Thousands Of Single Nucleotide Polymorphisms Research Articles

DistiLD Database: diseases and traits in linkage disequilibrium blocks

Genome-wide association studies (GWAS) have identified thousands of single nucleotide polymorphisms (SNPs) associated with the risk of hundreds of diseases. However, there is currently no database that enables non-specialists to answer the following simple questions: which SNPs associated with diseases are in linkage disequilibrium (LD) with a gene of interest? Which chromosomal regions have been associated with a given disease, and which are the potentially causal genes in each region? To answer these questions, we use data from the HapMap Project to partition each chromosome into so-called LD blocks, so that SNPs in LD with each other are preferentially in the same block, whereas SNPs not in LD are in different blocks. By projecting SNPs and genes onto LD blocks, the DistiLD database aims to increase usage of existing GWAS results by making it easy to query and visualize disease-associated SNPs and genes in their chromosomal context. The database is available at http://distild.jensenlab.org/.

A Low-Cost Genome-Wide Association Study in Endometriosis

Identification of genes responsible for endometriosis remains difficult, despite recent expensive genome-wide association studies (GWAS). We aimed to build a rapid and low-cost GWAS testing hundreds of thousands of single nucleotide polymorphisms (SNP) for the association with three endometriosis phenotypes in a Caucasian population: superficial endometriosis (SUP), ovarian endometrioma (OMA) and deep infiltrating endometriosis (DIE).

Biological Markers of Boar Fertility

The semen evaluation techniques used in most commercial artificial insemination centers, which includes sperm motility and morphology measurements, provides a very conservative estimate of the relative fertility of individual boars. As well, differences in relative boar fertility are masked by the widespread use of pooled semen for commercial artificial insemination (AI) in many countries. Furthermore, the relatively high sperm numbers used in commercial AI practice usually compensate for reduced fertility, as can be seen in some boars when lower numbers of sperm are used for AI. The increased efficiency of pork production should involve enhanced use of boars with strong reproductive efficiency and the highest genetic merit for important production traits. Given that the current measures of semen quality are not always indicative of fertility and reproductive performance in boars, accurate and predictive genetic and protein markers are still needed. Recently, significant efforts have been made to identify reliable markers that allow for the identification and exclusion of sires with reduced reproductive efficiency. This paper reviews the current status of proteomic and genomic markers of fertility in boars in relation to other livestock species.

Stacks: Building and Genotyping Loci De Novo From Short-Read Sequences

Advances in sequencing technology provide special opportunities for genotyping individuals with speed and thrift, but the lack of software to automate the calling of tens of thousands of genotypes over hundreds of individuals has hindered progress. Stacks is a software system that uses short-read sequence data to identify and genotype loci in a set of individuals either de novo or by comparison to a reference genome. From reduced representation Illumina sequence data, such as RAD-tags, Stacks can recover thousands of single nucleotide polymorphism (SNP) markers useful for the genetic analysis of crosses or populations. Stacks can generate markers for ultra-dense genetic linkage maps, facilitate the examination of population phylogeography, and help in reference genome assembly. We report here the algorithms implemented in Stacks and demonstrate their efficacy by constructing loci from simulated RAD-tags taken from the stickleback reference genome and by recapitulating and improving a genetic map of the zebrafish, Danio rerio.

Genome-Wide “Pleiotropy Scan” Identifies HNF1A Region as a Novel Pancreatic Cancer Susceptibility Locus

In genome-wide association (GWA) studies, hundreds of thousands of single-nucleotide polymorphisms (SNP) are tested for association with a disease trait. Typically, GWA studies give equal consideration to all SNPs tested, regardless of existing knowledge of an SNP's functionality or biological plausibility of association. Because many tests are conducted, very low statistical significance thresholds (P < 5 × 10(-8)) are required to identify true associations with confidence. By restricting GWA analyses to SNPs with enhanced prior probabilities of association, we can reduce the number of tests conducted and relax the required significance threshold, increasing power to detect association. In this analysis of existing GWA data on pancreatic cancer cases (n = 1,736) and controls (n = 1,802) of European descent (the PanScan study), we conduct a GWA scan restricted to SNPs that have been reported to associate with human phenotypes in previous GWA studies (with P < 5 × 10(-8)). Using this method, we drastically reduce the number of tests conducted (from ~550,000 to 1,087) and test only SNPs that are known to be (or tag) variants that influence human biological processes. Of the 1,087 SNPs tested, the strongest association observed was for HNF1A SNP rs7310409 (P = 3 × 10(-5); P(Bonferroni) = 0.03), an SNP known to associate with circulating C-reactive protein. This association was replicated in an independent sample of 1,094 cases and 1,165 controls (P = 0.02), producing a highly significant association in the combined data sets (P = 2 × 10(-6); P(Bonferroni) = 0.002). The HNF1A region also harbors variants that influence several human traits, including maturity-onset diabetes of the young, type 2 diabetes, low-density lipoprotein cholesterol, and N-glycan levels. This novel "pleiotropy scan" method may be useful for identifying susceptibility loci for other cancer phenotypes.

