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A nonparametric test for association with multiple loci in the retrospective case-control study.
Chan Wang1,2, Shufang Deng1, Leiming Sun1
1State Key Laboratory of Genetic Engineering, School of Life Sciences, Institute of Biostatistics, Fudan University, Shanghai, China.
Statistical Methods in Medical Research
|April 17, 2019
Summary
This study introduces SLIDE, a new statistical test for genome-wide association studies. SLIDE improves the detection of disease-associated genetic variants by leveraging linkage disequilibrium information.
Area of Science:
- Genetics
- Statistical genomics
- Bioinformatics
Background:
- Genome-wide association studies (GWAS) aim to identify genetic variants linked to common diseases.
- Common diseases are influenced by multiple single nucleotide polymorphisms (SNPs) often correlated due to linkage disequilibrium (LD).
- Existing methods may lack power in detecting associations involving complex genetic architectures.
Purpose of the Study:
- To propose a novel statistical test, SLIDE, for powerful association detection in GWAS.
- To incorporate linkage disequilibrium (LD) information among multiple SNPs to enhance signal detection.
- To improve the power of identifying variants associated with common diseases.
Main Methods:
- Developed the SLIDE (Sliding Window LD Difference) test.
- Derived the variance-covariance matrix for the test in a retrospective design.
- Utilized external LD databases to improve statistical power.
Main Results:
- SLIDE demonstrates superior performance compared to existing methods in simulations.
- The test is particularly effective for scenarios involving both common and rare variants.
- SLIDE shows efficacy in detecting both protective and deleterious variants.
Conclusions:
- SLIDE offers a powerful approach for detecting genetic associations in GWAS.
- The method effectively utilizes LD information, enhancing the identification of disease-related variants.
- SLIDE provides a valuable tool for genetic research, as demonstrated by its application to real-world data.
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