PCA-correlated SNPs for structure identification in worldwide human populations

Existing methods to ascertain small sets of markers for the identification of human population structure require prior knowledge of individual ancestry. Based on Principal Components Analysis (PCA), and recent results in theoretical computer science, we present a novel algorithm that, applied on genomewide data, selects small subsets of SNPs (PCA-correlated SNPs) to reproduce the structure found by PCA on the complete dataset, without use of ancestry information. Evaluating our method on a previously described dataset (10,805 SNPs, 11 populations), we demonstrate that a very small set of PCA-correlated SNPs can be effectively employed to assign individuals to particular continents or populations, using a simple clustering algorithm. We validate our methods on the HapMap populations and achieve perfect intercontinental differentiation with 14 PCA-correlated SNPs. The Chinese and Japanese populations can be easily differentiated using less than 100 PCA-correlated SNPs ascertained after evaluating 1.7 million SNPs from HapMap. We show that, in general, structure informative SNPs are not portable across geographic regions. However, we manage to identify a general set of 50 PCA-correlated SNPs that effectively assigns individuals to one of nine different populations. Compared to analysis with the measure of informativeness, our methods, although unsupervised, achieved similar results. We proceed to demonstrate that our algorithm can be effectively used for the analysis of admixed populations without having to trace the origin of individuals. Analyzing a Puerto Rican dataset (192 individuals, 7,257 SNPs), we show that PCA-correlated SNPs can be used to successfully predict structure and ancestry proportions. We subsequently validate these SNPs for structure identification in an independent Puerto Rican dataset. The algorithm that we introduce runs in seconds and can be easily applied on large genome-wide datasets, facilitating the identification of population substructure, stratification assessment in multi-stage whole-genome association studies, and the study of demographic history in human populations.     

Integrative analysis of single nucleotide polymorphisms and gene expression efficiently distinguishes samples from closely related ethnic populations

ABSTRACT: BACKGROUND: Ancestry informative markers (AIMs) are a type of genetic marker that is informative for tracing the ancestral ethnicity of individuals. Application of AIMs has gained substantial attention in population genetics, forensic sciences, and medical genetics. Single nucleotide polymorphisms (SNPs), the materials of AIMs, are useful for classifying individuals from distinct continental origins but cannot discriminate individuals with subtle genetic differences from closely related ancestral lineages. Proof-of-principle studies have shown that gene expression (GE) also is a heritable human variation that exhibits differential intensity distributions among ethnic groups. GE supplies ethnic information supplemental to SNPs; this motivated us to integrate SNP and GE markers to construct AIM panels with a reduced number of required markers and provide high accuracy in ancestry inference. Few studies in the literature have considered GE in this aspect, and none have integrated SNP and GE markers to aid classification of samples from closely related ethnic populations. RESULTS: We integrated a forward variable selection procedure into flexible discriminant analysis to identify key SNP and/or GE markers with the highest cross-validation prediction accuracy. By analyzing genome-wide SNP and/or GE markers in 210 independent samples from four ethnic groups in the HapMap II Project, we found that average testing accuracies for a majority of classification analyses were quite high, except for SNP-only analyses that were performed to discern study samples containing individuals from two close Asian populations. The average testing accuracies ranged from 0.53 to 0.79 for SNP-only analyses and increased to around 0.90 when GE markers were integrated together with SNP markers for the classification of samples from closely related Asian populations. Compared to GE-only analyses, integrative analyses of SNP and GE markers showed comparable testing accuracies and a reduced number of selected markers in AIM panels. CONCLUSIONS: Integrative analysis of SNP and GE markers provides high-accuracy and/or cost-effective classification results for assigning samples from closely related or distantly related ancestral lineages to their original ancestral populations. User-friendly BIASLESS (Biomarkers Identification and Samples Subdivision) software was developed as an efficient tool for selecting key SNP and/or GE markers and then building models for sample subdivision. BIASLESS was programmed in R and R-GUI and is available online at http://www.stat.sinica.edu.tw/hsinchou/genetics/prediction/BIASLESS.htm.     

