TY - GEN
T1 - Improving imputation accuracy by inferring causal variants in genetic studies
AU - Wu, Yue
AU - Hormozdiari, Farhad
AU - Joo, Jong Wha J.
AU - Eskin, Eleazar
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85018410954&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-56970-3_19
DO - 10.1007/978-3-319-56970-3_19
M3 - Conference contribution
AN - SCOPUS:85018410954
SN - 9783319569697
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 303
EP - 317
BT - Research in Computational Molecular Biology - 21st Annual International Conference, RECOMB 2017, Proceedings
A2 - Sahinalp, S.Cenk
PB - Springer Verlag
T2 - 21st Annual International Conference on Research in Computational Molecular Biology, RECOMB 2017
Y2 - 3 May 2017 through 7 May 2017
ER -