DOCSLIB.ORG

An Introduction to Coalescent Theory

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
An Introduction to Coalescent Theory An introduction to coalescent theory Nicolas Lartillot May 26, 2014 Nicolas Lartillot (CNRS - Univ. Lyon 1) Coalescent May 26, 2014 1 / 39 De Novo Population Genomics in Animals Table 3. Coding sequence polymorphism and divergence patterns in five non-model animals. species #contigs #SNPs pS (%) pN (%) pN/pS dN/dS aa0.2 aEWK vA turtle 1 041 2 532 0.43 0.05 0.12 0.17 0.01 0.43 0.92 0.17 60.03 60.007 60.02 60.03 60.18 60.15 hare 524 2 054 0.38 0.05 0.12 0.15 20.11 0.30 ,0 ,0 60.04 60.008 60.02 60.03 60.22 60.23 ciona 2 004 11 727 1.58 0.15 0.10 0.10 20.28 0.10 0.34 0.04 60.06 60.011 60.01 60.01 60.10 60.11 termite 4 761 5 478 0.12 0.02 0.18 0.26 0.08 0.28 0.74 0.20 60.01 60.002 60.02 60.02 60.10 60.11 oyster 994 3 015 0.59 0.09 0.15 0.21 0.13 0.22 0.79 0.21 60.05 60.011 60.02 60.02 60.12 60.13 doi:10.1371/journal.pgen.1003457.t003 Explaining genetic variation Figure 4. Published estimates of genome-wide pS, pN and pN/pS in animals. a. pN as function of pS; b. pN/pS as function of pS; Blue: vertebrates; Red: invertebrates;Gayral Full circles: et species al, analysed2013, in PLoS this study, Genetics designated by 4:e1003457 their upper-case initial (H: hare; Tu: turtle; O: oyster; Te: termite; C: ciona); Dashed blue circles: non-primate mammals (from left to right: mouse, tupaia, rabbit). Estimates were taken from Bustamante et al. 2005 (human), Hvilsom et al 2012 (chimpanzee), Carneiro et al 2012 (rabbit), Perry et al 2012 (other mammals), Begun et al 2007 (D. simulans) and Tsagkogeorga et al 2012 (C. intestinalis B = right-most circle). doi:10.1371/journal.pgen.1003457.g004 Fraction ofPLOS heterozygous Genetics | www.plosgenetics.org sites innuclear 9 genomes April 2013 | Volume 9 | Issue 4 | e1003457 humans: 0.1% (1 every 1000 nucleotide positions) drosophila: 3.3% (1 every 30 nucleotide positions) what determines levels of within-species genetic variation? Nicolas Lartillot (CNRS - Univ. Lyon 1) Coalescent May 26, 2014 2 / 39 Viral phylogenies Rates of HIV Evolution and AIDS Progression REVIEWS As a result, strains with advantageous mutations could, by chance, find themselves in individuals with low rates of partner exchange and so will not be transmitted far in the population. Of more debate is whether a bottleneck has a selective component, so that strains that are better adapted to new hosts (such as R5 strains) competitively establish themselves in primary infection60,or whether it is entirely neutral61 and thereby only magnifies the effects of genetic drift. Finally, some advantageous mutations, such as those conferring CTL escape, might not appear until relatively late in infection62.If these late-escape mutants do not arise until after most individuals have transmitted the virus, natural selection will be less effective at the pop- ulation level. As a consequence, HIV strains might not readily adapt to the HLA HAPLOTYPE distributions of their local populations63,because some CTL-escape mutants have little opportunity for further transmission. The data presented to support the adaptation of HIV to HLA haplotypes at the population level only considered within-host evolution, albeit in a large number of Figure 4 | Contrasting patterns of intra- and inter-host evolution of HIV. The tree was patients, and did not measure the effect of transmission. constructed using the NEIGHBOUR-JOINING METHOD on envelope gene-sequence data that was takenwithin from nine HIV-infected and patients48 (a total between of 1,195 sequences, 822 base hosts pairs in length), with Indeed, the fact that repeated individual adaptation was Figure 1. Internal, Backbone, and External Branches in a Within-Host HIV Genealogy, and Mean Nucleotide Substitution Rates for These Branchesthose in viruses sampled from each patient depicted by a different colour. In each case, intra-host observed in these patients indicates that the HIV popu- Nine Longitudinally Sampled HIV-1 Patients HIV evolution is characterized by continual immune-driven selection, such that there is a lation as a whole was not adapted to the host HLA dis- To avoid the influence of deleterious mutations segregating to external branches in intrapatient HIV genealogies, we estimate mean substitutionsuccessive rates selective replacement of strains through time, with relatively little genetic diversity at tribution. Moreover, although certain CTL-escape for the set of internal and backbonewithin branches. Thesehost branch sets are depicted in color in the maximum a posteriori tree for ‘‘patient 1’’ obtained byRambaut et al, 2004, Nature Rev Genet 5:52 Bayesian relaxed-clock inference [26] (backbone, red; internal, blue; external, black). The backbone represents the central trunk of trees shaped byany rapid time point. By contrast, there is little evidence for positive selection at the population level mutants can be transmitted through the population64,it lineage turnover and can be defined phylogenetically (see Methods). Note that the set of internal branches also includes the backbone branches.