DISTMIX: direct imputation of summary statistics for unmeasured SNPs from mixed ethnicity cohorts

Authors

Donghyun Lee, Virginia Commonwealth University
T. Bernard Bigdeli, Virginia Commonwealth University
Vernell S. Williamson, Virginia Commonwealth University
Vladimir I. Vladimirov, Virginia Commonwealth University, Johns Hopkins UniversityFollow
Brien P. Riley, Virginia Commonwealth University
Ayman H. Fanous, Virginia Commonwealth University
Silviu-Alin Bacanu, Virginia Commonwealth University

Document Type

Article

Original Publication Date

2015

Journal/Book/Conference Title

Bioinformatics

Volume

31

Issue

19

DOI of Original Publication

10.1093/bioinformatics/btv348

Comments

Originally published at http://dx.doi.org/10.1093/bioinformatics/btv348

Date of Submission

November 2015

Abstract

Motivation: To increase the signal resolution for large-scale meta-analyses of genome-wide association studies, genotypes at unmeasured single nucleotide polymorphisms (SNPs) are commonly imputed using large multi-ethnic reference panels. However, the ever increasing size and ethnic diversity of both reference panels and cohorts makes genotype imputation computationally challenging for moderately sized computer clusters. Moreover, genotype imputation requires subject-level genetic data, which unlike summary statistics provided by virtually all studies, is not publicly available. While there are much less demanding methods which avoid the genotype imputation step by directly imputing SNP statistics, e.g. Directly Imputing summary STatistics (DIST) proposed by our group, their implicit assumptions make them applicable only to ethnically homogeneous cohorts.

Results: To decrease computational and access requirements for the analysis of cosmopolitan cohorts, we propose DISTMIX, which extends DIST capabilities to the analysis of mixed ethnicity cohorts. The method uses a relevant reference panel to directly impute unmeasured SNP statistics based only on statistics at measured SNPs and estimated/user-specified ethnic proportions. Simulations show that the proposed method adequately controls the Type I error rates. The 1000 Genomes panel imputation of summary statistics from the ethnically diverse Psychiatric Genetic Consortium Schizophrenia Phase 2 suggests that, when compared to genotype imputation methods, DISTMIX offers comparable imputation accuracy for only a fraction of computational resources.

Rights

© The Author 2015. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

Is Part Of

VCU Psychiatry Publications