Identification of Novel Schizophrenia Loci by Homozygosity Mapping Using DNA Microarray Analysis

The recent development of high-resolution DNA microarrays, in which hundreds of thousands of single nucleotide polymorphisms (SNPs) are genotyped, enables the rapid identification of susceptibility genes for complex diseases. Clusters of these SNPs may show runs of homozygosity (ROHs) that can be analyzed for association with disease. An analysis of patients whose parents were first cousins enables the search for autozygous segments in their offspring. Here, using the Affymetrix® Genome-Wide Human SNP Array 5.0 to determine ROHs, we genotyped 9 individuals with schizophrenia (SCZ) whose parents were first cousins. We identified overlapping ROHs on chromosomes 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 19, 20, and 21 in at least 3 individuals. Only the locus on chromosome 5 has been reported previously. The ROHs on chromosome 5q23.3–q31.1 include the candidate genes histidine triad nucleotide binding protein 1 (HINT1) and acyl-CoA synthetase long-chain family member 6 (ACSL6). Other overlapping ROHs may contain novel rare recessive variants that affect SCZ specifically in our samples, given the highly heterozygous nature of SCZ. Analysis of patients whose parents are first cousins may provide new insights for the genetic analysis of psychiatric diseases.

A flexible genome‐wide bootstrap method that accounts for rankingand threshold‐selection bias in GWAS interpretation and replication study design

The phenomenon known as the winner's curse is a form of selection bias that affects estimates of genetic association. In genome-wide association studies (GWAS) the bias is exacerbated by the use of stringent selection thresholds and ranking over hundreds of thousands of single nucleotide polymorphisms (SNPs). We develop an improved multi-locus bootstrap point estimate and confidence interval, which accounts for both ranking- and threshold-selection bias in the presence of genome-wide SNP linkage disequilibrium structure. The bootstrap method easily adapts to various study designs and alternative test statistics as well as complex SNP selection criteria. The latter is demonstrated by our application to the Wellcome Trust Case Control Consortium findings, in which the selection criterion was the minimum of the p-values for the additive and genotypic genetic effect models. In contrast, existing likelihood-based bias-reduced estimators account for the selection criterion applied to an SNP as if it were the only one tested, and so are more simple computationally, but do not address ranking across SNPs. Our simulation studies show that the bootstrap bias-reduced estimates are usually closer to the true genetic effect than the likelihood estimates and are less variable with a narrower confidence interval. Replication study sample size requirements computed from the bootstrap bias-reduced estimates are adequate 75-90 per cent of the time compared to 53-60 per cent of the time for the likelihood method. The bootstrap methods are implemented in a user-friendly package able to provide point and interval estimation for both binary and quantitative phenotypes in large-scale GWAS.

