NCRPIS Publications and Papers North Central Regional Plant Introduction Station

1995 Genetic Markers and Plant Genetic Resource Management P. K. Bretting United States Department of Agriculture

Mark P. Widrlechner Iowa State University, [email protected]

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This Book Chapter is brought to you for free and open access by the North Central Regional Plant Introduction Station at Iowa State University Digital Repository. It has been accepted for inclusion in NCRPIS Publications and Papers by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Plant Breeding Reviews, Volume 13 Edited by Jules Janick © 1995 John Wiley & Sons, Inc. ISBN: 978-0-471-57343-2 12 P. K. BRETTING AND M. P. WIDRLECHNER

C. Maintenance 1. Maintaining Trueness-to-Type a. Morphological Traits b. Secondary Metabolites c. Isozymes, Seed Proteins, and DNA Markers d. Comparative Studies e. Pollination Control Methods 2. Monitoring Shifts in Population Genetic Structure in Heterogeneous Germplasm a. Deviations from Random Mating b. Regeneration of Autogamous Species 3. Monitoring Genetic Shifts Caused by Differential Viability in Storage 4. Monitoring Genetic Shifts Caused by In Vitro Culture 5. Monitoring Germplasm Viability and Health D. Utilization 1. Developing Optimal Utilization Strategies from Genetic Marker Data 2. Exploiting Associations Among Traits of Interest and Genetic Markers 3. Genetic Enhancement IV. Concluding Remarks and Future Prospects Literature Cited

I. INTRODUCTION

Plant genetic resource (or simply germplasm) management comprises two phases. The first, germplasm conservation, includes acquisition, or securing germplasm in situ (by establishing reserves) or ex situ (by assembling collections through exchange or exploration). It also com­ prises maintenance: monitoring and protecting germplasm in reserves or storing it ex situ under controlled conditions, propagating it while preserving its original genetic profile with maximum fidelity, monitor­ ing its viability and health in storage, and maintaining associated passport and other data. Germplasm conservation also involves charac­ terization, assaying highly heritable morphological and molecular traits of germplasm, for taxonomic, genetic, quality assurance, and other management purposes. The second phase of germplasm management, encouraging utiliza­ tion, includes evaluation, assaying germplasm for agronomically or horticulturally meritorious traits with relatively low heritabilities and high components of environmental variance (e.g., yield, adaptation, and host-plant resistance to certain abiotic/biotic stresses). It also includes genetic enhancement, defined by Duvick (1990) as making particular genes more accessible and usable to breeders by adapting "exotic" germplasm to local environments without losing its essential genetic 2. GENETIC MARKERS AND PLANT GENETIC RESOURCE MANAGEMENT 13 profile, and/or introgressing high-value traits from exotic germplasm into adapted varieties. Simmonds (1993, p. 540) subdivides such en­ hancement activities into introgression "backcrossing into adapted stocks of few genes controlling desired characters," and incorporation "the large-scale development of locally adapted populations good enough to enter the adapted genetic bases of the crops concerned." We use both Duvick's and Simmonds's terminology in this review. The role ofgenetic markers in genetic enhancement is considered in the context ofgermplasm management as a whole (Chang 1985; Duvick 1990). This review acquaints those conducting crop improvement and basic plant science research with the means by which genetic markers may assist plant germplasm management, which when properly conducted provides scientists with high -quality raw genetic materials for analysis and breeding. This review seeks to update and expand Brown's (1978) seminal discussion of isozyme markers in relation to the genetic conservation of crops (synonymous with domesticated plants here) and their wild or weedy relatives. Its organization parallels Smith's (1989) brief review of the role of genetic markers in maize germplasm management. The scope of this review resembles that of an extensive review of germplasm characterization via isozyme analysis (Simpson and Withers 1986) but emphasizes post-1986 publications and covers a wider variety of genetic markers and plant germplasm management activities. Ex situ management is emphasized, but when germane, examples of in situ management oftraditional crops or ofwild-weedy crop relatives are also appraised. For more extensive accounts of ex situ crop genetic resource management's nature and scope, see Frankel and Soule (1981), Holden and Williams (1984), Brown et al. (1989a,b, 1990), and Stalker and Chapman (1989). The role of genetic markers in relation to in situ management of essentially wild flora is reviewed comprehensively by Schaal et al. (1991) and Hamrick et al. (1991). Avise (1994) contains a valuable discussion of genetic markers' roles in conservation biology in general. The contributions of genetic markers to gene mapping or to plant breeding per se are not considered in detail; this subject has been reviewed by Burr et al. (1983), Tanksley (1983), Tanksley and Orton (1983a,b), Helentjaris et al. (1985), Beckmann and Soller (1986), Soller and Beckmann (1988), Tanksley et al. (1989), Paterson et al. (1991), Edwards (1992b), Stuber (1992), and Dudley (1993). Smith and Smith (1992) have thoroughly reviewed the role of genetic markers in finger­ printing commercial germplasm. 14 P. K. BRETTING AND M. P. WIDRLECHNER

II. PLANT GERMPLASM, GENETIC MARKERS, AND ANALYTICAL METHODS

A. Types of Plant Germplasm Plant germplasm can be categorized in many ways, but from the perspec­ tive of ex situ germplasm management, the most important features are (1) selection intensity (Table 2.1), (2) genetic profile (Table 2.2), and (3) breeding system: (a) strictly asexual; (b) mixed asexual and sexual; (c) strictly sexual, including strictly allogamous, strictly autogamous, or mixed allogamous and autogamous (adapted from Hamrick and Godt 1990). Generally, the classes included in the preceding three features should be viewed as continuous rather than discrete. These germplasm attributes are presented here because collectively, they determine the types ofgenetic markers that are most suitable for various plant germplasm management applications.

B. Genetic Markers Traits that serve as genetic markers are by definition polymorphic; the more polymorphic the trait, the greater its potential value to germplasm management. As polymorphism at a locus increases, so does the number of potentially diagnostic genotypes (Porter and Smith 1982). Neverthe­ less, characters that vary significantly from organ to organ, or tissue to tissue, on an individual plant, or vary within a taxon as much as they do among taxa, are not particularly useful genetic markers for most plant germplasm management applications. Useful genetic markers should be highly heritable [see Nyquist (1991) for a lucid definition of heritability and Murray et al. (1988), Chapman (1989), Smith (1989), and Smith and Smith (1992) for definitions of genetic markers] with phenotypes generally not strongly affected by environment (i.e., with minimal genotype by environment interactions) (Falconer 1989). Therefore, in general, useful genetic markers can be assayed without replicated trials or experiments once inheritance pat­ terns have been established.

Table 2.1. Relative intensity of human selection on plant germplasm.

Type of plant germplasm Effect of human selection

Wild Nil or weak Weedy Moderate Traditional race or variety Substantial Elite cultivars Strong 2. GENETIC MARKERS AND PLANT GENETIC RESOURCE MANAGEMENT 15

Table 2.2. Genetic profiles for various types of plant germplasm.

Genes

Genotype Homozygous Heterozygous

Homogeneous Inbred lines Clones derived from an allogamous species Heterogeneous Autogamous, Allogamous landraces traditional cultivars or modern multiline cultivars

Elucidatinggenetic controlmaybeinstrumental in determiningwhether similar genetic markers in two or more accessions ortaxa are homologous (i.e., similar because they are descended from a common ancestor) (Stuessy 1990). Only homologous genetic markers (e.g., alleles of the same isozyme locus) should be compared across taxa or accessions. Plant genetic resource managers generally deal with germplasm accessions that have differentiated only at the populational or species level, so that inadvertently comparing nonhomologous (analogous) features may be less probable than when comparing more distantly related plants. The issue of homology may seem trivial for morphological markers, but the increasing use of molecular markers has heightened its impor­ tance. Determining molecular homologies involves sequence alignment, a subject outside the scope of this review (see Miyamoto and Cracraft 1991). Distinguishing homologous from analogous molecular markers may be complicated by the difficulty of recognizing serial homology, or paralogy, in which nucleotide sequence similarity in two genes, with similar or identical function(s), or which encode similar or identical product(s), has arisen via a duplication event in the ancestral gene (Fitch 1970). In contrast, homologous genes in two different species may be orthologous when they have arisen from a speciation event. Many germplasm management applications, such as genetic diversity estimations, require genetic data interpretable by a locus/allele model, and accordingly, markers with simple genetic control are preferred (ideally, single Mendelian genes with codominant alleles and no or negligible pleiotropic or epistatic effects). For such applications, a suite of Mendelian genes well dispersed throughout the genome is advanta­ geous (Murray et al. 1988; Smith and Smith 1992). Choosing statistically independent and, ideally, physically unlinked markers helps minimize character correlations arising not only from 16 P. K. BRETTING AND M. P. WIDRLECHNER

genetic linkage but also from functional relationships (e.g., kernel width and kernel row number on a maize cob are highly correlated), pleiotropy, selection, and several other evolutionary genetic phenomena. Correlated characters provide less independent information than do uncorrelated characters and also diminish the power of certain statistical analyses (Sneath and Sokal1973; Morrison 1976; Dunn and Everitt 1982; Manly 1986; Sokal1986). Finally, practical considerations prescribe that genetic markers should (1) be easily scored throughout the plant's life cycle, ideally, at the embryonic or seedling stage (e.g., Chunwongse et al. 1993); (2) have no or negligible effects on plant growth, reproductive fitness, or mating structure (i.e., they should be selectively neutral); and (3) be rapidly, safely, and inexpensively scored (Murray et al. 1988; Smith 1989; J. S. C. Smith, pers. comm.). The ensuing sections summarize specific techniques; the reader is directed to standard references for particulars. These sections emphasize the strengths and weaknesses ofgenetic markers or classes in relation to genetic resource management applications.

1. Morphology. Polymorphic, highly heritable morphological traits were some ofthe earliest genetic markers employed in scientific investigations (Mendel 1866; DeVries 1912), and they may still be optimal for certain plant germplasm management applications such as trueness-to-type in homogeneous lines (Section IHC1). Morphological assays generally re­ quire neither sophisticated equipment nor preparatory procedures, so monogenic or oligogenic morphological traits are generally simple, rapid, and inexpensive to score, even from preserved specimens (e.g., herbarium sheets). Until recently, scientific plant classification was based nearly exclusively on morphological traits (Stuessy 1990), some of which may serve as genetic markers (Gottlieb 1984; Hilu 1984) suitable for plant germplasm management (Stanton et al. 1994). Plant germplasm managers generally deal with plant taxa which, when viewed in the context of evolutionary relationships, are relatively closely related; these taxa may differ by allelic frequencies rather than by possessing different alleles altogether, so there may be relatively few diagnostic morphological traits available for germplasm management purposes. Furthermore, the few oligogenic morphological traits avail­ able may not be homologous. For example, paired crosses between wild and domesticated Mexican and Andean Chenopodium 1. species (Heiser and Nelson 1974) determined that seed color in Andean and Mexican chenopods was governed by two different loci and, consequently, that seed color in each was not homologous. Although color may be consid- 2. GENETIC MARKERS AND PLANT GENETIC RESOURCE MANAGEMENT 17

ered a biochemical rather than a morphological feature, the lesson is clear: whenever possible, the genetic control ofpotential markers should be determined. Polygenic morphological traits also often serve as genetic markers for various plant germplasm management and taxonomic applications (Stuessy 1990), but their lower heritabilities and substantial genotype x environment interactions can dramatically increase the complexity and expense of assaying them (Patterson and Weatherup 1984), although computerized imaging systems may assist with this effort (Keefe and Draper 1988). Furthermore, determining genetic control of morpho logi­ cal traits and, thereby, distinguishing homology from analogy may be more costly and complicated than with other genetic markers (Camussi et al. 1985; Zeng et al. 1990). Indeed, Smith and Smith (1992) argue strongly that lower heritabilities or uncertain homologies make most morphological markers essentially unsuited for certain germplasm man­ agement applications, especially for accurately determining genetic proximities among elite cultivars, lines, or hybrids.

2. Karyotypic/Cytogenetic. Karyotypic/cytogenetic observationsrequire specialized microscopic equipment but preparative protocols are other­ wise relatively simple and inexpensive (Dyer 1979). Correctly interpret­ ing certain cytomorphological features does require considerable train­ ing, experience, and sometimes advanced knowledge of cytogenetics. Two primary types of karyotypic/cytogenetic markers are enlisted in plant germplasm management: chromosome number and chromosomal morphology. Chromosome number is highly heritable, but mitotic number may vary considerably among specialized tissue via endopolyploidy, so one must examine undifferentiated meristematic cells (Dyer 1979). Chromosome numbers are, of course, more valuable to plant germplasm management when they are highly polymorphic, as in the polyploid series in wheats and potatoes (Zeven 1980), rather than highly conserved or relatively invariant as in Lycopersicon Mill. (Rick 1979). In addition to chromo­ some number, DNA content may differ interpopulationally or interspecifically (Price 1988a,b). Chromosome morphological features, such as size, centromere posi­ tion, meiotic configurations, and occurrence of satellites, knobs, or highly heterochromatic B chromosomes, are observable following stain­ ing (Dyer 1979). Specialized staining protocols (e.g., Cor G banding) and in situ hybridization of DNA probes may reveal otherwise cryptic cytogenetic variability (Fuchida 1984; Sessions 1990). All these preceding karyotypic features have contributed critical data 18 P. K. BRETTING AND M. P. WIDRLECHNER to plant systematic-evolutionary studies (Bennett 1984). Nevertheless, changes in chromosome number and morphology seem to occur ran­ domly, and their variability is generally not interpretable by a locus/ allele model. Consequently, cytological markers have found relatively limited use in plant germplasm management, except for monitoring the genomic stability of germplasm preserved in vitro (Towill 19S5). Griesbach (1987) and Larkin (1987) have documented numerous examples of changes in chromosome number and structure induced in vitro. Nearly all of these changes were associated with culture systems based on undifferentiated tissues or single cells, leading Towill (19S8) and Withers (19SS) to recommend that clones be conserved in vitro as organized meristems. Nevertheless, variants generated by meristem culture have occasionally been reported (Swartz et al. 1981; Towill 1985; Pecaut and Martin 1991), although their genetic bases were undeter­ mined. This issue has been of special concern in the maintenance of Musa 1. germplasm through shoot-tip culture (Vuylsteke 19S9). Ex­ amples of phenotypic variants in populations of Musa derived from culture have been widely documented (Krikorian 1989; Vuylsteke 1989; Vuylsteke et al. 1991), but as with other genera, the genetic bases for variation from meristem cultures remain to be elucidated. Several recent reports of in vitro maintenance of germplasm collec­ tions (Fukai and Oe 1990; Bowes and Curtis 1991; Fukai et al. 1991; Mix­ Wagner and Schittenhelm 1991) compared gross chromosomal morphol­ ogy but not karyotypic markers to document genomic stability, even though the need for karyotypic analysis is widely recognized (Towill 1988; Fukai and Oe 1990; Fukai et al. 1991), especially when freezing injury disrupts meristem structure, as can occur with the cryogenic storage of cultures.

3. Secondary Metabolites. Pigments and other secondary metabolites were enlisted very early as genetic markers (DeVries 1912; Wright 1943). Pigments, generally classes of anthocyanin or flavonoid com­ pounds, are often highly heritable and polymorphic at the infraspecific or specific level. They were quite popular as genetic markers during the mid-1960s tothe mid-1970s (McKee 1973; Harborne etal. 1975; Harborne and Mabry 1982). During this period, analyses offlavonoid compounds facilitated germplasm characterization (Singh and Thompson 1961; Brown et al. 1969, 1971). Nonetheless, as with some other traits, pigments may vary consider­ ably among tissues or organs of a single plant (Schilling and Heiser 1981). Genotype x environment interactions generally affect the quan­ tity, rather than type, of pigments produced (Harborne et al. 1975; 2. GENETIC MARKERS AND PLANT GENETIC RESOURCE MANAGEMENT 19

Harborne and Mabry 1982). There is ample evidence for these pig­ ments' selective value (Miller 1988), which may diminish their utility for some genetic marker applications. Pigments most useful as genetic markers can be scored visually, hence, rapidly, inexpensively, and easily (e.g., stem or corolla color). In the past, relatively large quantities of tissues were required for fractionating mixtures of these compounds chromatographically via paper or thin­ layer chromatography, but newer analytical procedures such as gas chromatography, high-performance liquid chromatography, and capillary electrophoresis require smaller quantities (Harborne and Mabry 1982). Pigments are more versatile as genetic markers whenever their genetic bases are known (Kulakow et al. 1985). Nevertheless, pigment phenotypes may be determined by complicated biosynthetic pathways difficult to interpret by a locus/allele model (Giannasi 1978; Crawford and Levy 1978). Consequently, during the last 15 years, isozymes have generally replaced flavonoid/anthocyanin pigments and other second­ ary metabolites as the genetic markers of choice for many germplasm management applications.

