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Causal effect estimation in sequencing studies: a Bayesian method to account for confounder adjustment uncertainty
Chi Wang1, Jinpeng Liu2, David W Fardo3
1Department of Biostatistics, College of Public Health, University of Kentucky, 725 Rose St, Lexington, KY 40536 USA ; Biostatistics and Bioinformatics Shared Resource Facility, Markey Cancer Center, University of Kentucky, 800 Rose St, Lexington, KY 40536 USA.
BMC Proceedings
|December 17, 2016
Summary
The Bayesian adjustment for confounding (BAC) method efficiently estimates genetic variant effects, adjusting for confounding factors in genetic studies. This approach aids in analyzing genome-wide association studies data to understand variant impacts.
Area of Science:
- Genetics
- Statistical genetics
- Bioinformatics
Background:
- Estimating causal effects of single nucleotide variants (SNVs) on clinical phenotypes is crucial in genetic research.
- Linkage disequilibrium and demographic/clinical factors can confound SNV effect estimations.
- Selecting relevant confounders from large datasets is a significant challenge.
Purpose of the Study:
- To explore the application of the Bayesian adjustment for confounding (BAC) method in genetic studies.
- To assess the efficacy of BAC in estimating causal effects of genetic variants using real-world data.
Main Methods:
- Utilized the Bayesian adjustment for confounding (BAC) method.
- Applied BAC to Genetic Analysis Workshop 19 exome sequencing data.
- Assessed the method's performance in handling confounding variables.
Main Results:
- Demonstrated that BAC efficiently estimates the causal effect of genetic variants.
- Showcased effective adjustment for confounding factors in genetic analyses.
- Validated BAC's utility with exome sequencing data.
Conclusions:
- The Bayesian adjustment for confounding (BAC) method is a valuable tool for genetic studies.
- BAC facilitates accurate causal effect estimation in the presence of numerous potential confounders.
- BAC can enhance genome-wide association studies (GWAS) data analysis for assessing genetic variant impacts.
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