(bold lines connecting patients), so that multiple lineages are able to coexist at any time point. Samples for each time point are indicated by the dotted line. Mean nucleotide substitution rates and their standard deviations on internal, external, and is possible that CTL-escape mutations that are passed to A major BOTTLENECK is also likely to occur when the virus is transmitted to new hosts. backbone branches are shown for all longitudinally sampled HIV-1 patients. The consistently higher substitution rate on external branches might be indicativeLemey of higher et mutation al, 2007load on these PLoS branches. Comput Biol 3:e29 individuals with the ‘wrong’ HLA background will doi:10.1371/journal.pcbi.0030029.g001 sometimes be deleterious and removed by purifying to progression time, and three continuous parameters that be attributed to differences in time length of sampling. In selection. In summary, inter-host HIV evolution is not relate to disease progression: progression time, the rate of general, backbone dS rates before progression time seem to (neutral) spatial and temporal diffusion of the virus, merely intra-host evolution played out over a longer CD4 T cell count change over time, and the rate of log viral show little temporal fluctuation in the trees since dS þ 2 with viral lineages co-existing for extended time peri- timescale, and the evolutionary process that occurs whatload (log VL) change tree over timeshape (Figure 3). The log of can the divergence say accumulated about in a linear fashion, infection with R values dynamics 2 ods. Indeed, there is little evidence that fitness differ- within hosts will not select for viruses with enhanced backbone rate of synonymous divergence shows a strong close to 0.96, except for patient 9 (R 0.83). We also ¼ negative correlation with both progression time (Pearson investigated the heterogeneity of synonymous and non- ences determine subtype structure and distribution. For transmissibility. correlation coefficient r 0.79, p 0.011) and the change in synonymous substitution rates within the env C2V3 gene, example, experimental studies have revealed that sub- ¼À ¼ CD4 T cell count (r 0.72, p 0.028), and a moderate because strong site-to-site variation in synonymous rates has whatþ global¼À ¼ tree can tell us about the global pandemicstype C viruses consistently have lower in vitro fitness Recombination and HIV diversity. Genetic recombina- positive correlation with the change in log VL (r 0.65, p the potential to bias dS estimates [30]. Although this analysis ¼ ¼ 0.059). No significant correlations were observed for non- revealed very strong site-to-site variation in dS rates, the than those assigned to subtype B (REF.56).Although cau- tion is an integral part of the HIV lifecycle, occurring synonymous divergence rates (r 0.32, p 0.40 for inferred rate distributions were nearly identical among all tion should be shown when extrapolating from the lab- when reverse transcriptase switches between alternative ¼À ¼ progression time; r 0.54, p 0.135 for CD4 T cell count patients (Table S3). Finally, recombination rate estimates ¼À ¼ þ oratory to nature, this indicates that the high prevalence genomic templates during replication. As already men- change; r 0.14, p 0.58 for log VL change). Similar results (Table S2) did not provide any evidence that recombination ¼À ¼ of subtype C in sub-Saharan Africa is the result of its tioned, the recombination rate of HIV is one of the were obtained when divergence rates were estimated from could be the cause of the differences between dS estimates. Nicolas Lartillotinternal branches (CNRS (Figure S2). - InUniv. contrastLyon to backbone 1) and The variability in dS rates couldCoalescent reflect differences in either chance entry into populationsMay with26, high rates 2014 of partner 3highest / 39 of all organisms, with an estimated three recom- internal rates, no significant correlations were observed for viral mutation rate or viral generation time, but only the exchange. However, it is unclear whether the success of bination events occurring per genome per replication both dS and dN rates on external branches. Similar results latter provides a likely explanation for the correlation HIV-1 group M, relative to groups N and O, is the result cycle65, thereby exceeding the mutation rate per replica- were also obtained when datasets were restricted to samples between dS rates and disease progression. However,NEIGHBOUR-JOINING viral METHOD up to about 70 months after seroconversion (Figure S3), generation time is also expected to affect dN rates to some of some intrinsic property of the virus that enhances tion.