4. Proteins. When sequenced, or analyzed electrophoretic ally or chro­ matographically, proteins and DNA can be considered semantides­ molecules that convey information regarding genes or their immediate transcripts (Zuckerkandl and Pauling 1965). Compared to most morpho­ logical and pigment traits, fewer biosynthetic steps lie between a gene sequence and the protein or DNA genotypes that serve as genetic markers. Crawford (1990) has reviewed the preceding factor. Conse­ quently, protein and DNA may provide superior markers for certain germplasm management applications. Proteins are inferior to DNA as genetic markers (Bernatzky and Tanksley 1989; Smith and Smith 1992) because they represent only the products of genes that encode polypeptides, and these are not randomly dispersed throughout the genome (Gepts 1990). Mutations in genes that encode polypeptides may be undetectable via standard protein analyses, because no changes in migration rate (determined by net surface charge, shape, or size) or conformational structure are evident. Finally, post­ translational processes may modify proteins such that their amino acid sequence and conformational structure may greatly differ from those encoded by the original mRNA transcript. a. Serological Assays. Serotaxonomic protein assays (Fairbrothers 1977) involve injecting plant-protein extracts into mammals, then killing and bleeding the latter to extract antibodies that the animals 20 P. K. BRETTING AND M. P. WIDRLECHNER produced against the plant protein antigens (Axelsen et al. 1975; Maxson and Maxson 1990). Mammalian antibodies are then challenged against plant-protein extracts in agarose gels; the resulting precipitin or bound-product profiles, when visualized, serve as genetic markers. Such profiles are generally highly heritable, with relatively minor genotype x environment interactions (Fairbrothers 1977; Maxson and Maxson 1990). But the genetic control for these profiles is rarely elucidated, so determining homologies and interpretating polymor­ phisms via locus/allele models are generally impossible. Today, serotaxonomic analyses are conducted relatively infrequently, not only because of the complications involved with animal experi­ mentation but because more discriminative and less complicated mark­ ers are available. Serological techniques have become important in enzyme-linked immunosorbent assays (ELISA), where an enzyme re­ porter (often peroxidase), linked to the antibody, reveals specific antigen-antibody reactions. The technology of producing monoclonal antibodies to specific antigens (e.g., a diagnostic bacterial cell wall component) has enabled ELISA to become a standard tool for disease diagnosis (Clark 1981; Catello et al. 1988). ELISA has also been enlisted in systematic studies of suprageneric taxa (Esen and Hilu 1989), maize (Zea mays L.) racial diversity (Yakoleff et al. 1982), and maize inbred lines (Esen et al. 1989). b. Seed Proteins. Seed protein polymorphisms may serve as genetic markers for plant germplasm management because they can be quite polymorphic, generally substantially more so than are isozymes (Gepts 1990), and the variability is generally highly heritable (Smith and Smith 1986). Such proteins [e.g., zeins (ZeaL.), glutens (Triticum L.), phaseolins (Phaseolus L.)], often organ ortissue specific, arebest assayed from seeds, where they are more highly concentrated than in other plant organs and where they often function in storage. Although seed proteins can be fractionated by high-performance liquid chromatography (Smith and Smith 1986) and other techniques, polyacrylamide gel electrophoresis [(PAGE), generallyin sodium dodecyl sulfate (SDS) gels] is currently the favored technique for rapid analysis (Cooke 1984), whereas two-dimensional electrophoresis, often incorpo­ rating isoelectric focusing, may be required for certain more demanding applications (Celis and Bravo 1984; Beckstrom-Sternberg 1989). Protein fractionation by SDS-PAGE (Stegemann and Pietsch 1983) is relatively rapid and inexpensive compared to isozyme and some DNA analyses, especially when conducted with precast minigels. In contrast, two-dimensional electrophoresis will often reveal an astounding number 2. GENETIC MARKERS AND PLANT GENETIC RESOURCE MANAGEMENT 21 of different seed proteins simultaneously, but it is relatively slow and demands considerable technical skill and experience. Furthermore, sophisticated and expensive computer analytical software may be needed for reproducible analyses of the patterns formed by the hundreds of different polypeptides so revealed (Higginbotham et al. 1991). As with isozyme analysis, seed protein polymorphisms may be inter­ preted according to a locus/allele model (with codominant alleles) following determination of their genetic control. Unlike isozyme analy­ sis, the electrophoretic procedures employed to assay specific, multiple, seed protein-encoding loci simultaneously are slower and more expen­ sive than are similar analyses ofmultiple isozyme loci. Furthermore, loci in seed proteins often refer to several tightly linked genes (Gepts 1990). Accordingly, distinguishing paralogous from orthologous variation may be particularly problematic, and gene diversity statistics estimated from seed proteins typically are not directly comparable to those derived from isozyme or RFLP data. c. Isozymes. During the last 20 years, isozymes revealed through starch gel electrophoresis (SGE) have been the genetic markers most frequently employed for plant germplasm management. Consequently, the literature associated with isozymes is voluminous. Comprehensive discussions of SGE technology appear in Shields et al. (1983), Soltis et al. (1983), Vallejos (1983), Werth (1985), Richardson et al. (1986), Wendel and Weeden (1989), Weeden and Wendel (1989), Murphy et al. (1990), and Kephart (1990). The pivotal roles of isozyme data in plant systematics and in statistical-populational genetics are covered by Gottlieb (1981), Tanksley and Orton (1983a,b), Soltis and Soltis (1989), and Crawford (1990). Doebley (1989a,b) examined applications to crop evolution. The utility of isozymes as genetic markers (Simpson and Withers 1986) is generally attributed to their frequent polymorphism; codominance; single gene-Mendelian inheritance; rapid, simple, and relatively inexpensive assay; and their ubiquity in plant tissues and organs (even in embryos and pollen). Although the selective neutrality of isozymes has been debated (Koehn and Hilbish 1987; Hillis and Moritz 1990; DiMichele et al. 1991), it seems highly probable that they are adaptive under certain circumstances. This is the case in maize ADH variants under anaerobiosis (Goodman and Stuber 1983). Although the typical isozyme locus encodes codominant alleles with no genotype x environment interaction, in maize, wheat (Triticum aestivum L.), and tomato (Lycopersicon esculentum Mill.)-the crops best characterized isozymatically-alleles governingnull (tomato, wheat, 22 P. K. BRETTING AND M. P. WIDRLECHNER

and maize), dominant [maize and Sorghum bicolor(L.) Moench (Doebley et al. 1986)], and epistatic isozymes (maize and tomato) have been identified. Genotype x environment interactions occur when different isozymes are generated in different tissues of the same plant or when comparative migration rates of electromorphs vary substantially in gels with different pHs (Goodman and Stuber 1983; Rick 1983; Hart 1987; Stuber et al. 1988).

5. DNA. Polymorphic DNA is thought to provide ideal genetic markers because (1) nucleotide sequence variation is presumably selectively neutral, at least for noncoding sequences (Kimura 1983; Nei 1987); (2) certain complications that may reduce heritability of protein analyses (undetectable mutations, posttranslational modification) may be mini­ mized; and (3) plant cells include three distinct genomes (nuclear, chloroplast, mitochondrial), which may each evolve according to differ­ ent modes and tempos. Accordingly, different genomes may be better suited for different plant germplasm management applications. Crawford (1990) and Soltis et al. (1992) review applications of DNA analyses to plant genetic diversity assessment and systematics in general, whereas Doebley's (1992) review emphasizes studies of crops and their wild or weedy relatives. Given that most crop cultivars have diverged from their wild-weedy relatives relatively recently, during the last 10,000 years (Heiser 1990; Harlan 1992), most DNA marker applications to crop germplasm man­ agement focus on nuclear DNA (nDNA) because it generally evolves more rapidly than does plant organellar DNA, especially the chloroplast genome (cpDNA) (Doebley and Wendel 1989) and hence is more poly­ morphic, at least when analyzed for restriction fragment length polymor­ phisms (RFLPs). Nevertheless, structural rearrangements (e.g., inver­ sions) and subsequent recombination occur relatively frequently in mitochondrial DNA (mtDNA), although mtDNA's nucleotide sequence is apparently more highly conserved than even that of cpDNA (Palmer and Herbon 1988). These structural arrangements may more frequently necessitate detailed mapping ofmtDNA genomes to elucidate the genetic bases for the observed polymorphisms (Doebley 1992). In many angiosperms, organelle genomes are generally uniparentally inherited (Birky 1991), which may contribute to different genomic evolutionary rates (Wolfe et al. 1987). With three different genomes that may evolve essentially independently of one another, DNA analyses have proved invaluable to plant systematics (Crawford 1990), especially for investigating putative cases of hybridization and/or introgression (Rieseberg and Brunsfeld 1992; Rieseberg and Wendel 1993). 2. GENETIC MARKERS AND PLANT GENETIC RESOURCE MANAGEMENT 23

u. RFLPs. Plant germplasm managers should consult relevant sections of Ausubel (1989), Sambrook et al. (1989), DeVerna and Alpert (1990), Walton (1990), Dowling et al. (1990), and Hillis and Moritz (1990) for technical advice regarding RFLP analytical techniques. Typically, RFLPs are produced by cleaving genomic DNA with restriction enzymes at specific nucleotide sequences, fractionating the resulting polymorphic DNA fragments electrophoretically, and finally, probing the fragments with specific radioactively or chemically labeled nucleotide sequences (probes), which hybridize to low-copy-number genes. On the whole, RFLPs are superior genetic markers because they are (1) ubiquitous throughout plant tissues and throughout the plant genome's coding and noncoding sequences, (2) highly heritable, (3) relatively highly polymorphic (at least in nDNA and mtDNA), (4) apparently selectively neutral, and (5) codominantly inherited (Helentjaris and Burr 1989). At present, though, RFLP analysis is relatively slow, labor inten­ sive, and expensive and may involve radioactive labeling. As with isozymes, the genetic bases for RFLP banding patterns should be deter­ mined through formal genetic analyses. Ideally, the probes used in RFLP analyses have been mapped to specific genomic loci (Smith and Smith 1992). Choosing an optimal set of probes is an essential step in RFLP analysis. Smith and Smith (1992) evaluated key elements of this process: (1) choosing among random genomic DNA, cDNA transcribed from mRNA, or among custom-synthe­ sized probe sequences (Murray et al. 1988; Bernatzky and Tanksley 1989); (2) recognizing DNA sequences that hybridize readily (provide a strong signal) but selectively (no background labeling) to the DNA under analysis; and (3) through pilot studies, selecting probes that are uni­ formly dispersed throughout the genome (Le., two or more per chromo­ some arm) and that hybridize to relatively rare target sequences (low copy-number) in the germplasm under study.

b. AFLPs. Amplification fragment length polymorphisms (AFLPs) are DNA fragments with different nucleotide sequences (Caetano-Anolles et al. 1991), of which millions of copies have been synthesized (i.e., amplified) via the polymerase chain reaction [(PCR), Mullis et al. 1986; Erlich et al. 1991]. Only the DNA sequence interval (several thousand nucleotides) between the sites where one or more base pairs (bp) of polynucleotide (15 to 35 bp) or oligonucleotide (2 to 10 bp) primers anneal to a DNA template is so amplified. Like RFLPs, the AFLPs are highly heritable, polymorphic, apparently selectively neutral, and are nearly ubiquitous in plant tissue. As with RFLPs, AFLPs are revealed through fractionation by agarose or polyacrylamide gel electrophoresis, 24 P. K. BRETTING AND M. P. WIDRLECHNER but unlike the radioactive-biochemical probing in RFLP analysis, the amplified DNA is generally stained with ethidium bromide, silver salts, or another nonspecific stain for DNA (Kwoh and Kwoh 1990; Caetano­ Anolles et al. 1991). With randomly amplified polymorphic DNA [(RAPD), Williams et al. 1990,1993; also termed AP-PCR; Welsh and McClelland 1990], the PCR primers are oligonucleotides (generally, 12 or fewer base pairs) chosen arbitrarily and, consequently, the sequences amplified are also arbitrary. The ease with which a theoretically unlimited number ofgenetic markers can be generated via the AP-PCR, RAPD, or DAF (DNA amplification fingerprinting) techniques has made AFLP analysis ubiquitous in mo­ lecular marker labs worldwide (Williams et al. 1990, 1993; Caetanno-Anolles et al. 1991). The DNA fragments produced via arbi­ trary priming are generally inherited in a simple dominant-Mendelian fashion, with fragment absence recessive. The RAPD analyses generally detect the occurrence of a single allele, whereas isozyme, RFLP, and other DNA techniques (see below) can distinguish among many alleles at specific loci (Williams et al. 1993). In this respect, RAPD markers may be inferior to codominant genetic markers, although the frequency ofalleles coding for fragment occurrence or absence may be estimated by maxi­ mum-likelihood procedures (Edwards 1992a), and nucleotide diver­ gence can be estimated from RAPD data via relevant statistical analyses (Clark and Lanigan 1993). In the experience ofsome workers, template DNA and reagent concen­ trations, primer sequence and length, and experimental conditions (e.g., annealing temperature) must all be strictly controlled during DNA amplification, because they may strongly affectbanding patterns (Tingey et al. 1992; Williams et al. 1993). Nevertheless, Weeden et al. (1992) contend that RAPD analysis is robust to substantial variation in experi­ mental conditions, provided that the template DNA is pure. Also, with arbitrary amplification, the sequence homology of comigrating, amplified bands is less certain than with RFLP bands (Williams et al. 1993), especially with interspecific comparisons of polyploid species such as Brassica L. (Thormann and Osborn 1992). Fragments resulting from nonarbitrary amplification (see below), or which are tagged by oligonucleotide probing in subsequent RFLP analy­ ses, are more likely to be derived from the same genetic locus than truly random DNA fragments would be (Smith 1992). In addition to RAPDs, PCR and related techniques can amplify specific genetic loci containing variable numbers of tandemly repeated nucle­ otide sequences [(VNTRs) Nakamura et al. 1987] of about 10 to 50 base pairs (minisatellites; Jeffreys et al. 1985), or of as few as 2 base pairs 2. GENETIC MARKERS AND PLANT GENETIC RESOURCE MANAGEMENT 25

[simple sequence repeats (SSR), Jacob et al. 1991; microsatellites, Littand Luty 1989]. The number oftandem repeats at these mini - ormicrosatellite loci varies greatly (hence the term hypervariable loci), producing a wide diversity of DNA products with different sequence lengths, easily frac­ tionated electrophoretically, that behave genetically like the product of codominant alleles (Morgante and Olivieri 1993). Relatively great allelic diversity ofthese loci may make them valuable genetic markers for plant germplasm management applications (Akkaya et al. 1992; Senior and Heun 1993; Zietkiewicz et al. 1994). As with RFLPs, the variability at specific genomic loci is generally examined via this method, so that similar banding patterns or tandem repeat numbers probably represent homologous variation. With the currently relatively high cost of DNA polymerase, a key enzymatic ingredient of PCR, AFLP analyses may be more expensive than RFLP assays for certain applications (Ragot and Hoisington 1993). Nevertheless, AFLP analyses may be amenable to automation (Ziegle et al. 1992), require very little DNA for analysis, and do not involve blotting, probing, probe maintenance in bacteria, or other expensive steps re­ quired in RFLP analysis (Smith and Smith 1992).