Haplotypes of single cancer driver genes and their local ancestry in a highly admixed long-lived population of Northeast Brazil

Admixed populations have not been examined in detail in cancer genetic studies. Here, we inferred the local ancestry of cancer-associated single nucleotide polymorphisms (SNPs) and haplotypes of a highly admixed Brazilian population. SNP array was used to genotype 73 unrelated individuals aged 80-102 years. Local ancestry inference was performed by merging genotyped regions with phase three data from the 1000 Genomes Project Consortium using RFmix. The average ancestry tract length was 9.12-81.71 megabases. Strong linkage disequilibrium was detected in 48 haplotypes containing 35 SNPs in 10 cancer driver genes. All together, 19 risk and eight protective alleles were identified in 23 out of 48 haplotypes. Homozygous individuals were mainly of European ancestry, whereas heterozygotes had at least one Native American and one African ancestry tract. Native-American ancestry for homozygous individuals with risk alleles for HNF1B, CDH1, and BRCA1 was inferred for the first time. Results indicated that analysis of SNP polymorphism in the present admixed population has a high potential to identify new ancestry-associated alleles and haplotypes that modify cancer susceptibility differentially in distinct human populations. Future case-control studies with populations with a complex history of admixture could help elucidate ancestry-associated biological differences in cancer incidence and therapeutic outcomes.     

Tracing sub-structure in the European American population with PCA-informative markers

Genetic structure in the European American population reflects waves of migration and recent gene flow among different populations. This complex structure can introduce bias in genetic association studies. Using Principal Components Analysis (PCA), we analyze the structure of two independent European American datasets (1,521 individuals-307,315 autosomal SNPs). Individual variation lies across a continuum with some individuals showing high degrees of admixture with non-European populations, as demonstrated through joint analysis with HapMap data. The CEPH Europeans only represent a small fraction of the variation encountered in the larger European American datasets we studied. We interpret the first eigenvector of this data as correlated with ancestry, and we apply an algorithm that we have previously described to select PCA-informative markers (PCAIMs) that can reproduce this structure. Importantly, we develop a novel method that can remove redundancy from the selected SNP panels and show that we can effectively remove correlated markers, thus increasing genotyping savings. Only 150-200 PCAIMs suffice to accurately predict fine structure in European American datasets, as identified by PCA. Simulating association studies, we couple our method with a PCA-based stratification correction tool and demonstrate that a small number of PCAIMs can efficiently remove false correlations with almost no loss in power. The structure informative SNPs that we propose are an important resource for genetic association studies of European Americans. Furthermore, our redundancy removal algorithm can be applied on sets of ancestry informative markers selected with any method in order to select the most uncorrelated SNPs, and significantly decreases genotyping costs.     

Genetic Population Structure Analysis in New Hampshire Reveals Eastern European Ancestry

Genetic structure due to ancestry has been well documented among many divergent human populations. However, the ability to associate ancestry with genetic substructure without using supervised clustering has not been explored in more presumably homogeneous and admixed US populations. The goal of this study was to determine if genetic structure could be detected in a United States population from a single state where the individuals have mixed European ancestry. Using Bayesian clustering with a set of 960 single nucleotide polymorphisms (SNPs) we found evidence of population stratification in 864 individuals from New Hampshire that can be used to differentiate the population into six distinct genetic subgroups. We then correlated self-reported ancestry of the individuals with the Bayesian clustering results. Finnish and Russian/Polish/Lithuanian ancestries were most notably found to be associated with genetic substructure. The ancestral results were further explained and substantiated using New Hampshire census data from 1870 to 1930 when the largest waves of European immigrants came to the area. We also discerned distinct patterns of linkage disequilibrium (LD) between the genetic groups in the growth hormone receptor gene (GHR). To our knowledge, this is the first time such an investigation has uncovered a strong link between genetic structure and ancestry in what would otherwise be considered a homogenous US population.     