Barley: Emergence as a New Research Material of Crop Science

The completion of genome sequencing, advances in a wide range of analytic technologies, greater research platforms and the emergence of bioinformatics procedures, as well as the development of related resources, have contributed to improvements in the quality of research not only in model species but also in crop plants and livestock. Sustainable agricultural production is an urgent issue in the context of global climate change and food security (Brown and Funk 2008, Turner et al. 2009). In order to address this issue, the integration of a broad spectrum of analytical tools and resources and an understanding of genetic mechanisms for agriculturally important traits is needed (Yamamoto et al. 2009, Mochida and Shinozaki 2010). Several essential features of barley (Hordeum vulgare L.) contribute to the broad utilization of this crop in genetic studies. These features include (i) the crop’s diploid nature with a high degree of inbreeding; (ii) the low chromosome number (2n = 14) with large size; (iii) the ease of cross-breeding; and (iv) the ease of cultivation in a wide range of climatic conditions. Cultivated barley, which ranks fourth among cereals with respect to worldwide production, is also well known as an extensively studied plant species in the field of genetics. Regarding its geographic adaptability, barley is particularly noted for its tolerance to cold, drought, alkalinity and salinity. In recent years, to address its huge genome size ( 5,000 Mb), research resources essential for barley genomic studies have been developed, including a large number of expressed sequence tags (ESTs). These have been widely used for barley genome analyses such as DNA marker generation and the construction of microarrays. Recent innovations in sequencing technology, which allow for working on a massive scale by genotyping thousands of single nucleotide polymorphisms (SNPs) on a genome-wide scale, have enabled us to dissect many genetically and biologically agronomically important complex traits. Advanced mapping populations, including chromosome segment substitution lines (CSSLs), have worked as the engines of genetic dissection of quantitative trait loci (QTLs) (Fukuoka et al. 2010). Resources for barley functional genomics have improved over the last decade, and several high-density genetic maps utilizing various types of molecular markers have been constructed (Close et al. 2009, Schulte et al. 2009). Several CSSLs have also been developed, and these are being used for various QTL discovery and related analyses in barley (e.g. Hori et al. 2005). Furthermore, >370,000 barley germplasms are preserved as ex situ collections in representative genebanks, including Okayama University (http://www .shigen.nig.ac.jp/barley/), and worldwide. In this special issue on barley in Plant and Cell Physiology, we discuss the historic advantages of barley as a genetic research material, as well as crop improvements, and briefly outline recent achievements in the establishment of genomic infrastructure that have enabled us to examine the characteristic traits of barley and/or to compare findings with other plant species such as Arabidopsis, rice, maize and soybean. Sato et al. (pp. 728–737) provided new barley research resources in the form of a doubled haploid population derived from the cross between the malting variety ‘Haruna Nijo’ and the Japanese landrace ‘Akashinriki,’ as well as 35 CSSL introgressions from ‘Akashinriki’ on a ‘Haruna Nijo’ background. Several genes controlling barley flower and inflorescence morphology have been isolated by means of map-based cloning technology, including the genes encoding the six-rowed spike (Komatsuda et al. 2007) and naked caryopsis (Taketa et al. 2008). These morphological characteristics and the nonbrittleness (grain shattering) of barley are well-known characteristic traits of domesticated barley, and a model of the barley domestication process has been developed based on archeological evidence of these characteristics (Zohary and Hopf 2000). Sakuma et al. (pp. 738–749) provides an overview of the disarticulation systems and inflorescence characteristics, along with the genes underlying these traits, occurring in the Triticeae tribe. In spite of its large genome, barley is recognized as a good genomic model of the Triticeae tribe, which includes cultivated wheat (einkorn, durum and bread wheats), rye and their respective wild relatives (Schulte et al. 2009). The evolution of the polyploid wheats is distinctive in that domestication, natural hybridization and allopolyploid speciation have significant impacts on their diversification. In this special issue, Matsuoka (pp. 750–764) outlines the phylogenetic relationship between cultivated wheats and their wild relatives and provides an overview of the recent progress and remaining questions in our

Beyond missing heritability: prediction of complex traits.

Despite rapid advances in genomic technology, our ability to account for phenotypic variation using genetic information remains limited for many traits. This has unfortunately resulted in limited application of genetic data towards preventive and personalized medicine, one of the primary impetuses of genome-wide association studies. Recently, a large proportion of the “missing heritability” for human height was statistically explained by modeling thousands of single nucleotide polymorphisms concurrently. However, it is currently unclear how gains in explained genetic variance will translate to the prediction of yet-to-be observed phenotypes. Using data from the Framingham Heart Study, we explore the genomic prediction of human height in training and validation samples while varying the statistical approach used, the number of SNPs included in the model, the validation scheme, and the number of subjects used to train the model. In our training datasets, we are able to explain a large proportion of the variation in height (h2 up to 0.83, R2 up to 0.96). However, the proportion of variance accounted for in validation samples is much smaller (ranging from 0.15 to 0.36 depending on the degree of familial information used in the training dataset). While such R2 values vastly exceed what has been previously reported using a reduced number of pre-selected markers (<0.10), given the heritability of the trait (∼0.80), substantial room for improvement remains.

