Dataset Information

Selecting SNPs to identify ancestry.

ABSTRACT: An individual's genotypes at a group of single-nucleotide polymorphisms (SNPs) can be used to predict that individual's ethnicity or ancestry. In medical studies, knowledge of a subject's ancestry can minimize possible confounding, and in forensic applications, such knowledge can help direct investigations. Our goal is to select a small subset of SNPs, from the millions already identified in the human genome, that can predict ancestry with a minimal error rate. The general form for this variable selection procedure is to estimate the expected error rates for sets of SNPs using a training dataset and consider those sets with the lowest error rates given their size. The quality of the estimate for the error rate determines the quality of the resulting SNPs. As the apparent error rate performs poorly when either the number of SNPs or the number of populations is large; we propose a new estimate, the Improved Bayesian Estimate. We demonstrate that selection procedures based on this estimate produce small sets of SNPs that can accurately predict ancestry. We also provide a list of the 100 optimal SNPs for identifying ancestry.

SUBMITTER: Sampson JN

PROVIDER: S-EPMC3141729 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature

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Selecting SNPs to identify ancestry.

Sampson Joshua N JN Kidd Kenneth K KK Kidd Judith R JR Zhao Hongyu H

Annals of human genetics 20110701 4

An individual's genotypes at a group of single-nucleotide polymorphisms (SNPs) can be used to predict that individual's ethnicity or ancestry. In medical studies, knowledge of a subject's ancestry can minimize possible confounding, and in forensic applications, such knowledge can help direct investigations. Our goal is to select a small subset of SNPs, from the millions already identified in the human genome, that can predict ancestry with a minimal error rate. The general form for this variable ...[more]

PMID: 21668909

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Project description:BACKGROUND:The presence of population structure in a sample may confound the search for important genetic loci associated with disease. Our four samples in the Family Investigation of Nephropathy and Diabetes (FIND), European Americans, Mexican Americans, African Americans, and American Indians are part of a genome- wide association study in which population structure might be particularly important. We therefore decided to study in detail one component of this, individual genetic ancestry (IGA). From SNPs present on the Affymetrix 6.0 Human SNP array, we identified 3 sets of ancestry informative markers (AIMs), each maximized for the information in one the three contrasts among ancestral populations: Europeans (HAPMAP, CEU), Africans (HAPMAP, YRI and LWK), and Native Americans (full heritage Pima Indians). We estimate IGA and present an algorithm for their standard errors, compare IGA to principal components, emphasize the importance of balancing information in the ancestry informative markers (AIMs), and test the association of IGA with diabetic nephropathy in the combined sample. RESULTS:A fixed parental allele maximum likelihood algorithm was applied to the FIND to estimate IGA in four samples: 869 American Indians; 1385 African Americans; 1451 Mexican Americans; and 826 European Americans. When the information in the AIMs is unbalanced, the estimates are incorrect with large error. Individual genetic admixture is highly correlated with principle components for capturing population structure. It takes ~700 SNPs to reduce the average standard error of individual admixture below 0.01. When the samples are combined, the resulting population structure creates associations between IGA and diabetic nephropathy. CONCLUSIONS:The identified set of AIMs, which include American Indian parental allele frequencies, may be particularly useful for estimating genetic admixture in populations from the Americas. Failure to balance information in maximum likelihood, poly-ancestry models creates biased estimates of individual admixture with large error. This also occurs when estimating IGA using the Bayesian clustering method as implemented in the program STRUCTURE. Odds ratios for the associations of IGA with disease are consistent with what is known about the incidence and prevalence of diabetic nephropathy in these populations.

Dataset Information

Selecting SNPs to identify ancestry.

Publications

Selecting SNPs to identify ancestry.

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