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.     

Differentiating salmon populations at broad and fine geographical scales with microsatellites and single nucleotide polymorphisms

Single nucleotide polymorphisms (SNPs) are appealing genetic markers due to several beneficial attributes, but uncertainty remains about how many of these bi-allelic markers are necessary to have sufficient power to differentiate populations, a task now generally accomplished with highly polymorphic microsatellite markers. In this study, we tested the utility of 37 SNPs and 13 microsatellites for differentiating 29 broadly distributed populations of Chinook salmon ( n  = 2783). Information content of all loci was determined by In and     , and the top 12 markers ranked by In were microsatellites, but the 6 highest, and 7 of the top 10     ranked markers, were SNPs. The mean ratio of random SNPs to random microsatellites ranged from 3.9 to 4.1, but this ratio was consistently reduced when only the most informative loci were included. Individual assignment test accuracy was higher for microsatellites (73.1%) than SNPs (66.6%), and pooling all 50 markers provided the highest accuracy (83.2%). When marker types were combined, as few as 15 of the top ranked loci provided higher assignment accuracy than either microsatellites or SNPs alone. Neighbour-joining dendrograms revealed similar clustering patterns and pairwise tests of population differentiation had nearly identical results with each suite of markers. Statistical tests and simulations indicated that closely related populations were better differentiated by microsatellites than SNPs. Our results indicate that both types of markers are likely to be useful in population genetics studies and that, in some cases, a combination of SNPs and microsatellites may be the most effective suite of loci.     

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.     

Waste not,want not: Microsatellites remain an economical and informative technology for conservation genetics

Comparisons of microsatellites and single‐nucleotide polymorphisms (SNPs) have found that SNPs outperform microsatellites in population genetic analyses, questioning the continued utility of microsatellites in population and landscape genetics. Yet, highly polymorphic markers may be of value in species that have reduced genetic variation. This study repeated previous analyses that used microsatellites with SNPs developed from ddRAD sequencing in the black‐capped vireo source‐sink system. SNPs provided greater resolution of genetic diversity, population differentiation, and migrant detection but could not reconstruct parentage relationships due to insufficient heterozygosities. The biological inferences made by both sets of markers were similar: asymmetrical gene flow from source sites to the remaining sink sites. With the landscape genetic analyses, we found different results between the two molecular markers, but associations of the top environmental features (riparian, open habitat, agriculture, and human development) with dispersal estimates were shared between marker types. Despite the higher precision of SNPs, we find that microsatellites effectively uncover population processes and patterns and are superior for parentage analyses in this species with reduced genetic diversity. This study illustrates the continued applicability and relevance of microsatellites in population genetic research.     

Genotyping-by-Sequencing for Populus Population Genomics: An Assessment of Genome Sampling Patterns and Filtering Approaches

Continuing advances in nucleotide sequencing technology are inspiring a suite of genomic approaches in studies of natural populations. Researchers are faced with data management and analytical scales that are increasing by orders of magnitude. With such dramatic advances comes a need to understand biases and error rates, which can be propagated and magnified in large-scale data acquisition and processing. Here we assess genomic sampling biases and the effects of various population-level data filtering strategies in a genotyping-by-sequencing (GBS) protocol. We focus on data from two species of Populus, because this genus has a relatively small genome and is emerging as a target for population genomic studies. We estimate the proportions and patterns of genomic sampling by examining the Populus trichocarpa genome (Nisqually-1), and demonstrate a pronounced bias towards coding regions when using the methylation-sensitive ApeKI restriction enzyme in this species. Using population-level data from a closely related species (P. tremuloides), we also investigate various approaches for filtering GBS data to retain high-depth, informative SNPs that can be used for population genetic analyses. We find a data filter that includes the designation of ambiguous alleles resulted in metrics of population structure and Hardy-Weinberg equilibrium that were most consistent with previous studies of the same populations based on other genetic markers. Analyses of the filtered data (27,910 SNPs) also resulted in patterns of heterozygosity and population structure similar to a previous study using microsatellites. Our application demonstrates that technically and analytically simple approaches can readily be developed for population genomics of natural populations.     