Load more

Recommended publications
  • Population Size and the Rate of Evolution
    Review Population size and the rate of evolution 1,2 1 3 Robert Lanfear , Hanna Kokko , and Adam Eyre-Walker 1 Ecology Evolution and Genetics, Research School of Biology, Australian National University, Canberra, ACT, Australia 2 National Evolutionary Synthesis Center, Durham, NC, USA 3 School of Life Sciences, University of Sussex, Brighton, UK Does evolution proceed faster in larger or smaller popu- mutations occur and the chance that each mutation lations? The relationship between effective population spreads to fixation. size (Ne) and the rate of evolution has consequences for The purpose of this review is to synthesize theoretical our ability to understand and interpret genomic varia- and empirical knowledge of the relationship between tion, and is central to many aspects of evolution and effective population size (Ne, Box 1) and the substitution ecology. Many factors affect the relationship between Ne rate, which we term the Ne–rate relationship (NeRR). A and the rate of evolution, and recent theoretical and positive NeRR implies faster evolution in larger popula- empirical studies have shown some surprising and tions relative to smaller ones, and a negative NeRR implies sometimes counterintuitive results. Some mechanisms the opposite (Figure 1A,B). Although Ne has long been tend to make the relationship positive, others negative, known to be one of the most important factors determining and they can act simultaneously. The relationship also the substitution rate [5–8], several novel predictions and depends on whether one is interested in the rate of observations have emerged in recent years, causing some neutral, adaptive, or deleterious evolution. Here, we reassessment of earlier theory and highlighting some gaps synthesize theoretical and empirical approaches to un- in our understanding.
    [Show full text]
  • Joint Effects of Genetic Hitchhiking and Background Selection on Neutral Variation
    Copyright 2000 by the Genetics Society of America Joint Effects of Genetic Hitchhiking and Background Selection on Neutral Variation Yuseob Kim and Wolfgang Stephan Department of Biology, University of Rochester, Rochester, New York 14627 Manuscript received December 7, 1999 Accepted for publication March 20, 2000 ABSTRACT Due to relatively high rates of strongly selected deleterious mutations, directional selection on favorable alleles (causing hitchhiking effects on linked neutral polymorphisms) is expected to occur while a deleteri- ous mutation-selection balance is present in a population. We analyze this interaction of directional selection and background selection and study their combined effects on neutral variation, using a three- locus model in which each locus is subjected to either deleterious, favorable, or neutral mutations. Average heterozygosity is measured by simulations (1) at the stationary state under the assumption of recurrent hitchhiking events and (2) as a transient level after a single hitchhiking event. The simulation results are compared to theoretical predictions. It is shown that known analytical solutions describing the hitchhiking effect without background selection can be modi®ed such that they accurately predict the joint effects of hitchhiking and background on linked, neutral variation. Generalization of these results to a more appro- priate multilocus model (such that background selection can occur at multiple sites) suggests that, in regions of very low recombination rates, stationary levels of nucleotide diversity are primarily determined by hitchhiking, whereas in regions of high recombination, background selection is the dominant force. The implications of these results on the identi®cation and estimation of the relevant parameters of the model are discussed.
