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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
Increasing power in association studies by using linkage disequilibrium structure and molecular function as prior
1Department of Computer Science and Human Genetics, University of California, Los Angeles, Los Angeles, California 90095, USA. eeskin@cs.ucla.edu
Genome Research
|March 21, 2008
Summary
This study enhances genetic association studies by using prior genomic data to refine multiple-hypothesis testing. The new method significantly increases the power to detect genetic associations with diseases.
Area of Science:
- Genetics
- Bioinformatics
- Statistical genomics
Background:
- Genomic data offers prior information for genetic association studies, including linkage disequilibrium and disease-relevant regions.
- Traditional genetic association studies use uniform significance thresholds for multiple-hypothesis testing.
Purpose of the Study:
- To develop a novel framework for incorporating prior genomic information into genetic association studies.
- To enhance the power of association studies by optimizing significance thresholds based on prior knowledge.
Main Methods:
- Revisiting multiple-hypothesis correction by varying significance thresholds (t(i)) at each marker.
- Developing a numerical procedure to solve for optimized thresholds that maximize study power.
- Utilizing HapMap data for benchmark simulation experiments.
Main Results:
- Demonstrated a significant increase in association study power using the proposed framework.
- Validated the approach through benchmark simulations with HapMap data.
- Developed a Web server for applying the method and provided optimized thresholds for specific genotyping chips.
Conclusions:
- The novel framework effectively incorporates prior genomic information to boost association study power.
- The method offers a significant advancement for genetic association studies, aiding in disease gene discovery.
- The provided tools and optimized thresholds facilitate the application of this powerful framework in real-world analyses.
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