Dataset Information

POPSTR: Inference of Admixed Population Structure Based on Single-Nucleotide Polymorphisms and Copy Number Variations.

ABSTRACT: Statistical approaches for population structure estimation have been predominantly driven by a particular data type, single-nucleotide polymorphisms (SNPs). However, in the presence of weak identifiability in SNPs, population structure estimation can suffer from undesirable accuracy loss. Copy number variations (CNVs) are genomic structural variants with loci that are commonly shared within a specific population and thus provide valuable information for estimation of the ancestry of sampled populations. We develop a Bayesian joint modeling framework of SNPs and CNVs, called POPSTR, to better understand population structure than approaches that use SNPs solely. To deal with the increased data volume, we use the Metropolis Adjusted Langevin algorithm (MALA) that guides the target distribution in a computationally efficient way. We illustrate applications of our approach using the HapMap 2005 project data. We carry out simulation studies and show that the performance of our approach is comparable or better than that of popular benchmarks, STRUCTURE and ADMIXTURE. We also observe that using only CNVs can be remarkably efficient if SNP data are not available.

SUBMITTER: Ahn J

PROVIDER: S-EPMC5915226 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Publications

POPSTR: Inference of Admixed Population Structure Based on Single-Nucleotide Polymorphisms and Copy Number Variations.

Ahn Jaeil J Conkright Brian B Boca Simina M SM Madhavan Subha S

Journal of computational biology : a journal of computational molecular cell biology 20180102 4

Statistical approaches for population structure estimation have been predominantly driven by a particular data type, single-nucleotide polymorphisms (SNPs). However, in the presence of weak identifiability in SNPs, population structure estimation can suffer from undesirable accuracy loss. Copy number variations (CNVs) are genomic structural variants with loci that are commonly shared within a specific population and thus provide valuable information for estimation of the ancestry of sampled popu ...[more]

PMID: 29293371

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Project description:Human populations have interacted throughout history, and a considerable portion of modern human populations show evidence of admixture. Local ancestry inference (LAI) is focused on detecting the genetic ancestry of chromosomal segments in admixed individuals and has wide applications. In this work, we proposed a new LAI method based on population-specific single-nucleotide polymorphisms (SNPs) and applied it in the analysis of admixed populations in the 1000 Genomes Project (1KGP). Based on population-specific SNPs in a sliding window, we computed local ancestry information vectors, which are moment estimators of local ancestral proportions, for two haplotypes of an admixed individual and inferred the local ancestral origins. Then we used African (AFR), East Asian (EAS), European (EUR) and South Asian (SAS) populations from the 1KGP and indigenous American (AMR) populations from the Human Genome Diversity Project (HGDP) as reference populations and conducted the proposed LAI analysis on African American populations and American populations in the 1KGP. The results were compared with those obtained by RFMix, G-Nomix and FLARE. We demonstrated that the existence of alleles in a chromosomal region that are specific to a particular reference population and the absence of alleles specific to the other reference populations provide reasonable evidence for determining the ancestral origin of the region. Contemporary AFR, AMR and EUR populations approximate ancestral populations of the admixed populations well, and the results from RFMix, G-Nomix and FLARE largely agree with those from the Ancestral Spectrum Analyzer (ASA), in which the proposed method was implemented. When admixtures are ancient and contemporary reference populations do not satisfactorily approximate ancestral populations, the performances of RFMix, G-Nomix and FLARE deteriorate with increased error rates and fragmented chromosomal segments. In contrast, our method provides fair results.

Dataset Information

POPSTR: Inference of Admixed Population Structure Based on Single-Nucleotide Polymorphisms and Copy Number Variations.

Publications

POPSTR: Inference of Admixed Population Structure Based on Single-Nucleotide Polymorphisms and Copy Number Variations.

Similar Datasets

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