    [Show full text]
  • Coalescent Likelihood Methods
    Coalescent Likelihood Methods Mary K. Kuhner Genome Sciences University of Washington Seattle WA Outline 1. Introduction to coalescent theory 2. Practical example 3. Genealogy samplers 4. Break 5. Survey of samplers 6. Evolutionary forces 7. Practical considerations Population genetics can help us to find answers We are interested in questions like { How big is this population? { Are these populations isolated? How common is migration? { How fast have they been growing or shrinking? { What is the recombination rate across this region? { Is this locus under selection? All of these questions require comparison of many individuals. Coalescent-based studies How many gray whales were there prior to whaling? When was the common ancestor of HIV lines in a Libyan hospital? Is the highland/lowland distinction in Andean ducks recent or ancient? Did humans wipe out the Beringian bison population? What proportion of HIV virions in a patient actually contribute to the breeding pool? What is the direction of gene flow between European rabbit populations? Basics: Wright-Fisher population model All individuals release many gametes and new individuals for the next generation are formed randomly from these. Wright-Fisher population model Population size N is constant through time. Each individual gets replaced every generation. Next generation is drawn randomly from a large gamete pool. Only genetic drift affects the allele frequencies. Other population models Other population models can often be equated to Wright-Fisher The N parameter becomes the effective
    [Show full text]
  • Natural Selection and Coalescent Theory
    Natural Selection and Coalescent Theory John Wakeley Harvard University INTRODUCTION The story of population genetics begins with the publication of Darwin’s Origin of Species and the tension which followed concerning the nature of inheritance. Today, workers in this field aim to understand the forces that produce and maintain genetic variation within and between species. For this we use the most direct kind of genetic data: DNA sequences, even entire genomes. Our “Great Obsession” with explaining genetic variation (Gillespie, 2004a) can be traced back to Darwin’s recognition that natural selection can occur only if individuals of a species vary, and this variation is heritable. Darwin might have been surprised that the importance of natural selection in shaping variation at the molecular level would be de-emphasized, beginning in the late 1960s, by scientists who readily accepted the fact and importance of his theory (Kimura, 1983). The motivation behind this chapter is the possible demise of this Neutral Theory of Molecular Evolution, which a growing number of population geneticists feel must follow recent observations of genetic variation within and between species. One hundred fifty years after the publication of the Origin, we are struggling to fully incorporate natural selection into the modern, genealogical models of population genetics. The main goal of this chapter is to present the mathematical models that have been used to describe the effects of positive selective sweeps on genetic variation, as mediated by gene genealogies, or coalescent trees. Background material, comprised of population genetic theory and simulation results, is provided in order to facilitate an understanding of these models.
    [Show full text]
  • Population Genetics: Wright Fisher Model and Coalescent Process
    POPULATION GENETICS: WRIGHT FISHER MODEL AND COALESCENT PROCESS by Hailong Cui and Wangshu Zhang Superviser: Prof. Quentin Berger A Final Project Report Presented In Partial Fulfillment of the Requirements for Math 505b April 2014 Acknowledgments We want to thank Prof. Quentin Berger for introducing to us the Wright Fisher model in the lecture, which inspired us to choose Population Genetics for our project topic. The resources Prof. Berger provided us have been excellent learning mate- rials and his feedback has helped us greatly to create this report. We also like to acknowledge that the research papers (in the reference) are integral parts of this process. They have motivated us to learn more about models beyond the class, and granted us confidence that these probabilistic models can actually be used for real applications. ii Contents Acknowledgments ii Abstract iv 1 Introduction to Population Genetics 1 2 Wright Fisher Model 2 2.1 Random drift . 2 2.2 Genealogy of the Wright Fisher model . 4 3 Coalescent Process 8 3.1 Kingman’s Approximation . 8 4 Applications 11 Reference List 12 iii Abstract In this project on Population Genetics, we aim to introduce models that lay the foundation to study more complicated models further. Specifically we will discuss Wright Fisher model as well as Coalescent process. The reason these are of our inter- est is not just the mathematical elegance of these models. With the availability of massive amount of sequencing data, we actually can use these models (or advanced models incorporating variable population size, mutation effect etc, which are how- ever out of the scope of this project) to solve and answer real questions in molecular biology.
    [Show full text]
  • Frontiers in Coalescent Theory: Pedigrees, Identity-By-Descent, and Sequentially Markov Coalescent Models
    Frontiers in Coalescent Theory: Pedigrees, Identity-by-Descent, and Sequentially Markov Coalescent Models The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Wilton, Peter R. 2016. Frontiers in Coalescent Theory: Pedigrees, Identity-by-Descent, and Sequentially Markov Coalescent Models. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences. Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:33493608 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA Frontiers in Coalescent Theory: Pedigrees, Identity-by-descent, and Sequentially Markov Coalescent Models a dissertation presented by Peter Richard Wilton to The Department of Organismic and Evolutionary Biology in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Biology Harvard University Cambridge, Massachusetts May 2016 ©2016 – Peter Richard Wilton all rights reserved. Thesis advisor: Professor John Wakeley Peter Richard Wilton Frontiers in Coalescent Theory: Pedigrees, Identity-by-descent, and Sequentially Markov Coalescent Models Abstract The coalescent is a stochastic process that describes the genetic ancestry of individuals sampled from a population. It is one of the main tools of theoretical population genetics and has been used as the basis of many sophisticated methods of inferring the demo- graphic history of a population from a genetic sample. This dissertation is presented in four chapters, each developing coalescent theory to some degree.
