Causal effect estimation in sequencing studies: a Bayesian method to account for confounder adjustment uncertainty

BMC Proceedings

Proceedings

Jinpeng Liu¹  David W. Fardo²  Chi Wang³ 

[1] Biostatistics and Bioinformatics Shared Resource Facility, Markey Cancer Center, University of Kentucky, 800 Rose St, 40536, Lexington, KY, USA;Department of Biostatistics, College of Public Health, University of Kentucky, 725 Rose St, 40536, Lexington, KY, USA;Department of Biostatistics, College of Public Health, University of Kentucky, 725 Rose St, 40536, Lexington, KY, USA;Biostatistics and Bioinformatics Shared Resource Facility, Markey Cancer Center, University of Kentucky, 800 Rose St, 40536, Lexington, KY, USA;

关键词: Causal Effect;    Single Nucleotide Variant;    Bayesian Model Average;    Exposure Model;    Alternative Allele;   

DOI  :  10.1186/s12919-016-0064-3

来源: Springer

【 摘 要 】

Estimating the causal effect of a single nucleotide variant (SNV) on clinical phenotypes is of interest in many genetic studies. The effect estimation may be confounded by other SNVs as a result of linkage disequilibrium as well as demographic and clinical characteristics. Because a large number of these other variables, which we call potential confounders, are collected, it is challenging to select and adjust for the variables that truly confound the causal effect. The Bayesian adjustment for confounding (BAC) method has been proposed as a general method to estimate the average causal effect in the presence of a large number of potential confounders under the assumption of no unmeasured confounders. In this paper, we explore the application of BAC in genetic studies using Genetic Analysis Workshop 19 exome sequencing data. Our results show that BAC can efficiently estimate the causal effect of genetic variants with adjustment for confounding. Consequently, BAC may serve as a useful tool for genome-wide association studies data analysis to effectively assess the causal effect of genetic variants and the impact of potential interventions.

【 授权许可】

CC BY   
© The Author(s). 2016

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