Sperm whale population structure in the eastern and central North Pacific inferred by the use of single-nucleotide polymorphisms, microsatellites and mitochondrial DNA

We use mitochondrial DNA (mtDNA) (400 bp), six microsatellites and 36 single-nucleotide polymorphisms (SNPs), 20 of which were linked, to investigate population structure of sperm whales (Physeter macrocephalus) in the eastern and central North Pacific. SNP markers, reproducible across technologies and laboratories, are ideal for long-term studies of globally distributed species such as sperm whales, a species of conservation concern because of both historical and contemporary impacts. We estimate genetic differentiation among three strata in the temperate to tropical waters where females are found: California Current, Hawai`i and the eastern tropical Pacific. We then consider how males on sub-Arctic foraging grounds assign to these strata. The California Current stratum was differentiated from both the other strata (P < 0.05) for mtDNA, microsatellites and SNPs, suggesting that the region supports a demographically independent population and providing the first indication that males may exhibit reproductive philopatry. Comparisons between the Hawai`i stratum and the eastern tropical Pacific stratum are not conclusive at this time. Comparisons with Alaska males were statistically significant, or nearly so, from all three strata and individuals showed mixed assignment to, and few exclusions from, the three potential source strata, suggesting widespread origin of males on sub-Arctic feeding grounds. We show that SNPs have sufficient power to detect population structure even when genetic differentiation is low. There is a need for better analytical methods for SNPs, especially when linked SNPs are used, but SNPs appear to be a valuable marker for long-term studies of globally dispersed and highly mobile species.     

Population genomics and comparisons of selective signatures in two invasions of melon fly, <Emphasis Type="Italic">Bactrocera cucurbitae</Emphasis> (Diptera: Tephritidae)

Population genetics is a powerful tool for invasion biology and pest management, and useful for a range of questions from tracing invasion pathways to informing management decisions with inference of population demographics. Genomics greatly increases the resolution of population-scale analyses, yet outside of model species with extensive genomic resources, few studies have used population genomics in invasion biology. We use genome-wide single nucleotide polymorphisms (SNPs) to investigate population genomic structure with samples from across the range of melon fly, Bactrocera cucurbitae (Coquillett, 1849), a highly polyphagous pest of commercial produce. We then make use of a chromosome-scale genome assembly and gene set to compare signatures of selection across the melon fly’s genome, both across our sampling as a whole and in the context of two independent, established introductions. Using multiple approaches, we find support for six genetic clusters across melon fly’s distribution. Some of these agree with previously identified genetic clusters using microsatellites, but consensus of clusters in mainland and oceanic southeast Asia is confounded by variable sampling between studies. We find few adaptive signatures across the genome, and virtually no unique signatures when comparing the two independent introductions, which suggests that similar management strategies are appropriate across melon fly’s range. This is the first use of genome-wide data to characterize population structure in tephritid fruit fly pests, and our SNP dataset provides a foundation for objective and cost-effective genotyping of previously collected melon fly specimens. Future research needs to focus on truly comprehensive sampling across melon fly’s range to overcome the historic variability of range-wide estimates of population structure for this pest.     

Challenges in analysis and interpretation of microsatellite data for population genetic studies

Advancing technologies have facilitated the ever‐widening application of genetic markers such as microsatellites into new systems and research questions in biology. In light of the data and experience accumulated from several years of using microsatellites, we present here a literature review that synthesizes the limitations of microsatellites in population genetic studies. With a focus on population structure, we review the widely used fixation (F_ST) statistics and Bayesian clustering algorithms and find that the former can be confusing and problematic for microsatellites and that the latter may be confounded by complex population models and lack power in certain cases. Clustering, multivariate analyses, and diversity‐based statistics are increasingly being applied to infer population structure, but in some instances these methods lack formalization with microsatellites. Migration‐specific methods perform well only under narrow constraints. We also examine the use of microsatellites for inferring effective population size, changes in population size, and deeper demographic history, and find that these methods are untested and/or highly context‐dependent. Overall, each method possesses important weaknesses for use with microsatellites, and there are significant constraints on inferences commonly made using microsatellite markers in the areas of population structure, admixture, and effective population size. To ameliorate and better understand these constraints, researchers are encouraged to analyze simulated datasets both prior to and following data collection and analysis, the latter of which is formalized within the approximate Bayesian computation framework. We also examine trends in the literature and show that microsatellites continue to be widely used, especially in non‐human subject areas. This review assists with study design and molecular marker selection, facilitates sound interpretation of microsatellite data while fostering respect for their practical limitations, and identifies lessons that could be applied toward emerging markers and high‐throughput technologies in population genetics.     

