Novel probabilistic models of spatial genetic ancestry with applications to stratification correction in genome-wide association studies.

ABSTRACT:

Motivation

Genetic variation in human populations is influenced by geographic ancestry due to spatial locality in historical mating and migration patterns. Spatial population structure in genetic datasets has been traditionally analyzed using either model-free algorithms, such as principal components analysis (PCA) and multidimensional scaling, or using explicit spatial probabilistic models of allele frequency evolution. We develop a general probabilistic model and an associated inference algorithm that unify the model-based and data-driven approaches to visualizing and inferring population structure. Our spatial inference algorithm can also be effectively applied to the problem of population stratification in genome-wide association studies (GWAS), where hidden population structur

SUBMITTER: Bhaskar A 

PROVIDER: S-EPMC5860619 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature