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

Chi Wang; Jinpeng Liu; David W. Fardo

doi:10.1186/s12919-016-0064-3

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

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Abstract

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.

Original language	English
Article number	38
Journal	BMC Proceedings
Volume	10
DOIs	https://doi.org/10.1186/s12919-016-0064-3
State	Published - 2016

Bibliographical note

Publisher Copyright:
© 2016 The Author(s).

Funding

This work was partially supported by the National Institute on Aging (DWF: K25AG043546). The Genetic Analysis Workshop is supported by National Institutes of Health (NIH) grant R01 GM031575. The GAW19 whole genome sequence data were provided by the T2D-GENES Consortium, which is supported by NIH grants U01 DK085524, U01 DK085584, U01 DK085501, U01 DK085526, and U01 DK085545. The other genetic and phenotypic data for Genetic Analysis Workshop 18 were provided by the San Antonio Family Heart Study and San Antonio Family Diabetes/Gallbladder Study, which are supported by NIH grants P01 HL045222, R01 DK047482, and R01 DK053889. The Genetic Analysis Workshop is supported by NIH grant R01 GM031575. This article has been published as part of BMC Proceedings Volume 10 Supplement 7, 2016: Genetic Analysis Workshop 19: Sequence, Blood Pressure and Expression Data. Summary articles. The full contents of the supplement are available online at http://bmcproc.biomedcentral.com/ articles/supplements/volume-10-supplement-7. Publication of the proceedings of Genetic Analysis Workshop 19 was supported by National Institutes of Health grant R01 GM031575.

Funders	Funder number
National Institutes of Health (NIH)	U01 DK085584, R01 DK047482, U01 DK085524, U01 DK085501, U01 DK085545, P01 HL045222, R01 DK053889, R01 GM031575, U01 DK085526
National Institute on Aging
Donald Woods Foundation	K25AG043546

ASJC Scopus subject areas

General Biochemistry, Genetics and Molecular Biology

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10.1186/s12919-016-0064-3

Cite this

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abstract = "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.",

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

Abstract

Bibliographical note

Funding

ASJC Scopus subject areas

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