A genomewide single-nucleotide-polymorphism panel with high ancestry information for African American admixture mapping

Admixture mapping requires a genomewide panel of relatively evenly spaced markers that can distinguish the ancestral origins of chromosomal segments in admixed individuals. Through use of the results of the International HapMap Project and specific selection criteria, the current study has examined the ability of selected single-nucleotide polymorphisms (SNPs) to extract continental ancestry information in African American subjects and to explore parameters for admixture mapping. Genotyping of two linguistically diverse West African populations (Bini and Kanuri Nigerians, who are Niger-Congo [Bantu] and Nilo-Saharan speakers, respectively), European Americans, and African Americans validated a genomewide set of >4,000 SNP ancestry-informative markers with mean and median F(ST) values >0.59 and mean and median Fisher's information content >2.5. This set of SNPs extracted a larger amount of ancestry information in African Americans than previously reported SNP panels and provides nearly uniform coverage of the genome. Moreover, in the current study, simulations show that this more informative panel improves power for admixture mapping in African Americans when ethnicity risk ratios are modest. This is particularly important in the application of admixture mapping in complex genetic diseases for which only modest ethnicity risk ratios of relevant susceptibility genes are expected.     

SNP discovery in wild and domesticated populations of blue catfish,Ictalurus furcatus,using genotyping‐by‐sequencing and subsequent SNP validation

Blue catfish, Ictalurus furcatus, are valued in the United States as a trophy fishery for their capacity to reach large sizes, sometimes exceeding 45 kg. Additionally, blue catfish × channel catfish (I. punctatus) hybrid food fish production has recently increased the demand for blue catfish broodstock. However, there has been little study of the genetic impacts and interaction of farmed, introduced and stocked populations of blue catfish. We utilized genotyping‐by‐sequencing (GBS) to capture and genotype SNP markers on 190 individuals from five wild and domesticated populations (Mississippi River, Missouri, D&B, Rio Grande and Texas). Stringent filtering of SNP‐calling parameters resulted in 4275 SNP loci represented across all five populations. Population genetics and structure analyses revealed potential shared ancestry and admixture between populations. We utilized the Sequenom MassARRAY to validate two multiplex panels of SNPs selected from the GBS data. Selection criteria included SNPs shared between populations, SNPs specific to populations, number of reads per individual and number of individuals genotyped by GBS. Putative SNPs were validated in the discovery population and in two additional populations not used in the GBS analysis. A total of 64 SNPs were genotyped successfully in 191 individuals from nine populations. Our results should guide the development of highly informative, flexible genotyping multiplexes for blue catfish from the larger GBS SNP set as well as provide an example of a rapid, low‐cost approach to generate and genotype informative marker loci in aquatic species with minimal previous genetic information.     

The effect of SNP discovery method and sample size on estimation of population genetic data for Chinese and Indian rhesus macaques (<Emphasis Type="Italic">Macaca mulatta</Emphasis>)

This study was designed to address issues regarding sample size and marker location that have arisen from the discovery of SNPs in the genomes of poorly characterized primate species and the application of these markers to the study of primate population genetics. We predict the effect of discovery sample size on the probability of discovering both rare and common SNPs and then compare this prediction with the proportion of common and rare SNPs discovered when different numbers of individuals are sequenced. Second, we examine the effect of genomic region on estimates of common population genetic data, comparing markers from both coding and non-coding regions of the rhesus macaque genome and the population genetic data calculated from these markers, to measure the degree and direction of bias introduced by SNPs located in coding versus non-coding regions of the genome. We found that both discovery sample size and genomic region surveyed affect SNP marker attributes and population genetic estimates, even when these are calculated from an expanded data set containing more individuals than the original discovery data set. Although none of the SNP detection methods or genomic regions tested in this study was completely uninformative, these results show that each has a different kind of genetic variation that is suitable for different purposes, and each introduces specific types of bias. Given that each SNP marker has an individual evolutionary history, we calculated that the most complete and unbiased representation of the genetic diversity present in the individual can be obtained by incorporating at least 10 individuals into the discovery sample set, to ensure the discovery of both common and rare polymorphisms.     

