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Improving Imputation Accuracy by Inferring Causal Variants in Genetic Studies

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Research in Computational Molecular Biology (RECOMB 2017)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10229))

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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.

Y. Wu and F. Hormozdiari—These authors contributed equally to this work.

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Authors and Affiliations

  1. Department of Computer Science, UCLA, Los Angeles, USA

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

  2. Program in Genetic Epidemiology and Statistical Genetics, Harvard University, Cambridge, USA

    Farhad Hormozdiari

  3. Department of Molecular and Medical Pharmacology, UCLA, Los Angeles, USA

    Jong Wha J. Joo

  4. Department of Human Genetics, UCLA, Los Angeles, USA

    Eleazar Eskin

Authors
  1. Yue Wu
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  2. Farhad Hormozdiari
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  3. Jong Wha J. Joo
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  4. Eleazar Eskin
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Corresponding author

Correspondence to Eleazar Eskin .

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Editors and Affiliations

  1. Indiana University Bloomington, Bloomington, Indiana, USA

    S. Cenk Sahinalp

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Wu, Y., Hormozdiari, F., Joo, J.W.J., Eskin, E. (2017). Improving Imputation Accuracy by Inferring Causal Variants in Genetic Studies. In: Sahinalp, S. (eds) Research in Computational Molecular Biology. RECOMB 2017. Lecture Notes in Computer Science(), vol 10229. Springer, Cham. https://doi.org/10.1007/978-3-319-56970-3_19

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  • DOI: https://doi.org/10.1007/978-3-319-56970-3_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56969-7

  • Online ISBN: 978-3-319-56970-3

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