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Iterative hard thresholding for model selection in genome-wide association studies.

Kevin L Keys1, Gary K Chen2, Kenneth Lange3

  • 1Department of Medicine, University of California, San Francisco, San Francisco, California, United States of America.

Genetic Epidemiology
|September 7, 2017
PubMed
Summary

Iterative hard thresholding (IHT) improves genome-wide association studies (GWAS) by enhancing single nucleotide polymorphism (SNP) selection accuracy over penalized regression methods like LASSO and MCP. This computational approach offers scalable and efficient analysis for geneticists.

Keywords:
genetic association studiesgreedy algorithmparallel computingsparse regression

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Area of Science:

  • Genetics
  • Computational Biology
  • Statistical Genetics

Background:

  • Genome-wide association studies (GWAS) identify correlations between genetic markers and traits.
  • Massive datasets in GWAS present computational challenges for accurate single nucleotide polymorphism (SNP) selection.
  • Penalized regression methods like LASSO and MCP are used but can suffer from false positives/negatives.

Purpose of the Study:

  • To compare the performance of Iterative Hard Thresholding (IHT) against LASSO and MCP penalized regression for SNP selection in GWAS.
  • To evaluate the accuracy and computational efficiency of IHT on simulated and real GWAS data.
  • To develop a parallelized implementation of IHT for large-scale GWAS analysis.

Main Methods:

  • Comparison of IHT, LASSO, and MCP penalized regression algorithms.
  • Application to both simulated and real genome-wide association study datasets.
  • Development of a parallelized IHT implementation utilizing SNP genotype compression, multi-core CPUs, and GPUs.

Main Results:

  • IHT demonstrates superior model selection accuracy compared to LASSO and MCP in GWAS.
  • IHT exhibits comparable computational speed to penalized regression methods.
  • The parallelized IHT implementation scales effectively for large numbers of causal markers.

Conclusions:

  • IHT is a more accurate and efficient method for SNP selection in GWAS.
  • Parallelized IHT enables large-scale genetic analyses on standard computing hardware.
  • This approach mitigates computational barriers in GWAS, improving the identification of trait-associated genetic variants.