Improving imputation accuracy by inferring causal variants in genetic studies

Yue Wu, Farhad Hormozdiari, Jong Wha J. Joo, Eleazar Eskin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Genotype imputation has been widely utilized for two reasons in the analysis of Genome-Wide Association Studies (GWAS). One reason is to increase the power for association studies when causal SNPs are not collected in the GWAS. The second reason is to aid the interpretation of a GWAS result by predicting the association statistics at untyped variants. In this paper, we show that prediction of association statistics at untyped variants that have an influence on the trait produces overly conservative results. Current imputation methods assume that none of the variants in a region (locus consists of multiple variants) affect the trait, which is often inconsistent with the observed data. In this paper, we propose a new method, CAUSAL-Imp, which can impute the association statistics at untyped variants while taking into account variants in the region that may affect the trait. Our method builds on recent methods that impute the marginal statistics for GWAS by utilizing the fact that marginal statistics follow a multivariate normal distribution. We utilize both simulated and real data sets to assess the performance of our method. We show that traditional imputation approaches underestimate the association statistics for variants involved in the trait, and our results demonstrate that our approach provides less biased estimates of these association statistics.

Original languageEnglish
Title of host publicationResearch in Computational Molecular Biology - 21st Annual International Conference, RECOMB 2017, Proceedings
EditorsS.Cenk Sahinalp
PublisherSpringer Verlag
Pages303-317
Number of pages15
ISBN (Print)9783319569697
DOIs
StatePublished - 2017
Event21st Annual International Conference on Research in Computational Molecular Biology, RECOMB 2017 - Hong Kong, China
Duration: 3 May 20177 May 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10229 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st Annual International Conference on Research in Computational Molecular Biology, RECOMB 2017
Country/TerritoryChina
CityHong Kong
Period3/05/177/05/17

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