Research Collection
Doctoral Thesis
A population genomics approach to host defense in a bumblebee- protozoan model system
Author(s): Bayer-Wilfert, Lena Katrin
Publication Date: 2006
Permanent Link: https://doi.org/10.3929/ethz-a-005281124
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ETH Library DISS. ETH NO. 16828
A POPULATION GENOMICS APPROACH TO HOST DEFENSE IN A BUMBLEBEE-PROTOZOAN MODEL SYSTEM
A dissertation submitted to the
SWISS FEDERAL INSTITUTE OF TECHNOLOGY ZURICH
for the degree of
Doctor of Sciences
presented by
LENA KATRIN BAYER-WILFERT
Diplom-Biologin, Universität Bayreuth, Germany
born 31.01.1978
citizen of Germany
Accepted on the recommendation of
Prof. Paul Schmid-Hempel, examiner
Assistant Prof. Jürgen Gadau, co-examiner
Prof. Bruce McDonald, co-examiner
2006 2
o8Ït0 L.60f / Blank Ifi^f 3
Seite Leer / Blank leaf 4
Seite Leer l\ Blank leaf 5
Table of Contents
Zusammenfassung 7
Summary 9
1. General Introduction 11
Population Genomics 12
The host-parasite system Bambus terrestris and Crithidia bomb 13
Thesis outline 15
General discussion 16
References 19
2. A core linkage map for the bumblebee Bombus terrestris 23
3. Sociality and recombination rates 51
4. Natural variation in the genetic architecture of a host-parasite interaction in the bumblebee
Bombus terrestris 73
5. The genetic architecture of investment in male immunity and reproduction in the
bumblebee Bombus terrestris 101
Acknowledgements ] 23
Curriculum Vitae 125 Seite Leer / Blank Isaf 7
Zusammenfassung
Wirt-Parasiten-Interaktionen unterliegen antagonistischer Koevolution, da die beteiligten
Parteien unter dem natürlichen Selektionsdruck stehen, sich aneinander anzupassen. Durch
Parasiten verursachte Selektion wird deshalb als ein wesentlicher Faktor in der Wirtsevolution angesehen, einschliesslich der Aufrechterhaltung genetischer Variation durch sexuelle
Fortpflanzung und meiotische Rekombination. Dies ist durch eine umfangreiche Sammlung theoretischer Arbeiten sowie indirekter empirischer Befunde belegt. Ein direkter Beweis, beispielsweise durch den Nachweis von Allelfluktuationen in natürlichen Populationen, wie sie von der „Red Queen"-Hypothese vorausgesagt werden, hat sich allerdings bisher nicht erbringen lassen, da das notwendige molekulare Rüstzeug nicht vorhanden war. Hier kann die
Kombination der Evolutionsökologie mit den Methoden der Populationsgenomik Abhilfe schaffen.
In meiner Dissertation habe ich einen QTL-Ansatz („quantitative trait locus", Genort für ein quantitatives Merkmal) gewählt, um die genetische Grundlage mehrerer fitness-relevanter
in Merkmale natürlichen Populationen der Hummel Bombus terrestris zu erkunden. Bei natürlichen Populationen und Nicht-Modell-Organismen sind QTL-Studien nach wie vor rar, da sich die Konstruktion der zugrundeliegenden genetischen Karten in diesen Systemen experimentell und statistisch schwierig gestaltet. In meiner Doktorarbeit konnte ich zeigen, dass sich diese Schwierigkeiten für die soziale haplo-diploide Hummel leicht umgehen lassen.
Ich habe für B, terrestris aus Daten von drei unabhängigen Kartierungspopulationen eine gemeinsame genetische Karte konstruiert, die als Vergleichswerkzeug für weitere genetische und molekulare Arbeiten dienen kann.
Genetische Karten erlauben zusätzlich Rückschlüsse auf die genomische Rekombinations¬ dichte, d.h. das Verhältnis aus genetischer und physikalischer Genomgrösse [cM/Mb]. In dieser Arbeit habe ich gezeigt, dass diese Dichte bei sozialen Hymenopteren herausragend hoch ist und zudem mit dem Grad der sozialen Organisation anzusteigen scheint. Diese
Ergebnisse stimmen mit der Auffassung überein, dass die meiotische Rekombination adaptiv ist. Sie unterstützen Hypothesen, die in verwandschaftsbasierten Sozialsystemen
Selektionsdruck für erhöhte genotypische Diversität in den Nachkommen vorhersagen. 8
QTL-Studien ermöglichen es, die für die natürliche Selektion ausschlaggebende genetische
Architektur aufzudecken, d. h. sie zeigen die Position, Wirkung und Interaktion von
Genorten, die für einen Teil der phänotypischen Varianz des untersuchten Merkmals
verantwortlich sind. In dieser Arbeit habe ich die genetische Grundlage der spezifischen
Anfälligkeit von B. terrestris für den Darmparasiten Crithidia bombi in drei unabhängigen
Kartierungspopulationen untersucht, mit dem Ziel, zum Verständnis der genetischen Dynamik dieser Wirt-Parasiten-Beziehung beizutragen. Zusätzlich habe ich die genetische Architektur der Investition sowohl in die Immunabwehr als auch in die Reproduktion bei männlichen
Hummeln untersucht, um durch diesen parallelen Ansatz mögliche mikroevolutionäre Trade¬ offs aufzudecken. Die genetische Architektur der hier untersuchten fitness-relevanten
Merkmale zeigte einige wichtige gemeinsame Eigenschaften: die genetische Grundlage beruhte im allgemeinen auf mehreren QTLs mit schwachen Effekten und auf epistatisch interagierenden Locus-Paaren, die wesentlich zur phänotypischen Varianz beitrugen.
Genetische Marker, die an QTLs und epistatische Loci für die Anfälligkeit für C. bombi gekoppelt sind, werden momentan zu leicht anwendbaren molekularen Werkzeugen weiterentwickelt. Diese Verbindung aus Evolutionsökologie und Populationsgenomik wird die direkte Untersuchung von Hypothesen über die Aufrechterhaltung genetischer Varianz ermöglichen. 9
Summary
Host-parasite interactions are characterized by the dynamics of antagonistic co-evolution,
with the involved players constantly being under natural selection to adapt to each other.
Consequently, parasite-mediated selection has been considered as a major force in host
evolution, including the maintenance of genetic variation via sexual reproduction and meiotic
recombination. There is a large body of theoretical and indirect or circumstantial empirical evidence for these processes. Yet direct verification, for example via allele fluctuations in natural populations as predicted by the Red Queen hypothesis, has so far proven elusive because the necessary molecular tools were not available. This can be remedied by combining evolutionary ecology with population genomics.
In this thesis, I have taken a QTL (quantitative trait locus) approach to uncover the genetic basis of several fitness-relevant traits in natural populations of the bumblebee
Bombus terrestris. Because of statistical and experimental complications in the construction of the underlying genetic linkage maps, QTL studies in non-model organisms and natural
are still populations rare. In my thesis, I have demonstrated that these difficulties can easily be overcome in the social haplo-diploid bumblebee. I have extracted a core genetic linkage map for B. terrestris from three independent natural mapping populations. This map will continue to serve as a reference tool for future genetic and molecular work in this emerging model species.
Genetic linkage maps provide an estimate of a species' recombination genome size. I demonstrate that social hymenoptera have outstandingly high genome-wide recombination densities, i.e. the ratio of genetic to physical genome size [cM/Mb], and that there is further evidence that this increases with the density degree of sociality. These results agree with the notion that meiotic recombination is adaptive, by supporting hypotheses predicting selection for increased genotypic diversity within offspring in kin-based social systems.
QTL studies allow revealing a trait's genetic architecture as relevant to natural selection, i.e. the location, effect and interaction of loci explaining part of the studied trait's phenotypic variation. I studied the genetic basis of specific susceptibility of B. terrestris to its gut parasite
Crithidia bombi in three independent mapping populations in order to understand the genetic 10
dynamics of this host-parasite interaction. Furthermore, I simultaneously analyzed the genetic
architecture of investment in immune defense and reproduction in male bumblebees so as to
investigate potential micro-evolutionary trade-offs between these costly functions. In my thesis, important common features characterize the genetic architecture of fitness-relevant traits as revealed in natural populations of B. terrestris: the traits' genetic basis generally consists of several QTLs of minor main effects, and of pairs of epistatically interacting loci contributing a major part of the phenotypic variation.
Markers linked to QTLs and epistatic loci controlling host susceptibility are currently being developed into versatile molecular tools. This combination of evolutionary ecology and population genomics will allow directly testing hypotheses on the maintenance of genetic variation. 11
1. General Introduction
Parasites are an ever-present threat to the survival and fitness of their hosts (e.g. Jarosz and
Davelos 1995; Lehmann 1993). Consequently, parasites have been invoked as a major
selective pressure in host evolution especially with respect to the maintenance of host genetic
diversity (e.g. Haldane 1949). This hypothesis suggests an effect on genes directly involved in
host-parasite interactions and, more generally, favoring the evolution and maintenance of
sexual reproduction and meiotic recombination (e.g. Hamilton et al. 1990).
Hosts have indeed evolved many means of escaping parasite infection, including behavioral
strategies, or physical and chemical barriers, e.g. the insect integument, and intricate systems
of immune defense (Schmid-Hempel and Ebert 2003). Given the strong selective pressure
exerted by parasites, one could expect that genes for resistance would be driven to fixation in
host populations. Yet, host populations are generally polymorphic in their degree of
resistance. Evolutionary ecology offers two main explanations for the maintenance of this
genetic variation (Jokela et al. 2000; Schmid-Hempel 2003; Siva-Jothy et al. 2005): the cost
of resistance and the specificity of host-parasite interactions.
The evolution of immunity might be limited by trade-offs against other fitness components
(Sheldon and Verhulst 1996). The maintenance and use of the immune system has been
shown to be costly in many systems (e.g. Moret and Schmid-Hempel 2000; van Boven and
Weissing 2004). Over evolutionary time, this can translate into "hard-wired" genetic trade¬
offs, i.e. negative genetic covariances, between resistance and other fitness-associated traits
as has been (Stearns 1992), demonstrated in selection experiments (reviewed e.g. in Schmid- Hempel 2003)).
A second important, but so far largely overlooked factor restricting the evolution of resistance
is the specificity of host-parasite interactions (Jokela et al. 2000; Schmid-Hempel and Ebert
2003). Parasites and hosts are known to adapt to each other. In the host-parasite system
Daphnia magna - Pasteuria ramosa, for example, the parasite P. ramosa's capacity to adapt to particular host lines has been demonstrated experimentally (Little et al. 2006). Likewise, natural of host D. populations the magna developed increased levels of resistance after an epidemic infection (Duncan et al. 2006). With the involved players constantly being under 12
natural selection to adapt to each other, host-parasite interactions are characterized by the
dynamics of antagonistic co-evolution (e.g. Hamilton 1980). If specificity is based on genetic-
interactions of hosts and parasites, this antagonistic co-evolution results in negative frequency-dependent selection, which automatically results in variation in the degree of
resistance (Jokela et al. 2000) and in the maintenance of genetic diversity of resistance alleles.
Negative frequency-dependent selection may not only preserve allelic diversity in particular
genes, but also be responsible for the genome-wide maintenance of genetic variation via
sexual reproduction and recombination. The origin and maintenance of sexual reproduction and recombination are much contended, because successful combinations of parental alleles
be may disrupted during meiosis. Yet, many models predict that the co-evolutionary arms- race between hosts and parasites may favor sexual reproduction and recombination, and thus,
maintain may genetic polymorphism in natural populations by favoring rare or novel allele combinations (the "Red Queen" model, e.g. Ebert and Hamilton 1996; Frank 1992; Haldane
1949; Hamilton 1980; Hamilton et al. 1981; Lively 1996).
While there is a large body of theoretical work on host-parasite interactions and their impact on the maintenance of genetic variation, experimental and empirical evidence has been mostly indirect. The Red-Queen model, for example, has been demonstrated repeatedly in silica (e.g.
Hamilton et al. 1990; Peters and Lively 1999; Schmid-Hempel and Jokela 2002) and has indirectly been supported by experimental and field data mostly in clonal populations (e.g.
Carius et al. 2001 ; Dybdahl and Lively 1998; Lively et al. 1990). Further support comes from a recent study demonstrating increased recombination rates in Tribalium castaneum lines co- evolved with their parasite Nosema whitei (Fischer and Schmid-Hempel 2005). The validation of the Red Queen hypothesis would call for fluctuations of alleles driven by parasites in natural populations of sexual species. Tools to tackle this question have been rare. Thus, direct evidence of the Red-Queen model has so far proven elusive.
Population genomics
Many crucial aspects of the hypotheses introduced above still await verification. For example, the cost of resistance could be directly investigated by tracking allele dynamics. In one of the first studies to adopt such a direct approach, Yan and Severson (2003) experimentally found that Plasmodium-$,uscept\b\e genotypes of the mosquito Aedes aegypti were favored in the 13
absence of parasite pressure. This direct test of the costliness of resistant alleles was made
possible only by previous genomic studies, providing the necessary molecular tools.
To further advance our understanding of the evolutionary ecology of host-parasite
interactions, it is essential to increase our knowledge of the genetic basis of these interactions
in ecological model species. This field will profit greatly from the application of genomic
methods (Thomas and Klaper 2004). Population genomics comprises of many different techniques, but generally consists of the simultaneous sampling of genetic information throughout the genome and the extraction of locus- or gene-specific effects (Black et al.
Such 2001). approaches include the genome-wide analysis of gene-expression, for example a
screen for genes differentially expressed in trypanosome-infected Tse-tse-flies (Lehane et al.
2003), or the screening for candidate genes, i.e. for antifungal peptides in termites (Bulmer and Crozier 2004).
In my thesis I have focused on the classical approach of searching for quantitative trait loci
(QTL). QTL studies rely on the simultaneous assessment of a phenotypic trait and of the
information genotypic needed for the construction of a genetic linkage map. This information is used to identify genetic loci explaining part of the trait's phenotypic variation. Unlike studies based on gene expression or candidate genes, this approach provides no mechanistic information and does not allow directly identifying the involved genes. While ignoring proximate mechanisms, this approach allows to study the underlying ultimate genetic basis.
QTL studies reveal the genetic architecture of the studied traits - the number, effect,
interaction and location of the involved loci - as relevant for natural selection. This is the starting point necessary for directly studying the role of host-parasite interactions in the maintenance of genetic variation in natural populations.
The host-parasite system Bombus teirestris - Crithidia bombi
The bumblebee Bombus terrestris is not only an economically important pollinator (Ghazoul
2005), but has also been intensively studied as a model organism for host-parasite interactions
(Baer and Schmid-Hempel 1999; Schmid-Hempel 2001) and ecological immunity (Moret and
Schmid-Hempel 2000; Sadd et al. 2005). The interaction with its parasite Crithidia bombi is particularly well understood. C. bombi (Trypanosomatidae) (Gorbunov 1987; Lipa and
Triggiani 1988) is a gut parasite that is transmitted via the ingestion of cells shed in the feces 14
of infected hosts. This parasite is prevalent in natural populations and can reach high infection
levels (Shykoff and Schmid-Hempel 1991). C. bombi is a condition-dependent parasite, and,
while it's effects are negligible under optimal conditions, mortality rates of infected bees can
increase by 50 % under poor host condition (Brown et al. 2000). Indeed, it dramatically reduces the success of colony founding by infected queens, thereby effectively castrating its
bumblebee host (Brown et al. 2003).
The B. terrestris - C. bombi association is an interesting system for studying the co-evolution of host-parasite interactions. This system shows genotype by genotype interactions (reviewed in Schmid-Hempel 2001), including a study in which the genotypic composition of infections was directly assessed by microsatellite analysis (Schmid-Hempel et al. 1999). Genetic diversity of the host has also experimentally been shown to influence parasite infection. For example, parasite loads, including Crithidia, for individual workers and entire colonies are significantly reduced in the field when the colony is genetically heterogeneous (Baer and
Schmid-Hempel 1999; Baer and Schmid-Hempel 2001; Liersch and Schmid-Hempel 1998).
As a social insect, B, terrestris is a host species that is particularly promising for analyzing the genetic architecture of co-evolved parasite resistance and susceptibility. After hibernation, a single B. terrestris queen will found a colony and start producing female workers. Towards the end of the season, she will produce sexuals, including up to several hundred males, if conditions are favorable. This social structure implicates that the offspring will face parasites that were able to previously adapt to genetically very similar individuals, which should accentuate the parasite-driven selection for increased genetic diversity in offspring (e.g.
Agrawal 2006; Schmid-Hempel 2000). Additionally, B. terrestris lends itself to QTL studies in natural populations with particular ease. It is a haplo-diploid hymenopteran with a single locus complementary sex determination system (Cook and Crozier 1995): females arise from diploid, fertilized eggs and males from haploid, unfertilized eggs. Thus, the queen's meiotic recombination frequency can be measured reliably and directly in her large number of male
which can offspring, simultaneously be screened for a wide range of phenotypic traits. In combination, this makes B. terrestris a highly competitive model species for ecological population genomics. 15
Thesis outline
Chapter 1: General Introduction and Discussion
Chapter 2: In my thesis I have taken a QTL approach to study the genetics of host defense.
The first step in this approach is to construct genetic recombination linkage maps. By
comparing maps from three independent natural mapping populations of B. terrestris,
I was able to extract a core linkage map for this species, which is presented in
chapter 2. This genetic tool will serve as a reference throughout my thesis and in
future genetic work in this emerging model species.
Chapter 3: If a species' physical genome size has been estimated, for example via flow
cytometry, the construction of a high-coverage genetic linkage map allows the
estimation of the genome-wide recombination density (i.e. the average number of
recombination events per unit of physical genome length (cM/MbJ). This density
varies along chromosomes, between the sexes and across species (Bell 1982). In
chapter 3,1 examine recombination densities across taxa, in order to test the prediction
that social insects will show elevated values of recombination due to strong selection
for increased genotypic diversity in the colony's worker force (Gadau et al. 2000).
Chapter 4: 1 have studied the genetic architecture of the specific interaction of B. terrestris
and its parasite C. bombi and of the general immune defense mechanism of
encapsulation in three natural mapping populations. The genetic basis of these
immune traits was found to be partly congruent, in contrast to the unrelated trait of
body size. This approach allowed for an assessment of the variability of genetic
architecture in un-manipulated populations and of the importance of epistatic
interactions.
Chapter 5: Hosken (2001) demonstrated micro-evolutionary trade-offs between investment in
immunity and reproduction in dung flies. To test whether similar trade-offs exist in the
bumblebee B. terrestris, I conducted a simultaneous QTL search for traits
characterizing the investment in immune defense and reproduction in males. 16
General discussion
The co-evolution of host-parasite interactions is widely recognized as an important factor in
the maintenance of genetic variation within populations, also including sexual reproduction
and recombination (e.g. Haldane 1949; Hamilton et al. 1990)). While this field has been
extensively studied using classical ecological approaches, many hypotheses have not been testable so far. For example, direct evidence for the Red Queen hypothesis on the maintenance of recombination and sexual reproduction via parasite-mediated negative frequency-dependent selection is so far lacking, in spite of a large body of theoretical and ecological work (Little 2002). Combining evolutionary ecology with population genomics promises to overcome many of these hurdles.
In my thesis I have taken a QTL mapping approach to study host defense and host-parasite interactions in the model system Bambus terrestris - Crithidia bombi. QTL studies reveal a trait's genetic architecture as relevant to both natural and artificial selection and have therefore become almost common place in economically important model organisms since the development of computerized QTL mapping procedures and economic genetic marker systems in the late 1980's. Nevertheless, QTL studies in non-model organisms and natural populations are still a rare exception, due to statistical and experimental complications in the construction of the underlying genetic linkage maps (Slate 2005). We have demonstrated that these difficulties can easily be overcome in the social haplo-diploid bumblebee (Gadau et al.
Wilfert et al. in In 2001; press). my thesis, 1 have extracted a core genetic linkage map for
B. terrestris from three independent natural mapping populations. This will continue to serve as a reference tool for future genetic and molecular work in this emerging model species.
The development of a stringent methodology for phase-unknown genetic linkage mapping will ease similar in other projects non-model hymenoptera. Results of my thesis have shown that such mapping projects would be highly desirable. We have demonstrated that social hymenoptera have outstandingly high genome-wide recombination densities, i.e. the ratio of genetic to physical genome size, and that there is further evidence that this density increases with the degree of sociality. These results are in favor of the notion that recombination rates are not neutral but adaptive by lending support to hypotheses predicting selection for high genotypic diversity within offspring in kin-based social systems, in order to fend off parasites or to stabilize sociality (Schmid-Hempel 2000; Schmid-Hempel and Crozier 1999; Sherman 17
1979; Sherman et al. 1988). These results are encouraging, but should be strengthened by
further studies on systems with varying degrees of sociality.
As a direct application of the constructed core linkage map, I have studied the genetic
architecture of several fitness-relevant traits in the bumblebee B. terrestris, including the
susceptibility to the gut parasite C. bombi, investment in immune defense and reproduction in
males. Important common features characterize the genetic architecture of fitness-relevant
traits as revealed in natural populations of B. terrestris: the traits genetic basis generally
consists of several QTLs of minor main effects, each explaining less than 10 % of phenotypic
variation (Tanksley 1993), and of pairs of epistatically interacting loci. A common feature
was the great importance of epistatic interactions; these interactions consistently contribute a
major part of the phenotypic variation of the studied traits. Similar results have been found in
other studies of the genetic architecture of fitness-relevant traits in natural populations (e.g.
et al. Malmberg 2005). Epistatic interactions have also been shown to be important in many
studies of host resistance in genetic model organisms, for example in the interaction of the tapeworm Hymenolepis diminuta and its host the red flour beetle Tribolium castaneum
(Zhong et al. 2003). Currently, the implications of such epistatic interactions for the evolution of quantitative traits are not understood in detail, but they have been implied in the evolution and maintenance of meiotic recombination (Otto and Michalakis 1998; Otto and Nuismer
2004).
The simultaneous analysis of the genetic basis of different traits allows the detection of "hard¬ wired" genetic correlations ((Fishman et al. 2002) or trade-offs (Zhong et al. 2005) caused by micro-evolutionary processes. For example, I have shown that there is considerable overlap in the genetic architecture of the general immune response of encapsulation and of the host- parasite specific susceptibility towards C. bombi in the two studied independent natural mapping populations of B. terrestris. Doums and Schmid-Hempel (2000) have shown that infection prevalence is negatively associated with encapsulation rate in the field; I could show the same result as a trend in a controlled experiment. Note that this finding can be interpreted in two on the ways: one hand, individuals with a higher standing encapsulation response could be protected from an infection, on the other, infection could lead to a reduction in the strength of the encapsulation reaction. Similarly, Mallon et al. (2003) demonstrated that the degree of specificity in a colony's susceptibility to C. bombi trades-off against the strength of the encapsulation of an artificial antigen. The revealed overlap in the genetic architecture of 18
specific host susceptibility to C. bombi and the encapsulation response, together with their
phenotypic association, indicate that micro-evolutionary processes may have favored linkage
of these host defense traits.
In a further study, I have analyzed a natural mapping population of B. terrestris for the occurrence of phenotypic and genetic trade-offs between investment immunity and reproduction, since such a micro-evolutionary trade-off has experimentally been demonstrated in dung flies (Hosken 2001). Yet, in the studied population, we found no evidence for such a negative correlation that could be interpreted as an explanation of the maintenance of genetic variation in host resistance. The existence of other hard-wired trade¬ offs involving investment in the immune system in this model organism cannot be ruled out.
However, taken together with empirical evidence for the variability of QTL loci involved in specific host-parasite interactions, this is rather in support of hypothesis stressing host- parasite specificity as a limiting factor in the evolution of resistance.
Besides the discussed insights into the evolution of genetic architecture discussed above, the results of this thesis will make many so-far elusive hypotheses on the co-evolution of host- parasite interactions amenable to stringent analysis. The ongoing development of linked loci into sequence-tagged markers with the help of a newly developed genomic BAC-Iibrary
(bacterial artificial chromosome library) will allow the testing of crucial predictions of the evolutionary impact of host-parasite interactions in bumblebees such as "Do natural host- parasite systems show allele fluctuations as predicted by the Red Queen hypothesis?", "Do individuals with rare alleles for host-parasite interactions have higher fitness in accordance with negative frequency dependent selection?", or "Are alleles for host resistance lost in introduced populations when co-evolved parasites are absent?"
Acknowledgements
I would like to thank Mathias Wegner, Oliver Otti, Ben Sadd and Paul Schmid-Hempel for comments on this chapter. 19
References
Agrawal, A. F. 2006. Similarity selection and the evolution of sex: revisiting the Red Queen.
Plos Biol. 4:1364-1371.
Baer, B., and P. Schmid-Hempel. 1999. Experimental variation in polyandry affects parasite
loads and fitness in a bumble-bee. Nature 397:151-154.
Baer, B., and P. Schmid-Hempel. 2001. Unexpected consequences of polyandry for
parasitism and fitness in the bumblebee, Bambus terrestris. Evolution 55:1639-1643.
Bell, G. 1982. The masterpiece of nature. University of California Press, Berkeley.
Black, W. C, C. F. Baer, M. F. Antolin, and N. M. DuTeau. 2001. Population genomics:
Genome-wide sampling of insect populations. Annu. Rev. Entomol. 46:441-469.
Brown, M. J. F., R. Loosli, and P. Schmid-Hempel. 2000. Condition-dependent expression of
virulence in a trypanosome infecting bumblebees. Oikos 91:421-427.
Brown, M. J. F., R. Schmid-Hempel, and P. Schmid-Hempel. 2003. Strong context-dependent
virulence in a host-parasite system: reconciling genetic evidence with theory. J. Anim.
Ecol. 72:994-1002.
Bulmer, M. S., and R. H. Crozier. 2004. Duplication and diversifying selection among termite
antifungal peptides. Mol. Biol. Evol. 21:2256-2264.
Carius, H. J., T. J. Little, and D. Ebert. 2001. Genetic variation in a host-parasite association:
Potential for coevolution and frequency-dependent selection. Evolution 55:1136-1145.
Cook, J. M., and R. H. Crozier. 1995. Sex determination and population biology in the
hymenoptera. Trends Ecol. Evol. 10:281-286.
Doums, C, and P. Schmid-Hempel. 2000. Immunocompetence in workers of a social insect,
Bombus terrestris L., in relation to foraging activity and parasitic infection. Can. J.
Zool.-Rev. Can. Zool. 78:1060-1066.
Duncan, A. B., S. E. Mitchell, and T. J. Little. 2006. Parasite-mediated selection and the role
of sex and diapause in Daphnia. J. Evol. Biol. 19:1183-1189.
Dybdahl, M. F., and C. M. Lively. 1998. Host-parasite coevolution: Evidence for rare
advantage and time-lagged selection in a natural population. Evolution 52:1057-1066.
Ebert, D., and W. D. Hamilton. 1996. Sex against virulence: The coevolution of parasitic
diseases. Trends Ecol. Evol. 1 LA79-A82.
Fischer, O., and P. Schmid-Hempel. 2005. Selection by parasites may increase host
recombination frequency. Biol. Lett. 1:193-195. 20
Fishman, L., A. J. Kelly, and J. H. Willis. 2002. Minor quantitative trait loci underlie floral
traits associated with mating system divergence in Mimulus. Evolution 56:2138-2155.
Frank, S. A. 1992. Models of plant pathogen coevolution. Trends Genet. 8:213-219.
Gadau, J., C. U. Gerloff, N. Kruger, H. Chan, P. Schmid-Hempel, A. Wille, and R. E. Page.
2001. A linkage analysis of sex determination in Bombus terrestris (L.) (Hymenoptera
: Apidae). Heredity 87:234-242.
Gadau, J., R. E. Page, J. H. Werren, and P. Schmid-Hempel. 2000. Genome organization and
social evolution in hymenoptera. Naturwissenschaften 87:87-89.
Ghazoul, J. 2005. Buzziness as usual? Questioning the global pollination crisis. Trends Ecol
Evol 20:367-373.
Gorbunov, P. S. 1987. Endo-parasitic flagellates of the genus Crithidia (Trypanosomatidae,
Zoomastigophorea) from alimentary canal of bumblebees. Zool. Zhurnal 66:1775-
1780.
Haldane, J. B. S. 1949. Disease and evolution. La ricerca scientifica 19:68-76.
Hamilton, W. D. 1980. Sex versus non-sex versus parasite. Oikos 35:282-290.
Hamilton, W. D., R. Axelrod, and R. Tanese. 1990. Sexual reproduction as an adaptation to
resist parasites (a review). Proc. Natl. Acad. Sei. U. S. A. 87:3566-3573.
Hamilton, W. D., P. A. Henderson, and N, A. Moran. 1981. Fluctuation of environment and
coevolved antagonist polymorphism as factors in the maintenance of sex. Pp. 363-381
in R. D. Alexander and D. W. Tinkle, eds. Natural Selection and Social Behavior,
Chiron Press, New York.
Hosken, D. J. 2001. Sex and death: microevolutionary trade-offs between reproductive and
immune investment in dung flies. Curr. Biol. 1LR379-R380.
Jarosz, A. M., and A. L. Davelos. 1995. Effects of disease in wild plant-populations and the
evolution of pathogen aggressiveness. New Phytol. 129:371-387.
Jokela, J., P. Schmid-Hempel, and M. C. Rigby. 2000. Dr. Pangloss restrained by the Red
Queen - steps towards a unified defence theory. Oikos 89:267-274.
Lehane, M. J., S. Aksoy, W. Gibson, A. Kerhornou, M. Berriman, J. Hamilton, M. B. Soares,
M. F. Bonaldo, S. Lehane, and N. Hall. 2003. Adult midgut expressed sequence tags
from the tsetse fly Glossina morsitans morsitans and expression analysis of putative
immune response genes. Genome Biol. 4:art. no.-R63.
Lehmann, T. 1993. Ectoparasites - Direct Impact on Host Fitness. Parasitol. Today 9:8-13.
Liersch, S., and P. Schmid-Hempel. 1998. Genetic variation within social insect colonies
reduces parasite load. Proc. R. Soc. Lond. Ser. B-Biol. Sei. 265:221-225. 21
Lipa, J. J., and O. Triggiani. 1988. Crithidia bombi Sp N. a flagellated parasite of a
bumblebee Bambus-Terrestris L (Hymenoptera, Apidae). Acta Protozool. 27:287-&.
Little, T. J. 2002. The evolutionary significance of parasitism: do parasite-driven genetic
dynamics occur ex silico? J. Evol. Biol. 15:1-9.
Little, T. J., K. Watt, and D. Ebert. 2006. Parasite-host specificity: Experimental studies on
the basis of parasite adaptation. Evolution 60:31-38.
Lively, C. M. 1996. Host-parasite coevolution and sex - Do interactions between biological
enemies maintain genetic variation and cross-fertilization? Bioscience 46:107-114.
Lively, C. M., C. Craddock, and R. C. Vrijenhoek. 1990. Red queen hypothesis supported by
parasitism in sexual and clonal fish. Nature 344:864-866.
Mallon, E. B., R. Loosli, and P. Schmid-Hempel. 2003. Specific versus nonspecific immune
defense in the bumblebee, Bombus terrestris L. Evolution 57:1444-1447.
Malmberg, R. L., S. Held, A. Waits, and R. Mauricio. 2005. Epistasis for fitness-related
quantitative traits in Arabidapsis thaliana grown in the field and in the greenhouse.
Genetics 171:2013-2027.
Moret, Y., and P. Schmid-Hempel. 2000. Survival for immunity: The price of immune system
activation for bumblebee workers. Science 290:1166-1168.
