Incorporating Functional Genomic Information in Genetic Association Studies Using an Empirical Bayes Approach

Genetic Epidemiology
Amy V SpencerKevin Walters

Abstract

There is a large amount of functional genetic data available, which can be used to inform fine-mapping association studies (in diseases with well-characterised disease pathways). Single nucleotide polymorphism (SNP) prioritization via Bayes factors is attractive because prior information can inform the effect size or the prior probability of causal association. This approach requires the specification of the effect size. If the information needed to estimate a priori the probability density for the effect sizes for causal SNPs in a genomic region isn't consistent or isn't available, then specifying a prior variance for the effect sizes is challenging. We propose both an empirical method to estimate this prior variance, and a coherent approach to using SNP-level functional data, to inform the prior probability of causal association. Through simulation we show that when ranking SNPs by our empirical Bayes factor in a fine-mapping study, the causal SNP rank is generally as high or higher than the rank using Bayes factors with other plausible values of the prior variance. Importantly, we also show that assigning SNP-specific prior probabilities of association based on expert prior functional knowledge of the disease mechanism can l...Continue Reading

References

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Citations

Feb 27, 2020·Genetic Epidemiology·Olga A Vsevolozhskaya, Dmitri V Zaykin
Aug 4, 2020·Human Molecular Genetics·Anna HutchinsonChris Wallace
Jul 10, 2019·Genetic Epidemiology·Kevin WaltersHannuun Yaacob
Jan 8, 2021·Genetic Epidemiology·Kevin WaltersHannuun Yaacob
Jan 27, 2021·European Journal of Human Genetics : EJHG·Junji MorisawaShigeyuki Matsui

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