Ancestral informative marker selection and population structure visualization using sparse Laplacian eigenfunctions

Identification of a small panel of population structure informative markers can reduce genotyping cost and is useful in various applications, such as ancestry inference in association mapping, forensics and evolutionary theory in population genetics. Traditional methods to ascertain ancestral informative markers usually require the prior knowledge of individual ancestry and have difficulty for admixed populations. Recently Principal Components Analysis (PCA) has been employed with success to select SNPs which are highly correlated with top significant principal components (PCs) without use of individual ancestral information. The approach is also applicable to admixed populations. Here we propose a novel approach based on our recent result on summarizing population structure by graph laplacian eigenfunctions, which differs from PCA in that it is geometric and robust to outliers. Our approach also takes advantage of the priori sparseness of informative markers in the genome. Through simulation of a ring population and the real global population sample HGDP of 650K SNPs genotyped in 940 unrelated individuals, we validate the proposed algorithm at selecting most informative markers, a small fraction of which can recover the similar underlying population structure efficiently. Employing a standard Support Vector Machine (SVM) to predict individuals' continental memberships on HGDP dataset of seven continents, we demonstrate that the selected SNPs by our method are more informative but less redundant than those selected by PCA. Our algorithm is a promising tool in genome-wide association studies and population genetics, facilitating the selection of structure informative markers, efficient detection of population substructure and ancestral inference.     

Signatures of adaptation and genetic structure among the mainland populations of Pinus radiata (D. Don) inferred from SNP loci

Insights into the relative contributions of locus specific and genome-wide effects on population genetic diversity can be gained through separation of their resulting genetic signals. Here we explore patterns of adaptive and neutral genetic diversity in the disjunct natural populations of Pinus radiata (D. Don) from mainland California. A first-generation common garden of 447 individuals revealed significant differentiation of wood phenotypes among populations (P _ST), possibly reflecting local adaptation in response to environment. We subsequently screened all trees for genetic diversity at 149 candidate gene single nucleotide polymorphism (SNP) loci for signatures of adaptation. Ten loci were identified as being possible targets of diversifying selection following F _ST outlier tests. Multivariate canonical correlation performed on a data set of 444 individuals identified significant covariance between environment, adaptive phenotypes and outlier SNP diversity, lending support to the case for local adaptation suggested from F _ST and P _ST tests. Covariation among discrete sets of outlier SNPs and adaptive phenotypes (inferred from multivariate loadings) with environment are supported by existing studies of candidate gene function and genotype–phenotype association. Canonical analyses failed to detect significant correlations between environment and 139 non-outlier SNP loci, which were applied to estimate neutral patterns of genetic differentiation among populations (F _ST 4.3 %). Using this data set, significant hierarchical structure was detected, indicating three populations on the mainland. The hierarchical relationships based on neutral SNP markers (and SSR) were in contrast with those inferred from putatively adaptive loci, potentially highlighting the independent action of selection and demography in shaping genetic structure in this species.     

Informativeness of genetic markers for inference of ancestry

Inference of individual ancestry is useful in various applications, such as admixture mapping and structured-association mapping. Using information-theoretic principles, we introduce a general measure, the informativeness for assignment (I(n)), applicable to any number of potential source populations, for determining the amount of information that multiallelic markers provide about individual ancestry. In a worldwide human microsatellite data set, we identify markers of highest informativeness for inference of regional ancestry and for inference of population ancestry within regions; these markers, which are listed in online-only tables in our article, can be useful both in testing for and in controlling the influence of ancestry on case-control genetic association studies. Markers that are informative in one collection of source populations are generally informative in others. Informativeness of random dinucleotides, the most informative class of microsatellites, is five to eight times that of random single-nucleotide polymorphisms (SNPs), but 2%-12% of SNPs have higher informativeness than the median for dinucleotides. Our results can aid in decisions about the type, quantity, and specific choice of markers for use in studies of ancestry.     

