Scaling probabilistic models of genetic variation to millions of humans.

TitleScaling probabilistic models of genetic variation to millions of humans.
Publication TypeJournal Article
Year of Publication2016
AuthorsGopalan, P, Hao, W, Blei, DM, Storey, JD
JournalNat Genet
Date Published2016 Dec
KeywordsAlgorithms, Bayes Theorem, Computational Biology, Disease, Genetic Markers, Genetic Predisposition to Disease, Genetics, Population, Humans, Models, Statistical, Polymorphism, Single Nucleotide

<p>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.</p>

Alternate JournalNat Genet
PubMed ID27819665
PubMed Central IDPMC5127768
Grant ListR01 HG006448 / HG / NHGRI NIH HHS / United States