C. Analytical Methods for Genetic Marker Data From the perspective of statistical genetic analysis, genetic-marker data fall into two broad categories: (1) quantitative traits (e.g., many morpho­ logical features) with a continuum ofphenotypes governed by several to many genes; and (2) qualitative traits [e.g., most genetic (especially molecular) markers] with discrete phenotypes governed by one to several genes. Importantly, these two types of traits may simply be variants of a single genetic theme, distinguishable only by the magnitude of allelic substitution effects (Comstock 1978; Robertson 1989). We will first briefly discuss quantitative genetic-marker analyses, then proceed to a more lengthy treatment of qualitative trait analyses.

1. Quantitative Genetic Markers. Wricke and Weber (1986) and Fal­ coner (1989) are probably the most valuable general references to germplasm managers who are analyzing quantitative traits. The empha­ sis in Wricke and Weber (1986) is on crop plant genetics, whereas Falconer (1989) presents a general approach but often uses examples from animal genetics. Falconer (1989) notes that as compared with qualitative traits, quantitative trait phenotypes may include substantial environmental components of variation, the relative magnitude of which should be estimated before deployment as genetic markers. 26 P. K. BRETTING AND M. P. WIDRLECHNER

Goodman and Paterniani (1969) provide a convenient and practical approach for scaling these variance components, by calculating the

T 2 (5 ~ (52 ratio t (5;/(5 te + where the terms are variance components, the subscript t refers to the taxonomic unit (e.g., germplasm collection, population, race, species) under study, the subscript e refers to the environment, and the subscript te refers to the taxonomic unit by environment interaction. Sanchez et al. (1993) tabulate the form of the preceding analysis of variance and describe how to calculate standard , errors for estimates of r t the ratio of variance components due to environment and taxonomic unit. As mentioned earlier, certain germplasm management applications require suites of genetic rnarkers well dispersed (on different chromo­ somes or linkage groups) throughout the genome. Determining conclu­ sively whether genes governing quantitative, polygenic traits are well dispersed throughout the genome requires far more resources than are generally available to germplasm managers. Nonetheless, Sanchez et al. (1993) describe a pragmatic approach for identifying suites ofnoncorre­ lated traits which, presumably, are governed by essentially unlinked genes free of major pleiotropic or epistatic effects. The method involves decomposing the underlying structure of an intertrait correlation matrix via eigenanalysis, so that the magnitude and direction of intertrait correlations are clearly evident. When quantitative traits are enlisted as genetic markers, such traits should be examined with the preceding approach whenever possible.

2. Qualitative Genetic Markers. Much of the following treatment is adapted from Nei's (1987) and Weir's (1990a) texts, currently the most comprehensive references for analyzing genetic data derived from dis­ crete, heritable, randomly transmitted genetic markers (Mendelizing units). These markers can be scored as either present or absent and may also be interpretable by locus/allele models. Qualitative marker geno­ types may simply be counted, summarized as frequencies (often of alleles), and analyzed with statistical estimators (means, variances) appropriate for multinomial (often binomial) distributions. When the genetic markers are codominant, genotypic counts are easily obtained, but when they are dominant (as with RAPD fragments; see section IB5b), maximum-likelihood methods are required to estimate allelic frequen­ cies (Weir 1990a).

3. Genetic Diversity. Kresovich and McFerson (1992) have highlighted the important role of genetic diversity assessment in plant genetic resource management. One simple estimate of the genetic diversity in a 2. GENETIC MARKERS AND PLANT GENETIC RESOURCE MANAGEMENT 27 given taxon, germplasm collection, or geographic region is the number of taxa included in the larger unit (e.g., the number of subspecies found in a species in a given region). But the number of subordinate taxa recog­ nized may vary substantially among taxonomic treatments, as may the actual level of genetic differentiation among such taxa (Bretting and Goodman 1989). Accordingly, diversity estimates derived from genetic marker data may be more valuable than counts of taxa for most germ­ plasm management applications, because such estimates can be more easily compared across taxa, and the focus may be on conserving genes rather than taxa. Variances of relatively highly heritable, quantitative genetic markers provide one estimate of genetic diversity. Sokal (1965) advocated calcu­ lating generalized variances (I S I, the determinant of the variance­ covariance matrix) derived from morphological characters as indices of intrapopulational diversity. For example, Goodman (1968) estimated the comparative intraaccession variability of several maize and cotton (Gossypium hirsutum L. and G. barbadense L.) germplasm collections via log-transformed generalized variances, and calculated their standard errors. The Shannon-Weaver diversity index, H/, has also served to summarize and compare morphological diversity (e.g., Engels 1994). Today, allelic variation in molecular genetic markers, especially isozymes, provides virtually all the raw data for genetic diversity estima­ tion. Variability in RFLP profiles may not be amenable to treatment by simple locus/allele models (Doebley and Wendel 1989) unless polymor­ phisms resulting from restriction site losses orgains can be distinguished from structural mutations, such as inversions (Engels 1981; Ewens et al. 1981; Doebley and Wendel 1989). Consequently, investigators such as Learn and Schaal (1987), Song et al. (1988), and Second and Wang (1992) have scored RFLP bands resulting from cleavage of sample DNA by one restriction enzyme, followed by hybridization with one probe, simply as absent or present rather than as alleles at specific loci, and have calcu­ lated from the presence or absence data various coefficients of genetic diversity/proximi ty such as Shannon's information measure (Brown and Weir 1983; Learn and Schaal 1987). When genetic marker data can be interpreted by locus/allele models, allelic diversity can be described by (1) P or PLP, the percentage of polymorphic loci, calculated by dividing the number of polymorphic loci by the total number ofloci assayed; (2) A, the mean number ofalleles per locus, calculated by dividing the total number of alleles detected by the number of loci assayed; and (3) total gene diversity or average expected heterozygosityH =1-I.I. p2./m ,wherep ..is the frequency of t 1 J---7m 1J 1J the ph allele at the i h of m loci (Nei 1973, 1987; Brown and Weir 1983). 28 P. K. BRETTING AND M. P. WIDRLECHNER

In many respects, P is an inferior estimator for allelic diversity. When relatively few loci are assayed, its values are subject to large statistical sampling error. Polymorphism, with reference to P, is defined arbitrarily (e.g., loci may be considered polymorphic when the predominant allele exceeds a frequency of 0.99, 0.95, or even 0.90). Thus an appropriate frequency threshold for the predominant allele should be chosen. For example, a sample size of 20 will probably not include low-frequency (i.e., p = 0.05 or 0.01) alleles, so a threshold frequency of 0.90 would be more appropriate (Brown and Weir 1983; Nei 1987). Although A serves frequently as an index of allelic richness and its frequency distribution is often described by the infinite neutral allele model (Ewens 1972), its numerical value is also strongly affected by sample size because small samples will not in general include rare alleles. Accordingly, A should be compared only across populations, species, and so on, represented in analyses by the same or similar sample size (Brown and Weir 1983; Nei 1987). Although the mean frequency of heterozygotes across all loci may serve to estimate genetic diversity in random-mating populations, it is not applicable to haploid or polyploid organisms, and it underestimates genetic diversity in primarily autogamous taxa, where individuals are primarily homozygous-although not necessarily for the same alleles.

) , Nei's (1973) measure ofgene diversity (Ht based on sampling probabili­ ties, can be calculated for taxa ofany ploidy level, is relatively unaffected by sample-size differences, and is perhaps the most useful single mea­ sure ofgenetic diversity (Brown and Weir 1983; Nei 1987). ButHtcannot exceed 1, is determined primarily by the two most common alleles' frequencies, is strongly affected by the number of loci assayed (Simon and Archie 1985), and is less sensitive to changes in allelic frequencies when they approach 1 (Brown and Weir 1983). The variances ofall the preceding estimates are affectedby the number of loci and by sample size-the number of progeny assayed per plant, plants assayed per population, or number of populations assayed per taxon (Brown and Weir 1983; Weir 1990c). Brown and Weir (1983) presented formulas for determining confidence intervals of allelic fre­ quency estimates and demonstrated that five progeny sampled per individual plant will suffice for most allelic frequency and gene diversity estimates. Various theoretical and empirical studies suggest that for precise estimates, number of loci assayed may be more critical than the sample size but that the latter should be as large as is practical (Nei 1987; Weir 1990a,b,c). Indeed, optimal sample sizes for gene diversity estima­ tion are functions ofstatistical efficiency and utilitarian factors (e.g., cost of analyzing more loci versus more plants). 2. GENETIC MARKERS AND PLANT GENETIC RESOURCE MANAGEMENT 29

4. Genetic Structure. Knowledge of plant populational genetic struc­ ture is fundamental for designing optimal germplasm acquisition and management strategies. Perhaps the simplest, yet most important model for populational genetic structure for outbreeding plant germplasm is the Hardy-Weinberg equilibrium (H-WE), in which, following a single generation ofrandom mating, genotypic frequencies for a gene with two

alleles = pJ+ 2PPj + pf, and multiallelic genotypic frequencies follow multinomial distributions. Germplasm can be tested for H-WE with genetic marker data as described by Frankel and Galun (1977), Hernandez and Weir (1989), and Weir (1990a). Maintaining H-WE in plant germplasm that was originally at or near that state helps to conserve original allelic and genotypic arrays and populational structures ex situ with maximum fidelity. Genotypic and allelic frequencies in plant populations will remain statistically constant at H-WE values over generations if (1) the plant is a sexually reproducing diploid; (2) generations are nonoverlapping (i.e., descendants do not ) mate with ancestral plants); (3) effective population sizes (Ne are suffi­ ciently high so that random drift (changes in allelic frequency resulting from sampling probabilities) is avoided; (4) rates of migration and mutation are negligible; and (5) the gene(s) of interest is (are) unaffected by selection during regeneration. Statistical tests demonstrating devia­ tion from H-WE indicate that one or more-but not which-of the foregoing conditions do(es) not hold. Furthermore, Weir (1990b) notes that "unfortunately, compliance with H-WE does not mean that all the assumptions are being met" (p. 380). Gene migration (gene flow), a primary cause for deviation from H-WE, must be regulated to maintain germplasm effectively. Gene flow must be measured and monitored accurately to be regulated; genetic markers are especially suitable for this task. Mathematical models and formulas for estimating gene flow abound (Selvin 1980; Shaw et al. 1981; Shaw and Brown 1982; Ritland 1983; Schoen and Clegg 1984; Ellstrand and Marshall 1985; Brown et al. 1985; Hamrick 1989) and are in general derived from the allelic frequencies of markers and their gene diversity (i.e., HJ Hamrick (1989) concludes that when feasible, paternity analysis (Selvin 1980; Ellstrand and Marshall 1985) is superior to the private

allele method of Slatkin (1985) and the Fst methods of Wright (1951) for estimating gene flow rates from genetic marker frequencies. As a rule, the allelic frequencies at a few, unlinked, highly polymorphic loci will yield gene flow estimates more robust than those derived from a greater number of less polymorphic genes (Brown et al. 1989a). Also, as a rule, the more allogamous the plant, the greaterthe number ofhighly polymor­ phic loci that should be assayed (Shaw and Brown 1982). Accordingly, 30 P. K. BRETTING AND M. P. WIDRLECHNER highly polymorphic molecular loci are superior markers for estimating gene flow, especially in allogamous species. Gene flow and the other factors noted above engender populational genetic structure (Hartl and Clark 1989). Populational genetic structure has been quantified by Cockerham's (1973) variance component method based on coancestries, Wright's (1978) F statistics, and Nei's (1973) gene diversity statistics. The latter two approaches are most frequently fol­ lowed currently (Hamrick et al. 1979, 1991; Hamrick 1989; Hamrick and Godt 1990); Nei's measures are used in this review. ) Calculation of total gene diversity (Ht for a group of taxa has been described above. Gene diversity, either within a particular organism or ' within particular taxa, H s is calculated simply by averaging the single­ = locus H values (Hs l-l;. p2.) over all loci. The fraction of H t appor­ tioned ~mongtaxa is Cst::::1~H/Ht'Notably, Cst' which estimates the relative degree of gene differentiation among taxa or populations, is mathematically highly dependent on both H t and the number of taxa or populations examined.

5. Genetic Proximity. Genetic proximity estimation is vital to formulat­ ing optimal germplasm management strategies and lies at the core of modern plant systematics and evolutionary biology (Nei 1987; Soltis and Soltis 1989; Crawford 1990; Stuessy 1990; Li and Graur 1991; Soltis et al. 1992). Plant systematists and evolutionary geneticists have developed techniques for analyzing genetic proximity that may be ideally suited for addressing certain germplasm management issues. Genetic proximity estimation generally involves multivariate statisti­ cal analyses and numerical taxonomy, subjects that have been well reviewed by Sneath and Sokal (1973), Morrison (1976), Duncan and Baum (1981), Dunn and Everitt (1982), Baum et al. (1984), Sokal (1986), and Manly (1986). Numerical taxonomy, often termed phenetics, ana­ lyzes character variability without weighting traits a priori and without reference to the evolutionary events underlying genetic proximity (Duncan and Baum 1981). The phenetic approach is most appropriate when analyzing taxa (e.g., crops and their wild-weedy relatives) which have diverged recently, because their overall genetic proximity as measured phenetic ally is generally still strongly congruent with the degree oftheir evolutionary divergence (Duncan and Baum 1981). Consequently, phe­ notypic traits which are similar in crops and their wild relatives may be in general homologous. The following paragraphs introduce some of the more important phenetic methods available for estimating genetic proximity. We urge the reader to consult the references cited earlier for more complete 2. GENETIC MARKERS AND PLANT GENETIC RESOURCE MANAGEMENT 31 treatments. Furthermore, Goodman's (1972) critical assessment of the role of multivariate analyses in genetic proximity estimation should be noted as well as the review of James and McCulloch (1990), especially their Table 3, which includes much valuable practical advice for apply­ ing multivariate analyses to germplasm characterization. A wide variety of pairwise genetic proximity measures is available (Sneath and Soka11973; Dunn and Everitt 1982; SokaI1986), but only a few have been widely applied. For quantitative (often morphological) traits, or a mixture of quantitative and qualitative characters, Gower's similarity coefficient (Gower 1971) is recommended. For qualitative locus/allele data, Nei's genetic identity (1) or distance (D) (Nei 1987) and modified Rogers distance (Rogers 1972; Wright 1978) are used most frequently. Rogers distance, unlike Nei's measures, is metric-a property considered desirable for many systematic applications (Goodman 1973; Rogers 1984). For RFLP data uninterpretable by a locus/allele model, the statistic d of Nei and Li (1979) is frequently calculated as a proximity measure, whereas Clark and Lanigan (1993) have derived an analogous proximity measure for RAPD analyses of relatively closely related taxa. Kresovich et al. (1994) similarly modified Nei and Li's d to measure genetic proximity with RAPD data. The statistical significance of differences between pairwise genetic proximities for three or more taxa, or for three or more germplasm accessions, is rarely tested because calculating variances for these proximities is quite complicated (Nei 1987; Weir 1990a). Weir (1990a) recommended resampling techniques such as bootstrapping (Efron and Tibshirani 1991) for this task [see Smith et al. (1991) for an application of bootstrapping]. Kresovich et al. (1994) tested genetic identities of clonal germplasm with a simple binomial probability measure. Lynch (1988, 1990) provides formulas for calculating sample variances and other error estimates for indices used to calculate DNA fingerprint similarities. Patterns of genetic proximity among taxa or germplasm collections can be visualized conveniently by cluster analysis and ordination. Ideally, these two multivariate techniques are deployed together because their strengths are complementary (Sneath and Sokal1973; Dunn and Everitt 1982; Sokal 1986). In cluster analysis, taxa, germplasm collec­ tions, or genetic markers are arranged in a hierarchy (called a phenogram or dendrogram) by an agglomerative algorithm according to patterns occurring in a matrix of pairwise genetic proximities. The hierarchies obtained from cluster analyses are highly dependent on both the proxim­ ity measure and the clustering algorithm used (Sneath and Sokal1973; Soka11986; Smith and Smith 1992). 32 P. K. BRETTING AND M. P. WIDRLECHNER

Some of the more popular statistical analytical packages currently available deploy Ward's error sum of squares method (Ward 1963) as a clustering algorithm, without warning that it may "divide dense clusters in an unacceptable manner" (Sneath and Soka11973, p. 204) and that it has other undesirable properties (Duncan and Baum 1981). In our experience, methods employing arithmetic means [unweighted-pair­ group means arithmetic (UPGMA) or weighted-pair-group means arith­ metic (WPGMA)] (Sneath and Soka11973) are more frequently used than neighbor-joining (Saitou and Nei 1987), Fitch-Margoliash, or other preferable procedures that involve fewer questionable assumptions regarding the evolutionary history of the germplasm under study (Nei 1987; Rohlf et al. 1990; Hillis et al. 1994). With ordination, the multidimensional variability in a pairwise, intertaxa or intermarker proximity matrix, or in a variance-covariance or correlation matrix is portrayed in one to several dimensions through eigenstructure analysis. Ordination is best suited to revealing interac­ tions and associations among taxa or germplasm accessions which are described by traits that vary continuously and quantitatively. Principal component, principal coordinate, and linear discriminant analyses are the ordination techniques most relevant for potential germplasm man­ agement applications (Sneath and Sokal 1973; Sokal 1986; James and McCulloch 1990). Supraspecific systematic relationships are best elucidated by phylo­ genetic methods (Duncan and Stuessy 1984; Swofford and Olsen 1990; Hillis et al. 1994). These methods sometimes can help estimate phyloge­ netic relationships among crops and related taxa (Doebley 1990), and, accordingly, may help determine whether a weedy crop relative is a crop progenitor or feral crop derivative (Pickersgill 1981). Nevertheless, unless such rigorous phylogenetic analyses are required, the germplasm manager will find standard phenetic approaches appropriate and suffi­ cient for the quantitative analysis of molecular marker data.

