Robustness of linkage maps in natural populations: a simulation study

Jon Slate 0 1 0 One contribution of 18 to a Special Issue 'Evolutionary dynamics of wild populations' 1 Department of Animal and Plant Sciences, University of Sheffield , Western Bank, Sheffield S10 2TN , UK In a number of long-term individual-based studies of vertebrate populations, the genealogical relationships between individuals have been established with molecular markers. As a result, it is possible to construct genetic linkage maps of these study populations by examining the co-segregation of markers through the pedigree. There are now four free-living vertebrate study populations for whom linkage maps have been built. In this study, simulation was used to investigate whether these linkage maps are likely to be accurate. In all four populations, the probability of assigning markers to the correct chromosome is high and framework maps are generally inferred correctly. However, genotyping error can result in incorrect maps being built with very strong statistical support over the correct order. Future applications of linkage maps of natural populations are discussed. 1. INTRODUCTION The last decade has witnessed a dramatic advance in evolutionary genetic studies of pedigreed natural populations of vertebrates. The principal reasons for this development are (i) the maturation of individual-based long-term study systems such that datasets are sufficiently large to undertake complex statistical analyses, (ii) the relative ease with which pedigrees can be inferred using molecular markers (Garant & Kruuk 2005; Pemberton 2008), and (iii) the uptake of the animal model approach to quantitative genetic studies (Kruuk 2004). Despite the logistical and analytical difficulties involved with inferring quantitative genetic parameters in natural populations, considerable success has been achieved in this area (Boag & Grant 1978), particularly since the animal model was first used to estimate the heritability of fitness traits in the wild (Reale et al. 1999; Kruuk et al. 2000). These pioneering studies paved the way to sophisticated examinations of the processes that determine (or constrain) microevolutionary changes (Kruuk et al. 2002), including investigations into gene by environmental variation (Merila et al. 2001; Charmantier & Garant 2005; Nussey et al. 2005; Wilson et al. 2006) and the role of genetic correlations between the traits (Sheldon et al. 2003) and sexes (Foerster et al. 2007). There is no doubt that pedigree-based studies of natural populations have contributed enormously to current understanding of the evolutionary process. However, quantitative genetic studies cannot pinpoint the loci responsible for phenotypic variation. One way in which loci of adaptive significance in natural populations can be identified is through linkage mapping studies (Slate 2005). Here, a suite of mapped markers that span the genome at roughly evenly spaced intervals are typed in a panel of related individuals, and the * (). presence of a quantitative trait locus (QTL) is inferred by co-segregation between marker alleles and phenotypic trait values. Map construction is possible only if large numbers of markers and a well-resolved pedigree comprising at least several hundred individuals are available, otherwise it is difficult to infer the correct marker order of closely linked markers. Mapping in natural populations is further complicated by the fact that marker phase can be difficult to infer when only one parent is known or when sibships are small. Therefore, most linkage maps have been constructed from specially created crosses in model (Lister & Dean 1993) or agriculturally important (Kappes et al. 1997; Groenen et al. 2000) organisms or from human pedigrees (Dib et al. 1996). More recently, linkage maps have now been constructed in four populations for which long-term individual-based datasets are available (table 1), and where natural pedigrees (rather than experimental breeding programmes) have been used to follow the co-segregation of marker alleles. Two of these mapping populations are in ungulate species (Slate et al. 2002b; Beraldi et al. 2006) and two are in passerine birds (Hansson et al. 2005; Backstr om et al. 2006a). There are several motivations for developing linkage maps in natural populations, but these can be categorized into addressing two types of broad question. First, there are questions relating to the evolution of genomes, karyotypes or recombination rates. For example, maps of related organisms can be compared to infer how genomes or karyotypes differ and the evolutionary explanations for such differences (Backstr om et al. 2006a; Dawson et al. 2007). Similarly, one might construct sex-specific linkage maps in order to detect and understand sex differences in recombination rate (heterochiasmy; Hansson et al. 2005). These questions directly consider map features such as gene order and chromosome lengths, both of which are properties of the population under study rather than of individuals. The second broad application of maps is to identify genomic regions that explain phenotypic variation between Downloaded from http://rspb.royalsocietypublishing.org/ on November 12, 2014 696 J. Slate Robustness of linkage maps Great reed warblers Acrocephalus arundinaceus Collared flycatchers Soay sheep Ficedula albicollis Ovis aries location Lake Kvismaren, Sweden N 812a generations 6 marker type microsatellelites & AFLPs number of markers 58Mb; 142 (59Mb/83Ab); 103 (53Mb/ 50Ab) reference Hansson et al. (2005); A kesson et al. (2007); Dawson et al. (2007) Beraldi et al. (2006) Slate et al. (2002b) a Current mapping panel comprises 1024 birds (value used in simulations). b M denotes microsatellites and A denotes AFLPs. c 53 SNPs typed across 23 genes. Intragenic SNPs scored as single locus haplotypes for linkage mapping such that 23 loci were mapped. d A small number of typed markers were allozymes (four in Soay sheep and three in red deer). individuals. Most obviously, linkage mapping can be used to identify loci responsible for variation at simple Mendelian (Beraldi et al. 2006; Gratten et al. 2007) or polygenic (Slate et al. 2002b; Beraldi et al. 2007a,b) traits. There are alternative approaches to identifying loci that explain trait variation. For example, association (or linkage disequilibrium) mapping does not (usually) require a pedigree to identify loci responsible for phenotypic variation, while heterozygosityfitness correlation studies may detect genomic regions where heterozygote advantage or associative overdominance is present (Hansson & Westerberg 2002). However, the inferences that can be made from these approaches are greatly limited without a map; indeed, linkage maps are useful tools to establish whether levels of linkage disequilibrium are sufficient to attempt association mapping in a natural population (Backstro m et al. 2006b; Slate & Pemberton 2007). In this second category of map-based analysis, the map is simply a tool to aid detect (...truncated)