Otto, S. P., and Y. Michalakis. 1998. The evolution of recombination in changing
environments. Trends Ecol. Evol. 13:145-151.
Otto, S. P., and S. L. Nuismer. 2004. Species interactions and the evolution of sex. Science
304:1018-1020.
Peters, A. D., and C. M. Lively. 1999. The red queen and fluctuating epistasis: A population
genetic analysis of antagonistic coevolution. Am. Nat. 154:393-405.
Sadd, B. M., Y. Kleinlogel, R. Schmid-Hempel, and P. Schmid-Hempel. 2005. Trans-
generational immune priming in a social insect. Biol. Lett. 1:386-388.
Schmid-Hempel, P. 2000. Mating, parasites and other trials of life in social insects. Microbes
Infect. 2:515-520.
Schmid-Hempel, P. 2001. On the evolutionary ecology of host-parasite interactions:
addressing the question with regard to bumblebees and their parasites.
Naturwissenschaften 88:147-158.
Schmid-Hempel, P. 2003. Variation in immune defence as a question of evolutionary
ecology. Proc. R. Soc. Lond. Ser. B-Biol. Sei. 270:357-366.
Schmid-Hempel, P., and R. H. Crozier. 1999. Polyandry versus polygyny versus parasites.
Philos. Trans. R. Soc. Lond. Ser. B-Biol. Sei. 354:507-515. 22
Schmid-Hempel, P., and D. Ebert. 2003. On the evolutionary ecology of specific immune
defence. Trends Ecol. Evol. 18:27-32.
Schmid-Hempel, P., and J. Jokela. 2002. Socially structured populations and evolution of
recombination under antagonistic coevolution. Am. Nat. 160:403-408.
Schmid-Hempel, P., K. Puhr, N. Kruger, C. Reber, and R. Schmid-Hempel. 1999. Dynamic
and genetic consequences of variation in horizontal transmission for a microparasitic
infection. Evolution 53:426-434.
Sheldon, B. C, and S. Verhulst. 1996. Ecological immunololgy: costly parasite defences and
trade-offs in evolutionary ecology. Trends Ecol. Evol. 11:317-321.
Sherman, P. W. 1979. Insect chromosome-numbers and eusociality. Am. Nat. 113:925-935.
Sherman, P. W., T. D. Seeley, and H. K. Reeve. 1988. Parasites, pathogens, and polyandry in
social hymenoptera. Am. Nat. 131:602-610.
Shykoff, J. A., and P. Schmid-Hempel. 1991. Incidence and effects of 4 parasites in natural
populations of bumble bees in Switzerland. Apidologie 22:117-125.
Siva-Jothy, M. T., Y. Moret, and J. Rolff. 2005. Insect immunity: An evolutionary ecology
perspective. Pp. 1-48. Advances in Insect Physiology, Vol 32.
J. Slate, 2005. Quantitative trait locus mapping in natural populations: progress, caveats and
future directions. Mol. Ecol. 14:363-379.
Stearns, S. C. 1992. Life-history evolution. Oxford University Press
Tanksley, S. D. 1993. Mapping polygenes. Annu. Rev. Genet. 27:205-233.
Thomas, M. A., and R. Klaper. 2004. Genomics for the ecological toolbox. 19:439-445. van Boven, M., and F. J. Weissing. 2004. The evolutionary economics of immunity. Am. Nat.
163:277-294.
Wilfert, L., J. Gadau, and P. Schmid-Hempel. in press. A core linkage map of the bumblebee
Bombus terrestris. Genome
Yan, G. Y., and D. W. Severson. 2003. Dynamics of molecular markers linked to the
resistance loci in a mosquito-plasmodium system. Genetics 164:511-519.
Zhong, D. B., A. Pai, and G. Y. Yan. 2003. Quantitative trait loci for susceptibility to
tapeworm infection in the red flour beetle. Genetics 165:1307-1315.
Zhong, D. B., A. Pai, and G. Y. Yan. 2005. Costly resistance to parasitism: Evidence from
simultaneous quantitative trait loci mapping for resistance and fitness in Tribolium
castaneum. Genetics 169:2127-2135. 23
2. A Core Linkage Map of the Bumblebee Bombus terrestris
(Lena Wilfert, Jürgen Gadau & Paul Schmid-Hempel, Genome, in press)
Abstract
The bumblebee Bombus terrestris is an economically important pollinator and an emerging model species for questions in quantitative and population genetics. We generated genetic linkage maps for three independent mapping populations of B. terrestris. The linkage map with the highest resolution had 21 linkage groups, which represents well the haploid chromosome number of B. terrestris (n = 18). This map can be considered saturated, with an average marker distance of 10.3 cM and an estimated genome coverage of 81 %. The estimate of the physical genome size of this species has increased to 625 Mb. With an estimated total recombination genome length of 2'760 cM, this results in a ratio of 226 kb/cM between the physical and genetic genome size. A recurring set of microsatellites and AFLP markers allowed the alignment of 14 linkage groups between the three maps. We propose to adopt this core map as a reference tool for future genetic and molecular work in B. terrestris.
Keywords: AFLP, microsatellite, RAPD, recombination frequency, genome mapping 24
Introduction
Genetic linkage maps are the starting point to an in-depth understanding of a species'
genomic make-up. They offer insights into the recombination frequency and genetic genome
size of a species. Furthermore, they allow the locations of genes underlying complex traits to
be identified by using Quantitative Trait Locus (QTL) analysis, and thus, provide an insight into the genetic architecture of fitness-relevant phenotypic variation. This knowledge is essential for many topics, from evolutionary genetics to molecular marker assisted breeding.
Research that stands to profit greatly from a genetic map is that dealing with the bumblebee,
Bombus terrestris, one of the most common native European species. B. terrestris is of economic importance as one of the most important natural pollinators of flowers in cool and temperate regions, including many commercial crops. For this reason, the commercial production of bumblebees has developed into a thriving branch of agribusiness (Ghazoul
2005).
Besides the considerable interest in bumblebees as commercial crop pollinators, they have also been used as a model species for research in ecology, behavior, physiology, foraging strategies and pollination (Goulson 2003). B. terrestris, in particular has received special attention in ecology and evolution, including host-parasite interactions, ecological immunology, developmental biology and social behavior (Baer and Schmid-Hempel 1999;
Lopez-Vaamonde et al. 2004; Moret and Schmid-Hempel 2000). Even though bumblebees and honeybees are phylogenetically related, their social systems differ considerably and bumblebees can be very useful for comparative studies on the evolution of sociality (Mares et al. 2005).
In contrast to its widespread use in basic and applied biology, the genetic and genomic research on bumblebees is lagging behind. Although there have been recent efforts to study gene expression (Pereboom et al. 2005; Spaethe and Briscoe 2005), even basic genomic
is still information very limited. As yet only one rudimentary low coverage genetic map based on RAPD-markers has been published (Gadau et al. 2001).
As with all hymenoptera, B. terrestris is haplo-diploid, i.e. has a single locus complementary sex determination system (Cook and Crozier 1995) with females arising from diploid, fertilized eggs and males from haploid, unfertilized eggs. B. terrestris is normally singly 25
mated (Schmid-Hempel and Schmid-Hempel 2000). A queen will found a colony in spring
and eventually produces up to a thousand haploid males. Due to the parthenogenetic origin of
males, hymenopteran males are sometimes called "flying oocytes". These features allow the
direct measurement of recombination frequencies in haploid individuals. Consequently, the
haplo-diploid system in combination with the large number of offspring derived from a single
individual readily allows the construction of linkage maps in natural populations. Generally,
such mapping in non-manipulated populations has relied on large numbers of sib-ships or on
detailed pedigree information (Slate 2005). This information is rarely available for most wild
populations, including those of bumblebees. However, we demonstrate in this article that
pedigree information for the determination of true linkage phase is not necessary for the
construction of accurate linkage maps in a haplo-diploid system. For that purpose, we
compared linkage maps produced phase-known and phase-unknown based on the same
mapping population (BBM-1). The phase of the markers in the mapping population BBM-1 was inferred indirectly by comparing the genotype of the queen of colony BBM-1 with those of her sisters.
To be of true value as a tool for basic and applied research, linkage maps have to be repeatable and comparable between labs and studies. We therefore used three independent natural mapping populations (BBM-1 to BBM-3) to develop a standardized core map for
B. terrestris using markers that vary within natural populations. Our core map of B. terrestris comprises of 14 linkage groups homologized via a set of recurring microsatellites and AFLP markers. This core map provides a starting point for more detailed and repeatable genetic and genomic studies on both applied and basic topics in B. terrestris. Beyond this, it allows for comparative genetic and genomic studies with the well-studied honeybee A. mellifera.
Materials and methods
Mapping populations
Three independent mapping populations were established from individual queens.
Populations BBM-2 and BBM-3 were raised directly from wild queens caught in
Northwestern Switzerland near Basel in 2003 (BBM-2) and 2000 (BBM-3) respectively. 26
Young queens produced by wild-caught queens from a population in Northeastern
Switzerland near Winterthur were mated and hibernated in the lab in autumn 2003, allowing
us to establish colony BBM-1 as a phase-known mapping population. All colonies were
maintained at standard conditions (red light, 28 °C and 60 % r.H.) and fed ad libitum with
sugar water and pollen (Gerloff and Schmid-Hempel 2005). Males were removed from their
maternal colonies as callows. They were later freeze-killed and stored at -80 °C. All
individuals were cared for in accordance with the principles and guidelines of the Canadian
Council of Animal Care.
Genetic markers
(i) AFLPs: For AFLP analysis DNA from half the thorax muscle of individual males was isolated with a modified CTAB-PhenoI extraction method (Toonen 1997). 500 ]i\ 2 x CTAB solution and 130 yig ProteinaseK were added to the dissected tissue and kept at 65 °C for 3 h.
After adding 2 pig RnaseA and thoroughly vortexing the samples, they were incubated for
10 min at 37 °C. This was followed by a standard Phenol-Chloroform extraction using
1/10 Vol 3 M NaOAC as a precipitant.
AFLP markers (Vos et al. 1995) were generated using EcoRI and Msel (New England Biolabs
NEB) as restriction enzymes. Genomic DNA was first digested with 3 U of EcoRI and 3 U of
Msel for 1 hour at 37 °C. Subsequently, ligation was carried out in a volume of 50 yi\ using
U T4 200 of Ligase (NEB) at 37 °C during 3 h. Pre-amplification was performed with C or G as pre-extensions to the standard MseZ-primer, using 35 picomoles of each primer and 2 \A of undiluted ligation reaction as a PCR template. All PCR-reactions were carried out using 0.5 U of Taq Polymerase (MBI Fermentas), 10 x PCR Buffer with (NH2)S04, 25 mM MgCl2 and
25 mM dNTPs in a volume of 25 ]a\ (pre-amplification) and 20 ]i\ (selective amplification).
For selective amplification, 5 pi\ of diluted pre-amplification reaction (1:20) and several combinations of selective EcoRI-primers (1.7 picomoles) with 3 selective base pairs and
Mve-Z-primers (4.8 picomoles) carrying 2 to 3 selective base pairs (see Table 1) were used.
Primers were labeled with FAM, NED, VIC and PET (population BBM-1), FAM, NED and
HEX (population BBM-2) and FAM, TAMRA and JOE (population BBM-3). Pre- amplification PCRs and touch-down selective PCRs were carried out as described in (Kaib et al. 2004). 27
Table 1: Coding of AFLP markers
Primer Extension added
Label 01 02 03 04 05 06 07 08 09 10 11 EcoRI Sequence AAC AAG ACA ACC ACG ACT AGC AGG AGT ATC ATG
Label ABC D E F G H J K L M P Msel Sequence CA CAA CAC CAG CAT CC CG CT CTT GA GC GC. GT
Note: Two or three selective nucleotides were used for selective amplification; these are coded as numbers for the EcoRI-primer and as a roman letter for Msel-primers. For example, the code A06G_190/198 indicates that this is a codominant AFLP-marker (A) amplified with the primer combination EcoRI-ACT and Msel-CG; its two alleles are 190 bp and 198 bp.
AFLP-fragments were separated on an ABI PRISM 310C Genetic Analyzer (APPLIED
BIOSYSTEMS) using HI-DI-formamide as carrier. Data were collected using the ABl
PRISM GeneScan Analysis Software (APPLIED BIOSYSTEMS), and samples were aligned via an internal size standard. We tested between 64 and 135 primer combinations per population; primer combinations were chosen according to the number of polymorphic bands and their size distribution. AFLP markers were named by an "A" followed by a number code for the EcoRI-Primer and a letter for the Msel-Primer as well as the fragment length (see
Table 1). For codominant AFLP markers, the fragment lengths of both alleles are indicated.
(ii) Microsatellites: DNA was extracted using a simple chelex-extraction as described in
Schmid-Hempel and Schmid-Hempel (2000). The reaction mixtures contained each 1-10 ng of total DNA; multiplex PCR of pairs of microsatellites (B10-11, 96, 100, 118, 119, 124, 126 and 132 (Estoup et al. 1993) and BL01-16, BT01-30 and Btern (Reber Funk et al. 2006)) were carried out according to Schmid-Hempel and Schmid-Hempel (2000). Gel electrophoresis was performed using Spreadex®-gels (Elchrom Scientific) and SYBR®Gold staining according to the manual.
(Hi) RAPDs: RAPD analysis in population BBM-3 was carried out as described in Gadau et al. (2001), following a standard phenol-chloroform DNA extraction (Gadau et al. 1996). 28
Genotyping
(i) Worker-produced males: By using codominant microsatellite markers, worker-produced
males could be removed from the mapping populations. In this singly mated haplo-diploid
species, all workers inherit the single paternal allele. If the paternal allele differs from the
maternal allele/s at a specific locus 50 % of worker-produced males can on average be
identified, because males will have a 50 % chance to inherit the parental allele. Analyzing
between 24 and 39 polymorphic microsatellite loci, worker-produced males could be
effectively removed from the colonies even though the paternal genotype was not available.
(ii) Segregation distortion: We tested for segregation distortion, i.e. significant deviation from a 1:1 ratio of alleles, via a chi-square test. Since linked markers are not mutually independent, we did not perform a Bonferroni test. Instead, we used a threshold of p < 0.01 to account for the large number of tests.
(Hi) Marker control: To reduce errors in the scoring of genotype information, all poorly amplified bands and markers with more than a third of missing marker information were re- scored. Potentially co-dominant AFLPs were scored twice to confirm that the two alleles were indeed complementary. Additionally, markers were rearranged according to their linkage after initial mapping to check for errors in data entry that result in blocks of genotype information being shifted from their correct position.
(iv) Phase-determination: The haploid male population BBM-1 (F2) was established from a lab-mated queen (F,); the queen (F,) and the drone were produced by unrelated colonies raised from two wild-caught queens (F0) in 2003. This breeding scheme yielded the pedigree information necessary for phase-known mapping. The relevant genetic information is the phase of alleles in the F„-generation, i.e. whether a particular allele was provided by the F0- queen or by her mate, because one set of chromosomes/markers of the F,-queen comes from her mother and the other from her father. No direct genotype information of the field drone was available. Therefore, the paternal allele was inferred by using the F0-queen and five full sisters of the F,-queen for co-dominant markers (see Table 2). For presence/absence markers, the segregation patterns of five haploid F2-progenies were additionally analyzed. This method can occasionally lead to ambiguous results, for instance the paternal allele cannot be determined if the FC)-queen and all F,-queens show the same heterozygous genotype for a co- 29
dominant marker. For a core of 177 markers, linkage phase could be unambiguously inferred
in mapping population BBM-1; all other markers were scored as phase-unknown. Every
linkage group contained at least two unambiguous phase-known markers; this allowed the
true phase to be established for all markers in this population (see under phase-unknown
mapping).
Table 2: Phase-determination in population BBM-1
F,-Queen FrQueen 5 F,-Sisters 5 F2-progenies Paternal allele
Allel es of co- dominant markers (fragment length polymorphisms A, B,C)
AB AC AB, BC B
AB BC AB, AC A
AB BB AB A
AB AA AB B
AB AB AB,AA A
AB AB AB, BB B
AB AB AB Arribiguousa)
Alleles of presence/absence polymorphisms (A/B)
"A "'>) BB "A" 100% A/B A
"A" "A" "A", BB 50 % A/B, 50% BB B
"A" "A" "A" 50 % A/B, 50% A/A A
"A" "A" "A" 100% A/B ambiguous0
a) The paternal allele cannot be determined, see text. b) with co-dominant markers, diploid individuals show the presence allele A whether they carry the absence allele B or not The paternal allele cannot be determined unambiguously; while this segregation would result if the F0-Queen is homozygous for the presence allele A and her mate carried allele B, it could also be the chance result of missing an FrQueen with the diagnostic AA-genotype.
Map construction
(/) General procedure: The mapping procedure with Mapmaker (Lander et al. 1987) followed a standard protocol. First, two-point linkage analysis was carried out using the "GROUP" command = theta = (setting: LOD 5.0; 0.35) to find a preliminary set of linkage groups. 30
Secondly, multi-point analysis within all putative linkage groups generated in step 1 was
carried out with the "FIRSTORDER" command (LOD = 5.0, theta = 0.35). This analysis
resulted in the most likely order of the markers in each linkage group. In the last step, the
order found in step 2 was tested within each linkage group for all possible three-point orders
of consecutive markers using the "RIPPLE" command. The most likely order for every
marker is shown. All map distances (cM) were calculated from recombination frequencies
(%) according to Kosambi's mapping function (Kosambi 1943). Kosambi's function was preferred over Haldane's function for two reasons: firstly, Kosambi's function resulted in less
map extension when the "drop marker" command was used; secondly, Solignac et al. (2004) could show that for A. mellifera, the ideal mapping function is closest to Kosambi's function.
(//) phase-unknown mapping: For populations BBM-2 and BBM-3, being derived directly from wild-caught queens, no phase information was available. Therefore, phase-unknown mapping was carried out as described in Gadau et al. (2001). Except for the initial two-point linkage analysis (step 1), this procedure is identical to the one described for the phase known mapping. Linkage group-wide phase is determined by using a doubled data set, in which each marker is represented in both possible phases (see Fig. 1). To achieve this, every marker allele was first arbitrarily assigned a phase ("1" for the "present" or the shorter allele of a fragment length polymorphism, "0" for the "absent" or longer allele respectively). Then, the genotype information was duplicated and added to the original data with the phase information being inverted (i.e. the "absent" or longer allele now being in phase "1"). This resulted in a data set in which each marker was represented twice with complementary phase information. This data set was used for two-point linkage analysis. Since this was the doubled data set, every linkage group had therefore to be present twice. All of the linkage groups we found followed this prediction. While this procedure cannot yield the true phase (maternal or paternal), it fixes the linkage phases of the markers within individual linkage groups. Once phase information was established, multi-point analysis could be carried out as with a true phase known data set, because one set of each linkage group was arbitrarily discarded, resulting in a data set in which each marker was represented only once in the correct phase. 31
LocusJ 01010111010101110101 Genotype data Locus_2 10101001101010011010
Duplication &
Locus 1 01010111010101110101 Locus 2 10101001101010011010 Locus 1d 01010111010101110101 Locus_2d 10101001101010011010
Phase of "d"-data set is inverted
Locus_1 01010111010101110101 Locus__2 10101001101010011010 LocusZld 10101000101010001010 Locus_2d 01010110010101100101
Two-point mapping analysis t&
Locus_1 01010111 Locus_1d 10101000 Locus_2d 01010110 Locus 2 10101001
-Locus_1d
-Locus_2 n a
Continue analyses with one set Discard the other set
Figure 1: Flow chart of the procedure for phase-unknown mapping
This chart exemplarily shows the procedure for phase-unknown mapping based on a data set of two genetic loci genotyped for 20 individuals. This method results in a data set of markers with consistent, albeit not true, phase allocation. With this data set, mapping is then carried out as with a true phase-known data set.
A comparison of phase-known and -unknown mapping for the core group of phase-known markers had shown that there were no differences between these maps. Thus, 65 additional markers whose phase had not been directly determined were included for population BBM-1 by establishing phase information via a partially doubled data set in two-point linkage analysis. By joining the phase-known data set and the doubled phase-unknown data set, two sets of linkage groups were obtained - those containing only markers of unknown phase and those of established phase containing both phase-known and unknown markers. Every linkage group contained at least 50 % of true phase-known markers. Thus, the correct phase for all markers could be established in population BBM-1.
(Hi) Alignment of linkage groups: Linkage groups from different populations are considered to represent the same genetic area if they contain identical microsatellite or AFLP markers. 32
(iv) Genome map size estimation: To obtain an estimate for the minimum recombination
genome length GM, the maximum distance between two markers (43.5 cM, 34.7 cM and
32.2 cM in maps BBM-1, BBM-2 and BBM-3 respectively) were added for each linkage
group exceeding the haploid karyotype of 18 chromosomes (Hoshiba et al. 1995). The total
genome length was estimated using a method-of-moments approach with the estimated
genome length GE being calculated as GE = N(N-1)X/K (Chakravarti et al. 1991; Hulbert et al.
1988). In this formula, N is the number of mapped markers, X the maximum distance between two markers at the threshold LOD score of 5 and K the number of marker pairs at this minimum LOD score, which can be obtained via the "LODs" command in Mapmaker (Li
et al. 2005). The achieved rate of genome coverage was obtained by dividing the total genome
size G by GF.
(v) Saturation curve: The degree of saturation of the maps was addressed by fitting a first order saturation model (Stadler et al. 2004). For this approach, the raw genotype data of population BBM-1 and an unpublished data set of 1021 RAPD markers in a population of 141
A. mellifera individuals (raw data provided by Greg Hunt) was randomly re-sampled (three
subsets each of 120, 160, 200 and 240 markers for BBM-1 and the A. mellifera data set and an additional three subsets of 320 and 400 markers for the latter). Linkage maps were constructed using MapMaker with a minimum LOD of 3 and a maximum theta of 0.34 to maximize the comparability with existing A. mellifera maps (Hunt and Page 1995). The minimum genome length estimate for these maps (GM) and the number of mapped markers (n) were used to fit a first order saturation model described by the function G(ll) = Gtotal* (l-e"cn)
using the nls module of R (R Core Development Team 2005).
Flow cytometry
The physical genome size of B. terrestris was estimated by flow cytometry. Single cell suspensions from worker flight muscles were obtained by applying a modification of the method of Lamatsch et al. (2000) for fish fin clips. The tissue was chopped in 2.1 % citric acid/0.5 % Tween 20, and incubated at room temperature (RT) with gentle stirring for 10 min.
For propidum iodide measurements, the cells were resuspended directly in staining buffer containing 154 mM NaCl, 100 mM Tris-Cl pH 7.4, 1 mM CaC12, 0.5 mM MgCl2, 0.2 %
BSA, 0.1 % NP40, 25 U/ml RNAseA, and 50//g/ml propidium iodide, and stained for approx. 1 h at 4 °C in the dark. Heparinized red blood cells from female chicken (Gallus 33
gallus) were used as standard (crbc). Whole blood was diluted approximately 1:100 in
minimal essential medium containing a final concentration of 10 % dimethyl sulfoxide,
aliquoted and stored at -20 °C. After centrifugation the cells were treated like the sample
cells. The concentration of the samples was approximately 2x 105 cells per ml, the
concentration of crbc slightly higher. Sample cells and reference cells were mixed at a ratio of
2/3 to 1/3 to obtain optimal results. Immediately before analysis, the samples were filtered
through a 50 mm nylon mesh to prevent obstruction of the flow chamber with cliitin. DAPI
measurements were performed on a Cell Analyzer CAII (Partec, Muenster, Germany). PI
measurements were performed on a BD-L3R (Becton Dickinson) equipped with a 488 nm
argon-ion laser with 20 mW power output. At least lO'OOO cells were measured per sample.
To determine the nuclear DNA content, the ratio of the channel numbers from the sample
(bumblebee) and chicken was multiplied by the known DNA content of crbc (2.5 pg/nucleus;
Vinogradov 1998).
Results
Genotyping
The mapping populations BBM-2 and BBM-3 correspond to the males produced by single
colonies founded in the laboratory by spring queens collected from natural populations in
Northwestern Switzerland. The males of the mapping population BBM-1 were produced by a daughter queen of a lab-reared colony originating from an area in Northeastern Switzerland, thus providing pedigree information.
A large number of informative méioses are necessary to achieve high resolution in genetic mapping. For the high-resolution mapping population BBM-1, all males produced by this colony were collected. Of these 577 males, 37 (6.4 %) were excluded because micro-satellite analysis proved them to be worker-derived. While only mated queens can produce females in bumble bees, workers will frequently lay male eggs in large colonies (Alaux et al. 2005).
Removing worker-derived males is important because they introduce random variation into the linkage information, potentially increasing the estimated recombination frequency between markers. The ratio of worker-derived males increased from an initial 3 % at the onset of male production to 14 % in the last batch of males produced by the colony BBM-1. This 34
increase reflects heightened reproductive competition as the colony cycle unfolds (Duchateau and Velthuis 1988). The BBM-1 mapping population thus provided 540 individuals with a mean of 392 ± 136 (S.D.) informative méioses per marker. This allowed reliable marker ordering down to less than 1 cM.
For the other two maps, the number of males in the mapping populations was 146 (colony
BBM-2) and 182 (colony BBM-3), respectively. Both populations had an average of 119 informative méioses per marker. None of the males from population BBM-2 or BBM3 proved to be worker-produced. These two less extensive mapping populations allow markers to be stringently arranged if they are linked at 2 cM (Hunt and Page 1995).
Three different types of genetic markers were used for mapping: Microsatellites, AFLPs
(Amplified Fragment Length Polymorphism), and RAPDs (Randomly Amplified
Polymorphic DNA). All of the currently available 60 microsatellites for Bombus spp. (Estoup et al. 1995: Estoup et al. 1993; Reber Funk et al. 2006) were tested for polymorphism in each mapping population. On average, 54 % of the available microsatellites were polymorphic in any of our mapping populations (39 of 60 tnicrosatellites in BBM-1, 34 in BBM-2 and 24 in
BBM-3); any two populations share on average 21 ±7.0 (S.D.) polymorphic microsatellites.
For the development of AFLP markers, 32 primer combinations were chosen for population
BBM-1, resulting in 219 reliably amplifying fragments. For populations BBM-2 and BBM-3,
12 and 8 combinations were tested, resulting in 103 and 50 polymorphic bands, respectively.
Since the degree of polymorphism of different primer combinations was highly variable between mapping populations, for economic reasons, no particular emphasis was put on using identical primer combinations in different populations. However, populations BBM-1 and
BBM-2 shared 6 primer combinations (A02E, A02G, A03F, A05B, A06G, and A07F),
14 resulting in potentially homologous markers (36 % of markers per primer combination in population BBM-1, 25 % in population BBM-2). Populations BBM-2 and BBM-3 had three combinations in common (A02H, A03D, and A07A), producing 3 potentially homologous markers. AFLPs are considered primarily dominant, but we discovered a considerable number of co-dominant AFLP markers (42 out of 207 in BBM-1, 15 of 102 in BBM-2, and 2 of 50 in
BBM-3). RAPD markers were used only for population BBM-3; the selected Operon primers resulted in 63 reliably amplifying polymorphic bands. 35
Segregation distortion
To avoid biasing the maps, e.g. by including AFLP markers showing homoplasy (Vekemans
et al. 2002), only markers that did not show significant segregation distortion were included
in map construction. Therefore, 28 markers were removed in total (13 RAPDs, all in
population BBM-3; 13 AFLPs, five in BBM-1, one in BBM-2, and seven in BBM-3; two microsatellites, one each in BBM-1 and BBM-3).
Recovery rate
For the phase-known population BBM-1, the ratio of genotyped maternal and paternal alleles,
rates of the recovery alleles, was investigated. We found that overall the chance of recovery,
/0, is normally distributed (ratio of female to male alleles; mean ± SD = 1.03 ± 0.12,
Kolmogoroff-Smirnoff-Test p = 0.69), but the recovery rate is slightly biased towards maternal alleles. The mean of^, = 1.03 ±0.12 (S.D.) significantly deviates from the expected ratio of 1 (One-sample t-test, / = 3.878, p < 0.001). This recovery distortion is not caused by markers showing individual segregation distortion; if only markers that show no significant segregation distortion (p > 0.05) are included in the analysis, the result remains the same
(One-sample t-test, / = 3.958, p< 0.001). This bias towards the female f0 allele could potentially be caused by them being more frequently the "absent" allele in AFLPs. With dominant markers, it is difficult to distinguish the absent allele from a mere accidental failure of amplification. Yet, this is not the case in this data set (chi-square-test, x2 = 0.824, p = 0.364). The biased recovery rate towards the grand mother's alleles in this population indicates the possibility of meiotic drive or some other biasing process in this haplo-diploid system. However, the present study cannot clarify this point further.
Phase-known and phase-unknown mapping
Two of the three linkage maps were constructed phase-unknown (BBM-2, BBM-3), because the pedigrees of their field-caught founding queens were unknown. When constructing a traditional genetic linkage map, it is necessary to assign a linkage phase to every marker, i.e. alleles are coded according to whether they were contributed by the grandmother or the grandfather. In order to circumvent this problem and to test our phase-unknown mapping approach, the mapping population BBM-1 was generated from a first-generation lab-reared 36
queen with known pedigree. To establish the reliability of the phase-unknown mapping approach, one third of all linkage groups of population BBM-1 were analyzed for the subset of markers whose phase had been determined by pedigree analysis. These results were compared to the results of mapping the same dataset as a phase-unknown population. There was no difference in the two sets of results, confirming that prior knowledge of linkage phase is not necessary for accurate genetic mapping in this system.
Genome size
The 246 markers of the high-resolution population BBM-1 mapped into 21 linkage groups, 20 of which consisted of at least three markers, while nine markers remained unmapped. This map spans a total of 2'222 cM. Since the karyotype of B. terrestris consists of 18 chromosomes (Hoshiba et al. 1995), the maximum distance between two linked markers of
43.5 cM has to be added for any excess linkage group; thus, the estimated minimal genome length, GM, for this population is 2'352 cM. The reduced number of markers in populations
BBM-2 and BBM-3 resulted in an inflated number of linkage groups and, consequently, a reduced genome length (see Table 3). The total genome length, GE, was estimated by a methods-of-moments approach (Chakravarti et al. 1991; Hulbert et al. 1988). This estimation yielded highly repeatable results with Gt-values of 2'734cM, 2'786 cM and 2'761 cM in maps BBM-1, BBM-2 and BBM-3, respectively. The two low-resolution maps therefore have an estimated genome coverage of 41 % to 44 %, whereas the high-resolution map achieves a coverage of 81 %.
Table 3: Summary of B. terrestris genome size 37
Table 3: Summary of B. terrestris genome size
BBM-1 BBM-2 BBM-3
Number of markers/unmapped 246/9 124/12 118/6
Linkage groups 21 25 30
linkage group >2 markers 20 19 21
homologized/not homologized 15/6 17/8 18/12
Map size (cM) 2221.8 1223.1 1124.3
Estimated minimal map size GM (cM) 2352.3 1462.6 1510.7
Estimated total genome length C b(cM) 2733.8 2785.5 2761.2
Physical recombination rate (kb/cM)* 229 224 226
Average marker spacing (cM) 10.3 12.5 12.8
* calculated as ratio of the estimated physical genome length of 625 Mb and the respective estimated total genetic genome length GE
The physical genome size of B. terrestris has been determined as being 1.54 times larger than the A. mellifera genome, which in prior calculations lead to an estimate of 274 Mb (Gadau et al. 2001). Subsequently, the physical size of the honeybee genome has been revised from the originally published 178 Mb (Jordan and Brosemer 1974) to 265 Mb (Johnston 2006, personal communication). Consequently, the estimate of the genome size of B. terrestris also required revision. Additionally, the initial estimate was biased as Gadau et al. (2001) used a method that preferentially stained the AT-portion of the genome. Using propidium iodide staining the B. terrestris genome has been estimated to be 2.71 times larger than the honey bee genome (= 575 MB). However, due to the current discrepancy in the estimated size of the
A. mellifera genome we provide here an estimate of the genome size independent of the honey bee genome size estimates (see Material & Methods). Our revised estimate of the physical genome size of B. terrestris is 625 Mb. This results in a rate of genome-wide recombination of 226 Kb/cM, assuming a total recombination length of GF = 2'760 cM.