Genomic diversity,population structure,and migration following rapid range expansion in the Balsam Poplar,Populus balsamifera

Rapid range expansions can cause pervasive changes in the genetic diversity and structure of populations. The postglacial history of the Balsam Poplar, Populus balsamifera, involved the colonization of most of northern North America, an area largely covered by continental ice sheets during the last glacial maximum. To characterize how this expansion shaped genomic diversity within and among populations, we developed 412 SNP markers that we assayed for a range‐wide sample of 474 individuals sampled from 34 populations. We complemented the SNP data set with DNA sequence data from 11 nuclear loci from 94 individuals, and used coalescent analyses to estimate historical population size, demographic growth, and patterns of migration. Bayesian clustering identified three geographically separated demes found in the Northern, Central, and Eastern portions of the species’ range. These demes varied significantly in nucleotide diversity, the abundance of private polymorphisms, and population substructure. Most measures supported the Central deme as descended from the primary refuge of diversity. Both SNPs and sequence data suggested recent population growth, and coalescent analyses of historical migration suggested a massive expansion from the Centre to the North and East. Collectively, these data demonstrate the strong influence that range expansions exert on genomic diversity, both within local populations and across the range. Our results suggest that an in‐depth knowledge of nucleotide diversity following expansion requires sampling within multiple populations, and highlight the utility of combining insights from different data types in population genomic studies.     

Genomic tools for evolution and conservation in the chimpanzee: Pan troglodytes ellioti is a genetically distinct population

In spite of its evolutionary significance and conservation importance, the population structure of the common chimpanzee, Pan troglodytes, is still poorly understood. An issue of particular controversy is whether the proposed fourth subspecies of chimpanzee, Pan troglodytes ellioti, from parts of Nigeria and Cameroon, is genetically distinct. Although modern high-throughput SNP genotyping has had a major impact on our understanding of human population structure and demographic history, its application to ecological, demographic, or conservation questions in non-human species has been extremely limited. Here we apply these tools to chimpanzee population structure, using ～700 autosomal SNPs derived from chimpanzee genomic data and a further ～100 SNPs from targeted re-sequencing. We demonstrate conclusively the existence of P. t. ellioti as a genetically distinct subgroup. We show that there is clear differentiation between the verus, troglodytes, and ellioti populations at the SNP and haplotype level, on a scale that is greater than that separating continental human populations. Further, we show that only a small set of SNPs (10-20) is needed to successfully assign individuals to these populations. Tellingly, use of only mitochondrial DNA variation to classify individuals is erroneous in 4 of 54 cases, reinforcing the dangers of basing demographic inference on a single locus and implying that the demographic history of the species is more complicated than that suggested analyses based solely on mtDNA. In this study we demonstrate the feasibility of developing economical and robust tests of individual chimpanzee origin as well as in-depth studies of population structure. These findings have important implications for conservation strategies and our understanding of the evolution of chimpanzees. They also act as a proof-of-principle for the use of cheap high-throughput genomic methods for ecological questions.     

Genome-wide Insights into the Patterns and Determinants of Fine-Scale Population Structure in Humans

