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

Anand Bhaskar; Adel Javanmard; Thomas A Courtade; David Tse

doi:10.1093/bioinformatics/btw720

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

Bioinformatics. 2017 Mar 15;33(6):879-885. doi: 10.1093/bioinformatics/btw720.

Authors

Anand Bhaskar^{1

2}, Adel Javanmard³, Thomas A Courtade⁴, David Tse^{4

5}

Affiliations

¹ Department of Genetics, Stanford University, Stanford, CA, USA.
² Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
³ Marshall School of Business, University of Southern California, Los Angeles, CA, USA.
⁴ Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.
⁵ Department of Electrical Engineering, Stanford University, Stanford, CA, USA.

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.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Algorithms
Evolution, Molecular*
Gene Frequency
Genome-Wide Association Study / methods*
Humans
Models, Genetic
Models, Statistical*
Phylogeography / methods*
Polymorphism, Single Nucleotide*
Population Groups / genetics
Principal Component Analysis
Software*

Grants and funding

R01 HG008140/HG/NHGRI NIH HHS/United States