Saturation curve
The re-sampled data sets of both population BBM-1 and an A. mellifera population (RAPD genotype data provided by G. Hunt) showed a highly significant fit to a first order saturation 38
model (p < 0.001 ). A comparison of both curves (Fig. 2) demonstrates that, while the Bombus
maps have not reached saturation, they are much closer to the saturated range of the curve compared to the re-sampled Apis maps and the published linkage maps of A. mellifera (Hunt and Page 1995; Rueppell et al. 2004; Solignac et al. 2004). This comparison adds further weight to the evidence of a higher recombination rate in A. mellifera (Gadau et al. 2000).
o o o o • re-sampled A. mellifera BBM-1 o O re-sampled o o + GM A. mellifera eo x GM ß. terrestris
0) N o o to o (D CD E o c O V % O o o c\ i ~i i r r 0 100 200 300 400 500 600 Mapped Markers Figure 2: Saturation curve for map BBM-1 and a RAPD map of A. mellifera. The curves were fitted to the model G(n) = Gtota* (l-e"c*n). Additionally, the estimated minimal genome sizes of the A. mellifera (Hunt and Page 1995; Rueppell et al. 2004; Solignac et al. 2004) and B. terrestris (Table 2, Gadau et al. 2001) maps are displayed (*). Marker distribution While the distances between markers for maps BBM-2 and BBM-3 are normally distributed (Kolmogoroff-Smirnoff-test, pnmi = 0.07, pBBM_3 = 0.63), BBM-1 showed a non-normal distribution (/?MM., < 0.05, mean = 10.3 ±0.9). In the latter, the distribution of marker distances was skewed to the left, indicating a tendency for markers to be clustered (Krutovskii et al. 1998) in this map. Seven of the linkage groups for this map show signs of clustering (two-tailed Kolmogoroff-Smirnoff test against a uniform distribution), with p< 0.001 for LG09, LG10 and LG13 and p < 0.05 for LG06, LG08, LG12 and the largest non-homologized linkage group. These clusters might denote the chromosomes' centromeric region (Gadau et al. 1999; Krutovskii et al. 1998). The length of linkage groups was overall highly correlated 39 with the number of markers per linkage group in all three maps (Pearson's correlation, < = 90.4 = 19.1 % and = p 0.001, R2BBM.j %, R2llltM_2 R2RnM < 86.1 %). This indicates that there is no bias in marker allocation between linkage groups. Core map Using primarily microsatellites and additional homologous AFLPs, 14 core linkage groups could be homologized across the different maps (see Fig. 3). Three pairs of linkage groups were homologized merely based on single AFLP polymorphisms (indicated by a black star in Fig. 3) and an additional two pairs based on co-dominant AFLPs (indicated by a white star). Eight of the potentially homologous AFLPs were found to reliably co-segregate with microsatellites or other AFLPs, demonstrating that these were indeed homologous. Three potentially homologous markers remained unlinked in one population; yet, this never lead to the rejection of the hypothesis of homology as would be the case if a homologous marker were missing from an otherwise well-mapped area. In no case were potentially homozygous markers mapped to different linkage groups. While this is strong evidence for the homology of AFLP markers, homology can only be strictly assumed if it has been demonstrated by sequencing. The homologized core linkage groups have been named LG01 to LG14 according to the name of mapped microsatellites (see Table 4). 40 Table 4: Alignment of core linkage groups naming locus additional microsatellites homologous AFLPs LG01 BIO BT 14, BT05,BT01 A05B_267 LG02 B96 BT10,B126 A06G_379/385 LG03 B100 BL06, BT 12, BT 17, BT30 LG04 Bl 18 BT07, BT08 LG05 B119 BT03, BT09, BT11,BT21, LG06 B124 BL16,BT04,BT19 A03F_189, A07F_064 LG07 B132 BL09, BL03, BT02, BT23, BTERN LG08 BL01 BL02, BL11,BT16,BT22 A05B_073 LG09 BL05 BT24 A02G_209/210 LG10 BL13 BT 15 A02GJM7/048, A02E_044 LGU BT 18 LG12 BT20 A02G_400/403, A02H_071 LG13 A03F_209/210 A03D_059, A05B_345 LG14 A02G 195 LG 01 LG02 LG03 A09E 2'9 LG04 1 B" - —A03F 33 P04 & r> BTir> AUF KlftSIO A01F D.t7 3 3~~ BL S rTf — AOIM 65 1K1 — A08G 91195 A01F \22,\& : —A09E 050 |—ACS &5D 19 5 — BT1C p 154 BT1S AD9P 092 I 1 381 B1D AO*L 11? 59 114 93 _ K 10 -—AG2H 153 iga "^AOSG 2S 30 6 "A11E 09° A02P sa* AD5D 442 A10K 326,^21 2 t EaeBa&" A05B 065 ^A01L346 am« -AÙlB OTC 39J— 1tl3 X T~ + H1D 400 H 7 BLM _,'acid 2"tse A06G1 377083- -t A02B 30? F396 14 2 - A09B A 6K F29 1: 13a 307^ 12 Ù.HPH 431"~ " a 1 A HTÎ4 AOBG 379.^85 37 . A06K 052 3—* OK 09 ST05 1t wrr Sfo AC9B SftïïJS 161—' B IS * 2^1 ù£aB — A[TF 123 A1 tE "61 1&0 — 2 3 4&—- Actp aift^a AC9P 33? 29 0- 5"* A09E 136 ; B1K -Ü-Btai - -: n î~ A{I4' Ï23- AlKC 0* A04t 1?3rt£4 2" 5 a^a 1^ h SÎ0S T1 330 ;;^" AlJIC 0e" A02G 170 ..s 15t '- 1^4, , AC5M 162. g'- AC 0 66 «J*F *2 12 3 BT3D A02G ^A5 1a r =° BTD7 AO 114 L AOSfl 2^7 f"S 15- It D M5Q XT A02E t45 ni 1 t BTH7 ; 5 65 BTDB 16"* ^2S^ A1CF 1" 13 AD4L 135 25 | ans 1C31 B119 AngE 091*134 *KiK IIB AOPD 0^. AC D 113115 123 nC1 164 DK 229.Ï30 - f A05S 073 64 : —AOEK.^9 B124 Ï D4fl AO A 1flf A02U_1&ü m Bim AD5B 073 BT19 BT19 ~BÏ23 1&2 ACÖE E6* BT2J - AOdE —. £r BL0Î BL0Î AOJF 064 BTM ce BTttf A02E 273 LG09 LG10 AO&G 346 Ï49 __l A02L095 ! A03K 16( g,— AD3D 220 A01K 46 ^-i a iy 144 ? "|r A03P 276 A06G 09 ' f" fiKL11£ AODB 313 10- Ï AJ31D Of AO F 130 - -A11Ê 091 AOIF 08* AOoG 05 AO«F 104 1G0 „. A01D 124 A«K I Ai D 0=2 AHM 042 ^0 ^-- BB-1 war s« population A11E M9 A02Ü 146 " \ AD1D 069 AfoK 340.144 A"3M -31 6? _ - Ml A02D 108 non-homologous AOBO ! A1GF 163 er ^ A01D 201 AOSE - A02E W& r ADSG 229 Ï42 mil ACbD ce-i A02G 12 3"^ m S2_; A3dE 1Q3 - A03K 093 -o BT2B AOIM Oll 55 ; " • I W1H 1 7 BB A05EÏ C**9 AOpG 370 A.3«E C61 BU3 -0 b M2G MrÄMSgj A^SE. 062 AQ!E 034 53 A92G 047/TMS ST15 BTls 6 9 -, A11H 1C« BT15 A06H ACBG ^ 054 A0«H 069 0 2 A02E D44 fl "AUE bfPF ÏÏ«.» — AG?0 ??? ~AWF 329 334 6 "*" 1B3 BLCB A06F 413 L BL05 ^ , AH4H 391 293 f>=f: : McG J G16 A~0 populationBB-2 Afl*F 239^242 a s —ü A06H 312 277 AB3F 202,3n6 j_ non-homologous —A32G ^35 LG11 AD6F 181 ftdFO lLG12 "£qä A06H 115 ia« äO Û * r, ^ W-*."^ -AOïG 4DCWD3 2^ C A34H 116 AC6H ,« 3Qg A09P 064C65 BT»] A02H 107 jt MG 361 -p—A'1» "A02D 223 ADSL 091 16.0 "A03M ?T> AD2L. MS A03F 13*= :03F 21ta BT1fl iffi "A10K ?5GP5P ^ AH! A11E 134 ï AOP -AlTIF ' "A02B 2^4 -P04 350 4-,r 149 51 AC2P I24 >A î " 095- *i02B O1^ -AD1B 27- " ADW AoaH on " A02B 045 A03D 161 A0TA 063 AOSÜ 15FW1B0 ftH*; !_? populationBB 3 A^tF 23ÉWfl6 A A16Û 094 non-homologous • A"Ee ?25 I 1 ' -k A07A 203 P AOa& 165 dioe 16 rwi II U| A''1^ 1 4 |^A04B 0 B A-MK J31 A32H 21^ PH2E 241 __H—UECIM Aoarj 210 i 510 «o-Ji^ElSSi BB-2 lGA Map I 63 | 1 AC2H 215 ,|Hl-|lri „. & 1 BB-1 LG13 LG143 Map A03E 144 — A12 610 Ï1 A04L 37417- BB-3 3 " - Map Fig The core LG01 to LG14 are AIHTJ 059 "•" " homologized linkagemaps depicted, 2*^fj4^AIBD AflSB 345 A03F 122 1 CS9 0 ~ ,« markers are in bold A02P 056 A01B 2W AD2G 196 i via homologous highlighted The linkagegroups from the S. AO^B 39t. ^^omolog^zecl single 3 H a 13 9 1 AC5M 2\ AD*B Ï4S AO&S 04^ — A03E 095 15? I1 26 5 5 4—: AO A 1Gâ * marker high-resolution BB-1 are at the center of core The I5 - A02B 1&1 AßiH 1Ftf i— *02G t« AFLP map linkage groups —*—fi.M& ÏD9JÏ10,, AH3F M*51D AD1E 054 ,;i i^ACrjK 45? A iE _., via co- non are the 147/450 F1^ 920 Homologized homologous linkagegroups displayed per population on right dominant AFLP marker hand side Map distances are given in cM (Kosambi) 42 Discussion We present the first high-resolution standardized map of the bumblebee B. terrestris. With an average marker spacing of 10.3 cM and an estimated genome coverage of 81 %, the map of population BBM-1 comes very close to being saturated. Yet, this map still contains three excess linkage groups as compared to the karyotype of B. terrestris. The number of excess linkage groups is decreasing with the number of markers per map (see Table 2). This is consistent with results from studies on A. mellifera. Solignac et al. (2004) found that the number of linkage groups dropped from 32 (with 297 markers) to 24 (with 541 markers). Nevertheless, their map still includes eight excess linkage groups and the authors estimated that it would take hundreds of additional markers to saturate the Apis map. This caveat also applies to B. terrestris, although the low number of excess linkage groups indicates that the high-resolution Bombus map presented in this study is much closer to saturation. A relatively high degree of saturation is also demonstrated by a comparison of the saturation curves for BBM-1 and an Apis mellifera mapping population (see Fig. 2). This difference is probably due to the significant difference in recombination frequency between A. mellifera and B. terrestris. Genetic linkage maps are ideally built with markers for which sequence information is available such as microsatellites, SNPs or STS-markers. Since B. terrestris is as yet not an extensively developed genetic model system, only a limited set of 60 polymorphic microsatellites has been developed so far (Estoup et al. 1995; Estoup et al. 1993; Reber Funk et al. In 2006). order to generate the large numbers of genetic markers necessary for linkage mapping, it was therefore necessary to resort to AFLPs and RAPDs. These markers provide the opportunity of rapidly generating hundreds of polymorphic markers at low cost. Although AFLPs have been shown to be highly repeatable within populations (Jones et al. 1997), they are often considered to be inappropriate for comparisons between populations (Slate 2005). While these concerns are valid in many systems, results from this study as well as from the literature show that AFLPs nevertheless can be valid markers not only for genetic mapping but also for inter-population comparisons. An often mentioned concern is the largely dominant nature of AFLP markers, which leads to a loss of information (Bensch and Akesson 2005). Yet, since B. terrestris is, like all hymenopterans, haplo-diploid, this problem is effectively circumvented by genotyping the haploid males where the absent allele is not 43 masked. Indeed, AFLP analysis can also generate truly co-dominant markers (Wong et al. 2001). These can easily be identified in large mapping population as co-segregating bands in repulsion phase, and thus, add further valuable information. For populations BBM-1 and BBM-2, in which AFLPs were thoroughly assessed for co-dominance, the rate of co- dominant markers ranged between 20.3 % and 14.7 %, respectively. Although there is little information on co-dominant markers in mapping studies, rates close to 20 % seem to be usual in plants and animals (Fishman et al. 2001; Parsons and Shaw 2002). A more serious concern is whether fragments of the same size can be considered to be homologous or whether they merely show homoplasy. For inter-species comparisons, this question has been addressed experimentally by O'Hanlon and Peakall (2000). They conclude that AFLPs are suitable for phylogenetic analysis of closely related taxa. Parsons and Shaw (2002) confirmed the homology of same-size AFLP bands of the closely-related cricket species Laupala paranigra and L. kohalensis by sequencing the fragments. Homologous AFLP bands were used for aligning linkage groups in genetic maps, e.g. in barley (Waugh et al. 1997) and in crickets (Parsons and Shaw 2002). In this study, we constructed three genetic maps from unrelated mapping populations of B. terrestris using microsatellites, AFLPs and (in population BBM-3) RAPDs. We could show that there are recurring patterns of linked microsatellites and AFLP markers (see Fig. 3, Table 4) and that there is no case in which the assumption of homology would be violated by discrepancies in linkage group assignment. This is very strong evidence for the widespread homology of AFLP markers in B. terrestris. To be valuable as a tool for basic and applied research, linkage maps have to be stable and between labs comparable and studies. The recurrence of markers between the genetic maps allowed a group of repeatable core linkage groups to be defined (see Fig. 3, Table 4). The considerable number of microsatellites and co-dominant AFLPs on the core linkage groups promises that genetic variability in most crosses or populations will be sufficient to anchor genetic information to the developed linkage maps. We propose to use these homologized core linkage groups as a reference for future genetic work in B. terrestris. This will allow for independent genomic studies to be related to each other, and enable meta-analyses of the combined results, e.g. to study the variance of recombination frequency in natural populations. While bumblebees are also kept as semi-domesticated animals, there is the potential, and 44 need, to study natural populations. While genetic mapping in applied or basic model species has become almost commonplace in the last two decades, linkage maps of natural populations still rare are (Slate 2005). Most work on "wild" species involves either inter-species crosses, such as for the Laupala crickets (Parsons and Shaw 2002), or intricate crossing schemes involving several generations, such as for example in the butterfly Heliconius melpomene (Jiggins et al. 2005). Here, we show that this is not necessary in the haplo-diploid system and that accurate linkage maps can be constructed because large numbers of progeny are available from a single reproductive individual. The availability of a genetic linkage map is the foundation of Quantitative Trait Locus (QTL) analysis. A large proportion of QTL studies are concerned with applied agricultural research, as is demonstrated by 35 % of all studies featuring „QTL" in their title belonging to the subject category „agronomy" (Web of Science, January 25 2006). In this field, QTLs are of practical interest for marker assisted selection, especially to select for disease resistance in livestock (Andersson 2001). In this approach, breeders aim at increasing and monitoring the breeding success by genotyping their stocks for markers associated with relevant QTLs, e.g. resistance to pathogens or product quality (see Collard et al. 2005). While bumblebees have been reared for commercially much of the last half of the 20,h century, little effort has gone into increasing their agricultural value, e.g. their pollinating efficiency, by selective breeding. Selecting bumblebees is currently prohibitively difficult and time-intensive, not only because of the extensive monitoring of phenotypic traits but also because continued inbreeding results in a high proportion of colonies producing effectively sterile diploid males (Ducnateau et al. 1994; Zayed 2004). These difficulties could be overcome with marker-assisted breeding. QTL studies are also of major importance in basic research, such as in the study of reproductive isolation (Orr 2001) or of fitness relevant traits and epistasis (Malmberg et al. 2005). The standardized core map presented in this paper serves as a basis for the comparative analysis of the bumblebee genome, and for further detailed studies in evolutionary and population genetics of social insects. Acknowledgements The authors would like to thank Yvonne Merki and Daniel Heinzmann for assistance in genotyping and Boris Baer for collecting population BBM-3. We further want to thank 45 D. Lamatsch for help with the flow-cytometry measurements and D. Schindler and R. Friedl for the opportunity to use their facilities. We are also grateful for technical help supplied by W. Durka from the UFZ Halle-Leipzig and A. Widmer and M. Bratteler from ETH Zurich. G. Hunt generously provided genotype data of A. mellifera. This project was funded by the DFG SFB 554-TB1 (JG) and by the ETH Zuerich via an ETH Research Grant TH-19/03-2 (PSH and LW). References Alaux, C, P. Jaisson, and A. Hefetz. 2005. Reproductive decision-making in semelparous colonies of the bumblebee Bombus terrestris. Behav. Ecol. Sociobiol. 59:270-277. Andersson, L. 2001. Genetic dissection of phenotypic diversity in farm animals. Nat. Rev. Genet. 2:130-138. Baer, B., and P. Schmid-Hempel. 1999. Experimental variation in polyandry affects parasite loads and fitness in a bumble-bee. Nature 397:151-154. Bensch, S., and M. Akesson. 2005. Ten years of AFLP in ecology and evolution: why so few animals? Mol. Ecol. 14:2899-2914. Chakravarti, A., L. K. Lasher, and J. E. Reefer. 1991. A Maximum-Likelihood Method for Estimating Genome Length Using Genetic-Linkage Data. Genetics 128:175-182. Collard, B. C. Y., M. Z. Z. Jahufer, J. B. Brouwer, and E. C. K. Pang. 2005. An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: The basic concepts. Euphytica 142:169-196. Cook, J. M., and R. H. Crozier. 1995. Sex determination and population biology in the hymenoptera. Trends Ecol Evol 10:281-286. Duchateau, M. J., H. Hoshiba, and H. H. W. Velthuis. 1994. Diploid Males in the Bumble- Bee Bambus-Terrestris Sex Determination, Sex Alleles and Viability. Entomol. Exp. Appl. 71:263-269. Duchateau, M. J., and H. H. W. Velthuis. 1988. Development and Reproductive Strategies in Bombus Colonies. Behaviour 107:186-207. Estoup, A., A. Scholl, A. Pouvreau, and M. Solignac. 1995. Monoandry and Polyandry in Bumble Bees (Hymenoptera - Bombinae) as Evidenced by Highly Variable Microsatellites. Mol Ecol 4:89-93. 46 Estoup, A., M. Solignac, M. Harry, and J. M. Cornuet. 1993. Characterization of (Gt)N and (Ct)N Microsatellites in 2 Insect Species - Apis-Mellifera and Bombus-Terrestris. Nucleic Acids Res 21:1427-1431. Fishman, L., A. J. Kelly, E. Morgan, and J. H. Willis. 2001. A genetic map in the Mimulus guttatus species complex reveals transmission ratio distortion due to heterospecific interactions. Genetics 159:1701-1716. Gadau, J., C. U. Gerloff, N. Kruger, H. Chan, P. Schmid-Hempel, A. Wille, and R. E. Page. 2001. A linkage analysis of sex determination in Bombus terrestris (L.) (Hymenoptera : Apidae). Heredity 87:234-242. Gadau, J., J. Heinze, B. Holldobler, and M. Schmid. 1996. Population and colony structure of the carpenter ant Campanatus floridanus. Mol Ecol 5:785-792. Gadau, J., R. E. Page, and J. H. Werren. 1999. Mapping of hybrid incompatibility loci in Nasonia. Genetics 153:1731-1741. Gadau, J., R. E. Page, J. H. Werren, and P. Schmid-Hempel. 2000. Genome organization and social evolution in hymenoptera. Naturwissenschaften 87:87-89. Gerloff, C. U., and P. Schmid-Hempel. 2005. Inbreeding depression and family variation in a social insect, Bombus terrestris (Hymenoptera : Apidae). Oikos 111:67-80. Ghazoul, J. 2005. Buzziness as usual? Questioning the global pollination crisis. Trends Ecol Evol 20:367-373. Goulson, D. 2003. Bumblebees - their Behaviour and Ecology. Oxford University Press, New York. Hoshiba, H., M. J. Duchateau, and H. H. W. Velthuis. 1995. Diploid males in the bumblebee Bombus terrestris (Hymenoptera): karyotype analyses of diploid females, diploid males and haploid males. Jap. J. Entomol. 63:203-207. Hulbert, S. H., T. W. Ilott, E. J. Legg, S. E. Lincoln, E. S. Lander, and R. W. Michelmore. 1988. Genetic-Analysis of the Fungus, Bremia-Lactucae, Using Restriction Fragment Length Polymorphisms. Genetics 120:947-958. G. Hunt, J„ and R. E. Page. 1995. Linkage Map of the Honey-Bee, Apis-Mellifera, Based on Rapd Markers. Genetics 139:1371-1382. Jiggins, C. D., J. Mavarez, M. Beltran, W. O. McMillan, J. S. Johnston, and E. Bermingham. 2005. A genetic linkage map of the mimetic butterfly Heliconius melpomene. Genetics 171:557-570. Jones, C. J., K. J. Edwards, S. Castaglione, M. O. Winfield, F. Sala, C. vandeWiel, G. Bredemeijer, B. Vosman, M. Matthes, A. Daly, R. Brettschneider, P. Bettini, M. 47 Buiatti, E. Maestri, A. Malcevschi, N. Marmiroli, R. Aert, G. Volckaert, J. Rueda, R. Linacero, A. Vazquez, and A. Karp. 1997. Reproducibility testing of RAPD, AFLP and SSR markers in plants by a network of European laboratories. Mol. Breed. 3:381- 390. Jordan, R. A., and R. W. Brosemer. 1974. Characterization of DNA from 3 Bee Species. J. Insect Physiol. 20:2513-2520. Kaib, M., P. Jmhasly, L. Wilfert, W. Durka, S. Franke, W. Francke, R. H. Leuthold, and R. Brandi. 2004. Cuticular hydrocarbons and aggression in the termite Macrotermes subhyalinus. J. Chem. Ecol. 30:365-385. Kosambi, D. D. 1943. The estimation of map distances from recombination values. Ann. Eugenic. 12:172-175. Krutovskii, K. V., S. S. Vollmer, F. C. Sorensen, W. T. Adams, S. J. Knapp, and S. H. Strauss. 1998. RAPD genome maps of Douglas-fir. J. Hered. 89:197-205. Lamatsch, D. K., C. Steinlein, M. Schmid, and M. Schartl. 2000. Noninvasive determination of genome size and ploidy level in fishes by flow cytometry: Detection of triploid Poecilia formosa. Cytometry 39:91-95. Lander, E., J. Abrahamson, A. Barlow, M. Daly, S. Lincoln, L. Newburg, and P. Green. 1987. Mapmaker a Computer Package for Constructing Genetic-Linkage Maps. Cytogenet. Cell Genet. 46:642-642. Li, L., J. H. Xiang, X. Liu, Y. Zhang, B. Dong, and X. J. Zhang. 2005. Construction of AFLP- based genetic linkage map for Zhikong scallop, Chlamys farreri Jones et Preston and mapping of sex-linked markers. Aquaculture 245:63-73. Lopez-Vaamonde, C, J. W. Koning, R. M. Brown, W. C. Jordan, and A. F. G. Bourke. 2004. Social parasitism by male-producing reproductive workers in a eusocial insect. Nature 430:557-560. Malmberg, R. L., S. Held, A. Waits, and R. Mauricio. 2005. Epistasis for fitness-related quantitative traits in Arabidopsis thaliana grown in the field and in the greenhouse. Genetics 171:2013-2027. Mares, S., L. Ash, and W. Gronenberg. 2005. Brain allometry in bumblebee and honey bee workers. Brain Behav. Evol. 66:50-61. Moret, Y., and P. Schmid-Hempel. 2000. Survival for immunity: The price of immune system activation for bumblebee workers. Science 290:1166-1168. O'Hanlon, P. C, and R. Peakall. 2000. A simple method for the detection of size homoplasy among amplified fragment length polymorphism fragments. Mol. Ecol. 9:815-816. 48 Orr, H. A, 2001. The genetics of species differences. Trends Ecol. Evol. 16:343-350. Parsons, Y. M., and K. L. Shaw. 2002. Mapping unexplored genomes: A genetic linkage map of the Hawaiian cricket Laupala. Genetics 162:1275-1282. Pereboom, J. J. M., W. C. Jordan, S. Sumner, R. L. Hammond, and A. F. G. Bourke. 2005. Differential gene expression in queen-worker caste determination in bumble-bees. Proc. Roy. Soc. B 272:1145-1152. Reber Funk, C, R. Schmid-Hempel, and P. Schmid-Hempel. 2006. Microsatellite loci for Bombus spp. Mol. Ecol. Notes 6:83-86. Rueppell, O., T. Pankiw, D. 1. Nielsen, M. K. Fondrk, M. Beye, and R. E. Page. 2004. The genetic architecture of the behavioral ontogeny of foraging in honeybee workers. Genetics 167:1767-1779. Schmid-Hempel, R., and P. Schmid-Hempel. 2000. Female mating frequencies in Bombus spp. from Central Europe. Insect. Soc. 47:36-41. Slate, J. 2005. Quantitative trait locus mapping in natural populations: progress, caveats and future directions. Mol. Ecol. 14:363-379. Solignac, M., D. Vautrin, E. Baudry, F. Mougel, A. Loiseau, and J. M. Cornuet. 2004. A microsatellite-based linkage map of the Honeybee, Apis mellifera L. Genetics 167:253-262. Spaethe, J., and A. D. Briscoe. 2005. Molecular characterization and expression of the UV opsin in bumblebees: three ommatidial subtypes in the retina and a new photoreceptor organ in the lamina. J. Exp. Biol. 208:2347-2361. Stadler, P. F., C. Fried, S. J. Prohaska, W. J. Bailey, B. Y. Misof, F. H. Ruddle, and G. P. Wagner. 2004. Evidence for independent Hox gene duplications in the hagfish lineage: a PCR-based gene inventory of Eplalretus stoutii. Mol. Phylogenet. Evol. 32:686-694. Team, R. D. C. 2005. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. Toonen, R. J. 1997. Microsatellites for Ecologists: Non-radioactive isolation and amplification protocols for microsatellite markers. Vekemans, X., T. Beauwens, M. Lemaire, and I. Roldan-Ruiz. 2002. Data from amplified fragment length polymorphism (AFLP) markers show indication of size homoplasy and of a relationship between degree of homoplasy and fragment size. Mol. Ecol. 11:139-151. 49 Vinogradov, A. E. 1998. Genome size and GC-percent in vertebrates as determined by flow cytometry: The triangular relationship. Cytometry 31:100-109. Vos, P., R. Hogers, M. Bleeker, M. Reijans, T. Vandelee, M. Homes, A. Frijters, J. Pot, J. Peleman, M. Kuiper, and M. Zabeau. 1995. AFLP - a New Technique for DNA- Fingerprinting. Nucleic Acids Res. 23:4407-4414. Waugh, R„ N. Bonar, E. Baird, B. Thomas, A. Graner, P. Hayes, and W. Powell. 1997. Homology of AFLP products in three mapping populations of barley. Mol. Gen. Genet. 255:311-321. Wong, A., M. R. Forbes, and M. L. Smith. 2001. Characterization of AFLP markers in damselflies: prevalence of codominant markers and implications for population genetic applications. Genome 44:677-684. Zayed, A. 2004. Effective population size in Hymenoptera with complementary sex determination. Heredity 93:627-630. Seite Leer / Blank leaf 51 3. Sociality and Genomic Recombination Rates (Lena Wilfert, Jürgen Gadau & Paul Schmid-Hempel, submitted ta Heredity) Abstract Meiotic recombination is almost universal among sexually reproducing organisms. Because the process leads to the destruction of successful parental allele combinations and the creation of novel, untested genotypes for offspring, the evolutionary forces responsible for the origin and maintenance of this seemingly inefficient and counter-intuitive process are still enigmatic. However, the average recombination rate - the overall frequency of recombination events in the genome - varies considerably among organisms. Here, we have used newly available genetic data to compare genome-wide recombination rates. We show that among eukaryotes exceptionally high rates are found in the social insects. The results are supportive of hypotheses suggesting that sociality in insects selects for increased genotypic diversity in worker offspring to either meet the demands of a sophisticated caste system or to mitigate against the effects of parasitism. We anticipate our finding to stimulate more detailed research for the comparative study of recombination frequencies in taxa with different life histories or ecological settings and so help to understand the causes for the evolution and maintenance of this puzzling process. Keywords: recombination, karyotype, multiple mating, parasites, division of labour 52 Introduction Recombination occurs in almost all sexually living organisms. It involves both the segregation of entire chromosomes as well as the genetic exchange between homologous chromosomes (crossing-over) when gametes are formed during the process of meiosis (Bell 1982). As a consequence of meiotic recombination, the combinations of alleles in offspring differ from those found in the parents. Because the successful parental genotypes are thereby destroyed in seeming contradiction to the expectations of Darwinian natural selection, the evolutionary processes responsible for the origin and maintenance of recombination are still very controversial (Barton and Cliariesworth 1998; Otto and Lenormand 2002). However, if variation in recombination rate is not neutral but reflects the working of natural selection, differences in recombination among different taxa, environments or life histories, can shed some light on the adaptive value of recombination. Here, we report on newly available data that elucidate - the importance of recombination for an especially rewarding group of species the social insects. Social living is a highly successful strategy and has arisen several times independently. Besides its many advantages, such as cooperative brood care or collective defense, sociality also has several drawbacks. Social living, for example, entails a higher risk of disease transmission, facilitated by the close spatial proximity and interactions among group members. In social systems based on kin selection, parasite transmission is additionally promoted by the high genetic similarity of individuals belonging to the same society (Hamilton 1987; Schmid-Hempel 1998; Sherman et al. 1988). Similariy, the division of labour, especially in the social insects, may be constrained by low genotypic diversity within the worker force (Page et al. 1989). Consequently, it has been hypothesized that natural selection operating on social systems should strongly favour the genotypic diversification of offspring (Crozier and Fjerdingstad 2001), for example, by increasing the number of segregating chromosomes (Sherman 1979), or an increased intra-chromosomal recombination rate (via crossovers) (Gadau et al. 2000; Schmid-Hempel 2000). Note that higher chromosome numbers will additionally reduce the variance in relatedness between social group members and thereby stabilize the society in kin- based social systems (Sherman 1979; Templeton 1979); an analogous argument can also be made for increased intra-chromosomal recombination rates. 