Scaling probabilistic models of genetic variation to millions of humans. Author Prem Gopalan, Wei Hao, David Blei, John Storey Publication Year 2016 Type Journal Article Abstract A major goal of population genetics is to quantitatively understand variation of genetic polymorphisms among individuals. The aggregated number of genotyped humans is currently on the order of millions of individuals, and existing methods do not scale to data of this size. To solve this problem, we developed TeraStructure, an algorithm to fit Bayesian models of genetic variation in structured human populations on tera-sample-sized data sets (10 observed genotypes; for example, 1 million individuals at 1 million SNPs). TeraStructure is a scalable approach to Bayesian inference in which subsamples of markers are used to update an estimate of the latent population structure among individuals. We demonstrate that TeraStructure performs as well as existing methods on current globally sampled data, and we show using simulations that TeraStructure continues to be accurate and is the only method that can scale to tera-sample sizes. Keywords Humans, Computational Biology, Genetic Markers, Algorithms, Models, Statistical, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Genetics, Population, Bayes Theorem, Disease Journal Nat Genet Volume 48 Issue 12 Pages 1587-1590 Date Published 2016 Dec ISSN Number 1546-1718 DOI 10.1038/ng.3710 Alternate Journal Nat Genet PMCID PMC5127768 PMID 27819665 PubMedPubMed CentralGoogle ScholarBibTeXEndNote X3 XML