Population differentiation determined from putative neutral and divergent adaptive genetic markers in Eulachon (Thaleichthys pacificus,Osmeridae), an anadromous Pacific smelt

Twelve eulachon (Thaleichthys pacificus, Osmeridae) populations ranging from Cook Inlet, Alaska and along the west coast of North America to the Columbia River were examined by restriction‐site‐associated DNA (RAD) sequencing to elucidate patterns of neutral and adaptive variation in this high geneflow species. A total of 4104 single‐nucleotide polymorphisms (SNPs) were discovered across the genome, with 193 putatively adaptive SNPs as determined by F_ST outlier tests. Estimates of population structure in eulachon with the putatively adaptive SNPs were similar, but provided greater resolution of stocks compared with a putatively neutral panel of 3911 SNPs or previous estimates with 14 microsatellites. A cline of increasing measures of genetic diversity from south to north was found in the adaptive panel, but not in the neutral markers (SNPs or microsatellites). This may indicate divergent selective pressures in differing freshwater and marine environments between regional eulachon populations and that these adaptive diversity patterns not seen with neutral markers could be a consideration when determining genetic boundaries for conservation purposes. Estimates of effective population size (N_e) were similar with the neutral SNP panel and microsatellites and may be utilized to monitor population status for eulachon where census sizes are difficult to obtain. Greater differentiation with the panel of putatively adaptive SNPs provided higher individual assignment accuracy compared to the neutral panel or microsatellites for stock identification purposes. This study presents the first SNPs that have been developed for eulachon, and analyses with these markers highlighted the importance of integrating genome‐wide neutral and adaptive genetic variation for the applications of conservation and management.     

Effect of microsatellite selection on individual and population genetic inferences: an empirical study using cross‐specific and species‐specific amplifications

Although whole‐genome sequencing is becoming more accessible and feasible for nonmodel organisms, microsatellites have remained the markers of choice for various population and conservation genetic studies. However, the criteria for choosing microsatellites are still controversial due to ascertainment bias that may be introduced into the genetic inference. An empirical study of red deer (Cervus elaphus) populations, in which cross‐specific and species‐specific microsatellites developed through pyrosequencing of enriched libraries, was performed for this study. Two different strategies were used to select the species‐specific panels: randomly vs. highly polymorphic markers. The results suggest that reliable and accurate estimations of genetic diversity can be obtained using random microsatellites distributed throughout the genome. In addition, the results reinforce previous evidence that selecting the most polymorphic markers leads to an ascertainment bias in estimates of genetic diversity, when compared with randomly selected microsatellites. Analyses of population differentiation and clustering seem less influenced by the approach of microsatellite selection, whereas assigning individuals to populations might be affected by a random selection of a small number of microsatellites. Individual multilocus heterozygosity measures produced various discordant results, which in turn had impacts on the heterozygosity‐fitness correlation test. Finally, we argue that picking the appropriate microsatellite set should primarily take into account the ecological and evolutionary questions studied. Selecting the most polymorphic markers will generally overestimate genetic diversity parameters, leading to misinterpretations of the real genetic diversity, which is particularly important in managed and threatened populations.     

Can balancing selection on MHC loci counteract genetic drift in small fragmented populations of black grouse?

The ability of natural populations to adapt to new environmental conditions is crucial for their survival and partly determined by the standing genetic variation in each population. Populations with higher genetic diversity are more likely to contain individuals that are better adapted to new circumstances than populations with lower genetic diversity. Here, we use both neutral and major histocompatibility complex (MHC) markers to test whether small and highly fragmented populations hold lower genetic diversity than large ones. We use black grouse as it is distributed across Europe and found in populations with varying degrees of isolation and size. We sampled 11 different populations; five continuous, three isolated, and three small and isolated. We tested patterns of genetic variation in these populations using three different types of genetic markers: nine microsatellites and 21 single nucleotide polymorphisms (SNPs) which both were found to be neutral, and two functional MHC genes that are presumably under selection. The small isolated populations displayed significantly lower neutral genetic diversity compared to continuous populations. A similar trend, but not as pronounced, was found for genotypes at MHC class II loci. Populations were less divergent at MHC genes compared to neutral markers. Measures of genetic diversity and population genetic structure were positively correlated among microsatellites and SNPs, but none of them were correlated to MHC when comparing all populations. Our results suggest that balancing selection at MHC loci does not counteract the power of genetic drift when populations get small and fragmented.     