53 Social insects are a particularly suitable model to investigate hypotheses on offspring genotypic diversity because of their varying degrees of sociality and the ease of access for genetic studies. In fact, there is empirical evidence that a genotypically more diverse worker force is associated with improved colony growth (Cole and Wiernasz 1999; Oldroyd et al. 1992), improved foraging efficiency (Cole and Wiernasz 1999; Oldroyd et al. 1993; Oldroyd et al. 1994), or lower parasite loads (Baer and Schmid-Hempel 1999; Liersch and Schmid- Hempel 1998; Tarpy 2003). Apart from the genetic means of recombination (i.e. segregation and intra-chromosomal crossovers), genotypic diversity among workers of a social insect colony is also behaviorally affected by the degree of polyandry and polygyny. Therefore, social insect females should be under selection to mate multiply (polyandry) so as to generate high degrees of genotypic diversity in worker offspring (Schmid-Hempel and Crozier 1999; Sherman 1979). Empirical evidence indeed shows that social insect females, especially in advanced taxa, are characterized by unusually high degrees of multiple mating (e.g. Foster et al. 1999; Moritz et al. 1995). Moreover, the degree of genotypic diversity within a colony correlates with the reported parasite load of the species, as expected (Schmid-Hempel and Crozier 1999). While the possible adaptive values of increased polygyny and polyandry, and of increased chromosome numbers in socially advanced insect species have been tested previously (Schmid-Hempel and Crozier 1999; Sherman 1979), data to evaluate the possible role of intra-chromosomal recombination rates have not come easy. In fact, only very recently have data become available - in the form of genetic linkage maps - that allow the comparison of genomic recombination rates across many taxa. Linkage maps are based on the probability of recombination events between known markers along the genome, yielding a recombination distance (in cM) between marker pairs while the respective physical distance is given by the number of base pairs (in bp). The correlation between recombination and physical distance forms the basis of linkage mapping. However, there is ample evidence for considerable variation of recombination rate per unit of physical distance (here, we call this measure the "recombination density", in cM/Mb) on all levels, that is, along chromosomes, between sexes, individuals and species (Baker et al. 1976; Brooks 1988). Describing the first genetic linkage map for the honeybee, Hunt and Page (1995) found that with a recombination density of 19.38 cM/Mb (corresponding to a ratio of physical and 54 genetic genome size of 52 kb/cM, i.e. a short physical distance for every cM of recombination distance), Apis mellifera had the highest reported genome-wide recombination rate in any of the higher eukaryotes. In the meantime, several studies have formulated hypotheses for this extremely high recombination frequency (Gadau et al. 2000; Schmid-Hempel 2000; Sirviö et al. 2006), but as yet no consensus has been reached. Now, data on genomic recombination rates in many more organisms, including several hymenoptera and other insects, have become available. Furthermore, much of the data from previous studies had to be revised considerably. For example, the estimate of the physical genome size of the honeybee increased from an initial 178 Mb (Jordan and Brosemer 1974) to 265 Mb (S. Johnston, pers. comm. 2006), and from 274 Mb (Gadau et al. 2000) to 625 Mb in the bumblebee, Bombus terrestris (Wilfert et al. in press), due to more accurate analyses and the complete sequencing of the honey bee genome. Therefore a re-evaluation of the genomic data, including recombination rates of honeybees and other social hymenoptera as compared to other taxa, has become necessary. We have undertaken a comprehensive survey of the available data on recombination density for insects, and compare them with data from other animals, plants, fungi and protozoa. Material and Methods The published data on the physical genome size and the recombination length based on linkage mapping was assembled from the literature. The assembled database encompasses a comprehensive survey of the available data for insects, and examples from other animals, plants, fungi and protozoa. Care was taken to cover the full range of genomic recombination densities for these taxa. For example, for the vertebrates, we chose the animals with both the highest and the lowest recombination density known today (the chicken Gallus domesticus (Groenen et al. 2000) and the tiger salamander Ambystoma tigrinum tigrinum (Smith et al. 2005), respectively) as well as recent examples from the literature. Where several recombinational genome sizes for the same organism had been published, we chose the linkage map with the highest coverage and best quality of markers for the current analysis. For example, for the honeybee Apis mellifera, we chose the linkage map produced by Solignac et al. (2004) and neglected those published by Hunt and Page (1995) and Ruppell et al. (2004). The map of Solignac et al. (2004) map is based on 54] markers, most of which 55 are high-quality microsatellites, and shows an average resolution of 7.5 cM. Similarly, for the bumblebee B. terrestris, the genome size produced by the high-coverage linkage map BBM-1 from Wilfert et al. (in press) was used in this analysis instead of the low-resolution maps BBM-2 and BBM-3 from the same study or the RAPD-based map from Gadau et al. (2001 ). For low-coverage maps, the number of linkage groups often exceeds the number of actual chromosomes. These excess linkage groups have to be linked so as to conform to the karyotype. In most mapping studies, the maximum recombination frequency (O) is set to a value between 0.3 and 0.4, which roughly corresponds to a recombinational distance of 40 cM. Therefore, we added a conservative 40 cM per gap to make those maps comparable to more saturated maps. This should prevent artefacts due to underestimation of genomic recombination rates. Results Data pertaining to recombination densities are summarized in table Al. We found that recombination density for both, A. mellifera and B. terrestris differ by a small amount from the originally published estimates, given that the estimates of the respective genetic genome sizes also had to be modified in the meantime. Therefore, recombination densities had to be corrected from 3.85 cM/Mb (Gadau et al. 2001) to 4.42 cM/Mb (Wilfert et al. in press) for the bumblebee, and from 19.38 cM/Mb (Hunt and Page 1995) to 16.53 cM/Mb for the honeybee (based on a recombination genome size of 4'381 cM after Solignac et al. (2004), see table Al). Thus, the honeybee's genomic recombination frequency remains the highest among the known values for animals and plants. Our analysis suggests that there are three somewhat distinct ranges of genomic recombination densities (figure 1). According to table A1 (Fig. 1) the highest recombination densities are found in fungi and protozoa (with the exception of Toxoplasma gondii), two groups which are characterized by small physical genomes. There is some evidence that protochordata might also have high recombination rates. The only linkage map published so far for this taxon basal to all vertebrates reports a high genomic recombination density ranging from 20 cM/Mb to 40 cM/Mb (corresponding to 25 to 49 kb/cM) in the ascidian Ciona intestinalis (Kano et al. 56 2006). All other taxa have consistently lower recombination densities (Fig. 1) Yet the social hymenoptera occupy a position higher than for all other eukaryotes (Fig, 1). Plants (all data from Chagné et al. (2002)), for example, show a wide range of values, with some gymnosperms having recombination densities as low as 0.1 cM/Mb. But even the plants with the highest recombination rates, for example Arabidopsis thaliana, show only about one sixth of the recombination density that characterizes the honeybee, A. mellifera. With regard to insects, all Diptera (average: 0.38 ± 0.30 cM/Mb S.D., n = 4 species) show very low densities, different from the Hymenoptera (6.85 ± 4.95 cM/Mb, n = 8; /7-test, z = 2.733, P = 0.006) and from the Lepidoptera (4.73 ± 1.87 cM/Mb, n = 3; [/-test, z = 2.141, P = 0.032) with the average value for Coleoptera occupying the middle range between these extremes (2.50 ± 1.33 cM/Mb S.D., n = 4). 4 - (A d g CO J2 E o o (1) y 1 - Co CO CD o o E E N ;* ;* 3 (1) CO o in X X -t— J3 o to o o "cö CD Q. o CO Figure 1 : Recombination densities across taxa Average recombination densities (cM/Mb) vary across the large taxonomic groups considered in this study (cf. table Al) (Kruskal-Wallis H = 29.604, df= 7, P = 0.001). The social hymenoptera stand out among the eukaryotes whereas the protozoa have the highest recorded values of For the all. clarity, graph shows the In-transformed values; number of species per group (N) indicated at bottom. The horizontal line marks the median value, boxes indicate ± one quartile, and vertical lines indicate the range of observations. 57 Even with the limited set of data currently available, it thus seems that recombination rates differ among groups (figure 1) and that the social hymenoptera have unusually high rates. For the social hymenoptera, the known recombination densities range from 16.53 cM/Mb (given a recombinational genome size of 4'381 cM after Solignac et al. (2004)) for the highly eusocial A. mellifera, to 4.40 cM/Mb for the primitively eusocial bumblebee B. terrestris (Wilfert et al. in press), with the two investigated ant species Acromyrmex echinatior and Pogonomyrmex rugosus showing intermediate values (table Al). The solitary hymenoptera Nasonia and Bracon sp. near hebetor, by contrast, have much lower recombination rates than the social hymenopterans (table Al). Note that earlier data for Bracon hebetar indicated a high recombination density of 7.75 cM/Mb (Antolin et al. 1996). Reanalyzing their data, however, Holloway et al. (2000) reported a lower recombination genome size of 760 cM for this species. Since the map had one excess linkage group above the haploid karyotype of 10 chromosomes, we added 40 cM to the total recombination length, thus estimating the genome size for this species at 800 cM, resulting in a density of 4.85 cM/Mb. Together with Trichogramma brassicae, which shows a density of 5.41 cM/Mb (table Al), two of the parasitoid hymenoptera have a higher genomic recombination frequency than the primitively eusocial bumblebee. On average, however, the social hymenoptera (9.73 ± 5.77 cM/Mb, n = 4) have a higher average recombination density than all other insects (2.68 ± 2.02 cM/Mb, n = 16; [/-test, z = -2.507, P = 0.007). Discussion In social systems, natural selection is expected to favor mechanisms resulting in increased genotypic diversity by female multiple mating (Crozier and Fjerdingstad 2001), to counter the threat posed by parasites (Schmid-Hempel 2000; Sherman 1988) or to stabilize the division of labor (Sherman 1979). A similar argument pertains to recombination (Gadau et al. 2000; Schmid-Hempel 2000) with an increase predicted for social insects, especially in advanced taxa. The data assembled here are supportive of Gadau et al.'s (Gadau 2000) suggestions. In particular, we find that the socially advanced honeybee, A. mellifera, has a fourfold higher recombination density than its primitively eusocial counterpart, the bumble bee, B. terrestris. The eusocial ant species studied so far, P. rugosus and A. echinatior, also show elevated recombination rates. In fact, with 12.5 cM/Mb, P. rugosus has the second highest recombination rate so far reported for animals or plants, next to A. mellifera. 58 Alternatively to the natural-selection based hypotheses advanced above, the exceptionally high recombination density of A. mellifera may be the result of domestication, which is typically associated with strong directional selection exerted by breeders, which is known to increase recombination rates (Schmid-Hempel and Jokela 2002). Increased values of recombination, as measured by chiasmata frequencies, have in fact been observed in domesticated mammals (Burt and Bell 1987) and plants (Ross-Ibarra 2004). However, our data does not show this pattern in the insects. In the hymenoptera, the ant P. rugosus has a recombination rate similar to the honeybee, yet has never been subject to domestication. Vice versa, the domesticated silkworm Bombyx mori, does not show an increase in recombination frequencies as compared to other Lepidoptera (see Table Al). Note that this remains so if one prefers the higher estimate of genome size from a recent, nearly saturated SSR-based map of B. mori (Miao et al. 2005). We therefore can reject artificial selection through domestication as an explanation for the increased recombination rate of the honeybee and social insects in general. Besides variation in the intra-chromosomal recombination rate via crossovers the genotypic diversity of a mother's offspring is also affected by the number of independently segregating chromosomes. Sherman (1979) demonstrated that chromosome numbers are higher in eusocial taxa as compared to closely related solitary groups. Using colony size as an indicator of parasite load, Schmid-Hempel (1998) found that among 58 ant species chromosome number increases with the typical colony size (assumed to be an indicator of higher parasite loads and more sophisticated societies), and that this effect persisted after correcting for phylogenetic dependencies. Although our present dataset (table Al) includes only a small fraction of the available karyotype data, they are consistent with the results of Sherman (1979) as the social hymenoptera included in this study indeed have higher chromosome numbers, (haploid N, range 16-18 chromosomes) than their parasitoid counterparts (range 5-10; [/-test, Z = 2.397, p = 0.029). The results reported here are encouraging in suggesting that social insects have unusually high recombination frequencies even though more data clearly are needed to thoroughly evaluate this pattern and to elucidate the importance of sociality for the evolution of recombination frequencies. Data on closely related species with varying degrees of sociality would be highly valuable in this context, including examples from the isoptera and their non- social relatives. Moreover, the nature of the selective pressure for increased recombination in 59 social insects is still unclear, with both, selection for sophisticated division of labor (but see Brown and Schmid-Hempel (2003)) and selection to mitigate against parasites (Fischer and Schmid-Hempel 2005) being two major contenders. Social parasitic hymenoptera such as the cuckoo bumblebees or parasitic ant species that take over the worker force of a host colony face the same parasite pressure as their host, but lack division of labor since they have lost the worker caste. The genomic recombination of these species would be particularly interesting to study the two rival functional hypotheses. With data on genomic recombination frequencies now slowly accumulating the study of social insects has great promise to test theories on the evolution of recombination rates in a new context. Acknowledgements Financially supported by grants from the Swiss SNF (3100-066733 to PSH) and ETH Zurich (TH TH-19/03-2 to PSH and LW). References Antolin, M. F., C. F. Bosio, J. Cotton, W. Sweeney, M. R. Strand, and W. C. Black. 1996. Intensive linkage mapping in a wasp (Bracon hebetor) and a mosquito (Aedes aegypti) with single-strand conformation polymorphism analysis of random amplified polymorphic DNA markers. Genetics 143:1727-1738. Baer, B., and P. Schmid-Hempel. 1999. Experimental variation in polyandry affects parasite loads and fitness in a bumble-bee. Nature 397:151-154. Baker, B. S., A. T. C. Carpenter, M. S. Esposito, R. E. Esposito, and L. Sandler. 1976. Genetic-Control of Meiosis. Annu. Rev. Genet. 10:53-134. Barton, N. H., and B. Cliariesworth. 1998. Why sex and recombination? Science 281:1986- 1990. Bell, G. 1982. The masterpiece of nature. University of California Press, Berkeley. Brooks, L. D. 1988. The evolution of recombination rates. Pp. 87-105 in R. E. Michod and B. R. Levin, eds. The evolution of sex: an examination of current ideas. Sinauer, Sunderland, Massachusetts. 60 Brown, M. J. F., and P. Schmid-Hempel. 2003. The evolution of female multiple mating in social hymenoptera. Evolution 57:2067-2081. Burt, A., and G. Bell. 1987. Mammalian Chiasma Frequencies as a Test of 2 Theories of Recombination. Nature 326:803-805. Chagné, D., C. Lalanne, D. Madur, S. Kumar, J. M. Frigerio, C. Krier, S. Decroocq, A. Savoure, M. Bou-Dagher-Kharrat, E. Bertocchi, J. Brach, and C. Plomion. 2002. A high density genetic map of maritime pine based on AFLPs. Ann. For. Sei. 59:627- 636. Cole, B. J., and D. C. Wiernasz. 1999. The selective advantage of low relatedness. Science 285:891-893. Crozier, R. H., and E. J. Fjerdingstad. 2001. Polyandry in social Hymenoptera - disunity in diversity? Ann. Zool. Fenn. 38:267-285. Fischer, O., and P. Schmid-Hempel. 2005. Selection by parasites may increase host recombination frequency. Biol. Lett. 1:193-195. Foster, K. R., P. Seppa, F. L. W. Ratnieks, and P. A. Thoren. 1999. Low paternity in the hornet Vespa crabro indicates that multiple mating by queens is derived in vespine wasps. Behav. Ecol. Sociobiol. 46:252-257. Gadau, J., C. U. Gerloff, N. Kruger, H. Chan, P. Schmid-Hempel, A. Wille, and R. E. Page. 2001. A linkage analysis of sex determination in Bombus terrestris (L.) (Hymenoptera : Apidae). Heredity 87:234-242. Gadau, J., R. E. Page, J. H. Werren, and P. Schmid-Hempel. 2000. Genome organization and social evolution in hymenoptera. Naturwissenschaften 87:87-89. Groenen, M. A. M., H. H. Cheng, N. Bumstead, B. F. Benkel, W. E. Briles, T. Burke, D. W. Burt, L. B. Crittenden, J. Dodgson, J. Hillel, S. Lamont, A. P. de Leon, M. Soller, H. and Takahashi, A. Vignal. 2000. A consensus linkage map of the chicken genome. Genome Res. 10:137-147. Hamilton, W. D. 1987. Kinship, recognition, disease, and intelligence: constraints of social evolution. Pp. 81-102 in Y. Itô, J. L. Brown and J. Kikkawa, eds. Animal societies: theories and facts. Japanese Scientific Society Press, Tokyo. Holloway, A. K., M. R. Strand, W. C. Black, and M. F. Antolin. 2000. Linkage analysis of sex determination in Bracon sp near hebetor (Hymenoptera : Braconidae). Genetics 154:205-212. Hunt, G. J., and R. E. Page. 1995. Linkage Map of the Honey-Bee, Apis-Mellifera, Based on Rapd Markers. Genetics 139:1371-1382. 61 Jordan, R. A., and R. W. Brosemer. 1974. Characterization of DNA from 3 Bee Species. J. Insect. Physiol. 20:2513-2520. Kano, S., N. Satoh, and P. Sordino. 2006. Primary genetic linkage maps of the ascidian, Ciona intestinalis. Zool. Sei. 23:31-39. Liersch, S., and P. Schmid-Hempel. 1998. Genetic variation within social insect colonies reduces parasite load. Proc. R. Soc. Lond. Ser. B-Biol. Sei. 265:221-225. Miao, X. X., S. J. Xu, M. H. Li, M. W. Li, J. H. Huang, F. Y. Dai, S. W. Marino, D. R. Mills, P. Y. Zeng, K. Mita, S. H. Jia, Y. Zhang, W. B. Liu, H. Xiang, Q. H. Guo, A. Y. Xu, X. Y. Kong, H. X. Lin, Y. Z. Shi, G. Lu, X. L. Zhang, W. Huang, Y. Yasukochi, T. Sugasaki, T. Shimada, J. Nagaraju, Z. H. Xiang, S. Y. Wang, M. R. Goldsmith, C. Lu, G. P. Zhao, and Y. P. Huang. 2005. Simple sequence repeat-based consensus linkage map of Bombyx mori. Proc. Natl. Acad. Sei. USA 102:16303-16308. Moritz, R. F. A., P. Kryger, G. Koeniger, N. Koeniger, A. Estoup, and S. Tingek. 1995. High- Degree of Polyandry in Apis dorsata Queens Detected by DNA Microsatellite Variability. Behav. Ecol. Sociobiol. 37:357-363. Oldroyd, B. P., T. E. Rinderer, S. M. Buco, and L. D. Beaman. 1993. Genetic Variance in Honey-Bees for Preferred Foraging Distance. Anim Behav 45:323-332. Oldroyd, B. P., T. E. Rinderer, J. R. Harbo, and S. M. Buco. 1992. Effects of Intracolonial Genetic Diversity on Honey-Bee (Hymenoptera, Apidae) Colony Performance. Ann. Entomol. Soc. Am. 85:335-343. Oldroyd, B. P., H. A. Sylvester, S. Wongsiri, and T. E. Rinderer. 1994. Task Specialization in a Wild Bee, Apis-Florea (Hymenoptera, Apidae), Revealed by Rflp Banding. Behav, Ecol. Sociobiol. 34:25-30. Otto, S. P., and T. Lenormand. 2002. Resolving the paradox of sex and recombination. Nat. Rev. Genet. 3:252-261. Page, R. E., G. E. Robinson, and M. K. Fondrk. 1989. Genetic Specialists, Kin Recognition and Nepotism in Honeybee Colonies. Nature 338:576-579. Ross-Ibarra, J. 2004. The evolution of recombination under domestication: A test of two hypotheses. Am. Nat. 163:105-112. Ruppell, O., T. Pankiw, and R. E. Page. 2004. Pleiotropy, epistasis and new QTL: The genetic architecture of honey bee foraging behavior. J. Hered. 95:481-491. Schmid-Hempel, P. 1998. Parasites in Social Insects. Princeton University Press, Princeton, New Jersey. 62 Schmid-Hempel, P. 2000. Mating, parasites and other trials of life in social insects. Microbes Infect. 2:515-520. Schmid-Hempel, P., and R. H. Crozier. 1999. Polyandry versus polygyny versus parasites. Philos. Trans. R. Soc. Lond. Ser. B-Biol. Sei. 354:507-515. Schmid-Hempel, P., and J. Jokela. 2002. Socially structured populations and evolution of recombination under antagonistic coevolution. Am. Nat. 160:403-408. Sherman, P. W. 1979. Insect Chromosome-Numbers and Eusociality. Am. Nat. 113:925-935. Sherman, P. W., T. D. Seeley, and H. K. Reeve. 1988. Parasites, Pathogens, and Polyandry in Social Hymenoptera. Am. Nat. 131:602-610. Sirvio, A., J. Gadau, O. Rueppell, D. Lamatsch, J. J. Boomsma, P. Pamilo, and R. E. Page. 2006. High recombination frequency creates genotypic diversity in colonies of the leaf-cutting ant Acromyrmex echinatior. J. Evol. Biol. 19:1475-1485. Smith, J. J., D. K. Kump, J. A. Walker, D. M. Parichy, and S. R. Voss. 2005. A comprehensive expressed sequence tag linkage map for tiger salamander and Mexican axolotl: enabling gene mapping and comparative genomics in ambystoma. Genetics 171:1161-1171. Solignac, M., D. Vautrin, E. Baudry, F. Mougel, A. Loiseau, and J. M. Cornuet. 2004. A microsatellite-based linkage map of the Honeybee, Apis mellifera L. Genetics 167:253-262. Tarpy, D. R. 2003. Genetic diversity within honeybee colonies prevents severe infections and promotes colony growth. Proc. R. Soc. Lond. B. 270:99-103. Templeton, A. R. 1979. Chromosome-Number, Quantitative Genetics and Eusociality. Am. Nat. 113:937-941. Wilfert, L., J. Gadau, and P. Schmid-Hempel. in press. A core linkage map of the bumblebee Bombus terrestris. Genome Appendix A Summary of geneticdata used for this study.Studies are listed alphabeticallywithin taxonomic groups. The haploidkaryotypeand estimates of the geneticand physicalgenome sizes (incM and Mb, respectively)as well as the resultingrecombination density(incM /Mb) are indicated. Table Al : Genomic Recombination rates across different taxa Karyotype Genetic Physical Density Species Class Order Familiv Source (IN) size [cM] size [Mb] [cM/Mb] echinatior Acromyrmex Insecta Hymenoptera Formicidae IS T236-' 335 6.7 (Sirviöeta!. 2006) (Solignacet al,2004).pers. comm. S. Johnston, Apis mellifera Insecta Hymenoptera Apidae 16 4'38! 265 16.5 2006 Bombus terrestris Insecta I lymenoptera Apidae 2'760 625 4.4 (Gadau et ah 2001; Wilfert et ai,in press) Pogonomyrmex rugosus Insecta Hymenoptera Formicidae 18 3'558 255 14.0 (Sirviöet ai, 2006} On et ai, 1977; Antolin et al. 1996). " (Rasch genome Bracon hebetor Insecta Hymenoptera Braconidae 10 800 165 4.8 size followingHolloway etat (2000) Bracon hebetor Insecta sp. near Hymenoptera Bracon idae 10 536 165 3.2 (Rasch et al. 1977; Holloway et ai, 2000) Nasonia vitripermisx giraulti Insecta Hymenoptera Pteromalidae 5 765 312 2.5 (Rasch et ai. 1977; Gadau et al, 1999) Trichogramma brassica Insecta tidae 5 T330 5.4 et al. Johnston et Hymenoptera Trichogramma 246 (Laurent 1998; al,2004) (Warren and Crampton, 1991; Brown et ai,2001; Aedes aegypti Insecta Diptera Culicidae 205 780 0.3 Severson étal,2002) Insecta Anophelesgambiae Diptera Culicidae 3 215 260 0.8 (Besanskyand Powell. 1992; Zheng et al,1996) Armigeressubaibadts Insecta Diptera Culicidae 3 182 l'215!l 0.1 (RaoandRai. 1990; Ferdiget ai, 1998) (Gage, 1974: Yasukochi. 1998; Yamamoto étal, : Bombyx mon Insecta Lepidoptera Bombycidae 28 1 '305 495 2.6 2006) Culex pipiens Insecta Diptera Culicidae 3 166 528 0.3 (Jostand Mameli, 1972; Mori et al. 1999) Heiiconius erato Insecta Lepidoptera Nymphali dae 21 2'400 396 6.1 (Tobleret ai,2005) Helicon ins melpomene Insecta Lepidoptera Nymphalidae 21 1 '616 292 5.5 (Jigginset al,2005) Laitpalaspec. Insecta Orthoptera Gry]lidae 2'330 F900 1.2 (Petrov et al,2000; Parsons and Shaw. 2002) Species Class Order Familiy Karyotype Gen. size Phys.size Density Source decernlineata Insecta 18 2.3 'Ji Leptinotarsa Coleoptera Chrysomelidae T032 450 (Petitpierreet ai,1993; Hawthorne, 2001) p Rhyzoperthadominica Insecta Coleoptera Bos triehidae 9 390 476 0.8 {Schlipaliusertp/.2002) Tribal i' u htm castaneum Insecta Coleoptera Tenebrionidae 10 571 199 2.9 (Alvarez-Fusteret ai, 1991; Lorenzen et ai,2005) c Tribolium Insecta confusum Coleoptera Tenebrionidae 10 969 245'' 4.0 (Alvarez-Fusteret al,1991 ; Yezerski et ai,2003) Crassostrea gigas Bi vat via Ostreoida Ostreidae 10 1'096-51 890 1.2 (Gonzalez-Tizon et al,2000; Li and Guo. 2004) Crassostrea virginica Bivalvia Ostreoida Ostreidae 10 ro77-" 675 1.6 (Hinegardner,1974; Yu and Guo. 2003) (Yu and Guo, 2003),(Rouppe van der Voort et al. Globodera rostochiensis Chromadorea Tylenchida Heteroderidae 9 650 ca. 80 8.3 Ö 1999) Ë o (Lapp and Triantap.Ac,1972; Opperman and Bird. to 41 o Heterodera glycines Chromadorea Tylenchida Heterodertdae 9 687 93 7.4J> 1998;Atibalenljae(ö',2005) u (Chen et al, 1994; Ulimann et al.2003; Uilmann et Ixodes scapttlaris Arachnida Ixodida Ixodidae 14 616 2'107 0.3 c al,2005) Penaeus monodon Malacostraca " Decapoda Pen aidae 44 2"292 2'000 1.1 (Chow et ai,1990; Wilson et al,2002) " Penaeus vannamei Malacostraca Decapoda Pen aidae 44 4'015 2'393 1.7 (Chowetal, 1990; Perez étal, 2004) Pristionchus pacifiais Chromadorea 6 339 100 3.4 et Srinivasan Diplogasterida Neodiplogasteridae (Sommer al, 1996; et al,2002) On 51 Ambysloma tigrinum Amphibia Caudata Ambystomatidae 14 5'251 26"950:i 0.2 (Morescalchi and Olmo, 1982; Smith étal, 2005) cabal Ins Eqmis Mammalia Perissodactyla E quidae 32 Till 3'087!l 0.9 (Tiersch et al,1989; Swinburne étal, 2006) S1 rt Gallus gailus Aves Gailiformes Phasianidae 39 3'800 l'225:i 3.1 (Groenen et ai.2000) Homo sapiens Mammalia Primates Hominidae 23 3'615 3'19l 1.3 (Kong et al. 2002) (L> Kiacaca mulatto > Mammalia Primates Hominidae 21 2'048 3'077!1 0.7 (Manfredi, 1972; Rogers et al,2006) Mus muscuhts tia 1 Mammalia Roden Muridae 20 "361 3*179 0.4 (Dietriche; oj, 1996: Vinogradov, 1998) Takifugurubripes Osteichthyes Tetraodonti formes Tetraodontidae 22 ri3511Jl 3922h 2.9 (Brenner et al, 1993; Kai et al.2005) " Plasmodium chabaudi Aconoidasida Haemosporida Plasmodiidae 14 1'996 23 86.8 (Martinelliet al.2005) 2 Plasmodium falciparum Aeon oidas ida Haemosporida Plasm odiidae 14 1'556 25-30 58.8 (Wellemse/fl/,1987;Sue?o/. 1999) c Toxoplasma gondii Coccidia Eimeriida Sa reo cystidae 14 592 ca. 65 9.6 (Khan étal, 2005) Ttypatiosomabrucei Kinetoplastida Trypanos omai dae II T358" 25 54.3 (MacLeod étal,2005) thaliana n Arahidopsis Rosids Brassicales Brassicaceae 5 675 350 4.5 (Chagné ef«/, 2002) 2 Brassica rapa Rosids" Brassicales Brassicaceae 10 l"850 650 2.8 (Chagné étal, 2002) J3 (Chagné Eucalyptusgrandis Rosids4' Myrtales Myrtaceae 11 !'370 600 2.3 er öj, 2002) Hordeum vulgare Liliopsida Poales Poaceae 7 F250 5'500 0.2 (Chagné et ai.2002) Species Class Order Famihy Karyotype Gen. size Phys.size Density S Lactuca salira ds''1 Ästen Asterales Asleraceae 9 1"950 2'730 0.7 (Chagnéétal, 2002) esculentum Asterids" le Lycopersicon Sol ana s Sol anaceae 12 1*280 980 1.3 (Chagnéel al,2002) saliva Otyza Liliopsida Poales Poaceae 12 1"490 450 3.3 (Chagné et ai,2002) Pinus i pinaster Coniferopsida Con ferales Pinaceae 12 ]"850 25'700 0.1 (Chagnéet al.2002) Pinus iaeda Coniferopsida Coniferales Pinaceae 12 ['700 2F000 0.1 (Chagné et al. 2002) deltoïdes Rosids(" Populus Malphigiaies Sahcaceae 19 2'300 550 4.2 (Chagnéet al. 2002) '" Prunus perstca Rosids Rosa les Rosaceae 8 712 300 2.4 (Chagné et al. 2002) robur Rosids"1 Querctts Fagales Fagaceae 12 ['200 900 1.3 (Chagné et al. 2002) Solanum tuberosum Asterids'" Sol anal es Solanaceae 12 1'120 1'540 0.7 (Chagné et al. 2002) Tri lieurn iauschh Poales Liliopsida Poaceae 7 ['330 4'200 0.3 (Chagné et al,2002) Zea mays Liliopsida Poales Poaceae 10 I'860 2'500 0.7 (Chagnéétal, 2002) Ciyptococcusneoformans Heterobasidiomycetes Tremellales Tremellaceae 14 1500 20.2 74 3 (Marra et al,2004) cinereus - Coprinus Ilomobasidiomycetes Agaricales Agaricaceae 13 346 35.8 (Muraguclnet al,2003) Cochiiobolus sativus Doth ideomycêtes Pleosporales Pleosporaceae 35 849 33 25.7 (Zhong et ai.2002) Os on minimal genome size: we added 40 cM per excess linkagegroup to the geneticmap size providedby the originalauthors the physicalgenome size in MB was calculated from itsweightin pg as 1 pg = 980 MB, following(Cavallier-Smith,1985) averagedover the sexes averagedestimate genome size estimated by originalauthors subclass 66 Appendix References Alvarez-Fuster, A., C. Juan, and E. Petitpierre. 1991. Genome Size in Tribolium Flour- Beetles - Interspecific and Intraspecific Variation. Genet. Res. 58:1-5. Antolin, M. F., C. F. Bosio, J. Cotton, W. Sweeney, M. R. Strand, and W. C. Black. 1996. Intensive linkage mapping in a wasp (Bracon hebetor) and a mosquito (Aedes aegypti) with single-strand conformation polymorphism analysis of random amplified polymorphic DNA markers. Genetics 143:1727-1738. Atibalentja, N., S. Bekal, L. L. Domier, T. L. Niblack, G. R. Noel, and K. N. Lambert. 2005. A genetic linkage map of the soybean cyst nematode Heterodera glycines. Mol. Genet. Genomics 273:273-281. Besansky, N. J., and J. R. Powell. 1992. Reassociation Kinetics of Anopheles gambiae (Diptera, Culicidae) DNA. J. Med. Entomol. 29:125-128. Brenner, S., G. Elgar, R. Sandford, A. Macrae, B. Venkatesh, and S. Aparicio. 1993. Characterization of the Pufferfish (Fugu) Genome as a Compact Model Vertebrate Genome. Nature 366:265-268. Brown, S. E., D. W. Severson, L. A. Smith, and D. L. Knudson. 2001. Integration of the Aedes aegypti mosquito genetic linkage and physical maps. Genetics 157:1299-1305. Chagné, D., C. Lalanne, D. Madur, S. Kumar, J. M. Frigerio, C. Krier, S. Decroocq, A. Savoure, M. Bou-Dagher-Kharrat, E. Bertocchi, J. Brach, and C. Plomion. 2002. A high density genetic map of maritime pine based on AFLPs. Ann. For. Sei. 59:627- 636. Chen, C. S., U. G. Munderloh, and T. J. Kurtti. 1994. Cytogenetic Characteristics of Cell- Lines from Ixodes scapularis (Acari. Ixodidae). J. Med. Entomol. 31:425-434. Chow, S., W. J. Dougherty, and P. A. Sandifer. 1990. Meiotic Chromosome Complements and Nuclear-DNA Contents of 4 Species of Shrimps of the Genus Penaeus. J. Crust. Biol. 10:29-36. Dietrich, W. F., J. Miller, R. Steen, M. A. Merchant, D. DamronBoles, Z. Husain, R. Dredge, M. J. Daly, K. A. Ingalls, T. J. Oconnor, C. A. Evans, M. M. DeAngelis, D. M. Levinson, L. Kruglyak, N. Goodman, N. G. Copeland, N. A. Jenkins, T. L. Hawkins, L. Stein, D. C. Page, and E. S. Lander. 1996. A comprehensive genetic map of the mouse genome. Nature 380:149-152. 67 Ferdig, M. T., A. S. Taft, D. W. Severson, and B. M. Christensen. 1998. Development of a comparative genetic linkage map for Armigeres subalbatus using Aedes aegypti RFLP markers. Genome Res. 8:41-47. Gadau, J., C. U. Gerloff, N. Kruger, H. Chan, P. Schmid-Hempel, A. Wille, and R. E. Page. 2001. A linkage analysis of sex determination in Bombus terrestris (L.) (Hymenoptera : Apidae). Heredity 87:234-242. Gadau, J., R. E. Page, and J. H. Werren. 1999. Mapping of hybrid incompatibility loci in Nasania. Genetics 153:1731-1741. Gage, L. P. 1974. Bombyx mari Genome - Analysis by DNA Reassociation Kinetics. Chromosoma 45:27-42. Gonzalez-Tizon, A., A. Martinez-Lage, I. Rego, J. Ausio, and J. Mendez. 2000. DNA content, karyotypes, and chromosomal location of 18S-5.8S-28S rJbosomal loci in some species of bivalve molluscs from the Pacific Canadian coast. Genome 43:1065-1072. Groenen, M. A. M., H. H. Cheng, N. Bumstead, B. F. Benkel, W. E. Briles, T. Burke, D. W. Burt, L. B. Crittenden, J. Dodgson, J. Hillel, S. Lamont, A. P. de Leon, M. Soller, H. Takahashi, and A. Vignal. 2000. A consensus linkage map of the chicken genome. Genome Res. 10:137-147. Hawthorne, D. J. 2001. AFLP-based genetic linkage map of the Colorado potato beetle Leptinotarsa decemlineata: Sex chromosomes and a pyrethroid- resistance candidate gene. Genetics 158:695-700. Hinegardner, R. 1974. Cellular DNA Content of Mollusca. Comp. Biochem. Physiol. A: Physiol. 