    [Show full text]
  • Detecting the Form of Selection from DNA Sequence Data
    Outlook COMMENT Male:female mutation ratio muscular dystrophy, neurofibromatosis and retinoblast- outcome of further investigation, this unique transposition oma), there is a large male excess. So, I believe the evidence event and perhaps others like it are sure to provide impor- is convincing that the male base-substitution rate greatly tant cytogenetic and evolutionary insights. exceeds the female rate. There are a number of reasons why the historical Acknowledgements male:female mutation ratio is likely to be less than the I have profited greatly from discussions with my colleagues current one, the most important being the lower age of B. Dove, B. Engels, C. Denniston and M. Susman. My reproduction in the earliest humans and their ape-like greatest debt is to D. Page for a continuing and very fruitful ancestors. More studies are needed. But whatever the dialog and for providing me with unpublished data. References Nature 406, 622–625 6 Engels, W.R. et al. (1994) Long-range cis preference in DNA 1 Vogel, F. and Motulsky, A. G. (1997) Human Genetics: 4 Shimmin, L.C. et al. (1993) Potential problems in estimating homology search over the length of a Drosophila chromosome. Problems and Approaches. Springer-Verlag the male-to-female mutation rate ratio from DNA sequence Science 263, 1623–1625 2 Crow, J. F. (1997) The high spontaneous mutation rate: is it a data. J. Mol. Evol. 37, 160–166 7 Crow, J.F. (2000) The origins, patterns and implications of health risk? Proc. Natl. Acad. Sci. U. S. A. 94, 8380–8386 5 Richardson, C. et al.
    [Show full text]
  • Variation in Meiosis, Across Genomes, and in Populations
    REVIEW Identity by Descent: Variation in Meiosis, Across Genomes, and in Populations Elizabeth A. Thompson1 Department of Statistics, University of Washington, Seattle, Washington 98195-4322 ABSTRACT Gene identity by descent (IBD) is a fundamental concept that underlies genetically mediated similarities among relatives. Gene IBD is traced through ancestral meioses and is defined relative to founders of a pedigree, or to some time point or mutational origin in the coalescent of a set of extant genes in a population. The random process underlying changes in the patterns of IBD across the genome is recombination, so the natural context for defining IBD is the ancestral recombination graph (ARG), which specifies the complete ancestry of a collection of chromosomes. The ARG determines both the sequence of coalescent ancestries across the chromosome and the extant segments of DNA descending unbroken by recombination from their most recent common ancestor (MRCA). DNA segments IBD from a recent common ancestor have high probability of being of the same allelic type. Non-IBD DNA is modeled as of independent allelic type, but the population frame of reference for defining allelic independence can vary. Whether of IBD, allelic similarity, or phenotypic covariance, comparisons may be made to other genomic regions of the same gametes, or to the same genomic regions in other sets of gametes or diploid individuals. In this review, I present IBD as the framework connecting evolutionary and coalescent theory with the analysis of genetic data observed on individuals. I focus on the high variance of the processes that determine IBD, its changes across the genome, and its impact on observable data.
    [Show full text]
  • 1 1 Title: Background Selection and the Statistics of Population Differentiation: 2 Consequences for Detecting Local
    bioRxiv preprint doi: https://doi.org/10.1101/326256; this version posted May 24, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 2 Title: Background selection and the statistics of population differentiation: 3 consequences for detecting local adaptation 4 5 Authors: Remi Matthey-Doret1 and Michael C. Whitlock 6 7 Affiliation: Department of Zoology and Biodiversity Research Centre, University of 8 British Columbia, Vancouver, British Columbia V6T 1Z4, Canada 9 10 1 Corresponding author: [email protected] 11 12 1 bioRxiv preprint doi: https://doi.org/10.1101/326256; this version posted May 24, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 13 Abstract 14 Background selection is a process whereby recurrent deleterious mutations cause a 15 decrease in the effective population size and genetic diversity at linked loci. Several 16 authors have suggested that variation in the intensity of background selection could 17 cause variation in FST across the genome, which could confound signals of local 18 adaptation in genome scans. We performed realistic simulations of DNA sequences, 19 using parameter estimates from humans and sticklebacks, to investigate how 20 variation in the intensity of background selection affects different statistics of 21 population differentiation.
