Journal article
Penalized multimarker vs. single-marker regression methods for genome-wide association studies of quantitative traits
Genetics (Austin), Vol.199(1), pp.205-222
01/2015
DOI: 10.1534/genetics.114.167817
PMCID: PMC4286685
PMID: 25354699
Abstract
The data from genome-wide association studies (GWAS) in humans are still predominantly analyzed using single-marker association methods. As an alternative to single-marker analysis (SMA), all or subsets of markers can be tested simultaneously. This approach requires a form of penalized regression (PR) as the number of SNPs is much larger than the sample size. Here we review PR methods in the context of GWAS, extend them to perform penalty parameter and SNP selection by false discovery rate (FDR) control, and assess their performance in comparison with SMA. PR methods were compared with SMA, using realistically simulated GWAS data with a continuous phenotype and real data. Based on these comparisons our analytic FDR criterion may currently be the best approach to SNP selection using PR for GWAS. We found that PR with FDR control provides substantially more power than SMA with genome-wide type-I error control but somewhat less power than SMA with Benjamini-Hochberg FDR control (SMA-BH). PR with FDR-based penalty parameter selection controlled the FDR somewhat conservatively while SMA-BH may not achieve FDR control in all situations. Differences among PR methods seem quite small when the focus is on SNP selection with FDR control. Incorporating linkage disequilibrium into the penalization by adapting penalties developed for covariates measured on graphs can improve power but also generate more false positives or wider regions for follow-up. We recommend the elastic net with a mixing weight for the Lasso penalty near 0.5 as the best method.
Details
- Title: Subtitle
- Penalized multimarker vs. single-marker regression methods for genome-wide association studies of quantitative traits
- Creators
- Hui Yi - Virginia Bioinformatics Institute, Virginia Tech University, Blacksburg, Virginia 24061 Ph.D. Program in Genetics, Bioinformatics, and Computational Biology, Virginia Tech University, Blacksburg, Virginia 24061Patrick Breheny - Department of Biostatistics, University of Iowa, Iowa City, Iowa 52240Netsanet Imam - Virginia Bioinformatics Institute, Virginia Tech University, Blacksburg, Virginia 24061Yongmei Liu - Departments of Epidemiology and Prevention and Internal Medicine, Division of Public Health Sciences, Translational Research Institute, Wake Forest School of Medicine, Winston-Salem, North Carolina 27157Ina Hoeschele - Virginia Bioinformatics Institute, Virginia Tech University, Blacksburg, Virginia 24061 Department of Statistics, Virginia Tech University, Blacksburg, Virginia 24061 inah@vbi.vt.edu
- Resource Type
- Journal article
- Publication Details
- Genetics (Austin), Vol.199(1), pp.205-222
- Publisher
- United States
- DOI
- 10.1534/genetics.114.167817
- PMID
- 25354699
- PMCID
- PMC4286685
- ISSN
- 1943-2631
- eISSN
- 1943-2631
- Grant note
- R01HG005254 / NHGRI NIH HHS N01 AG062103 / NIA NIH HHS N01 AG062101 / NIA NIH HHS R01HL101250 / NHLBI NIH HHS R01 AG032098 / NIA NIH HHS R01 HG005254 / NHGRI NIH HHS HHSN268200782096C / NHLBI NIH HHS 1R01AG032098-01A1 / NIA NIH HHS N01 AG062106 / NIA NIH HHS R01 HL101250 / NHLBI NIH HHS HHSN268200782096C / NHGRI NIH HHS
- Language
- English
- Date published
- 01/2015
- Academic Unit
- Biostatistics
- Record Identifier
- 9983997469402771
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