Scaling probabilistic models of genetic variation to millions of humans.
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 (1012observed 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.
Department of Computer Science, Princeton University, Princeton, New Jersey, USA.
Prem
P
Gopalan
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA.
Wei
W
Hao
Department of Statistics, Columbia University, New York, New York, USA.|Department of Computer Science, Columbia University, New York, New York, USA.
David M
DM
Blei
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA.
John D
JD
Storey
D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D004194:Disease / Q000235:genetics*; D005819:Genetic Markers / Q000235:genetics*; D020022:Genetic Predisposition to Disease; D005828:Genetics, Population; D006801:Humans; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*
D005819:Genetic Markers