III. PLANT GENETIC RESOURCE MANAGEMENT

A. Genetic Markers and Systematic Relationships Systematics can be defined as the scientific study of types of organisms and of any and all relationships among the organisms (Sinlpson 1961). One of the most important roles of genetic markers in plant germplasm management is elucidating the systematic relationships and characteris­ tic genetic profiles of germplasm (Table 2.2). Such studies include 2. GENETIC MARKERS AND PLANT GENETIC RESOURCE MANAGEMENT 33 analyses of comparative degree(s) of evolutionary genetic divergence; amounts, patterns, and apportionment of genetic diversity; and the evolutionary and/or human selective forces molding the preceding characteristics. Genetic markers of all types, from morphological features in Cyphomandra C. Martius ex Sendtner (Bohs 1988), through immu­ nochemical precipitin bands in Coffea 1. (Lee 1977), glycoalkaloid occurrence in Solanum 1. (Johns et al. 1987), seed globulins in Lotus corniculatus 1. (Steiner and Poklemba 1994), to the latest molecular markers in Zea (Doebley 1992) and Brassica oleracea 1. (Kresovich et al. 1992) have been instrumental in characterizing systematic and evolu­ tionary genetic relationships and in establishing germplasm's taxonomic identity. For example, Bohac et al. (1993) determined from morphologi­ cal markers (calyx and corolla traits) that wild 4x Ipomoea 1. germplasm accessions maintained ex situ were not 1. trifida (Kunth) G. Don f. but, rather, a 4x race of the generally 6x sweetpotato [1. batatas (1.) Lam.]. This taxonomic realignment will probably change how these germplasm accessions are managed and utilized. Stanton et al. (1994) described the morphological variability within ex situ germplasm collections of Gossypium arboreum 1. and G. herbaceum 1. About 10% of the approximately 400 accessions examined were found to be misidentified at the species level. This morphological analysis thus can provide for a "more orderly use" (p. 521) of the collections. The infraspecific, racial classifications proposed for these cotton species by earlier workers were not supported by the results of Stanton et al.'s (1994) morphological analyses. As with Bohac et al.'s (1993) study, Stanton et al.'s analysis has changed the managerial focus for an ex situ collection substantially. Clarifying evolutionary relationships among intergrading or interme­ diate taxa (Gottlieb 1972; Heiser 1973; Avise 1986) may challenge the germplasm manager's judgment and acuity. It is particularly vital for germplasm management purposes to discriminate recently synthesized, naturally occurring hybrids and/or hybrid derivatives from taxonomi­ cally intermediate taxa originating from convergent-parallel evolution, clinal variation, recombinational speciation, and/or the retention of intermediate ancestral traits (the latterincluding the phenomenon termed lineage sorting, Avise 1986). Naturally occurring F1 hybrids sometimes have different legal status than do species, with some conservation legislation excluding them from protection altogether (O'Brien and Mayr 1991; Whitham et al. 1991), presumably because the hybrids' genes are already safeguarded in the unendangered parental taxa. Whitham et al. (1991) argue, though, that relatively recent hybrids and hybrid contact 34 P. K. BRETTING AND M. P. WIDRLECHNER

zones are particularly important for germplasm conservation because they are highly diverse hot spots and the hybrids serve as keystone organisms of local ecosystems. Studies conducted by Heiser (1949) and Rieseberg et al. (1988) of the domesticated sunflower (Helianthus annuus L.) and its allogamous, annual wild-weedy relatives exemplify how genetic markers provide systematic data that may substantially affect plant germplasm manage­ rial decisions. Morphological, flavonoid, isozyme, cpDNA, and nDNA markers showed that H. bolanderi Gray did not originate via introgres­ sion ofH. exilis Gray with H. annuus germplasm, as hypothesized earlier by Heiser (1949), but was a clearly distinct taxon of ancient origin (Rieseberg et al. 1988). Conversely, morphological, cytological, and molecular markers (Rieseberg et al. 1990) indicated that H. paradoxus Heiser is a distinctive hybrid derivative that originated relatively recently, via recombinational speciation (Grant 1981), fromH. annuus andH. petiolarisNutt. Although clearly a hybrid derivative, H. paradoxus has evolved substantially

beyond the point of being a newly synthesized F 1 hybrid. Based on these systematic data, Rieseberg (1991) recommended that germplasm of H. paradoxus and H. bolanderi (both endemic, relatively rare, and some­ what threatened species) should be protected both in situ and ex situ. To sum, a clear understanding of systematic relationships among a crop and its wild-weedy relatives is vital to sound genetic resource management (Bramwell 1984; Avise 1989; Kresovich et al. 1992) and to crop improvement as a whole (Romberger 1978; Hedberg 1979). Misun­ derstanding systematic relationships, besides embarrassing germplasm managers and/or bureaucrats (O'Brien and Mayr 1991), can leave endan­ gered germplasm such as Cucurbita okeechobeensis (Small) L. H. Bailey unprotected (Walters and Decker-Walters 1993), may engender faulty germplasm management practices (Avise and Nelson 1989; Avise 1989), or may complicate the choice of germplasm for basic research or crop improvement programs, as with the "nomenclatural mess" attending systematic relationships among potatoes and their wild relatives (Spooner and van den Berg 1992; Spooner et al. 1993).

B. AcquisitionlDistribution

1. Assessing Collection Gaps and Redundancies. Assessments of how completely a germplasm collection represents a particular species's ge­ netic profile or a crop's total gene pool should incorporate a variety of genetic markers. Schoen and Brown's (1993) computer simulations dem­ onstrate how marker-assisted acquisition strategies might yield collec- 2. GENETIC MARKERS AND PLANT GENETIC RESOURCE MANAGEMENT 35 tions with maximal allelic richness. Particularly instructive cases where genetic markers assessed collection gaps and redundancies follow. Contemporary germplasm managers still often rely on morphological traits, long the foundation of plant systematics (Grant 1981; Radford 1986; Stuessy 1990), to determine gaps and redundancies in germplasm collections. Chang (1984), Jahn (1986), and Engels (1986) identified redundancies in ex situ collections with morphological markers, al­ though the latter two cautioned that verification with molecular markers was needed. In contrast, the assessment ofthe morphological diversity by Stanton et al. (1994) in ex situ collections of A-genome cottons revealed gaps in the collection and infraspecific variants that were only poorly represented. As part of a concerted effort to uncover gaps in extant germplasm collections, Edmonds (1990) assessed phenotypic variability of African Corchorus L. (jute and its wild relatives) from herbarium specimens and uncovered ecogeographical regions where these annual or perennial taxa were highly variable morphologically. Plant explorations to sample that variability were then conducted successfully (Edmonds 1990). Morpho­ logical markers proved quite effective in this role, not only for jute but for a variety of other plants, not only seed propagated (e.g., Martin and Adams 1987a,b) but also perennial, clonal, and sexually propagated Rubus L. and Ribes L. (Jahn 1986) and Theobroma L. (Engels 1986). Isozyme and DNAmarkers, includingRAPDs, RFLPs, andminisatellite DNA, have increasingly revealed unique phenotypic patterns (finger­ prints) for clonally propagated cultivars of vegetables, such as Ipomoea batatas (L.) Lam. (Kennedy and Thompson 1991) and Solanum tuberosum L. (Gebhardt et al. 1989); for fruits, such as Actinidia deliciosa (A. Chev.) C.F. Liang &A.R. Ferguson (Messina et al. 1991), DiospyroskakiL. f. (Tao and Sugiura 1987), Malus Mill. (Chyi and Weeden 1984; Menendez et al. 1986; Vinterhalter and James 1986; Weeden and Lamb, 1987), and Rubus (Nybom et al. 1989; Cousineau and Donnelly 1992); and for ornamentals, such as Camellia japonica L. (Wendel and Parks 1983), Dianthus caryophyllus L. (Messeguer and Artis 1985), and Rosa L. (Hubbard et al. 1992; Torres et al. 1993). Plant genetic resource managers can employ these fingerprints to verify synonymy and thus reduce duplication in collections (Tao and Sugiura 1987), to examine the relatedness ofputative sports (Wendel and Parks 1983; Weeden and Lamb 1985; Nybom 1990), to note misidentifications (Cousineau and Donnelly 1992), and to understand the breadth and gaps in their holdings. Most studies (e.g., Kaemmer et al. 1992) have reported on a more limited sample of clones than those held by major genebanks. During the next few years molecular technologies 36 P. K. BRETTING AND M. P. WIDRLECHNER will be more widely employed for measuring the breadth and gaps in large collections of vegetatively propagated plants. Once the genetic spectra of these collections are so measured, curators can focus on acquiring new accessions and deactivating redundant or mislabeled accessions. Pickersgill (1981) outlines how knowledge of phylogenetic relation­ ships among various cultivars and their wild-weedy relatives can be crucial for optimal germplasm acquisition. Weeds ancestral to domesti­ cates are generally more highly diverse genetically than the derivative crop populations, and they may be genetically isolated from the latter. Weeds derived from domesticates as feral escapes may thereby have undergone a second genetic bottleneck (the first having occurred during crop domestication) before their consequent hybridization with wild relatives. Consequently, weedy feral escapes may harbor few orno alleles which are absent from the domesticate or its wild relatives. Accordingly, acquiring germplasm of weedy crop ancestors should have a higher priority than securing germplasm of feral weeds derived from crops (Pickersgill 1981). For example, flavonoid pigment markers in corollas and foliage suggest that the annual, allogamous herb Luffa acutangula (L.) Roxb. var. amara is the closest wild-weedy relative ofthe domesticated L. acutangula var. acutangula, but var. amara's lack of other flavonoids that occur in the domesticate suggest that the former may be a feral derivative, rather than the progenitor, ofthe latter (Schilling and Heiser 1981). In contrast, chromosomal morphology suggests that weedy Capsicum 1. species (generally autogamous annuals or short-lived perennials) are ancestral to domesticated chiles, so the former may contain valuable traits absent from the crop (Pickersgill 1971). Such evidence for genealogical relation­ ships among the crop and its wild-weedy relatives should help focus germplasm acquisition efforts for Capsicum, Luffa Mill., and other crops. An intriguing, recent RFLP study of cpDNA of an allogamous, peren­ nial, clonally propagated teosinte [Zea perennis (A. Hitchc.) Reeves and Mangelsdorf] suggests that another, scientifically unknown, teosinte has hybridized with Z. perennis (Doebley 1989b). These data have stimu­ lated curators ofsome ex situ teosinte collections to investigate filling the gap represented by this enigmatic "missing teosinte."

2. Sampling Strategies. Field sampling strategies should emanate from knowledge of the populational genetics and ecogeographic distribution of a particular species (Bretting and Goodman 1989; Zimmerer and Douches 1991). As noted earlier, genetic marker data may be instrumen­ tal for assembling a collection with maximum allelic diversity (Schoen 2. GENETIC MARKERS AND PLANT GENETIC RESOURCE MANAGEMENT 37

and Brown 1993). Such data are best secured by surveying diversity in a variety of genes from many populations located throughout the plant's ecogeographical range. Morphological markers may enable collectors to sample desired germplasm selectively in the field. Calyx shape is highly associated with ploidy in sweet potato (Bohac et al. 1993); consequently, Austin et al. (1993) could discriminate 4x (the focus of their plant exploration) from 6x sweet potato populations in the field via their diagnostic calyx morphologies. Isozyme markers have often been enlisted to develop optimal field sampling strategies (Simpson and Withers 1986; Marshall 1990). For example, the clearly clinal variation along coastal Peru, evident in several isozyme loci ofLycopersicon germplasm accessions, helped Rick et al. (1974) maximize the genetic diversity collected during later expe­ ditions (Rick 1979). In accessions oftraditional Iranian barley (Hordeum vulgare L.) cultivars, Brown and Munday (1982) found no significant correlation between estimates of genetic diversity calculated from six morphological loci and those calculated from 25 isozyme loci. Brown and Munday (1982) argued that in this case, isozyme markers were superior to morphological traits for developing optimal germplasm sampling strategies. Zimmerer and Douches (1991) investigated with isozyme analysis the apportionment of genetic diversity in local and regional populations of two domesticated potato species [Solanum stenotomum Juz. et Buk. and S. tuberosum L. ssp. andigena Hawkes] in highland Peru. Based on the results of these analyses, Zimmerer and Douches (1991) recommended that germplasm conservation strategies "focus on intensive sampling or [in situ] preservation in micro-regional areas due to the concentration of unique genotypes" (p. 176). In an extensive ecogeographical survey ofisozyme variability, Aldrich et al. (1992) identified localized centers of diversity in Sorghum bicolor (L.) Moench (a facultatively autogamous annual) in race virgatum and race arundinaceum in northern and central Africa. Those races and regions were accordinglyrecommended as priorities for furthergermplasm acquisition (Aldrich et al. 1992). Although currently isozymes may be the most cost-effective genetic markers for such germplasm diversity surveys, nDNA polYlllorphisms may be intrinsically superior for this task. In sorghum, Aldrich and Doebley (1992) found more alleles at nDNA RFLP loci than were detected at isozyme loci. Partially as a consequence, nDNA RFLP allelic frequencies were more often interme­ diate (between 25 and 75%) than were isozyme allelic frequencies. Alleles with intermediate frequencies contribute to greater gene diversi­ ties (Le., H ) and are generally superior to those alleles with more extreme t 38 P. K. BRETTING AND M. P. WIDRLECHNER frequencies for estimating genetic divergence and other applications (Buth 1984; Weir 1990a).