Studying genomic patterns of human population structure provides important insights into human evolutionary history and the relationship among populations, and it has significant practical implications for disease-gene mapping. Here we describe a principal component (PC)-based approach to studying intracontinental population structure in humans, identify the underlying markers mediating the observed patterns of fine-scale population structure, and infer the predominating evolutionary forces shaping local population structure. We applied this methodology to a data set of 650K SNPs genotyped in 944 unrelated individuals from 52 populations and demonstrate that, although typical PC analyses focus on the top axes of variation, substantial information about population structure is contained in lower-ranked PCs. We identified 18 significant PCs, some of which distinguish individual populations. In addition to visually representing sample clusters in PC biplots, we estimated the set of all SNPs significantly correlated with each of the most informative axes of variation. These polymorphisms, unlike ancestry-informative markers (AIMs), constitute a much larger set of loci that drive genomic signatures of population structure. The genome-wide distribution of these significantly correlated markers can largely be accounted for by the stochastic effects of genetic drift, although significant clustering does occur in genomic regions that have been previously implicated as targets of recent adaptive evolution.     

Development of two multiplex mini-sequencing panels of ancestry informative SNPs for studies in Latin Americans: an application to populations of the State of Minas Gerais (Brazil)

Admixture occurs when individuals from parental populations that have been isolated for hundreds of generations form a new hybrid population. Currently, interest in measuring biogeographic ancestry has spread from anthropology to forensic sciences, direct-to-consumers personal genomics, and civil rights issues of minorities, and it is critical for genetic epidemiology studies of admixed populations. Markers with highly differentiated frequencies among human populations are informative of ancestry and are called ancestry informative markers (AIMs). For tri-hybrid Latin American populations, ancestry information is required for Africans, Europeans and Native Americans. We developed two multiplex panels of AIMs (for 14 SNPs) to be genotyped by two mini-sequencing reactions, suitable for investigators of medium-small laboratories to estimate admixture of Latin American populations. We tested the performance of these AIMs by comparing results obtained with our 14 AIMs with those obtained using 108 AIMs genotyped in the same individuals, for which DNA samples is available for other investigators. We emphasize that this type of comparison should be made when new admixture/population structure panels are developed. At the population level, our 14 AIMs were useful to estimate European admixture, though they overestimated African admixture and underestimated Native American admixture. Combined with more AIMs, our panel could be used to infer individual admixture. We used our panel to infer the pattern of admixture in two urban populations (Montes Claros and Manhua?u) of the State of Minas Gerais (southeastern Brazil), obtaining a snapshot of their genetic structure in the context of their demographic history.     

High-density single-nucleotide polymorphism maps of the human genome

Here we report a large, extensively characterized set of single-nucleotide polymorphisms (SNPs) covering the human genome. We determined the allele frequencies of 55,018 SNPs in African Americans, Asians (Japanese-Chinese), and European Americans as part of The SNP Consortium's Allele Frequency Project. A subset of 8333 SNPs was also characterized in Koreans. Because these SNPs were ascertained in the same way, the data set is particularly useful for modeling. Our results document that much genetic variation is shared among populations. For autosomes, some 44% of these SNPs have a minor allele frequency > or =10% in each population, and the average allele frequency differences between populations with different continental origins are less than 19%. However, the several percentage point allele frequency differences among the closely related Korean, Japanese, and Chinese populations suggest caution in using mixtures of well-established populations for case-control genetic studies of complex traits. We estimate that approximately 7% of these SNPs are private SNPs with minor allele frequencies <1%. A useful set of characterized SNPs with large allele frequency differences between populations (>60%) can be used for admixture studies. High-density maps of high-quality, characterized SNPs produced by this project are freely available.     