SAQC: SNP Array Quality Control

BackgroundGenome-wide single-nucleotide polymorphism (SNP) arrays containing hundreds of thousands of SNPs from the human genome have proven useful for studying important human genome questions. Data quality of SNP arrays plays a key role in the accuracy and precision of downstream data analyses. However, good indices for assessing data quality of SNP arrays have not yet been developed.ResultsWe developed new quality indices to measure the quality of SNP arrays and/or DNA samples and investigated their statistical properties. The indices quantify a departure of estimated individual-level allele frequencies (AFs) from expected frequencies via standardized distances. The proposed quality indices followed lognormal distributions in several large genomic studies that we empirically evaluated. AF reference data and quality index reference data for different SNP array platforms were established based on samples from various reference populations. Furthermore, a confidence interval method based on the underlying empirical distributions of quality indices was developed to identify poor-quality SNP arrays and/or DNA samples. Analyses of authentic biological data and simulated data show that this new method is sensitive and specific for the detection of poor-quality SNP arrays and/or DNA samples.ConclusionsThis study introduces new quality indices, establishes references for AFs and quality indices, and develops a detection method for poor-quality SNP arrays and/or DNA samples. We have developed a new computer program that utilizes these methods called SNP Array Quality Control (SAQC). SAQC software is written in R and R-GUI and was developed as a user-friendly tool for the visualization and evaluation of data quality of genome-wide SNP arrays. The program is available online (http://www.stat.sinica.edu.tw/hsinchou/genetics/quality/SAQC.htm).

Brain expression quantitative trait locus mapping informs genetic studies of psychiatric diseases

Genome-wide association study (GWAS) can be used to identify genes that increase the risk of psychiatric diseases. However, much of the disease heritability is still unexplained, suggesting that there are genes to be discovered. Functional annotation of the genetic variants may increase the power of GWAS to identify disease genes, by providing prior information that can be used in Bayesian analysis or in reducing the number of tests. Expression quantitative trait loci (eQTLs) are genomic loci that regulate gene expression. Genetic mapping of eQTLs can help reveal novel functional effects of thousands of single nucleotide polymorphisms (SNPs). The present review mainly focused on the current knowledge on brain eQTL mapping, and discussed some major methodological issues and their possible solutions. The frequently ignored problems of batch effects, covariates, and multiple testing were emphasized, since they can lead to false positives and false negatives. The future application of eQTL data in GWAS analysis was also discussed.

Biotechnology applications for the sustainable management of goat genetic resources

Goats are usually raised under harsh environmental conditions and have a unique ability to take advantage of marginal areas. The diversity of goat genetic resources throughout the world reflects their adaptation to very different production systems, with a predominance of native breeds, often in danger of extinction, very well adapted to local conditions. There is an urgent need to establish programs aimed at the characterization, conservation and sustainable utilization of those genetic resources, and recent developments in the different branches of biotechnology may provide the opportunity for new approaches to achieve those goals. Biotechnologies can further enhance genetic improvement programs, provide tools to ensure the integrity of procedures in goat production, foster opportunities for new products to be developed and support animal health control. Of the biotechnologies currently available, genetic markers, genetic modifications and reproductive technologies are the ones with more potential for the sustainable management of goat genetic resources. For example, artificial insemination and embryo transfer/cryopreservation are essential in conservation and very useful in selection programs, while genetic modifications are primarily applied to obtain recombinant proteins with pharmaceutical or nutraceutical properties. Genetic markers, including neutral, sex-specific and non-neutral markers, as well as single nucleotide polymorphisms (SNPs), are essential for the appropriate management of goat genetic resources, especially in activities related to their characterization and conservation, and specific genetic markers, such as polymorphisms in the αs1-casein locus or in the PrP gene, can be used to strengthen selection programs. Recently, the availability of high-density marker panels which allow the simultaneous detection of thousands of SNPs, opened the possibility of practicing genomic selection, which could increase tremendously the rates of genetic progress currently achieved. Nevertheless, the costs involved are high, and the appropriate definition of breeding objectives, including fitness-related traits, remains a crucial issue, especially for goats. The integration of biotechnologies in goat breeding programs in extensive systems is challenging, as it requires an appropriate organization of the breeding and production systems, which could be based on a simplification of performance recording and the adoption of a flexible open nucleus breeding system.