Comparing RADseq and microsatellites for estimating genetic diversity and relatedness — Implications for brown trout conservation

The conservation and management of endangered species requires information on their genetic diversity, relatedness and population structure. The main genetic markers applied for these questions are microsatellites and single nucleotide polymorphisms (SNPs), the latter of which remain the more resource demanding approach in most cases. Here, we compare the performance of two approaches, SNPs obtained by restriction‐site‐associated DNA sequencing (RADseq) and 16 DNA microsatellite loci, for estimating genetic diversity, relatedness and genetic differentiation of three, small, geographically close wild brown trout (Salmo trutta) populations and a regionally used hatchery strain. The genetic differentiation, quantified as F_ST, was similar when measured using 16 microsatellites and 4,876 SNPs. Based on both marker types, each brown trout population represented a distinct gene pool with a low level of interbreeding. Analysis of SNPs identified half‐ and full‐siblings with a higher probability than the analysis based on microsatellites, and SNPs outperformed microsatellites in estimating individual‐level multilocus heterozygosity. Overall, the results indicated that moderately polymorphic microsatellites and SNPs from RADseq agreed on estimates of population genetic structure in moderately diverged, small populations, but RADseq outperformed microsatellites for applications that required individual‐level genotype information, such as quantifying relatedness and individual‐level heterozygosity. The results can be applied to other small populations with low or moderate levels of genetic diversity.     

Biodiversity of 20 chicken breeds assessed by SNPs located in gene regions

Twenty-five single nucleotide polymorphisms (SNPs) were analyzed in 20 distinct chicken breeds. The SNPs, each located in a different gene and mostly on different chromosomes, were chosen to examine the use of SNPs in or close to genes (g-SNPs), for biodiversity studies. Phylogenetic trees were constructed from these data. When bootstrap values were used as a criterion for the tree repeatability, doubling the number of SNPs from 12 to 25 improved tree repeatability more than doubling the number of individuals per population, from five to ten. Clustering results of these 20 populations, based on the software STRUCTURE, are in agreement with those previously obtained from the analysis of microsatellites. When the number of clusters was similar to the number of populations, affiliation of birds to their original populations was correct (>95%) only when at least the 22 most polymorphic SNP loci (out of 25) were included. When ten populations were clustered into five groups based on STRUCTURE, we used membership coefficient (Q) of the major cluster at each population as an indicator for clustering success level. This value was used to compare between three marker types; microsatellites, SNPs in or close to genes (g-SNPs) and SNPs in random fragments (r-SNPs). In this comparison, the same individuals were used (five to ten birds per population) and the same number of loci (14) used for each of the marker types. The average membership coefficients (Q) of the major cluster for microsatellites, g-SNPs and r-SNPs were 0.85, 0.7, and 0.64, respectively. Analysis based on microsatellites resulted in significantly higher clustering success due to their multi-allelic nature. Nevertheless, SNPs have obvious advantages, and are an efficient and cost-effective genetic tool, providing broader genome coverage and reliable estimates of genetic relatedness.     

Construction of Genetic Linkage Maps and Comparative Genome Analysis of Catfish Using Gene-Associated Markers