47:447-460. Holloway, A. K., M. R. Strand, W. C. Black, and M. F. Antolin. 2000. Linkage analysis of sex determination in Bracon sp near hebetor (Hymenoptera : Braconidae). Genetics 154:205-212. Jiggins, C. D., J. Mavarez, M. Beltran, W. O. McMillan, J. S. Johnston, and E. Bermingham. 2005. A genetic linkage map of the mimetic butterfly Heliconius melpomene. Genetics 171:557-570. Johnston, J. S., L. D. Ross, L. Beani, D. P. Hughes, and J. Kathirithamby. 2004. Tiny genomes and endoreduplication in Strepsiptera. Insect Mol. Biol. 13:581-585. Jost, E., and M. Mameli. 1972. DNA Content in 9 Species of Nematocera with Special Reference to Sibling Species of Anopheles maculipennis Group and Culex-Pipiens Group. Chromosoma 37:201-&. 68 Kai, W., K. Kikuchi, M. Fujita, H. Suetake, A. Fujiwara, Y. Yoshiura, M. Ototake, B. Venkatesh, K. Miyaki, and Y. Suzuki. 2005. A genetic linkage map for the tiger pufferfish, Takifugu rubripes. Genetics 171:227-238. Khan, A., S. Taylor, C. Su, A. J. Mackey, J. Boyle, R. Cole, D. Glover, K. Tang, I. T. Paulsen, M. Berriman, J. C. Boothroyd, E. R. Pfefferkorn, J. P. Dubey, J. W. Ajioka, D. S. Roos, J. C. Wootton, and L. D. Sibley. 2005. Composite genome map and recombination parameters derived from three archetypal lineages of Toxoplasma gondii. Nucleic Acids Res. 33:2980-2992. Kong, A., D. F. Gudbjartsson, J. Sainz, G. M. Jonsdottir, S. A. Gudjonsson, B. Richardsson, S. Sigurdardottir, J. Barnard, B. Hallbeck, G. Masson, A. Shlien, S. T. Palsson, M. L. Frigge, T. E. Thorgeirsson, J. R. Gulcher, and K. Stefansson. 2002. A high-resolution recombination map of the human genome. Nat. Genet. 31:241-247. Lapp, N. A., and Triantap.Ac. 1972. Relative DNA Content and Chromosomal Relationships of Some Meloidogyne, Heteradera, and Meloidodera Spp (Nematoda - Heteroderidae). J. Nematol. 4:287-&. Laurent, V., E. Wajnberg, B. Mangin, T. Schiex, C. Gaspin, and F. Vanlerberglie-Masutti. 1998. A composite genetic map of the parasitoid wasp Trichogramma brassicae based on RAPD markers. Genetics 150:275-282. Li, L., and X. M. Guo. 2004. AFLP-based genetic linkage maps of the Pacific oyster Crassostrea gigas Thunberg. Mar. Biotechnol. 6:26-36. Lorenzen, M. D., Z. Doyungan, J. Savard, K. Snow, L. R. Crumly, T. D. Shippy, J. J. Stuart, S. J. Brown, and R. W. Beeman. 2005. Genetic linkage maps of the red flour beetle, Tribolium castaneum, based on bacterial artificial chromosomes and expressed sequence tags. Genetics 170:741-747. MacLeod, A., A. Tweedie, S. McLellan, S. Taylor, N. Hall, M. Berriman, N. M. El-Sayed, M. Hope, C. M. R. Turner, and A. Tait. 2005. The genetic map and comparative analysis with the physical map of Trypanosoma brucei. Nucleic Acids Res. 33:6688-6693. Manfredi, M. G. 1972. Nuclear DNA Content and Area of Primate Lymphocytes as a Cytotaxonomical Tool. J. Hum. Evol. 1:23-40. Maria, R. E., J. C. Huang, E. Fung, K. Nielsen, J. Heitman, R. Vilgalys, and T. G. Mitchell. 2004. A genetic linkage map of Cryptococcus neoformans variety neoformans serotype D (Filobasidiella neoformans). Genetics 167:619-631. 69 Martinelli, A., P. Hunt, R. Fawcett, P. V. L. Cravo, D. Walliker, and R. Carter. 2005. An AFLP-based genetic linkage map of Plasmodium chabaudi chabaudi. Malar. J. 4:Art, No. 11. Morescalchi, A., and E. Olmo. 1982. Single-Copy DNA and Vertebrate Phylogeny. Cytogenet. Cell Genet. 34:93-101. Mori, A., D. W. Severson, and B. M. Christenson. 1999. Comparative linkage maps for the mosquitoes (Culex pipiens and Aedes aegypti) based on common RFLP loci. J. Hered. 90:160-164. Muraguchi, H., Y. Ito, T. Kamada, and S. O. Yanagi. 2003. A linkage map of the basidiomycete Coprinus cinereus based on random amplified polymorphic DNAs and restriction fragment length polymorphisms. Fungal Genet. Biol. 40:93-102. Opperman, C. H., and D. M. Bird. 1998. The soybean cyst nematode, Heterodera glycines: a genetic model system for the study of plant-parasitic nematodes. Curr. Opin. Plant Biol. 1:342-346. Y. Parsons, M., and K. L. Shaw. 2002. Mapping unexplored genomes: A genetic linkage map of the Hawaiian cricket Laupala. Genetics 162:1275-1282. Perez, F., C. Erazo, M. Zliinaula, F. Volckaert, and J. Calderon. 2004. A sex-specific linkage map of the white shrimp Penaeus (Litopenaeus) vannamei based on AFLP markers. Aquaculture 242:105-118. Petitpierre, E., C. Segarra, and C. Juan. 1993. Genome Size and Chromosomal Evolution in Leaf Beetles (Coleoptera, Chrysomelidae). Hereditas 119:1-6. Petrov, D. A., T. A. Sangster, J. S. Johnston, D. L. Hartl, and K. L. Shaw. 2000. Evidence for DNA loss as a determinant of genome size. Science 287:1060-1062. Rao, P. N., and K. S. Rai. 1990. Genome Evolution in the Mosquitos and Other Closely Related Members of Superfamily Culicoidea. Hereditas 113:139-144. Rasch, E. M., J. D. Cassidy, and R. C. King. 1977. Evidence for Dosage Compensation in Parthenogenetic Hymenoptera. Chromosoma 59:323-340. Rogers, J., R. Garcia, W. Shelledy, J. Kaplan, A. Arya, Z. Johnson, M. Bergstrom, L. Novakowski, P. Nair, A. Vinson, D. Newman, G. Heckman, and J. Cameron. 2006. An initial genetic linkage map of the rhesus macaque (Macaca mulatto) genome using human microsatellite loci. Genomics 87:30-38. Rouppe van der Voort, J. N. A. M., H. J. van Eck, P. M. van Zandvoort, H. Overmars, J. Helder, and J. Bakker. 1999. Linkage analysis by genotyping of sibling populations: a 70 genetic map for the potato cyst nematode constructed using a "pseudo-F2" mapping strategy. Mol. Gen. Genet. 261:1021-1031. Schlipalius, D. I., Q. Cheng, P. E. B. Reilly, P. J. Collins, and P. R. Ebert. 2002. Genetic linkage analysis of the lesser grain borer Rhyzopertha dominica identifies two loci that confer high-level resistance to the fumigant phosphine. Genetics 161:773-782. Severson, D. W., J. K. Meece, D. D. Lovin, G. Saha, and I. Morlais. 2002. Linkage map organization of expressed sequence tags and sequence tagged sites in the mosquito, Aedes aegypti. Insect Mol. Biol. 11:371-378. Sirviö, A., J. Gadau, O. Rueppell, D. Lamatsch, J. J. Boomsma, P. Pamilo, and R. E. Page. 2006. High recombination frequency creates genotypic diversity in colonies of the leaf-cutting ant Acromyrmex echinatior. J. Evol. Biol. 19:1475-1485. Smith, J. J., D. K. Kump, J. A. Walker, D. M. Parichy, and S. R. Voss. 2005. A comprehensive expressed sequence tag linkage map for tiger salamander and Mexican axolotl: enabling gene mapping and comparative genomics in ambystoma. Genetics 171:1161-1171. Solignac, M., D. Vautrin, E. Baudry, F. Mougel, A. Loiseau, and J. M. Cornuet. 2004. A microsatellite-based linkage map of the Honeybee, Apis mellifera L. Genetics 167:253-262. Sommer, R. J., L. K. Carta, S. Y. Kim, and P. W. Sternberg. 1996. Morphological, genetic and molecular description of Pristionchus pacificus sp n (Nematoda: Neodiplogastridae). Fund. Appl. Nematol. 19:511-521. Srinivasan, J., W. Sinz, C. Lanz, A. Brand, R. Nandakumar, G. Raddatz, H. Witte, H. Keller, I. Kipping, A. Pires-daSilva, T. Jesse, J. Miliare, M. de Both, S. C. Schuster, and R. J. Sommer. 2002. A bacterial artificial chromosome-based genetic linkage map of the nematode Pristionchus pacificus. Genetics 162:129-134. Su, X. Z., M. T. Ferdig, Y. M. Huang, C. Q. Huynh, A. Liu, J. T. You, J. C. Wootton, and T. E. Wellems. 1999. A genetic map and recombination parameters of the human malaria parasite Plasmodium falciparum. Science 286:1351-1353. Swinburne, J. E., M. Boursnell, G. Hill, L. Pettitt, T. Allen, B. Chowdhary, T. Hasegawa, M. Kurosawa, T. Leeb, S. Mashima, J. R. Mickelson, T. Raudsepp, T. Tozaki, and M. Binns. 2006. Single linkage group per chromosome genetic linkage map for the horse, based on two three-generation, full-sibling, crossbred horse reference families. Genomics 87:1-29. 71 Tiersch, T. R., R. W. Chandler, S. S. Wachtel, and S. Elias. 1989. Reference-Standards for Flow-Cytometry and Application in Comparative Studies of Nuclear-DNA Content. Cytometry 10:706-710. Tobler, A., D. Kapan, N. S. Flanagan, C. Gonzalez, E. Peterson, C. D. Jiggins, J. S. Johntson, D. G. Heckel, and W. O. McMillan. 2005. First-generation linkage map of the warningly colored butterfly Heliconius erato. Heredity 94:408-417. Ulimann, A. J., C. M. R, Lima, F. D. Guerrero, J. Piesman, and W. C. Black. 2005. Genome size and organization in the blacklegged tick, Ixodes scapularis and the Southern cattle tick, Boophilus microplus. Insect Mol. Biol. 14:217-222. Uilmann, A. J., J. Piesman, M. C. Dolan, and W. C. Black. 2003. A preliminary linkage map of the hard tick, Ixodes scapularis. Insect Mol. Biol. 12:201-210. Vinogradov, A. E. 1998. Genome size and GC-percent in vertebrates as determined by flow cytometry: The triangular relationship. Cytometry 31:100-109. Warren, A. M., and J. M. Crampton. 1991. The Aedes aegypti Genome - Complexity and Organization. Genet. Res. 58:225-232. Wellems, T. E., D. Walliker, C. L. Smith, V. E. Dorosario, W. L. Maloy, R. J. Howard, R. Carter, and T. F. McCutchan. 1987. A Histidine-Rich Protein Gene Marks a Linkage Group Favored Strongly in a Genetic Cross of Plasmodium falciparum. Cell 49:633- 642. Wilfert, L., J. Gadau, and P. Schmid-Hempel. in press. A core linkage map of the bumblebee Bombus terrestris. Genome Wilson, K., Y. T. Li, V. Whan, S. Lehnert, K. Byrne, S. Moore, S. Pongsomboon, A. Tassanakajon, G. Rosenberg, E. Bailment, Z. Fayazi, J. Swan, M. Kenway, and J. Benzie. 2002. Genetic mapping of the black tiger shrimp Penaeus monodon with amplified fragment length polymorphism. Aquaculture 204:297-309. Yamamoto, K., J. Narukawa, K. Kadono-Okuda, J. Nohata, M. Sasanuma, Y. Suetsugu, Y. Banno, H. Fujii, M. R. Goldsmith, and K. Mita. 2006. Construction of a single nucleotide polymorphism linkage map for the silkworm, Bombyx mori, based on bacterial artificial chromosome end sequences. Genetics 173:151-161. Yasukochi, Y. 1998. A dense genetic map of the silkworm, Bombyx mori, covering all chromosomes based on 1018 molecular markers. Genetics 150:1513-1525. Yezerski, A., L. Stevens, and J. Ametrano. 2003. A genetic linkage map for Tribalium confusum based on random amplified polymorphic DNAs and recombinant inbred lines. Insect Mol. Biol. 12:517-526. 72 Yu, Z. N., and X. M. Guo. 2003. Genetic linkage map of the eastern oyster Crassostrea virginica Gmelin. Biol. Bull. 204:327-338. Zheng, L. B., M. O. Benedict, A. J. Cornel, F. H. Collins, and F. C. Kafatos. 1996. An integrated genetic map of the African human malaria vector mosquito. Anopheles gambiae. Genetics 143:941-952. Zhong, S. B., B. J. Steffenson, J. P. Martinez, and L. M. Ciuffetti. 2002. A molecular genetic map and electrophoretic karyotype of the plant pathogenic fungus Cochliabolus sativus. Mol. Plant-Microbe Interact. 15:481-492. 73 4. Natural Variation in the Genetic Architecture of a Host- Parasite Interaction in the Bumblebee Bombus terrestris (Lena Wilfert, Jürgen Gadau, Boris Baer & Paul Schmid-Hempel, submitted to Molecular Ecology) Abstract The genetic architecture of fitness-relevant traits in natural populations is a topic that has remained almost untouched by quantitative genetics. Given the importance of parasitism for the host's fitness, we used QTL mapping to study the genetic architecture of traits relevant for host-parasite interactions in the trypanosome parasite, Crithidia bombi and its host, Bombus terrestris. The three traits analyzed were the parasite's infection intensity, the strength of the general immune response (measured as the encapsulation of a novel antigen) and body size. The genetic architecture of these traits was examined in three natural, un-manipulated mapping populations of B. terrestris. Our results indicate that the intra-colonial phenotypic variation of all three traits is based on a network of mostly minor QTLs and epistatic interactions. While these networks are similar between mapping populations in complexity and number of QTLs as well as in their epistatic interactions, the variability in the position of QTLs and the interacting loci was high. Only one QTL for body size was plausibly found in at least two populations. QTLs for encapsulation and Crithidia infection intensity were located on the same linkage groups, indicating that micro-evolutionary processes might have favoured genetic linkage of these traits. 74 Introduction Parasitism is ubiquitous and affects a wide array of host characteristics at all levels of biological organization. The severe effects of parasites can manifest themselves in drastic modifications of host behavior (Thomas and Poulin 1998; Webster 2001), reduction of the host's fitness (Brown et al. 1995) or a decline in host population size (Anderson and May 1979; Holt and Pickering 1985), to mention just a few. By many accounts, therefore, parasites are considered to be one of the main driving forces for host evolution (e.g. Hamilton et al. 1990). In turn, investigating the genetic basis of parasite resistance or susceptibility is clearly of importance for our understanding of the parasite's role in host evolution. Studying quantitative trait loci (QTL), in particular, can reveal the genetic architecture of host traits - the number, effect and location of the involved loci - that underlie these interactions. This method avoids limitations by a priori physiological or mechanistic assumptions implicit in candidate gene approaches. Our study uses QTL analyses to infer information about the genetic architecture of resistance/susceptibility loci of the bumblebee Bombus terrestris L. towards its parasite Crithidia bombi Lipa & Triggiani. Additionally, we studied the genetic basis of the encapsulation response, which is a general immune mechanism, and body size. The bumblebee B. terrestris is a primitively eusocial insect. Besides being an economically important pollinator (Ghazoul 2005), it has also been intensively studied as a model organism for host-parasite interactions (Baer and Schmid-Hempel 1999; Schmid-Hempel 2001) and ecological immunity (Moret and Schmid-Hempel 2000; Sadd et al. 2005). The interaction with its parasite C. bombi has been studied in particular depth. C. bombi (Trypanosomatidae) (Gorbunov 1987; Lipa and Triggiani 1988) is a gut parasite that is contracted by ingestion of infective cells. This parasite is prevalent in natural populations and shows high levels of infection during the season (Shykoff and Schmid-Hempel 1991b). The effect of C. bombi is condition-dependent. Mortality rates of infected bees can increase by 50 % under poor host condition (Brown et al. 2000). More importantly, C. bombi dramatically reduces the success of colony founding by infected queens, thus effectively castrating the host (Brown et al. 2003 b). A strong genetic component to Cnf/n't/itf-susceptibility has been demonstrated in previous studies. Transmission experiments, for example, have shown that the spread of a Crithidia- infection is slower in genetically heterogeneous groups (Shykoff and Schmid-Hempel 75 1991a, c). Similarly, parasite loads, including Crithidia, for individual workers and entire colonies are significantly reduced in the field when the colony is genetically heterogeneous (Baer and Schmid-Hempel 1999, 2001; Liersch and Schmid-Hempel 1998). Furthermore, genotype by genotype interactions have been demonstrated more directly in several experiments, with the success of Crithidia- infection s strongly depending on the parasite strain as well as the origin of the host bee (reviewed in Schmid-Hempel 2001)). These findings were supported by a study in which the genotypic composition of infections was directly assessed by microsatellite analysis (Schmid-Hempel et al. 1999). The strong evidence for genotype-by- genotype interactions makes this system an interesting model for the study of the genetic architecture of host susceptibility in host-parasite co-evolution. In contrast to the specific interactions of B. terrestris and C. bombi, encapsulation is a general insect immune response elicited by a wide range of antigens. It leads to the formation of a melanized hemocyte capsule around an invader and to its eventual elimination. The encapsulation response is based on the activation of the pro-Phenoloxidase-cascade (Soderhall and Cerenius 1998). The cascade can be triggered by implanting an antigen such as a piece of nylon serving as a novel, experimental parasite. In B. terrestris this encapsulation response has been shown to correlate positively with colony fitness under field conditions (Baer and Schmid-Hempel 2003). The strength of the encapsulation response varies among colonies and therefore is likely based on genotypic variance for this trait (König and Schmid- Hempel 1995; Schmid-Hempel and Schmid-Hempel 1998). Here, the genetic basis of the encapsulation response was studied to compare the genetic architecture of traits shaped either by general immune insults or by a specific host-parasite association. A second aim was to investigate whether there is a genetic correlation between the specific response to a C. bombi infection and the encapsulation response, i.e. whether the same genomic regions are involved in these immune defense strategies. Additionally, we investigated the genetic basis of body size as a benchmark trait that is presumably genetically independent of the immune system, while still being fitness-relevant (Owen 1988). Analyzing the genetic basis of trait variation in natural and wild populations remains a challenging task due to experimental and statistical difficulties (Slate 2005). QTL studies rely on the simultaneous assessment of a phenotypic trait and of the genotypic information needed for the construction of a genetic linkage map. The latter typically requires large numbers of sib-ships or detailed pedigree information (Slate 2005), which are difficult to obtain for un- 76 manipulated natural populations. Yet, this extensive procedure can be cut short in our model species, B. terrestris, because of its particular biology. Specifically, bumblebees, like all hymenoptera, are haplo-diploid, i.e. have a single locus complementary sex determination system (Cook and Crozier 1995). Thus, females arise from diploid, fertilized eggs and males from haploid, unfertilized eggs. Furthermore, queens of B. terrestris may produce up to a thousand haploid sons when reared in the laboratory. The large number of haploid offspring derived from a single mother readily allows the construction of linkage maps in natural populations, as was demonstrated by Gadau et al. (2001) and Wilfert et al. (in press). By studying three independent mapping populations, we were able to unveil the distribution of QTL effects and epistatic interactions for potential fitness traits as relevant in natural populations. Materials and Methods Mapping populations Three independent mapping populations (BBM-1, BBM-2 and BBM-3, referred to here as Bl, B2 and B3), described in detail in Wilfert et al. (in press), were established by collecting the sons of three individual queens. Populations B2 and B3 were raised directly from wild queens caught in North-western Switzerland in 2003 (B2) and 2000 (B3) respectively, and colony Bl was established in autumn 2003 from a first-generation lab-queen produced by a wild-caught queen from a population in North-eastern Switzerland. All colonies were maintained at standard conditions (red light, 28 °C and 60 % r.H.) and fed ad libitum with sugar water and pollen (Gerloff and Schmid-Hempel 2005). Males were removed from their maternal colonies as callows (i.e. newly emerged individuals) to control their age. The males were subjected to the same conditions and stored individually, except for mapping population B3, where males where kept in groups of 30 - 40 individuals. Crithidia infections All males were experimentally infected with C. bombi at maturity (4-7 days in Bl, 7 days in B2 and B3). To infect males, we collected C. bombi cells from the faeces of approximately 20 77 workers of infected source colonies. For mapping population B2, a cocktail of four isolates was used, whereas populations Bl and B3 were infected with single isolates. Males were infected per os with standard inocula of lO'OOO cells (Bl) or 16'000 cells (B2, B3) in 10//I or 20 \A of 50 % sugar water, respectively. Prior to infection, males were starved for 7-12 hours and were only included in the mapping population if they had been observed imbibing the inoculum. After infection, males were kept individually for another week under standard conditions. The intensity of Crithidia infection was measured by dissecting the male's gut and counting Crithidia cells in a Ringer solution of the extracted gut content in a Neubauer improved hemocytometer. Uninfected males were removed from the QTL analysis, in order to avoid wrongly scoring as "resistant" those individuals who failed to take up the inoculum permanently. Phenotypic comparison of Crithidia infections in workers and males While studying the haploid progeny of a B. terrestris queen allows genetic linkage mapping and QTL analyses in a natural population, this limits the phenotypic assessment to the males. In order to evaluate the phenotypic differences between the two sexes, workers and males from six colonies were infected as described above with standard inocula of lO'OOO cells of a Crithidia-cocktail. Infection levels were measured as above, except that Fast-Read 102 chambers (Immune Systems LTD) were used instead of Neubauer hemocytometers. Encapsulation reaction In population B3, the individuals from the infection assay were simultaneously scored for the encapsulation reaction at 14 days of age before being sacrificed in accordance with the overall infection protocol. In population Bl, in contrast, a separate batch of 77 individuals aged 4-7 days was used for the encapsulation assay. Encapsulation response was measured according to the standard protocol as described earlier (Gerloff et al. 2003; Schmid-Hempel and Schmid-Hempel 1998). In short, the males were anaesthetized and a piece of nylon thread (ca. 0.8- 1 mm) was inserted into the abdomen via an intersegmental membrane. Bees were freeze-killed in liquid nitrogen 2 h after treatment to stop the reaction. The nylon filament was dissected from the males' abdomen and mounted on a microscopic slide. Using the ImageJ program (Abramoff et al. 2004; available at: http://rsb.info.nih.gov/ij), we determined the 78 mean grey value of the density of the encapsulation response of the implant against the background grey value. Body size As a proxy for body size, the length of the radial cell of the forewing was measured. This measure correlates well with body size and other morphometric features in Bombus (Owen 1988, 1989; Schiestl and Barrows 1999) and can be measured unambiguously. The forewings were removed and mounted on a slide, and the length of the radial cell was determined using ImageJ (Abramoff et al. 2004; mean of 6 measurements). Construction of genetic linkage maps The construction of the linkage maps is described in detail in (Wilfert et al. in press). Briefly, populations were genotyped for 118 - 246 microsatellites, AFLPs and RAPDs (the latter only in B3). The high-resolution map Bl resulted in an estimated genome coverage of 81 %, spanning 2'222 cM in 21 linkage groups (for 18 known chromosomes) at an average marker distance of 10.3 cM. The less-detailed maps B2 and B3 (average marker distance 12.5 - 12.8 cM) had an estimated genome coverage of 41 % to 44 %, with a map length of 1 '223 cM and l'124cM, respectively. Using recurrent homologous microsatellites and AFLP markers to identify homologous genome regions, a core map of 14 linkage groups could be determined from the three independent maps. These groups will be referred to as LGOl to LG14, whereas the as yet non-homologized linkage groups are named by a combination of the mapping population and a number (e.g. Bl_15 for linkage group 15 in population Bl). QTL mapping MapQTL 4.0 (Van Ooijen et al. 1999) was used to identify QTL for all traits, following the basic procedure outlined in Gadau et al. (2002). Suggestive (chromosome-wide) and significant (genome-wide) QTL were statistically confirmed using the standard permutation test for interval mapping (Churchill and Doerge 1994) incorporated in the program. First, standard interval-mapping was carried out to identify the major QTL. Then MQM-mapping (multiple-QTL-model) was used to fit more than one QTL at a time. Suggestive QTLs determined in the interval mapping procedure were used as cofactors during the consecutive 79 MQM-mapping. If the LOD value for a QTL linked with the cofactor dropped below the suggestive threshold during the MQM-mapping, it was removed from the cofactor list and MQM was run again. This procedure was repeated until the cofactor list remained stable. Digenic epistasis EPI STAT (Chase et al. 1997) was used to search for epistatic interactions of QTLs. This program searches the whole genome for significant interactions between QTLs and uses log- likelihood ratios to compare the likelihood of explaining the effects by the null, additive, or epistatic models (see Chase et al. 1997; the program is available for download at http://64.226.94.9/epistat.htm). First, an automated search option was used to identify all digenic interactions. This search was constrained by a minimal group size of 10 and a threshold of 6 LLR (log-likelihood ratio) for the comparison of an additive model versus a non-additive model, as suggested by Chase et al. (1997). The interactions were analyzed with a Monte Carlo program implemented in EPISTAT to test for statistical significance. A total of I'OOO'OOO trials were done for each interaction. We transformed the p-value found in the Monte Carlo simulation (the probability that a single trial exceeded the observed value) into a corrected p-value of p' = l-(l-p)" (where n = number of mapped markers; nB1 = 235, nB2= 112 and nB_,= 112) to account for the high number of tests (Lark et al. 1995). For each interaction, we calculated the additive effects of the two involved loci (A/a and B/b) and their epistatic effect, the interaction deviation. The additive effects are calculated as Add_A = (GAB + GAb - GaB - Gab)/4 and Add_B = (GAB + GaB - GAb - GJ/4, respectively. The epistatic effect is calculated as Epi_ = ((GAB+ Gal,)/2 - (GAl,+ G.lB)/2)/2. In the phase-known population Bl, the allele combination "AB" (F0-mother) and "ab" (F0-father) are defined as the parental combinations. In populations B2 and B3, the identity of the parental allele combinations is not known. For these populations, only the absolute magnitude of the effects is therefore given, as the direction of epistatic effects depends on the identity of the parental combinations. The effect size was transformed to the percentage of the population mean. Linear models including the main effect QTLs detected via Interval- and MQM-mapping and the significant two-way interactions from the EPISTAT-analysis were constructed in order to compare their respective effects on the phenotype. The type III sums of squares of each term 80 of the analysis was divided by the total of the type III sums of squares to obtain the partial r of the respective term (main effect QTL or interaction term). This analysis is analogous to the determination of epistatic r implemented in the program EPISTACY (see (Holland 1998, 1997). While the revalues resulting from this analysis provide useful estimates of the relative contributions of epistatic interactions and QTLs to the trait's genetic architecture, they should not be taken at face value. Therefore, we only report the ratio of the percentage of phenotypic variation explained by epistatic interactions and main effect QTLs. Results Phcnotypes Crithidia infection patterns were analyzed in three mapping populations. Infection rates in the studied mapping populations ranged from over 90 % (98.6 % in population Bl and 93.4 % in B3) to 53.5 % in population B2. Only the infected individuals (nB1 = 276, nB2 = 145 and nB3 = 170) were considered in further analyses. Infection intensities varied between ' populations, with a mean of 1'386 ±1'804 (mean ± SD) cells p\ and a median of 440 cells /if1 in population Bl, 724 ± I '543 cells /d"1 and 80 cells #1"' in population B2, and 2'935 ± 3'178 cells ^1"' and 1'900 cells //I"1 in population B3, respectively. The raw infection intensities were roughly Poisson-distributed, being heavily skewed to the left with an approximately continuous tail to the right. Tilquin et al. (2001) could show that parametric QTL mapping approaches such as the maximum likelihood method used in this analysis suffer a loss in power if the assumption of normality is violated in this form typical of parasitic cell counts. Yet, they also demonstrated that this loss could be circumvented by transforming data, e.g. by a log-transformation, to normalize variances. The cell counts were therefore log-transformed to increase the power and accuracy of the QTL-analysis. The strength of the encapsulation reaction was measured in arbitrary units as the mean grey value of the artificial implant in populations Bl (population mean: 41.8 ± 14.3 units, n = 76) and B3 (36.5 ± 19.0 units, n = 173). The strength of the encapsulation reaction had previously been shown not to be affected by the infection intensities of an experimental Crithidia infection (Allander and Schmid-Hempel 2000). Crithidia infection intensity and encapsulation reaction were simultaneously assayed in population B3, where they showed no 81 correlation (p = 0.13, R = -0.12, n = 164). To test both traits independently, they were assayed in different subsets of individuals in population Bl. Body size was measured via the length of the radial cell of the forewing in population Bl (3.61 ±0.16 mm, n = 359) and B2 (3.58 ±0.13 mm, n = 85). Body size was not significantly correlated with any of the other traits (p > 0.1 ). The eusociality and haplo-diploid genetics of B. terrestris enabled us to exploit the haploid male progeny of queens to measure recombination directly. While this allows the genetic architecture of natural variation to be revealed in un-manipulated populations with ease, it limits the phenotypic assessment to the haploid males. Males and females often differ in many phenotypic traits; for example, parasite infections are male- or female biased in many arthropods (reviewed in Sheridan et al. 2000). For the traits used in this study, Gerloff et al. (2003) demonstrated that males are significantly larger than workers (radial cell length 3.44 vs. 2.83 mm) and that they show a significantly weaker encapsulation reaction in standard tests (46.43 vs. 56.53 units). For C. bombi infections, Shykoff and Schmid-Hempel (1991b) found that the infection rate was worker-biased in a natural field population. To test whether this bias also exists in controlled infections, workers and males of six colonies were experimentally infected with C. bombi. Infection rates ranged from 29.6 % to 79.6 % in males and from 52.1 % to 85.7 % in workers. The infection prevalence is significantly different for the two sexes (2x2-contingency tables, cumulative Chi-squared = 37'.7', p< 0.001, d.f.6, nmales = 297, nworkers- 351), showing a strong worker-bias. Note that in a similar experiment with low sample sizes, Ruiz-Gonzalez and Brown (2006) found no difference in prevalence between males and workers. The infection intensities in our experiment showed the typical left-skewed distribution, with median cell counts ranging from 200 to 625 cells //I"1 in males and from 318 to 1'042 cells /-l"1 in females. However, the median infection intensities are not significantly different in males and females over colonies (Wilcoxon Signed Rank test, Z= 1.05, p = 0.293). In a general linear model of the log-transformed data for infection intensity, colony is not significant as a factor (F = 0.48, p = 0.79) while sex is (F = 7.63, p < 0.01), yet explains very little of the total variation (r2 =1.3 %). Thus, the main difference in Crithidia infection patterns between males and workers resides in a generally reduced infection rate in males and, hence, analyzing the infection intensity data used for QTL analysis should be comparable for both sexes. 