In contrast to sorghum, estimates ofHt values for lentil (Lens culinaris Medikus) and its wild relatives, derived from RFLP data, were equivalent to those derived from isozyme data (Havey and Muehlbauer 1989). Within peanut (Arachis hypogaea L.), RFLP, RAPD, and peR-amplified/ restriction enzyme-cut DNA markers were all highly homogeneous relative to the infraspecific variability observed for morphological and agronomic traits and relative to marker variability in wild relatives of peanut (Halward et al. 1991). So the optimal genetic-marker type for developing germplasm acquisition strategies may be crop specific. Statistical genetic theory, when combined with crop- or gene pool­ specific genetic marker data, may help germplasm managers determine the optimal numbers of plants per population (e.g., Crossa et al. 1993), populations per taxon, and/or taxa per crop/species group to conserve (Schoen and Brown 1993). In a simple but perhaps effective approach, genetic diversity levels estimated from genetic marker data are compared graphically to the sample size analyzed, and optimal sample sizes relative to total genetic diversity are identified according to the slope and/ or inflection point. This approach was applied to tomato and its wild or weedy relatives, where species-specificrelationships between genetic diversityand sample size indicated to Miller and Tanksley (1990) that populations of a self­ incompatible Lycopersicon species were 20 times more likely to include a novel RFLP fragment than were populations of a self-compatible species. Accordingly, it was recommended that relatively more acces­ sions of self-incompatible species than self-compatible species be in­ cluded in germplasm banks. Similarly, the numbers ofunique genotypes of 18 morphological and disease resistance markers (heritabilities not reported) in barley germplasm accessions were graphed against the numbers of randomly chosen accessions assayed per country (Peeters 1988). The resulting functional relationships between genotypic vari­ ability and the number of accessions assayed revealed inflection points that differed substantially across countries but were potentially useful for determining optimal sample sizes for germplasm accessions.

3. Core Subsets. According to its originaldefinition (Frankel 1984),a core subset of a germplasm collection contains, with minimal redundancy, most of the entire collection's genetic diversity. Currently, defining core subsets and integrating them into germplasm management strategies are two of the most complex and controversial issues facing germplasm managers. As Brown (1989a) has noted, defining core subsets involves 2. GENETIC MARKERS AND PLANT GENETIC RESOURCE MANAGEMENT 39

ordering germplasm collections hierarchically, a process analogous to developing taxonomic classifications. Hence it should not be surprising that genetic marker analyses (Schoen and Brown 1993) and systematic or statistical genetic principles, operational procedures, and approaches (see Sections IIC and lIlA) are directly applicable to defining core subsets. At its simplest, delimiting core subsets involves estimating, by statis­ tical genetic theory (see Section IIC3), the probability that a particular number of randomly selected accessions (the core subset) will include a given proportion of alleles that occur in the total germplasm collection at a given frequency. From this statistical genetic theory, Brown (1989a,b) estimated that a core subset comprising about 10% ofthe total accessions (or at least 3000 individuals) from a collection that includes at least 10,000 accessions should probably (95% confidence level) include most (70% or more) of the alleles that are widespread throughout the collec­ tion, regardless of the alleles' intraaccession frequencies. Generally, alleles are distributed unevenly throughout the collection (Le., they can be locally common or rare). This uneven distribution increases-either dramatically or negligibly, depending on the model's assumptions-the size of the core subset required to capture the same diversity of locally distributed alleles as of widespread alleles (Brown 1989a,b). The pivotal question emanating from this theoretical approach (which, we postulate, estimates not the optimal but rather, the minimal, core subset size) is whether optimal core subsets can be assembled by combining statistical genetic theory with knowledge of the patterns of genetic divergence, allelic diversity, genetic structure, and linkage disequilibria ofthe constituent accessions (see Sections IIC3 to 5). Recent attempts to assemble such putatively optimal core subsets, or to test empirically their utility and efficacy relative to randomly chosen sub­ sets, have been guided by statistical genetic theory, by information about ecogeographical provenance, and by genetic marker data (Brown 1989b; Schoen and Brown 1993). For example, Brown (1989b) first segmented germplasm collections of Glycine tomentella Hayata, an autogamous, perennial wild relative of soybean, and of autogamous annual barley into provisional groups (of variable size) comprising accessions with common ecogeographical provenances and isozyme profiles. Then a sample of accessions was chosen from each provisional group whose size was either constant across groups, proportional to the group's size, or equivalent to the logarithm of the group's size. Based on how much of the germplasm collection's total isozyme allelic diversity was included in the core subsets, Brown (1989b) judged the logarithmic sampling strategy optimal, although the genotypic compositions ofseveral other core subsets appar- 40 P. K. BRETTING AND M. P. WIDRLECHNER ently were not significantly different statistically-not only interse-but also from a randomly chosen core subset. A core subset formed from a random sample of about 10% of an ex situ germplasm collection of autogamous, annual lentils included about 70% of that collection's total allelic diversity at 17 isozyme marker loci, a putative isozyme locus, and at three genes encoding pigments (Erskine and Muehlbauer 1991). All but two of these marker genes were unlinked and, presumably, were highly heritable. Although the genes were unlinked, multilocus genotypic associations were per­ vasive, diverse, and statistically very strong throughout the collection. The proportion of the collection's total allelic diversity for these markers included in the random core subset was not significantly different statistically from the proportions captured by two other core subsets that were defined via stratified sampling according to the geographical provenances of the accessions. Similarly, a core subset including about 11 % of the accessions in the U.S. National Plant Germplasm System's ex situ collection of autoga­ mous, annual peanut germplasm encompassed nearly all of that collection's range of diversity in six vegetative and seed morphological classes whose heritabilities and linkage relationships were unreported (Holbrook et al. 1993). Accessions were chosen for the subset through a combination of random and stratified sampling, primarily by ecogeographical provenance, and secondarily by cluster analysis of the collection's morphological diversity. In summary, core subsets documented in the literature to date have been assembled primarily from provenance data and, secondarily, from data derived from relatively few genetic markers, with sometimes un­ known heritabilities or genetic bases. Considering the ample experience of plant systematists with phenetic taxonomies developed from too few unlinked genetic markers with low heritabilities (Sokal19S6; Crawford 1990; Stuessy 1990), we wonder whether extant core subset/reserve subset hierarchies, often assembled from data derived from relatively limited segments of a crop's genome or gene pool, can accommodate additional evaluation and characterization data without requiring whole­ sale revision. Furthermore, will delimiting these subsets based on allelic diversity of genetic markers militate against conserving a large enough number ofsamples to include key quantitative trait alleles involved with genetic correlations, genotype x environment interactions, and adapta­ tion per se (Hamilton 1994)? As they become more affordable and accessible via technological developments and the establishment of high-volume service laborato­ ries, molecular genetic markers undoubtedly will be instrumental for 2. GENETIC MARKERS AND PLANT GENETIC RESOURCE MANAGEMENT 41 assembling and testing core subsets (Schoen and Brown 1993). Genetic variability will be assessed from relatively many accessions with genetic markers sufficiently numerous to saturate a crop's genome with loci (Smith and Smith 1992). These molecular markers will be increasingly important for core subset formation. Schoen and Brown (1993) first segmented a collection by ecogeographical provenance and then tested the utility of two marker-assisted strategies: (1) the H strategy, wherein gene diversity is maximized in populations that are assumed to be reproductively isolated, and (2) the M strategy, wherein populations are thought to exchange genes and, therefore, marker allelic richness is maximized. Schoen and Brown (1993) found that core subsets assembled by either the H or M strategies were more effective in retaining allelic richness than were core subsets based on including a constant number of accessions per region, a proportional number of accessions per region, the logarithm of the proportion of accessions per region, or simply including a random sample of accessions. Genetic marker data may also help determine the minimal size of germplasm collections (several hundred accessions? several thousand?) in which core subsets are practical. They may also indicate how strongly various patterns of genetic divergence and genetic diversity apportion­ ment influence optimal core subset size. Finally, they may help circum­ scribe durable core subsets, which can accommodate new genetic infor­ mation-ideally, without requiring substantial revision-perhaps better than extant subsets can.

4. Characterizing Newly Acquired Germplasm. There are relatively few reports of genetic markers providing key information for designing and implementing new in situ or ex situ germplasm management pro­ grams for newly acquired germplasm. It is somewhat surprising that such genetic assessments, articulated so clearly by Brown and Clegg (1983) a decade ago and reiterated more recently (Clegg 1993), apparently are not conducted frequently as a prelude to germplasm maintenance or utiliza­ tion efforts. In this section, several examples ofgenetic marker character­ ization that have preceded, or have helped redirect, plant germplasm management efforts are reviewed. Patterns ofisozyme variability in the narrowly endemic giant sequoia, Sequoiadendron giganteum (Lindl.) Buchh., suggested that wild sequoia populations were inbred (Fins and Libby 1982). Accordingly, Fins and Libby (1982) recommended that genetically distinct sequoia populations be maintained in ex situ nurseries where they could interbreed, and the more vigorous outcrossed, hybrid seedlings, rather than seedlings col­ lected in nature, would then be planted in in situ reforestation programs. 42 P. K. BRETTING AND M. P. WIDRLECHNER

It is unknown whether these recommendations, based on genetic marker data, were ever heeded. As has been the case in most crops, carefully conceived and conducted morphological studies (of fruit tissues, in this case) provided the earliest glimpse of Cocos L.(coconut) germplasm's diagnostic genetic profile (Harries 1978). Nonetheless, morphological, isozyme, and flavonoid analyses apparently revealed insufficient genetic variation to formulate guidelines for effective genetic resource management, so a recent coco­ nut germplasm management workshop recommended characterizing germplasm collections with RFLP or RAPD markers before intensifying managerial efforts in this vital tropical crop (IBPGR 1992). Morphological and isozyme marker data helped formulate an in situ germplasm conservation program for an endangered, autogamous, herbaceous, annual Limnanthes R. Br. subspecies that includes genes potentially valuable for oilseed crop improvement (Dole and Sun 1992). Isozyme and morphological marker data suggested that the subspecies was not hybridizing with another sympatric Limnanthes species. Isozyme analyses detected very little infrapopulational genetic variation (as qualified by low values of P, A, and HJ Infrasubspecific variation was distributed overwhelmingly interpopulationally, so an extraordinarily high Cst was encountered. Cluster phenograms of interpopulational I and D values (calculated from isozyme data) indi­ cated relatively high interpopulational differentiation and infrequent interpopulational gene flow. Accordingly, Dole and Sun (1992) recom­ mended that as many individual populations of this subspecies as possible be conserved in situ at the potential expense ofeach population's • N e An optimal combination of populations for conserving the plants' genetic diversity efficiently was identified. In light of the newly available data profile, it was recommended that an in situ germplasm management program, formulated several years earlier, be modified (Dole and Sun 1992). Hamon and van Sloten (1989) integrated isozyme, pigment, and quantitative and qualitative morphological and agronomic marker data into recommendations for managing West African germplasm accessions of okra [Abelmoschus esculentus (1.) Moench] and related taxa. Herita­ bilities and linkages were unreported for the markers, but intercharacter associations, identified by cluster analysis, led the authors to suggestthat the number of markers assayed routinely could be reduced according to such associations. Field experimental designs were modified early during the investigation by isozymatic data, which could discriminate okra variants from one another and from their wild relatives at the seedling stage. 2. GENETIC MARKERS AND PLANT GENETIC RESOURCE MANAGEMENT 43

' Gene diversity statistics derived from isozyme data (A, P, Ht' H s and Cst) indicated that the morphological forms of a forage legume complex endemic to the Canary Islands [vernacular names-tagasate and escobon; Chamaecytisus proliferus (1.f.) Link and related taxa] were genetically highly polymorphic, with most of the diversity distributed infrapopulationally. The morphological forms were not very divergent isozymatically; rather, infraspecific genetic divergence was strongly associated with insular isolation. Based on field surveys and these genetic marker data, general recommendations for conserving these plants in situ were formulated (Francisco-Ortega et al. 1993). Finally, Lamboy et al. (1994) assayed six isozyme loci from an ex situ germplasm collection of Brassica oleracea to identify genotypes and accessions and to assess infraspecific relationships and genetic struc­ ture. Although the loci assayed were too few to estimate genetic identities confidently, the data did permit comparisons of mean het­ erozygosities and apportionment of genetic diversity among accessions and among varieties. This information was useful for developing managerial strategies.

C. Maintenance

1. Maintaining Trueness-to-Type. Geneticmarkers have frequently docu­ mented outcrossing rates under defined conditions of cultivation and have measured how effectively various managerial methods maintain true-to-type populations (Le., accession integrity) (Astley 1992). Such work has been conducted to establish minimal isolation distances for commercial seed production, to investigate pollinator behavior, and to test various germplasm regeneration methods. We focus on studies conducted on scales relevant to germplasm management and on those that specifically test the utility of particular regeneration protocols. A summary ofscientific approaches for optimal germplasm regeneration is found in Breese (1989). These studies have incorporated a broad array of markers, including alleles controlling isozymes, morphological traits, pigmentation and other secondary products, male sterility, and re­ sponses to insect and disease infestation. Choosing markers for monitoring of trueness-of-type involves the accessibility of known markers (in many species, few marker stocks are available a priori), the speed and accuracy of scoring progenies, and any biases resulting from the effects of markers on experimental results (Porter and Smith 1982; and see Section IIB). Assays can be performed rapidly when markers diagnostic for genetic contamination are ex­ pressed in a dominant or codominant fashion either at seed maturity 44 P. K. BRETTING AND M. P. WIDRLECHNER

[e.g., endosperm color (Airy 1950) and texture (Bateman 1947b) in Zea mays] or at the seedling stage [e.g., seedling color (Bateman 1947a,b; Jain et al. 1982), or leaf shape (Maiti et al. 1981)]. Rapidly expressed markers are particularly helpful for tree species with prolonged juvenility, such as Prunus L. (Miller et al. 1989) and Pinus L. (Muona and Hariu 1989).

a. Morphological Traits. Historically, many genetic resource manage­ ment programs have relied on morphological traits to monitor and maintain germplasm's trueness-to-type (Porter and Smith 1982). With many species, morphological and pigment variants (e.g., foliage or floral morphology and seed color) characteristic of wild or weedy crop rela­ tives are generally genetically dominant to variants found in the domes­ ticated counterpart (Rick 1951; Schwanitz 1967; Bretting 1982). This phenomenon can facilitate designing strategies for monitoring out­ crosses in situ, or for conducting crop regenerations where the domesti­ cate and its relatives are sympatric. For example, Wilkes (1977) described dominantly expressed markers (e.g., control of rachis disarticulation) varying between maize and teosinte (subspecies of Zea mays) that distinguish interpopulational hybrids from parental plants in traditional agricultural systems in Mexico and Central America. Similarly, Nabhan et al. (1981) and Bretting (1982) recognized morphological and pigmen­ tation markers (e.g., testa color) that identified putative hybrid deriva­ tives or hybrids between wild-weedy and domesticated Proboscidea parviflora (Wool.) Wool. and StandI. ssp. parviflora. Importantly, those morphological markers that alter floral morphol­ ogy, either directly or through pleiotropic effects [e.g., the obtuse-leaf marker in Cajanus cajan (L.) Millsp. (Williams 1977)], or modify overall plant architecture, can influence outcrossing rates in wind-, insect-, and predominantly self-pollinating species [e.g., awn types in Hordeum vulgare (Jain etal. 1979), flower size and stigma exsertioninLycopersicon pimpinelhfolium (Jusl.) Mill. (Rick et al. 1978; Widrlechner 1987), floral morphology in Corchorus olitorius L. (Basak and Paria 1989), and plant architecture in Vigna unguiculata (L.) Walp. (Rawal et al. 1978)]. In contrast, Miller et al. (1989) reported that differences in floral morphol­ ogy and pigmentation did not influence outcrossing rates in Prunus persica (L.) Batsch. The potential association of specific marker phenotypes with out­ crossing rates also limits the utility of markers that cause male sterility. The few studies that used pollen sterility as a marker in this manner include that by Wilson (1989), where a male-sterile line of Helianthus annuus assayed the transport of viable foreign sunflower pollen by honeybees (Apis mellifera L.) into isolation cages, and that by Waller et 2. GENETIC MARKERS AND PLANT GENETIC RESOURCE MANAGEMENT 45 al. (1985), who compared the pollinating efficiency of carpenter bees (Xylocopa varipuncta Patton) to that of honeybees with isogenic male­ sterile/male-fertile lines of Gossypium hirsutum. b. Secondary Metabolites. Pigmentation markers can also bias the estimates of outcrossing frequency when insects effect pollination. If pollinators can differentiate between color phenotypes, they may visit only one phenotype, or at least favor the color form offering the greatest caloric reward (Leleji 1973; Widrlechner and Senechal 1992). Although flower-color markers have been used, Leleji (1973) and Onim (1981) noted, and Steiner et al. (1992) confirmed for Medicago L., that random mating should not be assumed in such field experiments. Examples of studies of outcrossing using such color markers include two predomi­ nantly, but not exclusively, self-pollinated species: Trifolium alexandrinum L. (Beri et al. 1985) and Lupinus albus L. (Faluyi and Williams 1981), and in an entomophilous species, Coriandrum sativum L. (Sethi 1981). Markers for othersecondary metabolites [e.g., cucurbitacins (Handel 1983; Handel and Le Vie Mishkin 1984) or essential oils] could also be perceived by insect pollinators. For example, Beker et al. (1989) reported that honeybees preferentially visited essential-oil chemotypes of Majorana syriaca (L.) Rafin. The best pigmentation markers for studying insect pollination in plant germplasm are expressed only post-pollination, such as seed color (when independent of corolla color) in Phaseolus vulgaris (Wells et al. 1988), Vicia faba L. (Robertson and Cardona 1986), and Vigna umbellata (Thunb.) Ohwi and Ohashi (Das and Dana 1987), and fruit color in Atropa belladonna L. (Dhar and Bhat 1982). Seed color markers may also have pleiotropic effects on outcrossing. An atypically high estimated out­ crossing rate in Phaseolus lunatus L. resulted when a locus-controlling seed-coat color was assayed (Harding and Tucker 1964). Stem color markers have also served to estimate outcrossing under controlled conditions for Amaranthus L. spp. (Jain et al. 1982; Hauptli and Jain 1985), Beta vulgaris L. (Bateman 1947b), Brassica rapa L. (Bateman 1947a), Cajanus cajan (Onim 1981), Corchorus olitorius (Datta et al. 1982; Basak and Paria 1989), and Raphanus sativus L. (Bateman 1947a), but there appear to be no tests of whether insect pollinators can sense stem color. Monitoring germplasm's trueness-to-type with markers affecting in­ sect and disease resistance (and with some morphological traits) may be limited by difficulties in accurately and reproducibly measuring gene expression. But a dominant allele, conferring scab resistance, served to measure pollen flow in a monoecious, inbred line of Cucumis sativus L. 46 P. K. BRETTING AND M. P. WIDRLECHNER