A genetic linkage map of the channel catfish genome (N = 29) was constructed using EST-based microsatellite and single nucleotide polymorphism (SNP) markers in an interspecific reference family. A total of 413 microsatellites and 125 SNP markers were polymorphic in the reference family. Linkage analysis using JoinMap 4.0 allowed mapping of 331 markers (259 microsatellites and 72 SNPs) to 29 linkage groups. Each linkage group contained 3–18 markers. The largest linkage group contained 18 markers and spanned 131.2 cM, while the smallest linkage group contained 14 markers and spanned only 7.9 cM. The linkage map covered a genetic distance of 1811 cM with an average marker interval of 6.0 cM. Sex-specific maps were also constructed; the recombination rate for females was 1.6 times higher than that for males. Putative conserved syntenies between catfish and zebrafish, medaka, and Tetraodon were established, but the overall levels of genome rearrangements were high among the teleost genomes. This study represents a first-generation linkage map constructed by using EST-derived microsatellites and SNPs, laying a framework for large-scale comparative genome analysis in catfish. The conserved syntenies identified here between the catfish and the three model fish species should facilitate structural genome analysis and evolutionary studies, but more importantly should facilitate functional inference of catfish genes. Given that determination of gene functions is difficult in nonmodel species such as catfish, functional genome analysis will have to rely heavily on the establishment of orthologies from model species.     

IIb‐RAD‐sequencing coupled with random forest classification indicates regional population structuring and sex‐specific differentiation in salmon lice (Lepeophtheirus salmonis)

The aquaculture industry has been dealing with salmon lice problems forming serious threats to salmonid farming. Several treatment approaches have been used to control the parasite. Treatment effectiveness must be optimized, and the systematic genetic differences between subpopulations must be studied to monitor louse species and enhance targeted control measures. We have used IIb‐RAD sequencing in tandem with a random forest classification algorithm to detect the regional genetic structure of the Norwegian salmon lice and identify important markers for sex differentiation of this species. We identified 19,428 single nucleotide polymorphisms (SNPs) from 95 individuals of salmon lice. These SNPs, however, were not able to distinguish the differential structure of lice populations. Using the random forest algorithm, we selected 91 SNPs important for geographical classification and 14 SNPs important for sex classification. The geographically important SNP data substantially improved the genetic understanding of the population structure and classified regional demographic clusters along the Norwegian coast. We also uncovered SNP markers that could help determine the sex of the salmon louse. A large portion of the SNPs identified to be under directional selection was also ranked highly important by random forest. According to our findings, there is a regional population structure of salmon lice associated with the geographical location along the Norwegian coastline.     

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.     

Microsatellites, from molecules to populations and back

Population genetics studies using microsatellites, and data on their molecular dynamics, are on the increase. But, so far, no consensus has emerged on which mutation model should be used, though this is of paramount importance for analysis of population genetic structure. However, this is not surprising given the variety of microsatellite molecular motifs. Null alleles may be disturbing for population studies, even though their presence can be detected through careful population analyses, while homoplasy seems of little concern, at least over short evolutionary scales. Interspecific studies show that microsatellites are poor markers for phylogenetic inference. However, these studies are fuelling discussions on directional mutation and the role of selection and recombination in their evolution. Nonetheless, it remains true that microsatellites may be considered as good, neutral mendelian markers.     

Historical processes and contemporary ocean currents drive genetic structure in the seagrass Thalassia hemprichii in the Indo‐Australian Archipelago

Understanding spatial patterns of gene flow and genetic structure is essential for the conservation of marine ecosystems. Contemporary ocean currents and historical isolation due to Pleistocene sea level fluctuations have been predicted to influence the genetic structure in marine populations. In the Indo‐Australian Archipelago (IAA), the world's hotspot of marine biodiversity, seagrasses are a vital component but population genetic information is very limited. Here, we reconstructed the phylogeography of the seagrass Thalassia hemprichii in the IAA based on single nucleotide polymorphisms (SNPs) and then characterized the genetic structure based on a panel of 16 microsatellite markers. We further examined the relative importance of historical isolation and contemporary ocean currents in driving the patterns of genetic structure. Results from SNPs revealed three population groups: eastern Indonesia, western Indonesia (Sunda Shelf) and Indian Ocean; while the microsatellites supported five population groups (eastern Indonesia, Sunda Shelf, Lesser Sunda, Western Australia and Indian Ocean). Both SNPs and microsatellites showed asymmetrical gene flow among population groups with a trend of southwestward migration from eastern Indonesia. Genetic diversity was generally higher in eastern Indonesia and decreased southwestward. The pattern of genetic structure and connectivity is attributed partly to the Pleistocene sea level fluctuations modified to a smaller level by contemporary ocean currents.     

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.