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Dissecting the colocalized GWAS and eQTLs with mediation analysis for high-dimensional exposures and confounders
Qi Zhang1, Zhikai Yang2, Jinliang Yang3
1Department of Mathematics and Statistics, University of New Hampshire, Durham, NH 03824, United States.
Biometrics
|May 27, 2024
Summary
MedDiC estimates indirect genetic effects for complex traits using a novel difference-in-coefficients approach. This method provides valid inference with higher power and faster computation for genetic regulation studies.
Area of Science:
- Genetics
- Statistical Genomics
- Bioinformatics
Background:
- Genome-wide association studies (GWAS) and quantitative trait loci (QTL) mapping are crucial for understanding genetic regulation.
- Investigating expression QTLs (eQTLs) that colocalize with QTL or GWAS peaks aids in mechanistic insights.
- Identifying causal variants and cis-driver genes is essential for explaining phenotypic variation.
Purpose of the Study:
- To propose MedDiC, a novel procedure for estimating the overall indirect effect (IE) in mediation problems with high-dimensional data.
- To address challenges in genetic regulation studies inspired by maize and mouse models.
- To provide a robust method for estimating mediation effects in complex biological systems.
Main Methods:
- Developed MedDiC, a procedure based on the difference-in-coefficients approach.
- Formulated biological studies as mediation problems with high-dimensional exposures, confounders, and mediators.
- Utilized simulation studies to evaluate MedDiC's performance against competing methods.
Main Results:
- MedDiC offers valid inference for the indirect effect (IE).
- Simulation studies demonstrated higher statistical power, shorter confidence intervals, and faster computing times for MedDiC.
- Application to maize and mouse datasets yielded reproducible results supported by external biological evidence.
Conclusions:
- MedDiC is a powerful and efficient method for estimating indirect genetic effects.
- The procedure provides reliable and reproducible findings in genetic regulation studies.
- MedDiC advances the analysis of complex traits by elucidating transcriptomic-level impacts of genetic variants.
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