(Wehner and Jenkins 1985); and resistance to spotted alfalfa aphid [(Therioaphis maculata (Buckton)] documented minimum isolation distances and border effects in two commercial cultivars of Medicago sativa L. (Brown et al. 1980, 1981). c. Isozymes, Seed Proteins, and DNA Markers. Seed protein polymor­ phisms are more stable than isozymes and can be evaluated from inviable seed lots (Sergio and Spagnoletti Zeuli 1992). Except for Triticum (Cox and Worrall 1987; Sergio and Spagnoletti Zeuli 1992), in which their genetic control is well understood, seed proteins have not been enlisted as extensively to test trueness-to-type as have isozymes. However, Gepts (1990) and Smith and Smith (1986, 1992) do discuss instances of seed protein polymorphisms serving for cultivar fingerprinting of legumes and grasses. Such fingerprints might test trueness-to-type, assuming that sufficient levels of polymorphism are present. This, unfortunately, was not the case for North American cultivars of Glycine max (L.) Merrill (Buehler et al. 1989). After considering all the preceding drawbacks of other markers, the popularity of isozyme markers for evaluating germplasm regeneration methods and trueness-to-type is understandable. Homogeneous lines or clones can be monitored quickly for outcrossing with one or a few isozyme marker loci (Jain et al. 1982; Ellstrand and Foster 1983; Schmidt­ Stohn et al. 1986; Miller et al. 1989), but it is best to rely on multilocus analyses for highly heterogeneous populations. Assays of4 to 14 isozyme loci have documented outcrossing in heterogeneous populations of Apium L. (Orton and Artis 1984), Hordeum (Jana and Khangura 1986; Wagner and Allard 1991), Zea (Pollak et al. 1984; Bijlsma et al. 1986; Kahleretal.1984, 1989), CucumisL. (Widrlechneretal.1992),Medicago (Quiros 1980, 1983), Secale L. (Perez de la Vega and Allard 1984; Cruz­ Pardilla et al. 1989; Vaquero et al. 1989), wild Triticum (Golenberg 1987, 1988), and inintergeneric hybrids ofLycopersicon and Solanum (DeVerna et al. 1987). Isozyme markers have also been pertinent for measuring mating systems and pollen transfer in managed plantings of forest trees (seed orchards). Many of these forestry studies were reviewed by Muller­ Starck (1985) and Muona and Hariu (1989). Other recent reports include those by Cheliak (1985) for Pinus sylvestris L., by Friedman and Adams (1985) for Pinus taeda L., by Ritland and El-Kassaby (1985), EI-Kassaby and Ritland (1986), and El-Kassaby etal. (1988) for Pseudotsugamenziesii (Mirb.) Franco, and by Moran et al. (1989) for Eucalyptus regnans F. Muell. Isozyme marker data, together with phenological and crossability information (Dickson et al. 1991), suggested that physical separation and 2. GENETIC MARKERS AND PLANT GENETIC RESOURCE MANAGEMENT 47

intrinsic crossability barriers effectively protect the genetic integrity of domesticated apple (Malus x domestica Borkh.) orchards and nearby (100 m distance) wild populations of North American Malus. During the last 10 years, isozyme markers have become common­

place tools in commercial seed production, especially of F1 hybrid crops [reviewed by Artis (1983)]. Cultivar purity in hybrid tomato (Tanksley and Jones 1981), maize (Smith and Wych 1986), Brassica spp. (Artis 1983), and many other species is assayed by isozyme analyses. With sufficiently many, relatively highly polymorphic isozyme loci, off-types encountered in lots ofhybrid seeds may be categorized­ with variable levels of statistical confidence (Artis 1983)-as se1£- or sib-pollinated seed parents, pollen parents, other hybrids, or out­ crossed seed plants (Artis 1983; Smith and Wych 1986; Surrs 1986; Bretting 1991; Shoemaker et al. 1992). We forecast that in the near future, some of the preceding approaches developed for assuring trueness-to-type in commercial seed production will be increasingly adopted to germplasm regeneration efforts by genebanks and by other noncommercial genetic resource management programs (see, e.g., Kresovich et al. 1994). These approaches will be especially cost-effective for crops, such as maize, for which the commer­ cial sector (Smith and Wych 1986) or the research community (Goodman and Stuber 1980; Stuber et al. 1988) have already developed diagnostic genetic markers and genetic-marker databases, which can then readily be adapted to noncommercial germplasm management. Although DNA technologies are currently prohibitively expensive for many commercial or germplasm bank trueness-to-type applications, new RFLP or AFLP allelic-specific probes or codominant SSR or RAPD markers under development hold considerable potential in this area (Kresovich et al. 1994; J. S. C. Smith, pers. comm.; Zietkiewicz et al. 1994). For example, Kresovich et al. (1994) analyzed a series of clonal germplasm accessions of vetiver grass [(Vetiveria zizanioides (L.) Nash ex Small)] with RAPD protocol. Several elite, commercial clones were genetically nearly identical for all the primers assayed, whereas a collection of clones maintained as a single germplasm accession were genetically quite heterogeneous. Further inquiry revealed that the latter accession had been regenerated by seed, which allowed genetic segrega­ tion to occur. d. Comparative Studies. In some cases, more than one type of genetic marker has documented outcrossing or other genetic changes resulting from seed regeneration. Jana and Khangura (1986) compared nine isozyme loci, four color markers, male sterility, and eight morphological charac- 48 P. K. BRETTING AND M. P. WIDRLECHNER

teristics (most under polygenic control) in early and advanced genera­ tions of heterogeneous bulk populations of barley. Mass seed regenera­ tion for several successive generations reduced overall genetic diversity, but the reduction was minimal for isozyme alleles and most pronounced for morphological characteristics, some of which may have conferred selective advantage under regeneration conditions. This difference might also result from the polygenic nature of many morphological traits; any of many loci could be influenced by regeneration conditions. Based on these results, Jana and Khangura (1986) proposed adequate single-plant sampling and head-to-row regeneration as an alternative to bulk regen­ erations. Spaced planting, along with the harvesting of perfect samples, could also reduce genetic shifts resulting from germplasm regeneration. Erskine and Muehlbauer (1991) characterized 105 lentil germplasm accessions for variability at three pigmentation loci, 17 isozyme loci, and a putative isozyme locus. The multilocus estimate of outcrossing fre­ quency (0.037) among accessions incorporated both pigmentation and isozyme loci. Allelic frequencies at all three pigmentation loci were significantly associated at the 0.01 level with the country of origin ofthe germplasm, whereas only 8 of the 18 enzyme loci were so correlated. Seedling color and isozyme markers gave similar estimates of out­ crossing in Amaranth us (Jain et al. 1982), but no single population was examined for variability in both pigmentation and isozymes. Assays 07a polymorphic esterase locus tracked varying levels of outcrossing a'ld confirmed assortative mating among different corolla color phenotyp8s of natural (Brown and Clegg 1984) and experimental (Schoen and Clegg 1985) populations of Ipomoea purpurea (1.) Roth. (1986) Smith and Wych assembled seed lots of F1 hybrid maize contaminated with known percentages of kernels from self-pollinated seed parents and assessed how accurately a suite of isozyme markers identified selfed plants as compared with identifications made by mor­ phological (cob color, ear and kernel shapes) and physiological (reduced growth due to inbreeding) traits (heritabilities were not reported). In general, estimates of percentages of selfed plants derived from the isozyme marker data were consistently more accurate than those derived from the morphological-physiological markers. e. Pollination ControlMethods. Although the literature contains many studies of outcrossing rates in various environments or over varying isolation distances, there are fewer published tests of the efficacy of particular pollination control methods. Robertson and Cardona (1986) enlisted a seed color marker to evaluate attractant (Brassica napus L.) and barrier (x Triticosecale Wittmack) crops as borders to reduce bee activity 2. GENETIC MARKERS AND PLANT GENETIC RESOURCE MANAGEMENT 49

and concomitant outcrossing in Vida faba. Genetic markers were vital to this study because although Brassica borders significantly reduced bee activity on Vida, the reduction in outcrossing rate was insignificant, leading the authors to suggest modified planting designs. In a more labor­ intensive procedure, Garditz and Aldag (1989) great!y reduced outcross­ ing in Lupinus albus by mechanical removal of certain corolla parts, making the flowers unattractive to bees. Variation in three morphological markers served as assays for differences in outcrossing rates. Progeny tests were not reported, but apparently no contaminants were detected from the surgically modified parental plants. Three recent studies have evaluated, with genetic markers, the effi­ cacy of caging and bagging for controlling pollen flow in germplasm plantings. Wilson (1989) estimated that 0.27% of sunflower achenes produced in screened isolation cages could have resulted from pollen carried into cages by insects. He used a pollen-sterile marker but did not test the progeny with other genetic markers to distinguish between outcross contamination and an incomplete expression ofpollen sterility. McAdam et al. (1987) tested six pollination bag types for permeability to Lalium L. pollen by tracking isozyme markers. Widrlechner et al. (1992) compared the isozyme profiles of 157 paired seed increases of Cucumis sativus germplasm accessions regenerated by cage and open pollination. The isozyme data suggested that the cage pollination system was supe­ rior for preserving the genetic integrity of this germplasm, but the study was hampered by lack of information regarding the original isozyme frequencies of the seed stocks.

2. Monitoring Shifts in Population Genetic Structure in Heterogeneous Germplasm

a. Deviations from Random Mating. Deviations from random mating, primarily in the form of assortative or consanguineous matings, have been widely studied in maize, with an emphasis on detailed multilocus isozyme analyses of one or two synthetic or open-pollinated maize varieties (Kahler et al. 1984, 1989; Pollak et al. 1984; Bijlsma et al. 1986). Bijlsma et al. (1986) and Kahler et al. (1989) evaluated both tasseled and detasseled plants for the relative contributions of selfing and assortative mating (Kahler et al. 1989) and/or gametophytic selection (Bijlsma et al. 1986). In general, levels of selfing did not exceed those expected under random-mating models, but significant deviations were caused by tem­ poral variation in the pollen pool or by gametophytic selection. Another large-scale study of deviations from random mating in germplasm accessions analyzed gene frequencies for four polymorphic 50 P. K. BRETTING AND M. P. WIDRLECHNER isozyme loci in 51 celery (Apium graveolens L. germplasm accessions; Orton and Artis 1984). Thirty-four samples contained at least one locus at which the predominant allele occurred at a frequency less than or equal to 0.8. These were statistically analyzed for deviations from H-WE following Frankel and Galun (1977). The other 17 samples comprised North American and Western European celery cultivars, indicating prior selection for uniformity. Single-locus estimates of outcrossing in the 34 polymorphic samples ranged from 19.7 to 100%. No information about the regeneration history or pollination control for these samples was presented. Loaiza-Figueroa and Weeden (1991) employed two to six polymor­ phic isozyme loci to compare the genetic profiles of multiple regenera­ tions of two germplasm accessions of onion (Allium cepa L.) They reported significant differences in allelic frequencies at three of six isozyme loci for one accession but no significant differences among samples ofthe other. Populations for regeneration as small as three to five plants had been harvested and exhibited significant, directional changes in allele frequencies, implicating the role of genetic drift. Geric et al. (1989) analyzed 21 isozyme loci for 20 plants from each of about 275 Yugoslavian maize landrace germplasm accessions. They noted a "tendency" (neither described nor tested statistically) for acces­ sions maintained in ex situ germplasm collections for longer periods than other accessions to be isozymically less variable (fewer alleles) and more distinctive (i.e., higher Nei's genetic distances). The tendency was thought to be the result of inbreeding during maintenance of these populations. b. Regeneration of Autogamous Species. Roos (1984b) documented genotypic frequency changes in artificial mixtures of eight Phaseolus vulgaris lines, distinguishable by testa color, which simulate the hetero­ geneous, homozygous mixtures encountered in germplasm accessions of traditional cultivars. Three cycles of field regeneration, combined with oto 6 weeks of artificial seed aging before field planting, led to the rapid elimination ofthose lines most sensitive to aging. Computer modeling of this process (Roos 1979) and statistical theory indicate that increasing sample size should delay, but not halt, such elimination of varieties during regeneration of germplasm mixtures. Martin and Adam's (1987a,b) characterization of seed morphological diversity in Malawian P. vulgaris represents a model for how genetic marker data can be integrated with knowledge of indigenous germplasm maintenance practices to develop strategies for conserving genetic diver­ sity in an autogamous species. 2. GENETIC MARKERS AND PLANT GENETIC RESOURCE MANAGEMENT 51

Widrlechner (1987) examined changes in floral morphological mark­ ers associated with regenerating accessions of Lycopersicon pimpinellifolium, a species that is highly autogamous throughout much of its native range but shows clinal variation in its breeding system. Regenerated accessions expected to be somewhat allogamous, based on their geographic origin, showed shifts toward autogamous floral mor­ phology. Perhaps this was caused by outcross contamination or by unintentional selection for plants that set fruits in the absence of native pollinators, which would reduce overall genetic variability in such populations. Because few pollinators were observed, the second possi­ bility is more likely, but this point remains to be tested. Cox and Worrall (1987) examined gliadin (storage protein) variability in 11 strains of 'Kharkof' wheat, Triticum aestivum, to evaluate long­ term genetic changes engendered by repeated regeneration of heterog­ enous traditional cultivars in different environments. Seven strains had polymorphic but overlapping isozyme pattern frequencies, which pointed to a common ancestry. The other strains were completely uniform (suggesting single plant selections); two had patterns that also occurred in the variable strains, and two were completely different. Although these strains were maintained as long-term check varieties at evaluation nurseries, and not as accessions in a genebank, the potential for genetic shifts over many regeneration cycles (since 1930) was striking. Gliadin phenotypes were also compared by Sergio and Spagnoletti Zeuli (1992) for three cycles of regenerating a heterogeneous germplasm bank acces­ sion of a traditional variety of durum wheat, Triticum turgidum 1. Both changes in phenotypic frequencies and increasing levels of contamina­ tion by Triticum aestivum were encountered.