82 QTL analysis Using both Interval-mapping and MQM-mapping, we identified two (Bl and B3) to three (B2) suggestive QTLs (i.e. QTLs significant on the chromosome level) for Crithidia infection (see Table 1). Cumulatively, they explained 6.6%, 13.6% and 11.5% of phenotypic variation via MQM-mapping in populations Bl, B2 and B3, respectively. For the strength of the encapsulation response, we also found two to three suggestive QTLs in populations Bl and B3, depending on the mapping method employed (see Table 1). Via MQM-mapping, these QTLs cumulatively explained 23.2 % (Bl) and 9.7 % (B3) of phenotypic variation. For the variation in body size, we each found one significant QTL (i.e. significant at the genome- wide threshold), and five and three suggestive QTLs, respectively, in populations Bl and B2. Using MQM mapping, these QTLs cumulatively explained 16 % (Bl) and 39 % (B2) of the observed phenotypic variance in the respective population. For body size, we found the QTL BS-4 in both populations B1 and B2 at the same position on LGOl. Except for BS-4, all of the QTLs were unique to their population. Table 1: Main effect QTLs determined via Interval (IM)-and MQM-mapping d) Trait Population Name Location IM-mapping MQM-maping Lod-threshold Mean allelicvalue Add L>') LG Marker Lod PVE Lod PVE per group genome wide ,c) Crithidia Bl Cri-1 BB1-I6 A04FJ04 1.6 2.9 i.8 1.6 2.9 2.46 2.74 -5.4 Crithidia Bl Cri-2 1.8 LG11 A02P 124 3.1 2.1 3.4 1.5 2.9 2.71 2.42 5.6 Crithidia B2 Cri-3 LG04 A02E_245 1.1 3.9 1.3 4.2 1.0 2.5 2.46 2.07 9.2 Crithidia B2 Cri-4 LG13 A03F_209 1.5 4.6 1.2 _d' 1.3 2.5 2.28 1.94 8.1 Crithidia B2 Cri-5 LG14 A03F_146 1.9 9.1 2.0 9.4 1.4 2.5 2.45 1.84 14.5 Crithidia B3 Cri-6 BB3-17 A03D161 1.2 - 1.5 5.3 1.3 2.8 3.43 3.26 2.6 Crithidia B3 Cri-7 BB3-22 A02H_213 0.8 - 1.2 6.2 1.0 2.8 3.42 3.27 2.3 Encaps. Bl Enc-1 LGtl A01D_223 1.6 9.0 1.7 8.2 1.5 3.1 38.4 46.6 -9.8 CO Encaps. Bl Enc-2 BB1-17 A06KJ60 1.9 11.0 i.8 8.5 1.5 3.1 37.3 46.7 -11.3 Encaps. Bl . Enc-3 BB1-16 AHM_129 1.2 - 1.4 6.5 1.4 3.1 38.6 46.1 -9.0 Encaps. B3 Enc-4 LG03 B100 1.8 5.3 1.8 5.1 1.3 2.6 39.0 31.8 9.9 Encaps. B3 Enc-5 LG05 BT11 1.4 4.7 1.5 4.6 1.1 2.6 37.3 28.7 11.8 Encaps. B3 Enc-6 BB3-21 A04B_165 1.3 6.6 0.8 - 1.1 2.6 36.4 27.4 12.3 Body Bl BS-1 LG08 A10K_258 3.0 4.1 3.9 4.7 1.7 3 3.65 3.58 i.O Body Bl BS-2 LG07 BL03 1.7 - 2.5 2.9 1.8 3 3.59 3.63 -0.6 Body Bl BS-3 LG03 BT30 2.0 2.6 1.8 2 1.8 3 3.58 3.63 -0.7 Body Bl BS-4 LGOl A02E 106 2.7 3.5 3.1 3.6 1.9 3 3.58 3.64 -0.8 Body Bl BS-5 BB1-15 A02E_280 1.6 - 2.3 2.8 1.7 3 3.63 3.58 0.7 Bodv Bl BS-6 LGOl A11E 061 2.3 2.9 1.2 - 1.9 3 3.59 3.64 -0.7 Trait Population Name Location IM-mapping MQM-maping Lod-threshold Mean allelicvalue Add_<" LG Marker Loda> PVE1" Loda) PVEb) per group genome wide Ac> a° Body B2 BS-4 LGOl A05B_267 0.9 - 2.4 9.6 1.7 2.6 3.61 3.55 0.8 Body B2 BS-7 LG06 BT04 1.2 6.1 2.1 7.8 1.2 2.6 3.61 3.55 0.8 Body B2 BS-8 LG10 A02E_044 1.0 - 1.6 6.6 1.2 2.6 3.62 3.57 0.7 Body B2 BS-9 BB2-15 A06H_312 1.8 9.5 3.6 15 1.5 2.6 3.62 3.54 1.1 group-wide Lod-valties above the or genome-wide Lod-threshold indicate a suggestiveor significantQTL, respectively b)PVE := phenotypicvariation explainedby the QTL [%] c> In populationBl, the mean allelicvalue of allele "A" refers to the mean phenotype value for the maternal allele whereas "a" refers to the paternalallele;in populationsB2 and B3, true phase could not be determined (see Wilfert et al. in press)and therefore allele A was randomly assignedto the higherphenotypicvalue. d) Additive effect d) If a QTL did not reach the group-wide significanceLod-threshold for interval- or MQM-mapping, no value is given for the explained S phenotypicvariance. 85 For all traits and populations, we found epistatic interactions (Table 2), even though we conservatively corrected for all mapped markers. The detection of epistatic interactions in a natural population was facilitated in this study by the use of haploid males. To correct for the large number of pair-wise comparisons, we chose a significance threshold that corrects for the number of all mapped markers at a level corresponding to p < 0.05. This approach is very conservative since the markers are not statistically independent due to linkage; many other studies therefore only correct for the number of linkage groups (e.g. Gadau et al. 2002; Malmberg et al. 2005). Frequently, several marker pairs were involved in the pair-wise interactions between the same linkage groups; in this case, we only report the most significant interaction. For the strength of the encapsulation response and the intensity of Crithidia infection, we found three epistatic interactions in each population (see Table 2, Fig. 1). Five epistatic interactions were detected for body size in population Bl, while only one was found in B2. For the Crithidia infection intensity and the strength of the encapsulation reaction, the epistatic effect of interactions was always much higher than the additive effects of the loci involved in the interactions (Table 2). For body size, this picture was less clear, with additive effects out-ranking epistatic effects within two interactions (Ebs-2 and Ebs-4; see Table 2). To assess the importance of main effect and epistatic QTLs to the explained phenotypic variation, we constructed general linear models with all QTLs and epistatic interactions identified in the respective analyses. For the Crithidia infection intensity and the strength of the encapsulation reaction, the total of the epistatic interactions generally explained more of the phenotypic variation than the combined main effect QTLs. The ratio Q ~ ^L.PHasj,/r2yTLswas larger than 1 for these traits in all populations (Qm!hldiaA = 8.7, Qc,,thiJk,3 = 1.9, &„,., = 2.0, ôenc-3 = 2.1), except for Crithidia infection in population B2 (Q,crithidia_2= 0.4). For body size, the opposite was the case, with the main effect QTLs responsible for the major proportion of explained phenotypic variation in both studied populations (g^., = 0.2, ÖlMdv.2 = 0.2). A comparison of the effects of main effect QTLs and interactions similarly shows the importance of epistasis, with the epistatic effects of the interactions (Table 2) generally being higher than the additive effect of QTLs (Table 1; Wilcoxon signed rank test, Z = -2.853, p<0.0\). Table 2: Digenicepistasis Trait Population Name Marker A Marker B GenotypicValues1 Effects' LG Nearest marker LG Nearest marker AB Ab ab aB Add__A Add_B Epi__ Crithidia Bl Ecri-1 LG03 BT30 LG06 A05K_320 2.23 2.72 2.41 2.98 -4.3 1.5 -10.3 Crithidia Bl Ecri-2 BB1-15 A04K_047 LG07 BTERN 2.84 2.41 2.76 2.35 1.4 0.4 8.1 Crithidia Bl Ecri-3 LG04 A07F_i23 LG02 A01L_226 2.35 2.75 2.50 2.93 -3.2 0.6 -8.0 Crithidia B2 Ecri-4 LG14 A03FJ32 BB2-22 A01G331 2.22 1.53 2.42 2.08 8.9 8.3 12.2 Crithidia B2 Ecri-5 LGOl BT05 LG12 BT20 1.89 2.48 1.81 2.25 3.7 3.6 12.2 Crithidia B2 Ecri-6 BB2-16 A02G_335 LG03 BT12 1.84 2.36 1.89 2.32 0.1 2.1 11.3 Crithidia B3 Ecri-7 LG10 A05D_084 LG13 A12610 2.20 2.48 2.16 2.63 0.8 2.9 5.7 Crithidia B3 Ecri-8 BB3-23 A04B_165 BB315 AY440b 2.44 2.27 2.49 2.09 1.0 3.5 4.3 Crithidia B3 Ecri-9 LG07 A03D_350 LG13 Al 2610 2.09 2.38 2.30 2.61 3.3 0.3 4.5 Encaps. B1 Eenc-1 LG07 A11E_273 LGJ A02G_067 47.9 25.2 47.6 25.9 -0.2 1.2 26.6 Encaps. Bl Eenc-2 LG11 A09P_064 LG03 A05K_286 38.0 22.8 47.6 34.1 -12.5 2.0 17.2 Encaps. Bl Eenc-3 LG08 A06G_055 LG05 BT21 47.6 31.9 40.5 25.7 8.0 1.1 18.3 Encaps. B3 Eenc-4 BB3-23 A04B_078 LG05 T17_250 39.7 28.6 45.7 36.8 9.7 3.0 13.7 Encaps. B3 Eenc-5 BB3-27 P16_230 LG06 P041300 45.0 36.1 48.2 37.3 3.0 2.7 13.5 Encaps. B3 Eenc-6 BB3-19 M08 480 LGOl 111 380 39.6 45.0 35.4 44.9 2.9 5.6 10.2 Trait Population Name Marker A Marker B GenotypicValues3' Effects^1 Add_ LG Nearest marker LG Nearest marker AB Ab ab aB Add_B Epi_ A Body Bl Ebs-1 LG03 A02L_346 LG05 BT11 3.58 3.66 3.60 3.66 -0.1 -0.3 -1.0 Body Bl Ebs-2 LGOl A11L_285 BBi-16 A02E100 3.52 3.68 3.64 3.68 -0.8 -1.7 -1.4 Body Bl Ebs-3 LG09 A08L_223 BB1-17 A11E106 3.63 3.57 3.67 3.59 -0.4 -0.3 1.0 Body Bl Ebs-4 LG07 A11M051 LG13 A05B_345 3.60 3.56 3.62 3.51 0.2 -1.0 1.0 Body Bl Ebs-5 LG11 A09P_064 BB1-15 A04K_047 3.67 3.58 3.63 3.58 0.3 0.6 1.0 Body B2 Ebs-6 LGOl A07F_145 LG03 BT26 3.52 3.64 3.54 3.61 0.1 0.7 1.3 Note: all interactions are significantat the p < 0.05 level,corrected for allmapped markers. Alleles are designatedby A/a or B/b for the two interactingloci. a) A/a and B/b representthe alleles at the two loci;For the phase-known mapping populationB1, the A- and B-alleies are providedby the Fo- -J mother and the a- and b-alleles by the Fu-father.For populationsB2 and B3, this assignationis random. In populationBl, the combinations "AB" and "ab" therefore represent the two possibleparentalgenotypes, whereas in the phase-unknown populationsboth AB/ab or Ab/aB could be the parentalallele combinations. b) = - - - - The additive effects are calculated as Add_A (Gab + GAb G3b Gab)/4and Add_B = (Gab + GaB Gai, Gab)/4,respectively;the epistatic effect,is calculated as Epi_= ((Gab+ Gai,,)/2- (GAb+ GaB)/2)/2.For B2 and B3, only the absolute amount of the effects is given,since the direction of epistaticeffects depends on the identityof the parentalcombinations. The effect size was transformed to the percentage of the populationmean. 88 Crithidia infection intensity - main effect T epistatic interaction Encapsulation V Body size Figure 1: Genetic architecture of the studied traits in the three populations (Bl, B2 and B3) Solid triangles indicate main effect QTLs for infection intensity (with digenic epistatic interactions indicated by solid lines), open triangles for encapsulation strength (broken lines), and dotted triangles for body size (dotted lines). In the graph, linkage groups are shown as ovals, with the number indicating the respective group. The linkage groups LGOl to LG14 (bold outline) are homologous among the three populations (Wilfert et al. in press). Plain ovals indicate non-homologous groups. In the phase-known population Bl (Wilfert et al. in press), the identity of alleles contributed by the F0-queen and her mate are known. In this mapping population, the main effect QTL alleles conferring a stronger encapsulation reaction were all contributed by the F()-father, while for Crithidia intensity and body size, both parents contributed alleles conferring higher or lower genotypic values. For the pair-wise epistatic interactions, there is a trend for the parental combinations ("AB" in the F0-mother and "ab" in the F0-father), rather than the novel F2-allele combinations ("Ab" and "aB"), to convey the potentially more favourable phenotypic value (lower infection intensities, stronger encapsulation, larger body size), with eight of eleven interactions showing this direction of epistatic effects (Fig. 2). 89 o CO CO C\J 93 cd c CO CM Q o CD c ^w£- CM CM CM AB Ab aB ab Allele combinations Figure 2: Direction of epistatic effect Shown are the digenic epistatic interactions (Ecri-1, Ecri-2 and Ecri-3) for infection intensity of C. bombi in population Bl. In the interactions Ecri-1 (circles and solid line) and Ecri-2 (diamond and dotted line), the parental allele combinations AB and ab convey the potentially more favourable phenotype (i.e. with lower mean infection intensities), whereas individuals carrying the novel allele combinations Ab and aB had the more susceptible genotype (i.e. higher cell counts). The interaction Ecri-2 (triangles and broken line) shows the inverse pattern, with parental combinations associated with higher infection intensities. Genetic Architecture The genetic architecture of the studied traits, that is, how the involved loci are distributed over the genome, suggests that the genetic basis of susceptibility to infection with C. bombi is not independent from that of the encapsulation reaction (see Figure 1 ). In population Bl, the two QTL for Crithidia infection intensity Cri-1 and Cri-2 on the linkage groups Bl_16 and LG11 co-reside with two loci for encapsulation strength (that is, Enc-3 and Enc-1; Table 1, Fig. 3). There is no such overlap between the other trait combinations. To test whether this aggregation is significant, we performed a test of association (similar to tests for species association (Ludwig and Reynolds 1988)). In this simplified approach, we did not correct for 90 the size of linkage groups. This procedure is conservative, since the linkage groups with the co-located loci are shorter than = 65.5 = 87.5 mean average (LLG11 cM, LB] 16 cM, length L = 105.8 ± 56.1 cM). We found that the association was highly significant (Chi-squared = 12.9, d.f. = 1, p < 0.001). While there was no overlap in main effect QTLs in population B3, the marker A04B_165 on linkage group B3_21 is associated both with a QTL for the strength of encapsulation (Enc-06) and an epistatic interaction for Crithidia infection intensity (Ecri-8). Together, this indicates that the genomic location of QTLs for these two traits is not independent of one another. 0 / \ ~--/~~V, J A & & ' * i A . ' v1 A A A AA • • •• 0 10 20 30 40 50 60 70 0 20 40 60 80 Linkage group LG11 [cM] Linkage group B1 „16 [CM] Figure 3: Association of QTLs for encapsulation strength and Crithidia intensities. The graphs show the Lod-curves of MQM-mapping of Crithidia-\nfaction intensity (dots and continuous line) and encapsulation (triangles and dashed line) on the linkage groups LG11 (left) and Bl_16 (right). The respective group-wide Lod-threshold is indicated for each trait and linkage group (Lod = 1.5 for both traits on linkage group LG11; Lodc,,„„,,,„ = 1.6 (continuous line) and Lodhnc.ipilll.il]t,n =1.4 (dashed line) on linkage group Bl__16). Discussion In this study, we have used samples from natural populations to study the genetic architecture of fitness relevant traits in a controlled environment. According to a recent review (Slate 2005), it appears that this is the first time that such an approach has been used, although two recent studies on Plasmodium resistance in the genetically well-characterized mosquito Anopheles gambiae have employed similar procedures (Menge et al. 2006; Niare et al. 2002). According to Slate (2005), such an experimental setting allows studying the distribution of QTL effects in a population, as relevant for the natural context from which the animals were 91 obtained. For the quantitative trait of Crithidia-infection intensity, we have found a total of seven main effect QTLs in the three independent mapping populations. In the two populations in which variation in encapsulation strength (Bl and B3) and body size (Bl and B2) were studied, we found six QTLs for encapsulation and nine QTLs for body size, respectively. Except for the QTL BS-9, which explains 15.0 % of phenotypic variation, all QTLs are of minor effect, explaining less than 10 % of phenotypic variation (Tanksley 1993). Furthermore, all QTLs were unique for their population, with the plausible exception of the QTL BS-04, which maps to the same region of linkage group LGOl in populations Bl and B2. It is indeed a common - finding in QTL studies including studies on artificially selected model organisms - that only a fraction of QTLs are ever found again in multiple assays differing even slightly in their experimental setup. For example, Arru et al. (2002) found three unique QTLs for resistance to the fungal pathogen Pyrenophora graminea in two crosses of barley (Hordeum vulgare). Furthermore, studying the resistance against two different parasite isolates in this system, Arru et al. (2003) found five QTLs, only one of which was significantly contributing to the response against both isolates. In studies on host-parasite interactions, the observation that QTLs are specific to the particular host line and parasite isolate used for mapping, can partly be explained by genotype-by-genotype interactions. However, a problem inherent in all methods of QTL mapping (Beavis 1994; Zeng 1994) is the bias towards underestimating the number of involved QTLs. This problem is exacerbated when QTL detection is based on the naturally available variation, as is the case here. The studied traits had not been selected or screened for by the experimenter. In our setup, QTLs can only be detected if the F,-queen happened to be heterozygous for the involved alleles. A further caveat is that the linkage maps of the three B. terrestris populations are not fully homologous. Only linkage groups LGOl to LG14 could be homologized via recurring patterns of microsatellites or AFLP markers (Wilfert et al. in press). For QTLs on non-homologous or not fully mapped chromosomes, no valid comment on their uniqueness is possible. For example, Cri-3 (LG04) and Cri-4 (LG13) are clearly not identical with any of the other Crithidia susceptibility loci, because the involved genomic areas are fully homologous in all three populations. For Cri-1, Cri-6 and Cri-7 on the other hand, no conclusion can be drawn because these QTL are located on non-homologous linkage groups. An important advantage, however, is that in this way the natural genetic variation 92 underlying the phenotypic variation of the studied traits is exposed just as it would become operational in any natural infection. The genetic architecture of the general immune defense of encapsulation and the specific susceptibility towards the parasite C. bombi show a similar pattern. Remarkably, QTLs responsible for the strength of encapsulation and for the intensity of Crithidia infection were co-located on the same linkage groups (Fig. 2). This non-random association must not be mistaken for pleiotropy, since the QTLs peaks are spaced 10 cM to 45 cM apart. This does, however, raise the interesting possibility that micro-evolutionary processes might have led to linkage of both traits. Interestingly, we found that phenotypically, Crithidia infection intensity was not correlated with encapsulation strength in either the present study nor in previous work by Allander and Schmid-Hempel (2000). Yet, Doums and Schmid-Hempel (2000) have shown that infection prevalence (analyzed as a binary factor: yes/no) is significantly negatively associated with encapsulation rate in the field. In population B3, in which encapsulation strength was measured in individuals that had previously been experimentally infected with Crithidia, the same association was seen as a trend (F = 3.728, p = 0.055). It should be noted that this association could both mean that individuals with a higher standing encapsulation response are less likely to become infected or that infection leads to a reduction in the strength of the encapsulation reaction. Additionally, Mallon et al. (2003) have experimentally demonstrated that the degree of specificity in a colony's susceptibility to C. bombi trades-off against the strength of the encapsulation of an artificial antigen. Unlike parasites such as Plasmodium spp. or trypanosomes of the brucei group, Crithidia do not invade the hemocoel. Instead, they are restricted to the lumen of the hindgut (Gorbunov 1996) partly as free choanomastigotes, partly attached to the gut wall. Evidence from water striders (Tieszen and Molyneux 1989; Tieszen et al. 1986) and triatomine bugs (Kollien et al. 1998) suggests that cells attach to the hindgut wall via specialized structures called hemidesmosomes. Insect defense against gut parasites is considered to involve lectins and lysis as well as melanization and encapsulation above the basal matrix as main immune mechanisms (reviewed in Kaslow and Welburn 1996). Accordingly, Lehane et al. (2003) demonstrated that the expression of a homolog of a Drosophila melanogaster pro- Phenoloxidase RNA, an enzyme involved in the encapsulation response, is up-regulated in the guts of both Trypanosoma orwcez'-infected and self-cleared Glossina morsitans flies. This 93 finding underpins the possibility that encapsulation in the gut might play a role in insect immune defense against trypanosomatidae. Furthermore, Nigam et al. (1997) showed that pro-Phenoloxidase from Glossina palpalis selected for refractoriness was activated more readily than that from G. morsitans individuals bred for susceptibility to T. brucei. Similarly, Brown et al. (2003a) found that an experimental C. bombi infection doubled the Phenoloxidase activity in the hemolymph. In the malaria-mosquito system, encapsulation and parasite refractoriness have an overlapping genetic architecture. In this system, a QTL region responsible for the melanotic encapsulation of P. cynomolgi B oocysts in the midgut of A. gambiae (Zheng et al. 1997) also contributes to the phenotypic variation of the encapsulation of Sephadex beads in the hemolymph of A. gambiae (Gorman et al. 1997). In the B. terrestris - C. bombi system, the respective traits do not suggest pleiotropic effects, but QTLs for encapsulation and parasite susceptibility appear to be linked. Our results show that the fitness-relevant phenotypes in B. terrestris are determined by a network of minor QTLs and epistatic interactions, rather than by few major QTLs as is often found in experiments using artificially selected model organisms (e.g. Zheng et al. 1997). In addition, epistasis is accounting for an important part of the phenotypic variation. This may be a general feature of the genetic architecture of fitness-relevant traits in natural populations. For example, Malmberg et al. (2005) have demonstrated similar networks of epistatic interactions and QTLs for flowering and germination traits for Arabidopsis thaliana in the field. As in the present study, the epistatic interactions explained a large proportion of the phenotypic variation in A. thaliana. At the moment, the implications of these epistatic interactions for the evolution of quantitative traits are not understood in detail. However, it is remarkable that in our study epistatic effects were important for susceptibility to infection by C. bombi whereas this was much less the case for a "household" trait like body size. Host-parasite interactions are at the focus of current evolutionary biology, because parasites have been implicated in the evolution of many facets of host biology. Especially, parasites are considered to be one of the main causal agents for the evolution of sex and recombination through negative frequency-dependent selection, which may favour recombination and maintain genetic polymorphism in natural populations (the "Red Queen" model, e.g. Ebert and Hamilton 1996; Frank 1992; Haldane 1949; Hamilton 1980; Hamilton et al. 1981; Lively 1996). This process has been demonstrated repeatedly in silico (e.g. Hamilton et al. 1990; Peters and Lively 1999; Schmid-Hempel and Jokela 2002) and has been supported by 94 experimental and field data mostly in clonal populations (e.g. Carius et al. 2001; Dybdahl and Lively 1998; Lively et al. 1990). Yet, direct evidence of the occurrence of Red Queen - dynamics in natural populations fluctuations of alleles driven by parasites - has so far proven elusive in sexual species. This could be remedied by studying the variation in alleles directly involved in host-parasite interactions, such as relevant markers identified in QTL studies (Little 2002). Epistasis is required for the explanation of how sex and recombination could have evolved by antagonistic host-parasite interactions (Barton and Charlesworth 1998; Peters and Lively 1999). In the phase-known population Bl, the recombined, novel allele combinations were associated with higher rather than lower susceptibility towards C. bombi (Table 2, Fig. 2). At first sight, this might seem to contradict the expectation that novel gene combinations enjoy a fitness advantage, yet these effects need to be studied under natural conditions. With the determination of QTLs and epistatic loci for infection intensity, the B. terrestris - C. bombi host-parasite system has become a prime candidate for testing hypothesis on the maintenance of sex and recombination in both field and laboratory experiments. Acknowledgments The authors would like to thank Yvonne Merki, Daniel Heinzmann, Michael Bretscher and Patricia Nigg for assistance in genotyping and phenotyping. We are grateful to Timothy Linksvayer for statistical advice. The manuscript was improved by comments from Mathias Wegner. This project was funded by the DFG SFB 554-TB1 (JG) and grants by the Swiss SNF (3100-066733 to PSH) and by the ETH Zurich (TH-19/03-2 to PSH and LW). References Abramoff, M. D., P. J. Magelhaes, and S. J. Ram. 2004. Image processing with ImageJ. Biophotonics Intl. 11:36-42. Allander, K., and P. Schmid-Hempel. 2000. Immune defence reaction in bumble-bee workers after a previous challenge and parasitic coinfection. Funct. Ecol. 14:711-717. 95 Anderson, R. M., and R. M. May. 1979. Population biology of infectious-diseases .1. Nature 280:361-367. Arru, L., E. Francia, and N. Pecchioni. 2003. Isolate-specific QTLs of resistance to leaf stripe (Pyrenophora graminea) in the 'Steptoe' x 'Morex' spring barley cross. Theor. Appl. Genet. 106:668-675. Arm, L., R. E. Niks, P. Lindhout, G. Vale, E. Francia, and N. Pecchioni. 2002. Genomic regions determining resistance to leaf stripe (Pyrenophora graminea) in barley. Genome 45:460-466. Baer, B., and P. Schmid-Hempel. 1999. Experimental variation in polyandry affects parasite loads and fitness in a bumble-bee. Nature 397:151-154. Baer, B., and P. Schmid-Hempel. 2001. Unexpected consequences of polyandry for parasitism and fitness in the bumblebee, Bombus terrestris. Evolution 55:1639-1643. Baer, B., and P. Schmid-Hempel. 2003. Effects of selective episodes in the field on life history traits in the bumblebee Bombus terrestris. Oikos 101:563-568. Barton, N. H., and B. Charlesworth. 1998. Why sex and recombination? 281:1986-1990. Beavis, W. D. 1994. The power and deceit of QTL experiments: lessons from comparative QTL studies. Proceedings of the 49th annual corn and sorghum industry research conference, Washington D.C. Brown, C. R., M. B. Brown, and B. Rannala. 1995. Ectoparasites reduce long-term survival of their avian host. Proc. R. Soc. Lond. Ser. B-Biol. Sei. 262:313-319. Brown, M. J. F., R. Loosli, and P. Schmid-Hempel. 2000. Condition-dependent expression of virulence in a trypanosome infecting bumblebees. Oikos 91:421-427. Brown, M. J. F., Y. Moret, and P. Schmid-Hempel. 2003a. Activation of host constitutive immune defence by an intestinal trypanosome parasite of bumble bees. Parasitology 126:253-260. Brown, M. J. F., R. Schmid-Hempel, and P. Schmid-Hempel. 2003b. Strong context- dependent virulence in a host-parasite system: reconciling genetic evidence with theory. J. Anim. Ecol. 72:994-1002. Carius, H. J., T. J. Little, and D. Ebert. 2001. Genetic variation in a host-parasite association: Potential for coevolution and frequency-dependent selection. Evolution 55:1136-1145. F. Chase, K., R. Adler, and K. G. Lark. 1997. Epistat: A computer program for identifying and testing interactions between pairs of quantitative trait loci. Theor. Appl. Genet. 94:724-730. 96 Churchill, G. A., and R. W. Doerge. 1994. Empirical threshold values for quantitative trait mapping. Genetics 138:963-971. Cook, J. M., and R. H. Crozier. 1995. Sex determination and population biology in the hymenoptera. Trends Ecol Evol 10:281-286. Doums, C.„ and P. Schmid-Hempel. 2000. Immunocompetence in workers of a social insect, Bombus terrestris L., in relation to foraging activity and parasitic infection. Can. J. Zool.-Rev. Can. Zool. 78:1060-1066. Dybdahl, M. F., and C. M. Lively. 1998. Host-parasite coevolution: Evidence for rare advantage and time-lagged selection in a natural population. Evolution 52:1057-1066. Ebert, D., and W. D. Hamilton. 1996. Sex against virulence: The coevolution of parasitic diseases. Trends Ecol. Evol. 1LA79-A82. Frank, S. A. 1992. Models of Plant Pathogen Coevolution. Trends Genet. 8:213-219. Gadau, J., C. U. Gerloff, N. Kruger, H. Chan, P. Schmid-Hempel, A. Wille, and R. E. Page. 2001. A linkage analysis of sex determination in Bombus terrestris (L.) (Hymenoptera : Apidae). Heredity 87:234-242. Gadau, J., R. E. Page, and J. H. Werren. 2002. The genetic basis of the interspecific differences in wing size in Nasonia (Hymenoptera; Pteromalidae): Major quantitative trait loci and epistasis. Genetics 161:673-684. Gerloff, C. U., B. K. Ottmer, and P. Schmid-Hempel. 2003. Effects of inbreeding on immune response and body size in a social insect, Bombus terrestris. Funct. Ecol. 17:582-589. Gerloff, C. U., and P. Schmid-Hempel. 2005. Inbreeding depression and family variation in a social insect, Bombus terrestris (Hymenoptera : Apidae). Oikos 111:67-80. Ghazoul, J. 2005. Buzziness as usual? Questioning the global pollination crisis. Trends Ecol Evol 20:367-373. Gorbunov, P. S. 1987. Endo-parasitic flagellates of the genus Crithidia (Trypanosomatidae, Zoomastigophorea) from alimentary canal of bumblebees. Zool. Zhurnal 66:1775- 1780. Gorbunov, P. S. 1996. Peculiarities of life cycle in flagellate Crithidia bombi (Protozoa, Trypanosomatidae). Zool. Zhurnal 75:803-810. Gorman, M. J., D. W. Severson, A. J. Cornel, F. H. Collins, and S. M. Paskewitz. 1997. Mapping a quantitative trait locus involved in melanotic encapsulation of foreign bodies in the malaria vector, Anopheles gambiae. Genetics 146:965-971. Haldane, J. B. S. 1949. Disease and evolution. La ricerca scientifica 19:68-76. Hamilton, W. D. 1980. Sex versus non-sex versus parasite. Oikos 35:282-290. 