Variable set enrichment analysis in genome-wide association studies

Complex diseases such as hypertension are inherently multifactorial and involve many factors of mild-to-minute effect sizes. A genome-wide association study (GWAS) typically tests hundreds of thousands of single-nucleotide polymorphisms (SNPs), and offers opportunity to evaluate aggregated effects of many genetic variants with effects that are too small to detect individually. The gene-set-enrichment analysis (GSEA) is a pathway-based approach that tests for such aggregated effects of genes that are linked by biological functions. A key step in GSEA is the summary statistic (gene score) used to measure the overall relevance of a gene based on all SNPs tested in the gene. Existing GSEA methods use maximum statistics sensitive to gene size and linkage equilibrium. We propose the approach of variable set enrichment analysis (VSEA) and study new gene score methods that are less dependent on gene size. The new method treats groups of variables (SNPs or other variants) as base units for summarizing gene scores and relies less on gene definition itself. The power of VSEA is analyzed by simulation studies modeling various scenarios of complex multiloci interactions. Results show that the new gene scores generally performed better, some substantially so, than existing GSEA extension to GWAS. The new methods are implemented in an R package and when applied to a real GWAS data set demonstrated its practical utility in a GWAS setting.

Map Making in the 21st Century: Charting Breast Cancer Susceptibility Pathways in Rodent Models

Genetic factors play an important role in determining risk and resistance to increased breast cancer. Recent technological advances have made it possible to analyze hundreds of thousands of single nucleotide polymorphisms in large-scale association studies in humans and have resulted in identification of alleles in over 20 genes that influence breast cancer risk. Despite these advances, the challenge remains in identifying what the functional polymorphisms are that confer the increased risk, and how these genetic variants interact with each other and with environmental factors. In rodents, the incidence of mammary tumors varies among strains, such that they can provide alternate ideas for candidate pathways involved in humans. Mapping studies in animals have unearthed numerous loci for breast cancer susceptibility that have been validated in human populations. In a reciprocal manner, knockin and knockout mice have been used to validate the tumorigenicity of risk alleles found in population studies. Rodent studies also underscore the complexity of interactions among alleles. The fact that genes affecting risk and resistance to mammary tumors in rodents depend greatly upon the carcinogenic challenge emphasizes the importance of gene x environment interactions. The challenge to rodent geneticists now is to capitalize on the ability to control the genetics and environment in rodent models of tumorigenesis to better understand the biology of breast cancer development, to identify those polymorphisms most relevant to human susceptibility and to identify compensatory pathways that can be targeted for improved prevention in women at highest risk of developing breast cancer.

Concordance Study of 3 Direct-to-Consumer Genetic-Testing Services

Several companies offer direct-to-consumer (DTC) genetic testing to evaluate ancestry and wellness. Massive-scale testing of thousands of single-nucleotide polymorphisms (SNPs) is not error free, and such errors could translate into misclassification of risk and produce a false sense of security or unnecessary anxiety in an individual. We evaluated 3 DTC services and a genomics service that are based on DNA microarray or solution genotyping with hydrolysis probes (TaqMan® analysis) and compared the test results obtained for the same individual. We evaluated the results from 3 DTC services (23andMe, deCODEme, Navigenics) and a genomics-analysis service (Expression Analysis). The concordance rates between the services for SNP data were >99.6%; however, there were some marked differences in the relative disease risks assigned by the DTC services (e.g., for rheumatoid arthritis, the range of relative risk was 0.9-1.85). A possible reason for this difference is that different SNPs were used to calculate risk for the same disease. The reference population also had an influence on the relative disease risk. Our study revealed excellent concordance between the results of SNP analyses obtained from different companies with different platforms, but we noted a disparity in the data for risk, owing to both differences in the SNPs used in the calculation and the reference population used. The larger issues of the utility of the information and the need for risk data that match the user's ethnicity remain, however.

Predicting functionally important SNP classes based on negative selection.