3. Monitoring Genetic Shifts Caused by Differential Viability in Storage. The genetic profiles of germplasm accessions can change dur­ ing the course of medium- or long-term storage. Storage effects fall into three broad categories: (1) the occurrence ofmutations, (2) the occurrence of chromosomal aberrations, and (3) shifts in gene frequencies resulting from differential genotypic viability in heterogeneous populations (Roos 1988). Roos (1988) comprehensively reviewed studies of storage effects on seeds, found little evidence for heritable changes in germplasm attributable to storage-induced chromosomal aberrations, and noted "little need for concern about mutation as a significant factor in altering the composition of germplasm collections" (p. 88). In contrast, differential seed longevity can markedly reduce genetic variability over time, as is well documented by experiments involving mixtures of eight bean lines (of P. vulgaris; Roos 1984a) and four seed 52 P. K. BRETTING AND M. P. WIDRLECHNER storage protein genotypes within a cultivar of wheat (T. aestivum; Stoyanova 1991). Differences in seed longevity associated with seed coat color (Kueneman and Costa 1987) and longevity loci (Verma and Ram 1987) in Glycine max and with luteus mutants in Zea mays (Weiss and Wentz 1937) have also been reported. All of the preceding studies [except for Weiss and Wentz (1937)J relied on stress tests or accelerated aging (Delouche and Baskin 1973) to measure viability differences. Because such tests only estimate the cumulative effects of unfavorable storage conditions, long-term studies documenting changes under actual storage conditions should also be initiated. As Roos (1984a) demonstrated, the relative proportions of morpho­ logically distinct seed types within a mixed germplasm sample can serve to monitor viability over time. But caryopses of certain grasses have cryptic differences visible only after phenol staining, which, until recently, was destructive and could not be correlated with viability. Fortunately, Tao et al. (1992) have developed a modified phenol-staining protocol for Triticum that is rapid and nondestructive, enabling the assay of genetic changes in stored germplasm whenever phenol staining color and intensity varies and whenever the genetic basis for differential staining can be established. An isozylne marker tracked gene frequency changes, associated with pollen storage, in progeny produced by pollinating sunflower inbred lines with pollen samples stored in liquid nitrogen and under conven­ tional refrigeration (Roath et al. 1988). No significant differences in gene frequencies were detected between the storage conditions, although overall pollen viability declined more rapidly in the refrigerated sample than in the sample stored in liquid nitrogen.

4. Monitoring Genetic Shifts Caused by In Vitro Culture. The genetic stability of germplasm maintained in tissue culture (in vitro) has gener­ ally been monitored with karyotypic markers (e.g., chromosome number and morphology) (D'Amato 1975). This is because cytological variability has been considered a primary cause of somaclonal variation (Scowcroft et al. 1985). Other genetic markers, such as isozymes (Arnison and Boll 1974; Wetter and Dyck 1983; Lassner and Orton 1983; Viterbo et al. 1994), cpDNA (Kung 1983; Scowcroftetal. 1985), andnDNA (Brown et al. 1991) have detected point mutations or chromosomal aberrations in such cultures. Notably, though, Lassner and Orton (1983) reported that isozymatically identical in vitro cultures of celery were markedly vari­ able cytologically. This finding should reinforce the concept that the genetic stability of in vitro cultures should be monitored with a battery of differentgenetic markers (Walbotand Cullis 1985), particularly those DNA 2. GENETIC MARKERS AND PLANT GENETIC RESOURCE MANAGEMENT 53

markers that collectively span a relatively large proportion ofthe genome. Such a program, to screen in vitro cultures of Manihot Mill. germplasm with a variety of genetic markers, is under way (Roca et al. 1989).

5. Monitoring Germplasm Viability and Health. As noted in Section II4a, ELISA is a disease detection procedure based on protein antibody markers diagnostic for plant pathogen genotypes/phenotypes rather than for plant genes or genomes. The ELISA protocol and other recently developed technologies involving DNA and RNA hybridization (Van Regenmortel1982; Baulcombe et al. 1984) can help monitor the health of plant germplasm collections through disease indexing (Clark 1981; Catello et al. 1988). These techniques are often combined with in vitro culture to produce disease-free propagules (Kartha 1986). The preceding assays for seed health may also yield valuable evaluations of host-plant resistanceto thepathogens. Forexample,theICRISAT Arachis1. germplasm collection is screened via ELISA not only for occurrence of, but also for host-plant resistance to, peanut mottle disease (Bharatan et al. 1984). Disease elimination in homogeneous samples should not engender direct genetic consequences, but the genetic composition of heterogeneous samples may be altered when disease is eliminated through roguing. Genetic markers other than those based on serology can document changes in genetic structure related to eliminating diseases from hetero­ geneous germplasm populations. Programs to eliminate seedborne vi­ ruses from germplasm collections may reduce genetic diversity. Alconero et al. (1985) measured (with seed color and isozyme markers) changes in genetic variability tracing to the elimination of pea seedborne mosaic virus from Pisum sativum 1. germplasm. Nine of 17 polymorphic populations suffered a reduction in genetic diversity associated with virus elimination. Based on genetic marker data, Alconero et al. (1985) recommended eliminating seedborne viruses from genetically heterog­ enous populations, without roguing, by producing virus-free plants for seed regeneration by tissue culture and chemotherapy. A later study examined in greater detail (12 biochemical and 20 morphological mark­ ers) 50 polymorphic Pisum populations regenerated with roguing from the same genebank (Recchio-Demmin et al. 1990). Results of the latter study suggested that genetic shifts occurred between source and regen­ erated samples for all populations, but genetic drift caused by regenera­ tion per se could not be distinguished from the effects ofvirus elimination. Klein et al. (1990) examined genetic shifts resulting from a program to eliminatethe seedbornebeancommon mosaicvirusfrom Phaseolusvulgaris accessions by comparing 120 paired samples from two genebanks; one practiced roguing, the other did not. Genetic diversity was calculated from 54 P. K. BRETTING AND M. P. WIDRLECHNER

a weighted average of a sample's distinct seed phenotypes (colors, sizes, shapes, and patterns). A loss of 44.5% in within-sample phenotypic diver­ sityand65.5% for overallphenotypic diversitywas attributedto theroguing program. Viral incidence across allthe roguedsamples was significantlyless than that for the unrogued samples (4.2 versus 6.0%), but concomitant selection for virus tolerance may have occurred. Unfortunately, unlike pea seedbornemosaic virus, bean common mosaicvirus is not easily eliminated from individual plants, so the recommended roguing level was less than 10% of the plants from any population (Klein et al. 1990). Alternatively, new gene combinations may be generated-increas­ ing genetic diversity in one sense-while simultaneously eliminating viruses. In species that are usually propagated vegetatively (e.g., Solanum tuberosum), viruses that are not seed borne may be elimi­ nated from germplasm collections by controlled hybridization and seed propagation of related clones. Analyses of molecular and mor­ phological markers have documented that traditional potato cultiva­ tion in Peru includes both seed propagation and the selection of desirable clones (Quiros et al. 1992), suggesting that a similar mixed regeneration system for germplasm collections maintained ex situ might balance the needs for conserving the health and the original genetic profiles of germplasm collections.

D. Utilization

1. Developing Optimal Utilization Strategies from Genetic Marker Data. Earlier (Section IlIA) we discussed how genetic-marker data, when interpreted in a plant systematic-statistical genetic context, may aid germplasm conservation programs. Knowledge of systematic relation­ ships may also provide guidance for germplasm utilization efforts. As noted in Section lIB1, germplasm management programs might focus on weedy crop ancestors before assessing weedy feral escapes, because the former are more likely to be sources ofunique alleles for conservation and for crop improvement. As the exact systematic relationships among a crop and its wild­ weedy relatives are better understood, germplasm utilization strate­ gies may change tangibly. For example, for at least a quarter century, maize evolutionists in general accepted the tripartite hypothesis of Mangelsdorf [originated in the 1930s and reviewed in Mangelsdorf (1974)], which postulated that maize evolved directly from an undis­ covered wild maize, and that teosinte was derived from a hybrid between maize and Tripsacum L. species. During that period, substan­ tial resources (relative to those devoted to similar programs with 2. GENETIC MARKERS AND PLANT GENETIC RESOURCE MANAGEMENT 55

teosinte) were allocated to improving maize with introgressed Tripsacum germplasm (Galinat 1977). More recent systematic investigations, many incorporating molecular genetic markers, have led to the now widely accepted notion that teosinte is ancestral to maize and that Tripsacum is more distantly related to the preceding crop and its congeneric wild relatives (Iltis and Doebley 1984; Larson and Doebley 1994). Karyological marker research also uncovered a diploid perennial teosinte that crossed readily with maize (Iltis et al. 1979). Subsequently, these systematic studies helped redirect germplasm evaluation and utilization efforts from Tripsacum to teosintes, especially to the newly recognized diploid perennial (Nault et al. 1982). The role that genetic markers play in delimiting core subsets of ex situ germplasm collections was discussed earlier (Section IIIB3). Genetic markers can also aid with assembling groups of accessions designed to facilitate efficient germplasm evaluation and enhancement. Beuselinck and Steiner (1992) proposed the standard range collection (SRC) for evaluation purposes. The SRC would "receive priority evaluation for all traits of interest and [be] the first to be distributed when requests for germplasm are received" (Beuselinck and Steiner 1992, p. 264). The SRC would include accessions with verifiable (not necessarily optimal) data regarding their taxonomic identity and provenance and would "be defined by high-priority descriptive characters [many of which would presumably be genetic markers], serve as a genetic standard, and repre­ sent the genetic diversity of accessions within a collection" (p. 263). The SRC is too recent a concept for its utility to be appraised, but it resembles a hybrid between a core subset and a test array. The latter is a provisional, ad hoc group of accessions assembled for particular evalua­ tion and/or enhancement efforts. For example, a test array comprising Cucumis germplasm accessions, endemic to midlatitude deserts, might be assembled for drought resistance evaluations conducted in the U.S. midwest. In this case the (1) perceived potential ofaccessions for including the desired trait(s) and (2) their adaptation to the test location are primary criteria for array membership, whereas the total genetic diversity (as mea­ sured by genetic markers) included in the array is of secondary concern. Marker-facilitated plant breeding is not reviewed extensively here, but certain genetic marker applications to crop improvement will be appraised briefly. Geneticmarkers can, in theory, help optimize germplasm utilization strategies by (1) identifying novel, sometimes latent, alleles of agronomically valuable traits with relatively low heritabilities (Knott and Dvorak 1976; Stalker 1980; Soller and Beckmann 1988; Bubeck et al. 1993); and (2) incorporating these valuable traits into breeding popula­ tions (Soller and Beckmann 1988; Bernatzky and Tanksley 1989). In the 56 P. K. BRETTING AND M. P. WIDRLECHNER ensuing sections we emphasize genetic markers' bipartite role in identi­ fying and manipulating favorable traits during germplasm utilization.

2. Exploiting Associations Among Traits of Interest and Genetic Markers. Genetic markers may help exploit valuable traits when the markers and traits are in linkage disequilibrium (i.e., associated geneti­ cally). Such markers and traits are often, but not necessarily, physically linked (Weir 1990a; Allard 1992). Cosegregation of the traits of interest and genetic markers is the basis ofbulked segregant analysis (Michelmore et al. 1991), an iterative procedure for identifying markers linked to specific traits, such as disease resistance. This procedure has the poten­ tial of greatly facilitating germplasm evaluation and subsequent en­ hancement or breeding. Linkage disequilibrium implies that one or more of the preconditions ofH-WE do not occur. Consequently, such associations are expected to occur more frequently in clones than in sexually reproducing germplasm, and in inbreeding populations more frequently than in outbreeding populations. Indeed, several recently reported examples of genetic marker-facilitated utilization involve highly autogamous or clonally propagated crops (Nevo et al. 1984, 1985; Weeden et al. 1984; Zhang and Tang 1987; Martin et al. 1991). Nevertheless, genetic markers are not effective in this role in every clonal or inbreeding species (e.g., Burdon and Jarosz 1989). After surveying isozyme and DNA variability reported in barley and maize, Allard (1992) distinguished three classes of allelic markers: (1) ubiquitous alleles that are either fixed or generally occur at high frequencies, (2) rare alleles that always occur in low frequencies and only in a small segment ofthe entire germplasm pool, and (3) alleles that occur at intermediate to high frequencies but not in all segments of the germplasm pool. Isozyme and nDNA allelic frequencies were in general significantly associated with agronomically meritorious traits such as adaptation and productivity, an association that might provide a means for evaluating the latter lower-heritability traits less expensively and more efficiently. Allard suggested that "if an accession carries one or more alleles ofthe first and/or third [frequency] classes described above, and these alleles are not present in the breeding stocks of a given ecogeographical region, the chances are good that this accession will be of value in crop improvement programs of that region" (p. 143). His assessment that molecular markers can be assayed sufficiently "quickly, precisely, and relatively inexpensively" (p. 143) for such broad-scale molecular evalu­ ations was perhaps overly optimistic in 1992, but such molecular 2. GENETIC MARKERS AND PLANT GENETIC RESOURCE MANAGEMENT 57 evaluation programs are now nearing practicality as the new amplifica­ tion-based technologies are perfected (e.g., Senior and Heun 1993; Rafalski and Tingey 1993). Genetic markers may not be required for efficiently exploiting valu­ able traits that are themselves highly heritable, although they may be vital for manipulating traits with lower heritabilities (Lande and Thomp­ son 1990; Edwards 1992b). Genetic marker-assisted evaluation and manipulation may be more efficient, especially when speed is crucial (Edwards 1992b; Tanksley et al. 1989), or more cost-effective than directly assaying and selecting valuable traits that are costly or difficult to score (Edwards 1992b), or when assays perhaps involve unacceptable risks such as environmental hazards. It should be recognized that polygenically inherited traits represent the cumulative expression of many genes and a rauge of possible gene actions (additive, dominant, epistatic, pleiotropic, or any combination of the preceding). Consequently, although the phenotype of an accession may appear agronomically worthless, the presence of some favorable genes may be masked or swamped by either more numerous or more dominant deleterious genes. As Soller and Beckmann (1988) indicate, the most valuable contribution of genetic markers to germplasm utiliza­ tion may be the efficient detection of these valuable latent genes. Genetic marker-assisted germplasm evaluations or manipulations can be placed into one ofseveralbroad classes. The first categoryinvolves genetic markers highly associated with relatively high-value plant prod­ ucts (e.g., agronomic-horticultural, industrial, or medicinal). For ex­ ample, seed protein markers are highly associated with the bread-making or baking qualities of wheat (Triticum aestivum) (Payne et al. 1979) and the malting qualities ofbarley (Hordeum vulgare) (Riggs et al. 1983). An allele governing an isozyme of shikimate dehydrogenase is strongly associated with higher protein content in rice (Oryza sativa L.) (Shenoy et al. 1990). Lewis's (1984) review of the role of taxonomic data in identifying and improving medicinal plants suggests that cytogenetic markers-specifically, polyploidy or aneuploidy-may be associated with elevated concentrations of drugs or drug precursors. Genetic markers have been particularly valuable for improving seed quality traits in legumes. Davies and Nielsen (1986,1987) and Davies et al. (1987) as well as Kitamura (1984) and Kitamura et al. (1985) have used isozymemarkers to detectand totransfernull alleles for three lipoxygenase loci, which reduce the characteristic beany taste of soybean. In this case, the isozyme markers and the milder taste are different phenotypic expressions ofthe same three lipoxygenase genes. The protein bands are much more highly heritable than is better taste (Davies et al. 1987) and 58 P. K. BRETTING AND M. P. WIDRLECHNER hence are superior genetic markers for crop improvement. The preceding work builds on earlier research (Hymowitz 1973; Orf and Hymowitz 1979) in which isozymes of Kunitz trypsin inhibitor (an antinutritional soybean seed protein) and of soybean seed lectins were manipulated to improve the nutritional properties of soybean (Stahlhut et al. 1981). By selecting isozyme or immunological markers with higher herita­ bilities or simpler genetic control relative to agronomically valuable traits, Bliss and associates introgressed into the common bean, from wild relatives, genes that encode higher protein content, accelerated rates of Nz fixation, and stronger host-plant resistance to insect predation (Bliss 1990). Similarly, diagnostic isozyme markers helped introgress genomic segments from wheat's wild relatives into wheat lines (Hart and Tuleen 1983) to produce genetic stocks valuable for basic and applied research. In the second category of genetic marker-assisted germplasm evalua­ tions, the association-linkage of genetic markers with genes for host-plant resistance to biotic or abiotic stresses is exploited (Martin et al. 1991; Nance et al. 1992). For example, Rick and Fobes (1974) discovered 20 years ago that in Lycopersicon spp., host-plant resistance to nematode infestation is very highly correlated with an acid phosphatase isozyme. Since then, tomato germplasm has been assayed routinely for that isozyme marker rather than being screened directly for nematode resis­ tance (DeVerna and Alpert 1990). Similarly, Ashri (1971) identified several easily visualized morpho­ logical traits in Carthamus tinctorius 1. germplasm that are highly associated with host-plant resistance to several diseases. Also, mutant morphologies (e.g., nectariless and okraleaj) in Gossypium 1. spp. cosegregated with host-plant resistance to pink bollworm [Pectinophora gossypiella (Saunders)] infestation (Wilson et al. 1979) and also with host-plant resistance to boll rot (Meredith et al. 1973). Evaluating host­ plant resistance to such insects is tedious, so selection for these genetic markers facilitated the introgression ofresistance genes into commercial cotton cultivars (Meredith et al. 1973). In the preceding examples, genetic markers were often associated with oligogenically controlled valuable traits. As noted by Frankel (1947), who early distinguished observable from nonobservable traits, genetic markers may help detect latent alleles that condition valuable polygenically controlled traits (Soller and Beckmann 1988). Nuclear DNA RFLP genetic markers have recently highlighted latent alleles for polygenic resistance to gray leafspot (incited by Cercospora zeae-maydis Theon and Daniels) in maize lines that are highly susceptible to the disease (Bubeck et al. 1993). Genetic (especially molecular) markers may help to incorporate such alleles into other partially resistant lines. 2. GENETIC MARKERS AND PLANT GENETIC RESOURCE MANAGEMENT 59