97 Hamilton, W. D., R. Axelrod, and R. Tanese. 1990. Sexual reproduction as an adaptation to resist parasites (a review). Proc. Natl. Acad. Sei. U. S. A. 87:3566-3573. Hamilton, W. D., P. A. Henderson, and N. A. Moran. 1981. Fluctuation of environment and coevolved antagonist polymorphism as factors in the maintenance of sex. Pp. 363-381 in R. D. Alexander and D. W. Tinkle, eds. Natural Selection and Social Behavior. Chiron Press, New York. Holland, J. B. 1998. EPISTACY: A SAS program for detecting two-locus epistatic interactions using genetic marker information. J. Hered. 89:374-375. Holland, J. B., H. S. Moser, L. S. Odonoughue, and M. Lee. 1997. QTLs and epistasis associated with vernalization responses in oat. Crop Sei. 37:1306-1316. Holt, R. D., and J. Pickering. 1985. Infectious-disease and species coexistence - a model of lotka-volterra form. Am. Nat. 126:196-211. Kaslow, D. C, and S. C. Welburn. 1996. Insect-transmitted pathogens in the insect midgut. Pp. 432-462 in M. J. Lehane and P. F. Billingsley, eds. Biology of the Insect Midgut. Chapman & Hall, London. Kollien, A. H., J. Schmidt, and G. A. Schaub. 1998. Modes of association of Trypanosoma cruz't with the intestinal tract of the vector Trialoma infestons. Acta Trop. 70:127-141. König, C, and P. Schmid-Hempel. 1995. Foraging activity and immunocompetence in workers of the bumble bee, Bombus terrestris L. Proc. R. Soc. Lond. Ser. B-Biol. Sei. 260:225-227. Lark, K. G., K. Chase, F. Adler, L. M. Mansur, and J. H. Orf. 1995. Interactions between quantitative trait loci in soybean in which trait variation at one locus is conditional upon a specific allele at another. Proc. Natl. Acad. Sei. USA 92:4656-4660. Lehane, M. J., S. Aksoy, W. Gibson, A. Kerhornou, M. Berriman, J. Hamilton, M. B. Soares, M. F. Bonaldo, S. Lehane, and N. Hall. 2003. Adult midgut expressed sequence tags from the tsetse fly Glossina morsitans morsitans and expression analysis of putative immune response genes. Genome Biol. 4:art. no.-R63. Liersch, S., and P. Schmid-Hempel. 1998. Genetic variation within social insect colonies reduces parasite load. Proc. R. Soc. Lond. Ser. B-Biol. Sei. 265:221-225. Lipa, J. J., and O. Triggiani. 1988. Crithidia bombi Sp N. a flagellated parasite of a bumblebee Bombus-Terrestris L (Hymenoptera, Apidae). Acta Protozool. 27:287-&. Little, T. J. 2002. The evolutionary significance of parasitism: do parasite-driven genetic dynamics occur ex silico? J. Evol. Biol. 15:1-9. 98 Lively, C. M. 1996. Host-parasite coevolution and sex - Do interactions between biological enemies maintain genetic variation and cross-fertilization? Bioscience 46:107-114. Lively, C. M., C. Craddock, and R. C. Vrijenhoek. 1990. Red queen hypothesis supported by parasitism in sexual and clonal fish. Nature 344:864-866. Ludwig, J. A., and J. F. Reynolds. 1988. Statistical Ecology - A primer on methods and computing. John Wiley & Sons, New York. Mallon, E. B., R. Loosli, and P. Schmid-Hempel. 2003. Specific versus nonspecific immune defense in the bumblebee, Bombus terrestris L. Evolution 57:1444-1447. Malmberg, R. L., S. Held, A. Waits, and R. Mauricio. 2005. Epistasis for fitness-related quantitative traits in Arabidopsis thaliana grown in the field and in the greenhouse. Genetics 171:2013-2027. Menge, D. M., D. B. Zhong, T. Guda, L. Gouagna, J. Githure, J. Beier, and G. Y. Yan. 2006. Quantitative trait loci controlling refractoriness to Plasmodium falciparum in natural Anopheles gambiae mosquitoes from a malaria-endemic region in western Kenya. Genetics 173:235-241. Moret, Y., and P. Schmid-Hempel. 2000. Survival for immunity: The price of immune system activation for bumblebee workers. Science 290:1166-1168. Niare, O., K. Markianos, J. Volz, F. Oduol, A. Toure, M. Bagayoko, D. Sangare, S. F. Traore, R. Wang, C. Blass, G. Dolo, M. Bouare, F. C. Kafatos, L. Kruglyak, Y. T. Toure, and K. D. Vernick. 2002. Genetic loci affecting resistance to human malaria parasites in a West African mosquito vector population. Science 298:213-216. Nigam, Y., I. Maudlin, S. Welburn, and N. A. Ratcliffe. 1997. Detection of phenoloxidase activity in the hemolymph of tsetse flies, refractory and susceptible to infection with Trypanosoma brucei rhodesiense. J. Invertebr. Pathol. 69:279-281. Owen, R. E. 1988. Body size variation and optimal body size of bumble bee queens (Hymenoptera, Apidae). Can. Entomol. 120:19-27. Owen, R. E. 1989. Differential size variation of male and female bumblebees. J. Hered. 80:39-43. Peters, A. D., and C. M. Lively. 1999. The red queen and fluctuating epistasis: A population genetic analysis of antagonistic coevolution. Am. Nat. 154:393-405. Ruiz-Gonzalez, M. X., and M. J. F. Brown. 2006. Males vs workers: testing the assumptions of the haploid susceptibility hypothesis in bumblebees. Behav. Ecol. Sociobiol. 60 501-509. 99 Sadd, B. M., Y. Kleinlogel, R. Schmid-Hempel, and P. Schmid-Hempel. 2005. Trans- generational immune priming in a social insect. Biol. Lett. 1:386-388. Schiestl, F. P., and E. M. Barrows. 1999. Queen and forager sizes of Bombus qffinis cresson (Hymenoptera : Apidae). Proc. Ent. Soc. Wash. 101:880-886. Schmid-Hempel, P. 2001. On the evolutionary ecology of host-parasite interactions: addressing the question with regard to bumblebees and their parasites. Naturwissenschaften 88:147-158. Schmid-Hempel, P., and J. Jokela. 2002. Socially structured populations and evolution of recombination under antagonistic coevolution. Am. Nat. 160:403-408. Schmid-Hempel, P., K. Puhr, N. Kruger, C. Reber, and R. Schmid-Hempel. 1999. Dynamic and genetic consequences of variation in horizontal transmission for a microparasitic infection. Evolution 53:426-434. Schmid-Hempel, R., and P. Schmid-Hempel. 1998. Colony performance and immunocompetence of a social insect, Bombus terrestris, in poor and variable environments. Funct. Ecol. 12:22-30. Sheridan, L. A. D., R. Poulin, D. F. Ward, and M. Zuk. 2000. Sex differences in parasitic infections among arthropod hosts: is there a male bias? Oikos 88:327-334. Shykoff, J. A., and P. Schmid-Hempel. 1991a. Genetic relatedness and eusociality - parasite- mediated selection on the genetic composition of groups. Behav. Ecol. Sociobiol. 28:371-376. Shykoff, J. A., and P. Schmid-Hempel. 1991b. Incidence and effects of 4 parasites in natural populations of bumble bees in Switzerland. Apidologie 22:117-125. Shykoff, J. A., and P. Schmid-Hempel. 1991c. Parasites and the advantage of genetic variability within social insect colonies. Proc. R. Soc. Lond. Ser. B-Biol. Sei. 243:55- 58. J. 2005. Slate, Quantitative trait locus mapping in natural populations: progress, caveats and future directions. Mol. Ecol. 14:363-379. Söderhall, K., and L. Cerenius. 1998. Role of the prophenoloxidase-activating system in invertebrate immunity. Curr. Opin. Immunol. 10:23-28. Tanksley, S. D. 1993. Mapping polygenes. Annu. Rev. Genet. 27:205-233. Thomas, F., and R. Poulin. 1998. Manipulation of a mollusc by a trophically transmitted parasite: convergent evolution or phylogenetic inheritance? Parasitology 116:431-436. 100 Tieszen, K. L., and D. H. Molyneux. 1989. Morphology and host-parasite relationships of Crithidia flexonema (Trypanosomatidae) in the hindgut and malpighian tubules of Gerris odontogaster (Hemiptera, Gerridae). J. Parasitol. 75:441-448. Tieszen, K. L., D. H. Molyneux, and S. K. Abdelhafez. 1986. Host-parasite relationships of Blastocrithidia familiaris in Lygaeus pandurus Scop (Hemiptera, Lygaeidae). Parasitology 92:1-12. Tilquin, P., W. Coppieters, J. M. Elsen, F. Lantier, C. Moreno, and P. V. Baret. 2001. Statistical power of QTL mapping methods applied to bacteria counts. Genet. Res. 78:303-316. Van Ooijen, J. W., M. P. Boer, R. C. Jansen, and C. Maliepaard. 1999. MapQTL (tm) Version 4.0: Software for the calculation of QTL positions on genetic maps. CPRO-DLO, Wageningen, The Netherlands. Webster, J. P. 2001. Rats, cats, people and parasites: the impact of latent toxoplasmosis on behaviour. Microbes Infect. 3:1037-1045. Wilfert, L., J. Gadau, and P. Schmid-Hempel. in press. A core linkage map of the bumblebee Bombus terrestris. Genome Zeng. Z. B. 1994. Precision mapping of quantitative trait loci. Genetics 136:1457-1468. Zheng, L. B., A. J. Cornel, R. Wang, H. Erfle, H. Voss, W. Ansorge, F. C. Kafatos, and F. H. Collins. 1997. Quantitative trait loci for refractoriness of Anopheles gambiae to Plasmodium cynomolgi B. Science 276:425-428. 101 5. The Genetic Architecture of Immune Defense and Reproduction in Male Bombus terrestris Bumblebees (Lena Wilfert, Jürgen Gadau & Paul Schmid-Hempel, submitted to Evolution) Abstract Understanding the architecture of genetic variation, i.e. the number, effect, location and interaction, of genes responsible for phenotypic variability in nature, is important for the understanding of micro-evolutionary processes. In this study, we have used a QTL approach to uncover the genetic architecture of fitness-relevant traits associated with reproduction and immune defense in male B. terrestris bumblebees. Three male reproductive investment traits, the number and length of the produced sperm and the size of the accessory glands, were studied. Two branches of the insect innate immune system, the activation of the Phenoloxidase-cascade and the hemolymph's antibacterial activity, were investigated. We found that variation in most of the studied traits is based on a network of minor QTLs and epistatic interactions. Unexpectedly, there was no evidence for phenotypic or genetic trade¬ offs between the presumably costly investment in immune defense and reproductive effort in this population for the measured traits. In fact, we found a positive correlation, both, in phenotype and genetic architecture for the number of produced sperm and antibacterial activity against an insect pathogen. A major finding for all traits analyzed was that the epistatic interactions accounted for a major proportion of the explained phenotypic variance. Especially for traits involved in immune defense, this pattern highlights the possible role of parasites in the evolution and maintenance of recombination and sexual reproduction. Keywords: natural variation, QTL, trade-off, epistasis, recombination. 102 Introduction Phenotypic variation, even for highly selected, fitness-relevant traits, abounds in nature. If this variation is based on differences in the underlying genes, i.e. is heritable, it represents the raw materia] for adaptation to a particular environment. This is why the understanding of the genetic basis for the phenotypic variation of fitness-relevant traits is essential for understanding the evolutionary processes shaping these traits. Today, studying quantitative trait loci (QTL) is a powerful tool to elucidate the detailed "genetic architecture" of traits - the number, location, effect and interaction of genetic loci explaining phenotypic variation - and, hence, to study the genetic foundations of evolutionary processes. For example, current theories on the evolution and maintenance of meiotic recombination are divided in their opinions on the need for epistatic gene interactions (Otto and Michalakis 1998; Otto and Nuismer 2004). QTL studies now allow for unprecedented insights into the reality and significance of such interactions and thereby aid clarifying the assumptions of different theories. In fact, QTL analyses of model or laboratory species have become almost commonplace over the last two decades, including the analysis of traits directly influencing the individual's fitness (e.g. Shook and Johnson 1999; Malmberg et al. 2005). However, studies on natural populations are still very rare due to experimental and statistical difficulties (Slate 2005). The first step in a QTL analysis is the construction of a genetic linkage map, which, in un- manipulated populations, is typically achieved by genotyping large numbers of sib-ships or mapping populations with detailed pedigree information (Slate 2005). However, these requirements can be bypassed in hymenopteran social insects, such as B. terrestris, the study organism used here. Hymenoptera are haplo-diploid with a single locus complementary sex determination system (Cook and Crozier 1995): females hatch from diploid, fertilized eggs and males from haploid, unfertilized eggs. Being social, a B. terrestris queen will found a colony after hibernation and eventually may produce several hundred haploid sons that can easily be collected from her nest. Thus, the queen's meiotic recombination frequency can be measured reliably and directly in her male offspring. As was demonstrated by Gadau et al. and (2001) Wilfert et al. (in press), these features allow genetic linkage maps to be readily constructed even in un-manipulated mapping populations of B. terrestris. 103 Here, we focus on the genetic architecture of fitness-relevant traits of B. terrestris males as measured in F2-offspring of a wild-caught queen. Bumblebee males are essentially semelparous, that is, their fitness is largely determined by their chances to survive until mating and their ability to inseminate queens during mating flights. Furthermore, the sex-ratio in bumblebees is heavily male-biased (Bourke 1997) and B. terrestris queens are monandrous (Estoup et al. 1995; Schmid-Hempel and Schmid-Hempel 2000). Males, on the other hand, - can at least under laboratory conditions - successfully mate up to eight times (Tasei et al. 1998). These conditions suggest that males should invest heavily in their reproductive effort (Baer et al. 2003). We studied three corresponding traits - sperm length, sperm number and the size of the accessory glands. The male accessory glands, for example, play a role in securing the success of the male's sperm, since they produce a mating plug that is transferred to the female's bursa copulatorix during mating (Duvoisin et ai. 1999). The plug prevents re-mating by reducing the female's copulation acceptance (Baer et al. 2001 ; Sauter et al. 2001). Since the size of the accessory glands is roughly halved after only one mating by the discharge of gland products (Lahusen 2003) and males may mate several times, selection for large accessory glands is likely in this species. Similarly, the male's capacity for repeated copulations could lead to selection for increased sperm number. In polyandrous species, sperm length is under heavy selection (e.g. Hosken 2003). Although this kind of selection should be absent in B. terrestris, where typically females mate only once (Estoup et al. 1995; Schmid-Hempel and Schmid-Hempel 2000), Baer et al. (2003) showed that B. terrestris exhibits variation in sperm length. While they found that most of the variation resides within the individual male, they also demonstrated that queens preferentially store a certain individual size range of their mate's sperm. Furthermore, sperm length is known to be a heritable trait (Baer et al. 2006). Hence, we included both sperm number and sperm length in our fitness measures. Although investment in reproduction is of major importance for a male's fitness, survival to mating is an essential pre-requisite. Parasites and pathogens are a threat to the survival of all organisms. In social insects such as the bumblebees, the spread of parasitic diseases might be facilitated by the large number of genetically similar individuals in close spatial proximity within colonies (Schmid-Hempel 1998). An active immune system should therefore be essential in social insects. Here, we studied the genetic architecture of two branches of the insect innate immune system, the activation of the Phenoloxidase-cascade (PO) and the 104 antibacterial activity of the hemolymph after an experimental immune challenge. The PO- cascade leads to melanization and is essential for the encapsulation of invaders (Söderhall and Cerenius 1998). This rapidly activated system is the insect's first line of defense and can be considered as their constitutive response (Schmid-Hempel and Ebert 2003). While this rapid response is un-specific, the hemolymph's slower antibacterial activity, caused by soluble peptides, has the potential for specificity (Schmid-Hempel 2005). A QTL approach has been successfully employed for studying the genetic basis of the encapsulation reaction in the mosquito Anopheles gambiae (Gorman et al. 1997). However, to our knowledge this is the first study using this method to simultaneously uncover the genetic architecture of the PO- cascade and the hemolymph's antibacterial activity, two of the most important mechanisms of the insect's innate immune system. The immune system is particularly costly, both in maintenance and when used to fight pathogens and parasites (e.g. Moret and Schmid-Hempel 2000). Therefore, trade-offs between functions of the immune system and direct fitness factors such as the investment in reproduction are to be expected and have been reported in several systems (e.g. Siva-Jothy et al. 1998; Verhulst et al. 1999; Unionen et al. 2000). Hosken (2001) has demonstrated that such trade-offs can arise through micro-evolutionary processes and thus might be hard-wired into the organism's genome. The signature of genetic trade-offs and correlations can be found in the traits' genetic architecture. For example, when Zhong et al. (2005) simultaneously mapped QTLs for fecundity and egg-to-larvae viability as well as for parasite resistance in the Flour beetle, Tribolium castaneum, they found that the QTLs for these traits were mostly co- located to the same genomic regions. We have analyzed the genetic architecture of immune and reproductive investment traits for evidence of genetic correlations or trade-offs in a field- derived mapping population of the bumblebee B. terrestris. Materials & Methods Mapping Populations The mapping population was established in autumn 2003 from a first-generation lab-queen produced by a wild-caught B. terrestris queen from a population in Northeastern Switzerland. This setup allowed the determination of phase-information (BBM-1; see Wilfert et al. in 105 press). All males were removed from their maternal colony as callows and kept individually under the same standard conditions (red light, 28 °C and 60 % r.H., fed ad libitum with sugar water and pollen (Gerloff and Schmid-Hempel 2005)). Phenotypic Measurements Immune and male sexual traits were measured in the same individuals. For the former, the males received an immune insult aged four to seven days and were sacrificed after a further four days. This protocol ensured that the sexual traits were measured in mature individuals, since sexual maturity is reached seven days post-eclosion in male bumblebees (reviewed in Baer 2003). The immune traits were measured as described in Körner and Schmid-Hempel (2004). First, the males were injected with 2 pi of a lipopolysaccharide-solution (LPS; 0.5 mg/ml in sterile Ringer solution) four to seven days after eclosing to elicit an immune response. After four days, 10 u\ of hemolymph were extracted by bleeding and the individuals were freeze-killed. The hemolymph was diluted in sodium cacodilate buffer (0.01 M Na-Cac, 0.005 M CaCL, pH 6.5; final dilutions of 1:6 for the measurement of antibacterial activity, 1:21 for the activation of the PO-cascade). The activation of the PO-cascade was measured photometrically as the vmax of the change in absorbance at 480 nm. The reaction was started by adding 20 ul of L-Dopa (4 mg/ml in H20) to a mixture of 20 \iI diluted hemolymph supernatant, 20/ri PBS (phosphate buffer saline) and 140/^1 H20). The absorbance was measured for 40 minutes at 30 °C in 10 second intervals. The antibacterial activity of the hemolymph was assessed via the zone of inhibition assay. It was tested against two gram-positive bacteria, Arthrobacter globiformis and Paenibacillus alvei. A. globiformis is a soil bacterium that has been used in several previous studies of ecological immunity in B. terrestris (e.g. Moret and Schmid-Hempel 2000). P. alvei was chosen because it is a potential insect pathogen, being a secondary associate of the severe honeybee disease European foulbrood, caused by Melisococcus plulon (Schmid-Hempel 1998). The inhibition of bacterial growth was measured by pipetting 2 //I of hemolymph supernatant into a hole in a bacterial agar plate (105 cfu ml"1 of A. globiformis or P. alvei, respectively). The plates were incubated for 24 hours at 30 °C. The size of the inhibition zone was measured using the image processing program ImageJ (Abramoff et al. 2004; available 106 at: http://rsb.info.nih.gov/ij) and calibrated via scale paper. Sperm length and sperm number as well as the size of the accessory glands were measured as the male sexual traits. For this purpose, the entire male sexual tract was dissected from the abdomen (as illustrated in Duvoisin et al. 1999). The inner sexual organs - testes, accessory testes and accessory glands - were separated from the sclerotized copulatory organ at the endophallus. The sexual organs were then placed in Ringer solution and displayed in a black microscopic well plate. The size of the accessory glands was measured at 12x magnification using the polygon function of ImageJ (Abramoff et al. 2004) and a Wacom Sapphire CTE- 630 pen tablet. Sperm number and length was analyzed by taking pictures of fixed sperm isolates from adults. To obtain these, we adapted the procedure outlined in Sauter et al. (2001). The production of sperm is completed when the individuals eclose from the pupa and sexually mature B. terrestris males store their sperm in the accessory testes (reviewed in Baer 2003). The accessory testes were placed in a V-vial (Infochroma) in 100 pi of KIEV medium (Moritz 1984) and the stored sperm were then gently squeezed out. A further 300 pi of KIEV medium were added, the mixture was sonificated for 3 minutes and vortexed. Three replicates of each sperm solution were fixed on a degreased superteflon well slide and stained with DAPI as described by Sauter et al. (2001). Sperm number was counted in two pictures per replicate, taken at 200x magnification with a fluorescence microscope (Nikon Eclipse with DAPI- filter). For sperm length, the size of one undamaged, uncoiled sperm per replicate was measured at 400x magnification using a Wacom Sapphire CTE-630 pen tablet and the segmented line function of ImageJ (Abramoff et al. 2004). Additionally, we measured body size as a general indicator for conditions during larval development (Sutcliffe and Plowright 1988). We measured the length of the radial cell of the forewing since it is known to correlate well with body size and other morphometric features in Bombus (Owen 1988; Owen 1989; Schiestl and Barrows 1999). The forewings were clipped and the length of the radial cell was measured using ImageJ (Abramoff et al. 2004) of 6 (mean measurements); the measurements were calibrated via scale paper. 107 Construction of a Genetic Linkage Map The construction of the linkage map is described in detail in Wilfert et al. (in press). Briefly, the population was genotyped for 246 microsatellites and AFLPs. The linkage map has an estimated genome coverage of 81 % and spans 2'222 cM. It is nearly saturated; the average marker distance is 10.3 cM and the haploid karyotype of 18 chromosomes is closely matched by 20 linkage groups. 14 linkage groups homologous in three genetic maps form a core linkage map of B. terrestris (Wilfert et al, in press). They are referred to as linkage groups LGOl to LG14, whereas the non-homologous linkage groups are named Bl_15 to Bl_20. QTL Mapping Main effect QTLs were mapped using MapQTL 4.0 (Van Ooijen 1999), adapting the approach described in Gadau et al. (2002). The standard permutation test for interval mapping (Churchill and Doerge 1994) was used to determine the LOD-threshold for declaring a suggestive (chromosome-wide) or a significant (genome-wide) QTL. Suggestive QTLs determined in the interval mapping procedure were used as cofactors during the consecutive MQM (multiple QTL model) mapping, which allows fitting more than one QTL at a time. MQM was repeated until the cofactor list remained stable, with all identified QTLs showing LOD-scores above the suggestive threshold. Digenic Epistasis The whole genome was scanned for significant epistatic interactions between all markers using the program EPISTAT (Chase et al. 1997; available at: http://64.226.94.9/epistat.htm). In this program, a log-likelihood approach is implemented that compares the log likelihood ratios (LLR) of explaining the observed effects by null, additive, or epistatic models. The initial search for all pair-wise epistatic interactions was limited by a minimal group size of 10 and an LLR-threshold of 6, as suggested by Chase et al. (1997). Statistical significance was established via Monte Carlo simulations, implemented in EPISTAT. Each simulation was specific to a given trait and pair of loci (I'OOO'OOO trials per interaction). To account for the high number of tests, we transformed the p-value determined in the Monte Carlo simulation into p' = 1 - (1 - p)n (number of mapped markers, n= 235; Lark 108 et al. 1995). We calculated the additive effects of the two involved loci (A/a and B/b) and their epistatic effect, the interaction deviation, for all interactions. The additive effects are - derived as Add_A - (GAB + GAb-GaB Gab)/4 and AddJB = (GAB + GaB - GAb - GJ/4, respectively. The epistatic effect is calculated as EpL = ((GAB + Gab)/2 - (GAb+ GaB)/2)/2. Statistics The power of parametric QTL mapping methods, such as the maximum likelihood approach used here, can be severely reduced if the assumption of normality is violated for an analyzed trait. Tilquin et al. (2001) could show that this loss in power can be circumvented by transforming data, so that the variable's distribution is approximately normal. Only the measures for the PO-cascade and the antibacterial-activity against A. globiformis deviated from a normal distribution (KoImogoroff-Smirnoff-test, PO-cascade: Z = 3.52, p< 0.001; antibacterial activity: Z = 1.68, p < 0.01). The antibacterial activity against A. globiformis was square root transformed, resulting in a normal distribution. The distribution of PO was strongly skewed to the left and was transformed to an approximately normal distribution using the natural logarithm. We used the transformed data for all further analyses. Results - In total, 175 males haploid offspring of one B. terrestris queen - were collected as callows over a period of two weeks. Table 1 gives the mean and variance for all measured traits. We first tested for phenotypic correlations both between and within immune traits and reproductive traits. Among the male reproductive traits, there were no significant correlations in this mapping population. For the immune defense traits, we found a highly significant negative correlation between the PO-cascade and the antibacterial activity against A. globiformis (Pearson r = -0.459, p < 0.001), suggesting a phenotypic trade-off between these two branches of innate immunity. Since both traits were likely to be influenced by "environmental" factors such as age or body size, or by the date on which the individual's immune system was challenged, we repeated the test with the traits' residuals after the effects of these variables were removed (see below). This should minimize the possibility that the observed trade-off might merely be caused by environmental variation. However, even when the = using residuals, a highly significant negative correlation was found (Pearson r -0.262, 109 p < 0.001), although the explained variance, r2, was reduced from 21.1 % to 6.9 %. Besides this negative correlation, there was a weak positive correlation between the antibacterial activities against both bacterial targets (Pearson r = 0.191, p < 0.05). Yet, this correlation is not significant when correcting for the large number of tests performed (n = 15) and is not confirmed when testing with the residuals of the antibacterial activity against A. globiformis (p = 0.58). Table 1: Means of the investigated traits N Mean ± Stdev. PO-cascade (vmaJ 173 309.2 498.7 Antibacterial activity (A. globiformis) [mm2J 170 18.0 3.9 Antibacterial activity (P. alvei) [mm2| 153 48.6 27.5 Accessory glands [mm2] 161 1.91 0.25 Sperm number 172 64 29 Sperm length [mm| 172 0.132 0.015 Considering immune defense and sexual traits, we found a highly significant positive correlation of sperm number and antibacterial activity against P. alvei (Pearson r = 0.357, p < 0.001), which was confirmed when using the residuals of sperm number after the effect of body size was removed (Pearson r = 0.322, p < 0.001 ). Furthermore, the size of the accessory glands was weakly correlated with the PO-cascade (Pearson r = 0.164, p < 0.05) and with antibacterial activity against A. globiformis (Pearson r = -0.161, p < 0.05). But these correlations were neither significant after a Bonferroni-correction nor where they confirmed when the residuals corrected for size = = using body (pP0 0.19, pA „hbll0,m„ 0.30). QTL Analysis For QTL mapping, environmental variation should be minimized. We therefore first checked whether the measured immune and sexual traits were influenced by the individuals' age or body size, or by the date on which their immune system was challenged. Generalized linear models showed that PO-activity, antibacterial activity against A. globiformis and the number 110 of sperm were partially influenced by these environmental variables. The date of the affected the activation of the PO-cascade = < while experiment (Fx 171 15.452, p 0.001), sperm number was correlated with size = < body (F] 1% 4.405, p 0.05). The antibacterial activity against A. globiformis was influenced by all three environmental variables (date and age as factors, size as = < < To body covariate; F7 148 6.288, pdaic 0.05, pa„e<0.01, /^ 0.05). increase the power of QTL mapping, we therefore used the traits' residuals after the effect of these environmental factors was removed. Interval- and MQM-mapping identified at least one main effect QTL for each investigated trait, except for sperm length (Table 2). All QTLs are of minor effect, explaining less than 10 % of the phenotypic variation (Tanksley 1993). For each of the immune investment traits, two suggestive QTLs (significant at the chromosome-wide level) were identified. Cumulatively, they explained 11.6% (PO-cascade), 13.0% (antibacterial activity against A. globiformis) and 12.0 % (antibacterial activity against P. alvei) of the phenotypic variation, respectively. While we unexpectedly (Baer et al. 2006) found no main effect QTLs explaining the phenotypic variation in sperm length in this population, we identified one suggestive QTL for the number of stored