Gene Flow between the Korean Peninsula and Its Neighboring Countries

SNP markers provide the primary data for population structure analysis. In this study, we employed whole-genome autosomal SNPs as a marker set (54,836 SNP markers) and tested their possible effects on genetic ancestry using 320 subjects covering 24 regional groups including Northern ( = 16) and Southern ( = 3) Asians, Amerindians ( = 1), and four HapMap populations (YRI, CEU, JPT, and CHB). Additionally, we evaluated the effectiveness and robustness of 50K autosomal SNPs with various clustering methods, along with their dependencies on recombination hotspots (RH), linkage disequilibrium (LD), missing calls and regional specific markers. The RH- and LD-free multi-dimensional scaling (MDS) method showed a broad picture of human migration from Africa to North-East Asia on our genome map, supporting results from previous haploid DNA studies. Of the Asian groups, the East Asian group showed greater differentiation than the Northern and Southern Asian groups with respect to Fst statistics. By extension, the analysis of monomorphic markers implied that nine out of ten historical regions in South Korea, and Tokyo in Japan, showed signs of genetic drift caused by the later settlement of East Asia (South Korea, Japan and China), while Gyeongju in South East Korea showed signs of the earliest settlement in East Asia. In the genome map, the gene flow to the Korean Peninsula from its neighboring countries indicated that some genetic signals from Northern populations such as the Siberians and Mongolians still remain in the South East and West regions, while few signals remain from the early Southern lineages.     

Conservation genomics of anadromous Atlantic salmon across its North American range: outlier loci identify the same patterns of population structure as neutral loci

Anadromous Atlantic salmon (Salmo salar) is a species of major conservation and management concern in North America, where population abundance has been declining over the past 30 years. Effective conservation actions require the delineation of conservation units to appropriately reflect the spatial scale of intraspecific variation and local adaptation. Towards this goal, we used the most comprehensive genetic and genomic database for Atlantic salmon to date, covering the entire North American range of the species. The database included microsatellite data from 9142 individuals from 149 sampling locations and data from a medium‐density SNP array providing genotypes for >3000 SNPs for 50 sampling locations. We used neutral and putatively selected loci to integrate adaptive information in the definition of conservation units. Bayesian clustering with the microsatellite data set and with neutral SNPs identified regional groupings largely consistent with previously published regional assessments. The use of outlier SNPs did not result in major differences in the regional groupings, suggesting that neutral markers can reflect the geographic scale of local adaptation despite not being under selection. We also performed assignment tests to compare power obtained from microsatellites, neutral SNPs and outlier SNPs. Using SNP data substantially improved power compared to microsatellites, and an assignment success of 97% to the population of origin and of 100% to the region of origin was achieved when all SNP loci were used. Using outlier SNPs only resulted in minor improvements to assignment success to the population of origin but improved regional assignment. We discuss the implications of these new genetic resources for the conservation and management of Atlantic salmon in North America.     

Matching strategies for genetic association studies in structured populations

Association studies in populations that are genetically heterogeneous can yield large numbers of spurious associations if population subgroups are unequally represented among cases and controls. This problem is particularly acute for studies involving pooled genotyping of very large numbers of single-nucleotide-polymorphism (SNP) markers, because most methods for analysis of association in structured populations require individual genotyping data. In this study, we present several strategies for matching case and control pools to have similar genetic compositions, based on ancestry information inferred from genotype data for approximately 300 SNPs tiled on an oligonucleotide-based genotyping array. We also discuss methods for measuring the impact of population stratification on an association study. Results for an admixed population and a phenotype strongly confounded with ancestry show that these simple matching strategies can effectively mitigate the impact of population stratification.     

Adaptive and neutral markers both show continent‐wide population structure of mountain pine beetle (Dendroctonus ponderosae)

Assessments of population genetic structure and demographic history have traditionally been based on neutral markers while explicitly excluding adaptive markers. In this study, we compared the utility of putatively adaptive and neutral single‐nucleotide polymorphisms (SNPs) for inferring mountain pine beetle population structure across its geographic range. Both adaptive and neutral SNPs, and their combination, allowed range‐wide structure to be distinguished and delimited a population that has recently undergone range expansion across northern British Columbia and Alberta. Using an equal number of both adaptive and neutral SNPs revealed that adaptive SNPs resulted in a stronger correlation between sampled populations and inferred clustering. Our results suggest that adaptive SNPs should not be excluded prior to analysis from neutral SNPs as a combination of both marker sets resulted in better resolution of genetic differentiation between populations than either marker set alone. These results demonstrate the utility of adaptive loci for resolving population genetic structure in a nonmodel organism.