| Title: | Iterative hard thresholding in genome-wide association studies: Generalized linear models, prior weights, and double sparsity |
| Journal: | GigaScience |
| Published: | 1 Jun 2020 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/32491161/ |
| DOI: | https://doi.org/10.1093/gigascience/giaa044 |
| URL: | https://academic.oup.com/gigascience/article-pdf/9/6/giaa044/33341096/giaa044.pdf |
Publication 5290
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Abstract
BACKGROUND: Consecutive testing of single nucleotide polymorphisms (SNPs) is usually employed to identify genetic variants associated with complex traits. Ideally one should model all covariates in unison, but most existing analysis methods for genome-wide association studies (GWAS) perform only univariate regression.</p>
RESULTS: We extend and efficiently implement iterative hard thresholding (IHT) for multiple regression, treating all SNPs simultaneously. Our extensions accommodate generalized linear models, prior information on genetic variants, and grouping of variants. In our simulations, IHT recovers up to 30% more true predictors than SNP-by-SNP association testing and exhibits a 2-3 orders of magnitude decrease in false-positive rates compared with lasso regression. We also test IHT on the UK Biobank hypertension phenotypes and the Northern Finland Birth Cohort of 1966 cardiovascular phenotypes. We find that IHT scales to the large datasets of contemporary human genetics and recovers the plausible genetic variants identified by previous studies.</p>
CONCLUSIONS: Our real data analysis and simulation studies suggest that IHT can (i) recover highly correlated predictors, (ii) avoid over-fitting, (iii) deliver better true-positive and false-positive rates than either marginal testing or lasso regression, (iv) recover unbiased regression coefficients, (v) exploit prior information and group-sparsity, and (vi) be used with biobank-sized datasets. Although these advances are studied for genome-wide association studies inference, our extensions are pertinent to other regression problems with large numbers of predictors.</p>
8 Authors
- Benjamin B Chu
- Kevin L Keys
- Christopher A German
- Hua Zhou
- Jin J Zhou
- Eric M Sobel
- Janet S Sinsheimer
- Kenneth Lange
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