    [Show full text]
  • Arxiv:1302.1148V1 [Q-Bio.PE] 5 Feb 2013 I
    Genetic draft, selective interference, and population genetics of rapid adaptation Richard A. Neher Max Planck Institute for Developmental Biology, T¨ubingen, 72070, Germany. [email protected] (Dated: January 8, 2018) To learn about the past from a sample of genomic sequences, one needs to understand how evolutionary processes shape genetic diversity. Most population genetic inference is based on frameworks assuming adaptive evolution is rare. But if positive selection operates on many loci simultaneously, as has recently been suggested for many species including animals such as flies, a different approach is necessary. In this review, I discuss recent progress in characterizing and understanding evolution in rapidly adapting populations where random associations of mutations with genetic backgrounds of different fitness, i.e., genetic draft, dominate over genetic drift. As a result, neutral genetic diversity depends weakly on population size, but strongly on the rate of adaptation or more generally the variance in fitness. Coalescent processes with multiple merg- ers, rather than Kingman's coalescent, are appropriate genealogical models for rapidly adapting populations with important implications for population genetic inference. Contents I. Introduction 1 II. Adaptation of large and diverse asexual populations 2 A. Clonal Interference 3 B. Genetic background and multiple mutations 5 III. Evolution of facultatively sexual populations 6 IV. Selective interference in obligately sexual organisms 8 V. Genetic diversity, draft, and coalescence 9 A. Genetic draft in obligately sexual populations 9 B. Soft sweeps 10 C. The Bolthausen-Sznitman coalescent and rapidly adapting populations 11 D. Background selection and genetic diversity 11 VI. Conclusions and future directions 12 Acknowledgments 13 References 13 A.
    [Show full text]
  • Rapid Estimation of SNP Heritability Using Predictive Process Approximation in Large Scale Cohort Studies
    bioRxiv preprint doi: https://doi.org/10.1101/2021.05.12.443931; this version posted May 14, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Rapid Estimation of SNP Heritability using Predictive Process approximation in Large scale Cohort Studies Souvik Seal1, Abhirup Datta2, and Saonli Basu3 1Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus 2Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health 3Division of Biostatistics, University of Minnesota Twin Cities Abstract With the advent of high throughput genetic data, there have been attempts to estimate heritability from genome-wide SNP data on a cohort of distantly related individuals using linear mixed model (LMM). Fitting such an LMM in a large scale cohort study, however, is tremendously challenging due to its high dimensional linear algebraic operations. In this paper, we propose a new method named PredLMM approximating the aforementioned LMM moti- vated by the concepts of genetic coalescence and gaussian predictive process. PredLMM has substantially better computational complexity than most of the existing LMM based methods and thus, provides a fast alternative for estimating heritability in large scale cohort studies. Theoretically, we show that under a model of genetic coalescence, the limiting form of our approximation is the celebrated predictive process approximation of large gaussian process likelihoods that has well-established accuracy standards. We illustrate our approach with ex- tensive simulation studies and use it to estimate the heritability of multiple quantitative traits from the UK Biobank cohort.
    [Show full text]
  • Genomic Insights Into Positive Selection
    Review TRENDS in Genetics Vol.22 No.8 August 2006 Genomic insights into positive selection Shameek Biswas and Joshua M. Akey Department of Genome Sciences, University of Washington, 1705 NE Pacific, Seattle, WA 98195, USA The traditional way of identifying targets of adaptive more utilitarian benefits, each target of positive selection evolution has been to study a few loci that one has a story to tell about the historical forces and events hypothesizes a priori to have been under selection. that have shaped the history of a population. This approach is complicated because of the confound- Several genome-wide analyses for positive selection ing effects that population demographic history and have been performed in a variety of species. In this review, selection have on patterns of DNA sequence variation. In we summarize some of the recent studies, primarily principle, multilocus analyses can facilitate robust focusing on humans, critically evaluate what genome- inferences of selection at individual loci. The deluge of wide scans for selection are and are not likely to find and large-scale catalogs of genetic variation has stimulated suggest future avenues of research. A brief overview of many genome-wide scans for positive selection in statistical methods used to detect deviations from several species. Here, we review some of the salient neutrality is summarized in Box 1. For more detailed observations of these studies, identify important chal- discussions, see Refs [6,7]. lenges ahead, consider the limitations of genome-wide scans for selection and discuss the potential significance Thinking genomically of a comprehensive understanding of genomic patterns Positive selection perturbs patterns of genetic variation of selection for disease-related research.
    [Show full text]