BackgroundWith the advent of cost-effective genotyping technologies, genome-wide association studies allow researchers to examine hundreds of thousands of single nucleotide polymorphisms (SNPs) for association with human disease. Recently, many researchers applying this strategy have detected strong associations to disease with SNP markers that are either not in linkage disequilibrium with any nonsynonymous SNP or large distances from any annotated gene. In such cases, no well-established standard practice for effective SNP selection for follow-up studies exists. We aim to identify and prioritize groups of SNPs that are more likely to affect phenotypes in order to facilitate efficient SNP selection for follow-up studies.ResultsBased on the annotations available in the Ensembl database, we categorized SNPs in the human genome into classes related to regulatory attributes, such as epigenetic modifications and transcription factor binding sites, in addition to classes related to gene structure and cross-species conservation. Using the distribution of derived allele frequencies (DAF) within each class, we assessed the strength of natural selection for each class relative to the genome as a whole. We applied this DAF analysis to Perlegen resequenced SNPs genome-wide. Regulatory elements annotated by Ensembl such as specific histone methylation sites as well as classes defined by cross-species conservation showed negative selection in comparison to the genome as a whole.ConclusionsThese results highlight which annotated classes are under purifying selection, have putative functional importance, and contain SNPs that are strong candidates for follow-up studies after genome-wide association. Such SNP annotation may also be useful in interpreting results of whole-genome sequencing studies.

Computational and statistical approaches to analyzing variants identified by exome sequencing

New sequencing technology has enabled the identification of thousands of single nucleotide polymorphisms in the exome, and many computational and statistical approaches to identify disease-association signals have emerged.

False-negative-rate based approach selecting top single-nucleotide polymorphisms in the first stage of a two-stage genome-wide association study

Genome-wide association (GWA) studies, where hundreds of thousands of single-nucleotide polymorphisms (SNPs) are tested simultaneously, are becoming popular for identifying disease loci for common diseases. Most commonly, a GWA study involves two stages: the first stage includes testing the association between all SNPs and the disease and the second stage includes replication of SNPs selected from the first stage to validate associations in an independent sample. The first stage is considered to be more fundamental since the second stage is contingent on the results of the first stage. Selection of SNPs from stage one for genotyping in stage two is typically based on an arbitrary threshold or controlling type I errors. These strategies can be inefficient and have potential to exclude genotyping of disease-associated SNPs in stage two. We propose an approach for selecting top SNPs that uses a strategy based on the false-negative rate (FNR). Using the FNR approach, we proposed the number of SNPs that should be selected based on the observed p-values and a pre-specified multi-testing power in the first stage. We applied our method to simulated data and a GWA study of glioma (a rare form of brain tumor) data. Results from simulation and the glioma GWA indicate that the proposed approach provides an FNR-based way to select SNPs using pre-specified power.

Protein microarrays and personalized medicine

Over the last 10 years, DNA microarrays have achieved a robust analytical performance, enabling their use for analyzing the whole transcriptome or for screening thousands of single-nucleotide polymorphisms in a single experiment. DNA microarrays allow scientists to correlate gene expression signatures with disease progression, to screen for disease-specific mutations, and to treat patients according to their individual genetic profiles; however, the real key is proteins and their manifold functions. It is necessary to achieve a greater understanding of not only protein function and abundance but also their role in the development of diseases. Protein concentrations have been shown to reflect the physiological and pathologic state of an organ, tissue, or cells far more directly than DNA, and proteins can be profiled effectively with protein microarrays, which require only a small amount of sample material. Protein microarrays have become wellestablished tools in basic and applied research, and the first products have already entered the in vitro diagnostics market. This review focuses on protein microarray applications for biomarker discovery and validation, disease diagnosis, and use within the area of personalized medicine. Protein microarrays have proved to be reliable research tools in screening for a multitude of parameters with only a minimal quantity of sample and have enormous potential in applications for diagnostic and personalized medicine.

Ancestry informative marker panels for African Americans based on subsets of commercially available SNP arrays

Admixture mapping is a widely used method for localizing disease genes in African Americans. Most current methods for inferring ancestry at each locus in the genome use a few thousand single nucleotide polymorphisms (SNPs) that are very different in frequency between West Africans and European Americans, and that are required to not be in linkage disequilibrium in the ancestral populations. Modern SNP arrays provide data on hundreds of thousands of SNPs per sample, and to use these to infer ancestry, using many of the standard methods, it is necessary to choose subsets of the SNPs for analysis. Here we present panels of about 4,300 ancestry informative markers (AIMs) that are subsets respectively of SNPs on the Illumina 1 M, Illumina 650, Illumina 610, Affymetrix 6.0 and Affymetrix 5.0 arrays. To validate the usefulness of these panels, we applied them to samples that are different from the ones used to select the SNPs. The panels provide about 80% of the maximum information about African or European ancestry, even with up to 10% missing data.