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Multivariate genome-wide association analysis by iterative hard thresholding
Benjamin B Chu1, Seyoon Ko1,2, Jin J Zhou2,3
1Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095-1554, United States.
Bioinformatics (Oxford, England)
|April 17, 2023
Summary
Analyzing multiple traits simultaneously in genome-wide association studies (GWAS) is more effective. A new algorithm, MendelIHT, offers a faster and more accurate approach for multivariate GWAS, improving upon existing methods.
Area of Science:
- Genetics
- Computational Biology
- Statistical Genetics
Background:
- Simultaneous analysis of multiple correlated traits in genome-wide association studies (GWAS) offers advantages over single-trait analyses.
- Existing multivariate GWAS methods are often computationally intensive and marker-by-marker, limiting scalability.
Purpose of the Study:
- To develop and implement an efficient algorithm for multivariate genome-wide association studies.
- To enable the simultaneous analysis of numerous correlated traits and genetic variants.
Main Methods:
- A sparsity-constrained regression algorithm based on iterative hard thresholding (IHT) was developed.
- The algorithm was implemented in a Julia package named MendelIHT.jl.
- Performance was evaluated using simulations and UK Biobank data.
Main Results:
- MendelIHT demonstrated comparable true positive rates and lower false positive rates than existing methods like GEMMA and mv-PLINK in simulations.
- The method achieved significantly faster execution times compared to conventional approaches.
- Analysis of UK Biobank data showed efficient joint analysis of three and 18 traits, handling large datasets with substantial memory usage.
Conclusions:
- MendelIHT provides a computationally efficient and accurate solution for multivariate genome-wide association studies.
- The package facilitates the simultaneous modeling of single nucleotide polymorphisms (SNPs) and multiple traits, advancing genetic research.
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