Pyramiding of resistant alleles and, perhaps more importantly, the concurrent elimination of latent susceptible alleles, might enhance the strength and the durability of quantitative host-plant resistance (Soller and Beckmann 1988; Bubeck et al. 1993)-provided that the gene pyramider compensates for the substantial genotype x environment interactions characteristic of many such genetic marker-quantitative trait associations (Bubeck et al. 1993). Finally, in the last category of marker-assisted evaluation, genetic markers are crucial for evaluating germplasm with extended juvenile periods for traits critical for crop improvement and germplasm manage­ ment. For example, a genetic marker can identify dwarf Malus clones early in their growth (Zhang and Tang 1987). Another isozyme marker in apple has been found tightly linked to the S gene(s) governing incompat­ ibility (Manganaris and Alston 1987). Under certain conditions, nucellar seedlings in Citrus L. spp. can be distinguished isozymatically from those resulting from sexual reproduction (Torres et al. 1982, Torres 1989), which substantially aids breeding efforts as well as facilitating efficient clonal germplasm propagation.

3. Genetic Enhancement. According to Duvick (1990), genetic enhance­ ment (or pre-breeding) involves partially adapting alien unadapted germplasm to local conditions while retaining the latter's essentialgenetic contributions (Le., its genetic diversity) and perhaps also its favorable genetic cassettes (Peterson 1992). The goals of such enhancement efforts, which Simmonds (1993) terms incorporation, are to (1) deter genetic homogeneity by generating genetic diversity, and (2) elevate yields. Genetic enhancement may also refer to the transfer of high-value trait(s) such as host-plant resistance or specialty proteins into locally adapted germplasm, a process that Simmonds terms introgression. Suc­ cessful instances of introgressing high-value traits from unadapted germplasm into elite, adapted material were discussed in Section IIID2. Whatever such programs may be termed, successful genetic enhance­ ment should make a wider variety of genes and gene combinations accessible to a wider variety of crop improvement programs. The proportions of unadapted and adapted genomes and/or geno­ types persisting in enhanced germplasm will differ according to the particular goal(s) of the enhancement program. Incorporation pro­ grams seek to increase genetic diversity by maximizing the proportion of unadapted genome/genotypes that is retained. In contrast, when introgressing adapted germplasm with high-value traits, only the requisite high-value genes should be transferred. Finally, yield en­ hancement efforts seek whichever proportion of the unadapted and 60 P. K. BRETTING AND M. P. WIDRLECHNER adapted genome/ genotypes that optimizes the yield ofthe desired end product. Traditionally, the persistence of unadapted genotypes or genes that were incorporated into adapted germplasm was inferred from breeding pedigrees, with random gene assortment assumed. For example, the conversion ofunadapted, photoperiod-sensitive germplasm to photope­ riod insensitivity by making an initial cross with photoperiod-insensi­ tive, adapted germplasm, followed by recurrent backcrossing to the unadapted parent and selection for photoperiod insensitivity, presum­ ably results in germplasm that retains a proportion of the unadapted genome which increases by 50% with each backcross [e.g., 75%, 87.5%, and 93.75% for the first, second, and third backcross (Simmonds 1979)]. To date, the most important contribution of genetic rnarkers to incorpo­ ration has been the expostfacto demonstrationthat incorporationprograms can yield genetically enhanced, yet locally adapted germplasm that incor­ porates substantial proportions of the unadapted germplasm's genome/ genotype. For instance, diagnostic isozyme markers suggested that genes from unadapted, tropical germplasm did persist in tropical-temperate sweet corn composite germplasm (Rubino and Davis 1991) and in tropical dent corn composite-temperate, Corn Belt Dentgermplasm (P.K. Bretting et al., unpubl.). Both of these maize populations were random mated and subjected to natural and relatively weak agronomic selection in the temper­ ate regions ofthe United States for several to many generations before se1£­ pollination and stronger agronomic selection began. Similarly, Koester et al. (1993) documented the retention of RFLP variants, diagnostic for the Canadian maize landrace 'Gaspe Flint', in Iowa and South Carolina lines that had been crossed to 'Gaspe Flint', then recurrently backcrossed to the non-'Gaspe Flint' parent while selecting for early maturity. Persistence of genetic markers diagnostic for the unadapted germplasm (and, presumably, which are linked to agronomi­ cally valuable traits) demonstrates that at least in maize, genomic segments of unadapted germplasm can be incorporated and are at least partly retained during selection for local adaptation. Edwards (1992b) warned that for genetic enhancement, genetic markers such as isozymes "have numerous limitations and require specific genetic situations to demonstrate competitive potential" (p. 248). At present it must be conceded that published evidence that genetic markers have advanced programs for genetically enhancing yield is scarce. Isozyme markers have confirmed that genomic segments of Sinapis alba 1. were introgressed successfully into Brassica napus via an initial intergeneric cross followed by backcrossing to B. napus (Ripley et al. 1992). Isozyme, RFLP, and morphologicalmarkers diagnostic for chromosomes oftomato's 2. GENETIC MARKERS AND PLANT GENETIC RESOURCE MANAGEMENT 61 wild-weedy relatives aided efforts to introgress wild genomic segments into elite tomato-breeding germplasm (DeVerna et al. 1987,1990). As a result, the elite germplasmreceivedwild genomic segments that increased the yield ofhorticulturally valuable traits (Rick 1988). Notably, as a result ofthis program, modern tomato cultivars may be genetically more diverse than heirloom, vintage varieties (Williams and St. Clair 1993). Stuber and Sisco (1991) and collaborators introgressed into a maize inbred line adapted to Iowa yield-enhancing genomic segments from an inbred line adapted to Texas by (1) identifying the favorable segments through yield trials coordinated with molecular (RFLP and isozyme) marker genotyping; and (2) transferring into the Iowaline, with the help of molecular marker genotyping, only the favorable segments from the Texas line. Although favorable segments were identified in field trials conducted in the diverse environments ofNorth Carolina, Iowa, and Illinois, both the recipient and the donor lines per se could be considered somewhat alien to the primary breeding site for this program in North Carolina. Neverthe­ less, this study and one by S. Furbeck (unpubl.) do represent cases in which genetic markers apparently facilitated yield enhancement success­ fully. Inthe latterstudy, genomic segments from a Peruvian maizerace that were introgressed into another elite, inbred line adapted to Missouri increased the grain yield and other valuable traits in hybrids between the enhanced Missouri line and a nonenhanced Iowa line.

IV. CONCLUDING REMARKS AND FUTURE PROSPECTS

Several generalities emerged from the preceding review of the many applications ofgenetic markers to plant genetic resource management. A generalized ideal genetic marker can be described as highly heritable and polymorphic (Section lIB1), but no specific class of genetic marker is inherently superior to all others for every managerial task. For instance, the value of a genetic marker for evaluating or introgressing valuable traits depends primarily on the marker's higher heritability or simpler genetic control relative to the valuable traits and its fortuitous close linkage to the gene(s) governing those traits. Consequently, a genetic marker's utility is always relative and is determined by the particular managerial application and by the specific natural history and genetic architecture (Table 2.2) of the germplasm. For example, one to a few somewhat variable, highly heritable, morphological markers may be relatively as effective, but relatively less expensive, for detecting gene flow among highly homogeneous culti­ vars than are assays of hypervariable loci and RAPD genetic markers, 62 P. K. BRETTING AND M. P. WIDRLECHNER

the latter employed by Philbrick (1993). In contrast, accurately assuring trueness-to-type of heterozygous and/or heterogeneous germplasm may require assays of several, highly polymorphic molecular marker loci (see Section HIC1). Similarly, accurately determining genetic identities demands many polymorphic loci which collectively saturate the plant's genome with markers, so RFLPs or other DNA markers are generally superior to morphological markers for this application (Smith and Smith 1992). Isozymes have long been (e.g., Brown 1978), and continue to be, the class ofgenetic markers that is useful for a wider range ofplant germplasm managerial tasks than is any other class. The proven value of highly polymorphic isozyme loci to plant germplasm management should not be overlooked in the rush to adopt DNA markers. For example, in barley, at least 15 isozyme alleles occur at esterase locus Est2 (Brown 1983), whereas in a Lycopersicon species more than 50 alleles occur at peroxi­ dase locus Prx-4 (Rick 1983). As compared with RAPD DNA markers, hypervariable isozyme loci analyzed by now-venerable technology have the added advantage of known genetic bases and allelic homologies. Nevertheless, isozyme loci are, in general, too few for genetic identity assessment and similar applications (e.g., Lamboy et al. 1994), where RFLP and related techniques are preeminent. Furthermore, ever fewer scientists are being trained in starch gel electrophoresis, so this tech­ nique is increasingly becoming an arcane art. Interestingly, hypervariable loci have not yet been detected in all plants. In morphologically diverse domesticated peanut germplasm, it is apparently rather difficult to detect DNA polymorphisms via analyses of RFLPs, RAPDs, or PCR-amplified-then cleaved genomic fragments (Halward et al. 1991). With respect to some of these newer DNA genetic markers, one might recall an earlier caveat regarding isozymes, the then new genetic marker: "Rarely is the currently popular fad the panacea that its most exhortant promoters imply" (Goodman and Stuber 1980, p. 26). In particular, many of these DNA amplification analyses must be con­ ducted according to rigorously defined experimental conditions, or artifactual patterns of variation can result (Williams et al. 1993). The PCR and related DNA-amplification protocols for generating genetic markers are much more amenable to automation (Frank et al. 1988; Ziegle et al. 1992) than are other genetic-marker technologies such as isozyme and RFLP analyses (Kresovich and McFerson 1992). With the rising cost of skilled technical labor, automation will be an increasingly important factor for determining genetic markers' utility. Consequently, automated DNA-amplification techniques (Rafalski and Tingey 1993) can be expected to displace isozymes as superior genetic markers for the 2. GENETIC MARKERS AND PLANT GENETIC RESOURCE MANAGEMENT 63 broadest range of germplasm managerial applications. As this review is being written, automated PCR-mediated amplification of sequences flanked by simple sequence repeats (SSR) is under development (e.g., Zietkiewicz et al. 1994). This, and related techniques, hold much prom­ ise for generating many highly polymorphic genetic markers quickly and inexpensively. Plant systematists (Simpson 1961; Radford 1986; Crawford 1990; Hillis and Moritz 1990; Stuessy 1990) have cautioned that whenever possible, systematic/evolutionary relationships and genetic diversity levels should be assessed by more than one class of genetic marker (e.g., morphology together with isozymes and/or nDNA). Recent plant germplasm characterizations with several classes of genetic markers demonstrate this approach's prudence (Beer et al. 1993; Liu and Furnier 1993). This precaution is also highly relevant to applying genetic marker data to germplasm management, where reliance on only one marker class, regardless of its sophistication, flexibility, and potency, might engender faulty managerial practices (Section lIlA). For example, Clegg et al. (1984) found that cpDNA variability in wild [Hordeum vulgare ssp. spontaneum (Koch) TheIl.] and domesticated (H. vulgare) barley was poorly correlated with isozymatic variation. Such incongruities may be encountered more frequently in crops than in wild plants, because strong human selection for a certain marker class (often morphology, e.g., fruit size or number) may lead to its evolving with a mode and tempo different from those of other marker classes (Doebley 1989a, 1992). As a counterexample, systematic rela­ tionships among naranjilla (Solanum quitoense Lam.) and its wild relatives, as revealed by morphological data, were fully congruent with those derived from allozyme data (Whalen and Caruso 1983), demon­ strating, once again, that a genetic marker's value to germplasm man­ agement is determined largely by the intrinsic qualities ofthe gene pool being managed. Despite the considerable congruence among some data sets derived from different marker classes, computer-assisted statistical genetic or numerical taxonomic procedures are required for reconciling data sets that are more often incongruent. To a certain extent, such incongruities may result from phenotypic similarity resulting from convergent and parallel evolution, or from evolutionary reversals (homoplasy), rather than from inheritance of the same genes from a common ancestor (Stuessy 1990; Swofford and Olsen 1990). Homoplasy may be common in crops for traits under direct human selection, such as the similar fruit traits that apparently evolved independently in different Capsicum species (Pickersgill1981). Joint cladistic and phenetic analyses might be 64 P. K. BRETTING AND M. P. WIDRLECHNER valuable for harmonizing conflicting data sets by identifying homoplasy, and minimizing its deleterious effects, or for identifying cases ofreticu­ late evolution (Sokal1986; Swofford and Olsen 1990). Genetic markers may be instrumental for addressing controversial issues facing germplasm managers. Some of the most contentious de­ bates involve methods for determining optimal germplasm sampling and core subsets: (1) Is A (mean number of alleles per locus), as calculated from genetic marker data, an optimal index of genetic diversity? Should its statistical drawbacks (Section IIC3) bar it from use in assembling core subsets and testing their utility? (2) Are levels of diversity in genetic markers congruent with levels of diversity in quantitative trait loci (QTLs), which, presumably together with regulatory genes, determine the phenotypes for such traits as yield, adaptation, and their genotypes x environment interactions? [See Hamilton (1994) for a provocative discussion of this point.] (3) Is it feasible to condense large germplasm collections-without concomitantly discarding unique genes-by me­ thodically removing apparently redundant accessions? We conclude by stressing that genetic marker data will complement, not replace, managerial experience with germplasm, prudent judgment, and keen knowledge of a plant's natural history. Genetic marker data should be weighed judiciously before basing germplasm management decisions on them. For example, Les et al. (1991) warn that without recognizing the importance ofmaintaining diversity in sporophytic se1£­ incompatibility (5) alleles, genetic-marker (isozyme, in this case) data might engender a managerial strategy that conserves too few 5 alleles to assure that the rare plant Aster furcatus Burgess would survive in nature. Nonetheless, when exploited carefully, genetic markers do have enor­ mous, generally unrealized (Section IIIB3) potential for optimizing germplasm conservation and utilization (Kresovich et al. 1993), espe­ cially by providing the precise details of plant germplasm's genetic architecture (Avise 1994) which are so vital for effective and efficient germplasm management.

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