Bhaskar A, Javanmard A, Courtade TA, Tse D. Novel probabilistic models of spatial genetic ancestry with applications to stratification correction in genome-wide association studies.
Bioinformatics 2017;
33:879-885. [PMID:
28025204 PMCID:
PMC5860619 DOI:
10.1093/bioinformatics/btw720]
[Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Revised: 10/18/2016] [Accepted: 11/10/2016] [Indexed: 11/12/2022] Open
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 structure can create fictitious associations when population ancestry is correlated with both the genotype and the trait.
Results
Our algorithm Geographic Ancestry Positioning (GAP) relates local genetic distances between samples to their spatial distances, and can be used for visually discerning population structure as well as accurately inferring the spatial origin of individuals on a two-dimensional continuum. On both simulated and several real datasets from diverse human populations, GAP exhibits substantially lower error in reconstructing spatial ancestry coordinates compared to PCA. We also develop an association test that uses the ancestry coordinates inferred by GAP to accurately account for ancestry-induced correlations in GWAS. Based on simulations and analysis of a dataset of 10 metabolic traits measured in a Northern Finland cohort, which is known to exhibit significant population structure, we find that our method has superior power to current approaches.
Availability and Implementation
Our software is available at https://github.com/anand-bhaskar/gap .
Contacts
abhaskar@stanford.edu or ajavanma@usc.edu.
Supplementary information
Supplementary data are available at Bioinformatics online.
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