sperm, explaining 5.1 % of variation. For the size of the accessory glands, we identified four to seven QTLs, depending on the mapping method. Using MQM- mapping, the seven identified QTLs cumulatively explained 40.9 % of phenotypic variation. Three QTLs for gland size (Gla 4, Gla 6 and Gla_8) were significant at the genome-wide level. Table 2: Main effect QTLs determined via Interval and MQM mapping Trait Name Location 1M- mapping MQM -mapping Lod-threshold Mean allelic \a!ue AddJ" LG Position [cM] Marker Loda' PVEb) [%] Lod PVE [%] per group genome wide Acl ac! el PO-cascade PO-1 LG02 100.1 B126 2.8 7.2 2.6 1.6 2.9 -0.45 0.47 -0.46 PO-cascade PO-2 LG08 0 BT22 1.7 4.4 1.4 - i.6 2.9 0.26 -0.27 0.27 AAtJ AA-1 LG14 34.4 A02GJ95 1.5 4.5 1.6 6.4 1.3 2.9 0.11 -0.11 0.11 AA AA-2 B1J9 23.4 A01M_457 2.1 6.4 2.3 6.6 1.3 2.9 -0.16 0.14 -0.15 AP*' AP-1 LG08 100.9 A05K_069 1.5 - 1.8 5.2 1.7 3.0 54.1 42.7 11.72 AP AP-2 B1_20 0 A09E081 1.7 6.1 2.0 6.8 LI 3.0 59.2 39.0 20.77 Glands Gla-1 LG11 19.3 A03M_277 1.5 4.1 0.7 - 1.4 2.9 1.87 1.96 -2.36 Glands Gia-2 LG13 59.0 A03F_209 0.6 - 2.3 4.8 1.4 2.9 1.96 1.89 1.83 Giands GIa-3 LG12 18.4 BT20 1.1 - 2.0 3.9 1.7 2.9 1.95 1.88 1.83 Glands Gla-4 LG05 70.2 BT03 2.5 8.0 3.2 7.9 1.5 2.9 1.84 1.96 -3.14 Glands Gla-5 LG08 27.4 BL01 1.0 - 2.3 4.7 1.9 2.9 1.96 1.87 2.36 Glands Gla-6 LGOl 110.7 BIO 2.0 6.4 2.9 6.5 1.7 2.9 1.85 1.98 -3.40 Glands Gla-7 Bl 20 10 A02EJ195 0.7 - 1.7 4.0 1.1 2.9 1.87 1.95 -2.09 Glands Gla-8 LG03 46.4 A05M__227 2.6 7.8 3.9 9.1 1.8 2.9 1.83 1.97 -3.66 Spermnr. Spn-1 LG08 100.9 A05K_069 1.7 5.1 - - 1.7 3.0 6.45 -6.69 6.57 al Lod-values above the group-wide or genome-wide Lod-threshold indicate a suggestiveor significantQTL, respectively h) PVE := phenotypicvariation explainedby the QTL c) The mean allelicvalue of allele "A" refers to the mean phenotypevalue for the maternal allele whereas "a" refers to the paternalallele dl The additive effect was calculated as Add_ = (GA - Ga)/2.The additive effect is givenas the percentage of the populationmean for those traitswere the raw phenotypicvalues were used for analysis(antibacterialactivityagainstP. alvei,accessory glands).For the other traits (PO-cascade, antibacterial activity againstA. globiformis,sperm number), where residuals were used for mapping,the untransformed additive effect is presented,since the traitvalues are alreadystandardized. e) If a QTL did not reach the group-wide significanceLod-threshold for interval- or MQM-mapping, no value is given for the explainedphenotypicvariance. f) E) Antibacterial activityagainstA. globiformis Antibacterial activityagainstP. alvei 112 Epistatic Interactions We scanned the entire linkage map for digenic epistatic interactions using the program EPISTAT (Chase et al. 1997). The focus on haploid males facilitated the detection of pair- wise epistatic interactions because it reduced the number of genotype classes to four. We conservatively corrected for the large number of pair-wise comparisons by adjusting the significance threshold of p < 0.05 to the number of markers mapped on the linkage map. This procedure is very conservative since linked markers are not statistically independent; in many studies, therefore, only the number of linkage groups is corrected for (e.g. Gadau et al. 2002; Malmberg et ai. 2005). We furthermore often found that interactions between the same regions of two linkage groups were significant for several marker pairs; in such cases, we here only report the most significant interaction, which should give a conservative view. We identified four digenic interactions for the activation of the PO-cascade (Table 3). For the strength of the antibacterial reaction, we found one epistatic interaction each for the activity against A. globiformis and P. alvei, respectively. For the male reproductive investment traits, we found three (accessory glands), four (sperm length) and two (sperm number) digenic epistatic interactions, respectively. Table 3: Digenicepistasis Genotypic Marker A Marker B : Values' Effects" Trait Name LG Position [cM] Marker LG Position [cM] Marker AB Ab ab aß Add_A Add_B Epi_ PO-cascade Epo-1 LGOl 57 AllE_06i LG02 77 A07F_065 0.33 -0.65 1.27 -0.31 -0.32 -0.15 0.64 PO-cascade Epo-2 Bl_15 79.4 A11L_047 LG09 22.9 BT24 -0.92 0.45 -0.32 0.57 -0.18 -0.12 -0.57 PO-cascade Epo-3 LG12 117.2 A01F_236 LG02 39.5 A10K_226 0.66 -0.43 0.39 -0.85 0.17 -0.04 0.58 PO-cascade Epo-4 LG14 39.7 A06K_452 LG07 130.8 BL03 -0.59 0.54 -0.88 0.28 0.14 0.01 -0.57 AAC1 Eaa-1 LG07 U7.5 A04K_048 Bl_15 41.8 A02E-280 0.14 -0.07 0.16 -0.38 0.07 -0.08 0.19 AP"» Eap-1 Bl_16 27.6 A09B_318 LG03 Î 14.4 A01L_346 64.6 41.7 61.1 43.1 1.08 2.52 21.0 Glands Egla-1 Bl_15 0 A10F_135 LG12 126.9 A05B_223 1.75 1.98 1.83 1.92 -0.26 -1.83 -4.19 Glands Egla-2 LG09 178.6 A01M_096 LGOl 0 A02E_106 2.07 1.86 1.92 1.82 2.49 1.44 4.06 Glands Egla-3 Bl_16 27.6 A09B_318 Bl_17 0 A03K_229 1.77 1.96 1.82 1.97 -0.79 -0.52 -4.45 Sperm length Espl-1 LG06 36.3 A01L_Î64 LG02 39.5 A10K_226 0.127 0.134 0.128 0.138 -0.95 0.57 -3.22 Sperm length Espi-2 LG13 43.2 A06GJM2 LG04 0 A09E_239 0.138 0.124 0.137 0.131 -1.14 1.52 3.79 Sperm length Espl-3 LG08 41.7 A02L_258 LG09 124.6 A02P_294 0.136 0.129 0.135 0.126 0.76 -0.38 3.03 Sperm length Espl-4 Bl_18 53.6 A01D_062 LG13 20.9 A05B_345 0.136 0.129 0.139 0.126 0.00 -1.14 3.79 Sperm nr Espn-1 LG06 28.7 B124 LG13 31.2 A02P_086 -5.86 7.40 -[4.8 7.92 2.09 2.35 -8.98 Sperm nr. Espn-2 LG07 45.5 A06K_056 LG02 9.3 A01M_165 -3.87 l.!7 -22.3 23.7 -1.03 10.24 -12.3 Note: all interactions are significantat the p < 0.05 level,corrected for all mapped markers. Alleles are designatedby A/a or B/b for the two interactingloci. al A/a and B/b represent the alleles at the two loci (allelesA and B contributed by the mother);as the phase is known in this mapping population,the allele combinations "AB" and "ab" represent the two possibleparentalgenotypes h) = = The additive effects are calculated as Add_A (GAb + GAb - GaB - Gab)/4and Add_B (GAb + Gaß - G,\b - Gat,)/4,respectively;the epistaticeffect,or = interaction deviation,is calculated as Epi_ ((GAB+Gai,)/2-(GAb+Gila)/2)/2.If the residuals were used for mapping (PO-cascade. antibacterial activity againstA. globiformisand sperm number), the raw effects are givensince the traitvalues are alreadystandardized. For the other traits(antibacterial activityagainstP. alvei,accessory glandsand sperm length),the effect size was transformed to the percentage of the populationmean. cl dl Antibacterial activityagainstA. globiformis Antibacterial activityagainstP. alvei 114 Genetic Correlations and Trade-Offs The genetic architecture of the different traits shows some overlap (Fig. 1). For example, linkage groups LG08 and Bl_20 carry main effect QTLs for several traits. We thus additionally analyzed the spatial pattern of the traits' genetic basis with respect to possible genetic correlations, testing whether co-localized QTLs or epistatic loci are significantly associated rather than randomly distributed across linkage groups. For this purpose, we performed association tests (Ludwig and Reynolds 1988). However, most of the co- localizations of main effect QTLs were either not significantly different from random associations (due to the large number of QTLs identified for the phenotypic variation in accessory gland size,) or involve loci that, albeit located on the same linkage group, are spaced further than 50 cM apart and thus in practice appear un-linked. Yet, two QTLs for sperm number (SPN_1) and for the antibacterial activity against P. alvei (AP_1) show a highly significant association (Chi-squared- 10.0, p < 0.001). These QTLs coincide at the genetic marker A05K_069 on linkage group LG08 (Fig. 2). ^P Accessory glands ^v PO-cascade *\P Sperm number ^n^ Activity against A. globiformis ' Sperm length • Activity against P. alvei Figure 1: Genetic architecture of male investment in reproductive effort (left) and immune defense (right). Main effect QTLs are indicated by symbols and digenic epistatic interactions by lines (see legend). Linkage groups are represented as ovals (linkage groups LGOl to LG14 (bold lines) have been homologized with linkage groups from two other mapping populations, see (Wilfert et al. in press). 115 © o o ^ o V) i o o d Linkage group LG08 [cM] Figure 2: Overlapping QTL Overlapping QTL for the antibacterial activity against P. alvei (dots and solid line) and the number of sperm (triangles and broken line) on linkage group LG08. The group-wide Lod- threshold (Lod = 1.7) is indicated by a horizontal line. For the epistatic interactions, we found no absolute congruence in patterns, i.e. there are no parallel interactions in different traits (Fig. 1) at this level of significance. Yet. there are several instances, where the same genetic region is implied in epistatic interactions for different traits (at intervals of < 50 cM; Table 2). For the activation of the PO-cascade and for the antibacterial activity against A. globiformis, the epistatic QTL are significantly aggregated (Chi-squared = 4.13, p < 0.05). 116 Effect of Environmental Variation on QTL Mapping As described above, for three traits - the activation of the PO-cascade, the antibacterial activity against A. globiformis and the number of sperm - the residuals of environmental or experimental covariates were used for interval- and MQM-mapping as well as in the search for epistatic interactions. This approach overall increased the power of QTL mapping. For example, while the QTLs PO_l and AA_1 (antibacterial activity against A. globiformis) were all found using both residuals and the uncorrected values, for both the PO-cascade and the antibacterial activity against A. globiformis one additional QTL each, PO_2 and AA_2, respectively, was revealed when using the residuals as compared to uncorrected values. In turn, employing the uncorrected values for the activation of the PO-cascade, one additional QTL was found that could not be retrieved with the residuals. This QTL coincides with Gla_4, a significant QTL for the size of the accessory glands. For the epistatic interactions, all interactions were also found when using the uncorrected trait values, with the exception of Epo_2 and Espn„2, which were not significant at the conservative level used in this study. Between two and five additional interactions were found for the uncorrected values. Interestingly, this included epistatic interactions on linkage group LG11 that were fully congruent in the correlated traits "PO" and "antibacterial activity against A. globiformis". Hence, we conclude that, overall, our results were not systematically distorted and the analysis improved by using the residuals in the mapping procedure. Discussion We have simultaneously mapped QTLs and epistatic interactions for immune and reproductive investment traits in bumblebee males. This allowed us to test for trait correlations both at the phenotypic and at the genetic level in a field-derived mapping population. Within the costly insect innate immune system, a phenotypic trade-off between the activation of the constitutive PO-cascade and the slower, but potentially more specific antibacterial activity has been found both in the bumblebee B. terrestris (Moret and Schmid- Hempel 2001) and in the mealworm beetle Tenebrio molitor (Moret and Siva-Jothy 2003). In our population, we found the same phenotypic trade-off as described by Moret and Schmid- Hempel (2001). This is paralleled by the genetic architecture with a significant overlap of 117 genomic regions implicated in epistatic interactions for the two traits (see Fig. 1 ). The reproductive investment traits showed no phenotypic association with each other, although trade-offs within the reproductive investment are frequently found. For example, Gage and Morrow (2003) demonstrated a trade-off between the number and length of sperm in the cricket Gryllus bimaculatus. We also did not find a correlation between body size and the length of sperm, two traits that were shown to correlate in B. terrestris by Baer et al. (2003). This correlation appears to be colony specific rather than a general feature of B. terrestris, since in earlier studies, a correlation between body size and length of sperm was found among but not within colonies (Baer et al. 2006). By contrast, the number of sperm correlated with body size in our mapping population. A similar correlation was experimentally found in the honeybee A. mellifera (Schluns et al. 2003). Contrary to our initial expectation, we found no trade-off between investment in reproductive effort and immunity in this male population. Instead, we found a positive correlation between the number of sperm produced and the antibacterial activity against the potential insect pathogen P. alvei. This is mirrored in the traits' genetic architecture, with one genetic marker being linked to a QTL for both traits. Note that this analysis cannot distinguish whether this highly significant co-localization is due to one pleiotropic QTL or two mechanistically independent but closely linked QTLs. As is to be expected for positively correlated traits, the direction of effects was the same for both traits, with the maternal allele conveying the higher phenotypic value in this population. Generally, trade-offs between costly traits are expected because individuals are restricted in their investment by the available resources. We have reared the males used in this study under stable and optimal conditions, providing them with ample resources at all life-stages. These benign conditions might mask existing trade-offs between immune defense and reproductive investment. Alternatively, it has to be considered that investment in immune defense and in reproductive effort can coincide. This could explain the positive correlation of sperm number and antibacterial activity against P. alvei in our study. Such parallel investments have previously been demonstrated in insects. For example, in Drosophila melanogaster, it has been demonstrated that males transfer antimicrobial peptides to their mate in the ejaculate (Lung et al. 2001 ). The genetic architecture of immune and reproductive investment traits in this mapping population is characterized by a network of minor QTLs and epistatic interactions (with the 118 exception of sperm length, for which no main effect QTLs were found). This pattern indicates that the investigated traits are polygenic. An important finding of our study is that epistatic interactions play a major role in these fitness-relevant traits, with the combined epistatic effects being consistently larger than the additive effects of main effect QTLs. Specifically, the observed ubiquity of epistasis for traits determining immune defense lends support to a major assumption of theories that assign parasites a role in the evolution and maintenance of recombination, or even sexual reproduction (Otto and Michalakis 1998; Otto and Nuismer 2004). In this study, the genetic architecture unveiled for the studied traits is based on the variation found in one B. terrestris queen randomly chosen from an un-manipulated wild population, without experimental selection or screening for particular phenotypes. The drawback of this ecological approach to genetic mapping is that it does not allow for an exhaustive QTL search. QTLs and epistatic interactions could only be detected if the Frqueen happened to be heterozygous for the involved alleles. Additionally, all QTL mapping methods are inherently biased towards underestimating the number of involved QTLs (Beavis 1994; Zeng 1994). Therefore, the results of this study should not be interpreted as representing the full genetic basis of the studied traits' phenotypic variation in this species. Instead, the QTLs and epistatic interactions found here represent the extant genetic variation shaped by natural selection in one wild B. terrestris queen. In turn, therefore, this study highlights the degree and structure of heritable variation in parasite-related and reproductive traits that is amenable to selection in nature. Acknowledgments The authors would like to thank Y. Merki and D. Heinzmann for assistance in genotyping. We are obliged to P. Nigg for her valuable assistance in measuring phenotypes. We would like to thank O. Soyer for his suggestions on network analysis and T. Linksvayer for statistical advice. B. Sadd provided helpful comments on the manuscript. This project was funded by the German Research Foundation (SFB 554-TBl to JG) and grants by the Swiss National Science Foundation (3100-066733 to PSH) and by the Swiss Federate Institute of Technology Zurich (TH-19/03-2 to PSH and LW). 119 References Abramoff, M. D., P. J. Magelhaes, and S. J. Ram. 2004. Image processing with ImageJ. Biophotonics Intl. 11:36-42. Baer, B. 2003. Bumblebees as model organisms to study male sexual selection in social insects. Behav. Ecol. Sociobiol. 54:521-533. Baer, B., E. D. Morgan, and P. Schmid-Hempel. 2001. A nonspecific fatty acid within the bumblebee mating plug prevents females from remating. Proc. Natl. Acad. Sei. USA 98:3926-3928. Baer, B., P. Schmid-Hempel, J. T. Hoeg, and J. J. Boomsma. 2003. Sperm length, sperm storage and mating system characteristics in bumblebees. Insect. Soc. 50:101-108. Baer, B., G. de Jong, R. Schmid-Hempel, P. Schmid-Hempel, J. T. H0eg, and J. J. Boomsma. 2006. Heritability of sperm length in the bumblebee, Bombus terrestris. Genetica 127:11-23. Beavis, W. D. 1994. The power and deceit of QTL experiments: Lessons from comparative QTL studies. Proceedings of the 49lh annual corn and sorghum industry research conference, Washington D.C. Bourke, A. F. G. 1997. Sex ratios in bumble bees. Philos. Trans. R. Soc. Lond. B 352:1921- 1932. Chase, K., F. R. Adler, and K. G. Lark. 1997. Epistat: A computer program for identifying and testing interactions between pairs of quantitative trait loci. Theor. Appl, Genet. 94:724-730. Churchill, G. A., and R. W. Doerge. 1994. Empirical threshold values for quantitative trait mapping. Genetics 138:963-971. Cook, J. M., and R. H. Crozier. 1995. Sex determination and population biology in the Hymenoptera. Trends Ecol. Evol. 10:281-286. Duvoisin, N., B. Baer, and P. Schmid-Hempel. 1999. Sperm transfer and male competition in a bumblebee. Anim. Behav. 58:743-749. Estoup, A., A. Scholl, A. Pouvreau, and M. Solignac. 1995. Monoandry and polyandry in bumble bees (Hymenoptera - Bombinae) as evidenced by highly variable microsatellites. Mol. Ecol. 4:89-93. Gadau, J., R. E. Page, and J. H. Werren. 2002. The genetic basis of the interspecific differences in wing size in Nasonia (Hymenoptera; Pteromalidae): Major quantitative trait loci and epistasis. Genetics 161:673-684. 120 Gadau, J., C. U. Gerloff, N. Kruger, H. Chan, P. Schmid-Hempel, A. Wille, and R. E. Page. 2001. A linkage analysis of sex determination in Bombus terrestris (I.) (Hymenoptera : Apidae). Heredity 87:234-242. Gage, M. J. G., and E. H. Morrow. 2003. Experimental evidence for the evolution of numerous, tiny sperm via sperm competition. Curr. Biol. 13:754-757. Gerloff, C. U., and P. Schmid-Hempel. 2005. Inbreeding depression and family variation in a social insect, Bombus terrestris (Hymenoptera : Apidae). Oikos 111:67-80. Gorman, M. J., D. W. Severson, A. J. Cornel, F. H. Collins, and S. M. Paskewitz. 1997. Mapping a quantitative trait locus involved in melanotic encapsulation of foreign bodies in the malaria vector, Anopheles gambiae. Genetics 146:965-971. Hosken, D. J. 2001. Sex and death: Microevolutionary trade-offs between reproductive and immune investment in dung flies. Curr. Biol. 1 LR379-R380. —. 2003. Sperm biology: Size indeed matters. Curr. Biol. 13:R355-R356. Unionen, P., T. Taarna, and D. Hasselquist. 2000. Experimentally activated immune defence in female pied flycatchers results in reduced breeding success. Proc. R. Soc. Lond. B 267:665-670. Körner, P., and P. Schmid-Hempel. 2004. In vivo dynamics of an immune response in the bumble bee Bombus terrestris. J. Invertebr. Pathol. 87:59-66. Lahusen, A. 2003. Age- and species-dependent differences in the male bumblebee reproductive system. University of Zurich, Zurich. Lark, K. G., K. Chase, F. Adler, L. M. Mansur, and J. H. Orf. 1995. Interactions between quantitative trait loci in soybean in which trait variation at one locus is conditional upon a specific allele at another. Proc. Natl. Acad. Sei. USA 92:4656-4660. Ludwig, J. A., and J. F. Reynolds. 1988. Statistical ecology - a primer on methods and computing. Wiley & Sons, New York. Lung, O., L. Kuo, and M. F. Wolfner. 2001. Drosophila males transfer antibacterial proteins from their accessory gland and ejaculatory duct to their mates. J. Insect Physiol. 47:617-622. Malmberg, R. L., S. Held, A. Waits, and R. Mauricio. 2005. Epistasis for fitness-related quantitative traits in Arabidopsis thaliana grown in the field and in the greenhouse. Genetics 171:2013-2027. Moret, Y., and P. Schmid-Hempel. 2000. Survival for immunity: The price of immune system activation for bumblebee workers. Science 290:1166-1168. —. 2001. Entomology - immune defence in bumble-bee offspring. Nature 414:506-506. 121 Moret, Y., and M. T. Siva-Jothy. 2003. Adaptive innate immunity? Responsive-mode prophylaxis in the mealworm beetle, Tenebrio molitor. Proc. R. Soc. Lond. B 270:2475-2480. Moritz, R. F. A. 1984. Semen diluents and homogenous semen mixing for artificial- insemination of the honey bee queen (Apis mellifera 1). Apidologie 15:269-271. Otto, S. P., and Y. Michalakis. 1998. The evolution of recombination in changing environments. Trends Ecol. Evol. 13:145-151. Otto, S. P., and S. L. Nuismer. 2004. Species interactions and the evolution of sex. Science 304:1018-1020. Owen, R. E. 1988. Body size variation and optimal body size of bumble bee queens (Hymenoptera, Apidae). Can. Entomol. 120:19-27. —. 1989. Differential size variation of male and female bumblebees. J. Hered. 80:39-43. Sauter, A., M. J. F. Brown, B. Baer, and P. Schmid-Hempel. 2001. Males of social insects can prevent queens from multiple mating. Proc. R. Soc. Lond. B 268:1449-1454. Schiestl, F. P., and E. M. Barrows. 1999. Queen and forager sizes of Bombus affinis cresson (Hymenoptera : Apidae). Proc. Ent. Soc. Wash. 101:880-886. Schluns, H., E. A. Schluns, J. van Praagh, and R. F. A. Moritz. 2003. Sperm numbers in drone honeybees (Apis mellifera) depend on body size. 34:577-584. Schmid-Hempel, P. 1998. Parasites in social insects. Princeton University Press, Princeton, New Jersey. —. 2005. Natural insect host-parasite systems show immune priming and specificity: Puzzles to be solved. Bioessays 27:1026-1034. Schmid-Hempel, P., and D. Ebert. 2003. On the evolutionary ecology of specific immune defence. Trends Ecol. Evol. 18:27-32. Schmid-Hempel, R., and P. Schmid-Hempel. 2000. Female mating frequencies in Bombus spp. From central Europe. Insect. Soc. 47:36-41. Shook, D. R., and T. E. Johnson. 1999. Quantitative trait loci affecting survival and fertility- related traits in Caenorhabditis elegans show genotype-environment interactions, pleiotropy and epistasis. Genetics 153:1233-1243. Siva-Jothy, M. T., Y. Tsubaki, and R. E. Hooper. 1998. Decreased immune response as a proximate cost of copulation and oviposition in a damselfly. Physiol. Entomol. 23:274-277. Slate, J. 2005. Quantitative trait locus mapping in natural populations: Progress, caveats and future directions. Mol. Ecol. 14:363-379. 122 Söderhall, K., and L. Cerenius. 1998. Role of the prophenoloxidase-activating system in invertebrate immunity. Curr. Opin. Immunol. 10:23-28. Sutcliffe, G. H., and R. C. Plowright. 1988. The effects of food-supply on adult size in the bumble bee Bombus terricola kirby (Hymenoptera, Apidae). Can. Entomol. 120:1051- 1058. Tanksley, S. D. 1993. Mapping polygenes. Annu. Rev. Genet. 27:205-233. Tasei, J. N., C. Moinard, L. Moreau, B. Himpens, and S. Guyonnaud. 1998. Relationship between aging, mating and sperm production in captive Bombus terrestris. J. Apic. Res. 37:107-113. Tilquin, P., W. Coppieters, J. M. Elsen, F. Lantier, C. Moreno, and P. V. Baret. 2001. Statistical power of QTL mapping methods applied to bacteria counts. Gen. Res. 78:303-316. Van Ooijen, J. W. 1999. Lod significance thresholds for QTL analysis in experimental populations of diploid species. Heredity 83:613-624. Verhulst, S., S. J. Dieleman, and H. K. Parmentier. 1999. A tradeoff between immunocompetence and sexual ornamentation in domestic fowl. Proc. Natl. Acad. Sei. USA 96:4478-4481. Wilfert, L., J. Gadau, and P. Schmid-Hempel. in press. A core linkage map of the bumblebee Bombus terrestris. Genome Zeng, Z. B. 1994. Precision mapping of quantitative trait loci. Genetics 136:1457-1468. Zhong, D. B., A. Pai, and G. Y. Yan. 2005. Costly resistance to parasitism: Evidence from simultaneous quantitative trait loci mapping for resistance and fitness in Tribolium castaneum. Genetics 169:2127-2135. 123 Acknowledgements Many people have contributed to this thesis. I would like to thank my supervisor Paul Schmid-Hempel for giving me the opportunity to join the Ecology&Evolution group, for bearing with me through the many draft revisions and for giving me the right nudges. This work would have been impossible without my unofficial co-supervisor Jürgen Gadau. Many thanks to him for introducing me to QTL mapping. I am grateful to Bruce McDonald for agreeing to be my co-examiner and for initiating me to the miracles of plant genetics. I am truly indebted to Rita Jenny for letting me forget that two thirds of a thesis are usually bureaucracy. Christine Reber-Funk, Roland Loosli and Regula Schmid-Hempel always made sure that the lab was running smoothly, for which I am very grateful. Thanks are owed to the helpers who made this work possible: Yvonne Merki and Daniel Heinzmann in the DNA lab; Daniel Trujillo in the bumblebee dungeons; and Patricia Nigg and Michi Bretscher, immersed in bumblebee private parts. 1 am grateful to Boris Baer for collecting the first Bombus- Crithidia mapping population and for his advise throughout my thesis. Walter Durka and Magali Sole from the UFZ Halle-Leipzig and Alex Widmer and Tinu Bratteler from the ETH have helped establishing AFLPs in bumblebees. For discussion on yet more population genomics in bumblebees, I thank Jay Evans, Gene Robinson and Florian Schiestl. Tim Linksvayer gave important statistic advice on epistasis in quantitative genetics. Almost every one from Ecology&Evolution has played an important role in this thesis. Particularly, I would like to thank Ben Sadd and Maze Wegner for comments on manuscripts; Roland Regoes, Lucy Crooks, Roger Kouyos, Orkun Soyer, Olin Silander, Oliver Otti and Chris Yourth for statistical advice; Almut Scherer for help with R graphics; and Regula Schmid-Hempel, Alice Johnson and Chrissy Gerloff for initiating me into the secrets of bee lore. Most importantly, I thank all people who assisted me in ranting & raving and who never tired of insisting to go for drinks - now! A complete list would make this thesis very heavy, so I'll limit it to Almut, Maja, Eamonn, Chris, Alice, Otti, Ben, Maze and Thomas - Cheers! Finally, I would like to thank my husband Floh, my family and friends for their support and understanding. I would like to acknowledge ETH Zürich for funding my thesis and the German National Merit Foundation for immaterial support. 124 Seite Leer / Blank Isaf 125 Curriculum Vitae for Lena Bayer-Wilfert Date of Birth 3V' January 1978, Hofa.d. Saale, Germany Nationality German Education and Qualifications 1997 - 2002 University of Bayreuth, Germany, Diploma in Biology (equivalent to M.Sc.) 1997 - 1999 University of Bayreuth, Germany, Biology,Vordiplom (equivalent to B.Sc.) 1992 -1997 Otto-Hahn-Gymnasium Marktredwitz, Germany, Abitur (equivalent to High School leaving certificate) Scholarships 2003 - 2006 Doctoral Scholarship of the German National Merit Foundation (Studienstiftung des deutschen Volkes) August 2004 Travel grant from the Huber-Kudlich-Stiftung for visiting the XXII International Congress of Entomology in Brisbane (Australia); 1609 CHF 1998 - 2002 Scholarship of the German National Merit Foundation 1997 - 2002 Scholarship of the Bavarian fellowship for highly talented students Research Experience A Population Genomics Approach to Host-Parasite Interactions in a Bumble Bee - Microsporidian Model System Ph.D. thesis, Oct. 2002 - present, ETH Zürich, Switzerland (supervision: Prof. Paul Schmid-Hempel) "Population Differentiation of Schedorhinotermes lamanianus in East Africa: a Biogeographical Analysis" Diploma thesis, Oct. 2001 -Jul. 2002, University of Bayreuth, Germany (supervision: Dr. Manfred Kaib, Prof. Roland Brandi) Analysis of genetic similarity via AFLPs between colonies of Macrotermes subhyalinus as part of a study on colony recognition, March 2001, Ufz Halle-Leipzig, Germany (supervision: Dr. Walter Durka) Chemotaxonomy of European species of Reticulilermes termites Aug. 2000 - Oct. 2000, CNRS Marseille, Groupe de Communication Chimique, France (supervision: Dr. Anne-Geneviève Bagnères) 126 Teaching Experience - organizing and running a seminar for graduate students (Zürich Interaction Seminar) (2003-2006) supervising a one-week field course on evolutionary ecology, advising students on designing a scientific project from the initial idea to the final presentation (2003) teaching assistant for a physiology class (2001) supervising and correcting several exams (2003-present) Languages - German (native language) English and French (fluent on all levels) Spanish (advanced) Publications Clément, J.L., Bagnères, A.-G., Uva, P., Wilfert, L., Quintana, A., Reinhard, J., Dronnet, S., (2001): "Biosystematics of Reticulitermes termites in Europe: morphological, chemical and molecular data." Insectes Sociaux 48: 202-215 Kaib, M., Jmhasly, P., Wilfert, L., Durka, W., Franke, S., Francke, W., Leuthold, R.H., Brandi, R. (2004): "Cuticular hydrocarbons and aggression in the termite Macrotermes subhyalinus." Journal of Chemical Ecology 30(2): 365-385 Wilfert, L., Kaib, M., Durka, W., Brandi, R. (in press): "Differentiation between populations of a termite in eastern Africa: Implications for biogeography. "Journal of Biogeography Wilfert, L., Gadau, J., Schmid-Hempel, P. (in press): "A core linkage map for the bumblebee Bombus terrestris." Genome Submitted manuscripts Wilfert, L., Gadau, J., Baer, B., Schmid-Hempel, P.: " Natural variation in the genetic " architecture of a host-parasite interaction in the bumblebee Bombus terrestris. (submitted to Molecular Evolution) Wilfert, L., Gadau, J., Schmid-Hempel, P.: "The genetic architecture of investment in male immunity and reproduction in the bumblebee Bombus terrestris" (submitted to Evolution) Wilfert, L., Gadau, J., Schmid-Hempel, P.: "Sociality and recombination rates" (submitted to Heredity as a commissioned review)