ArticleGenetics2015
Penalized multimarker vs. single-marker regression methods for genome-wide association studies of quantitative traits.
Article in Genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Genome-Wide Association Study and Genomic Prediction of Fusarium Wilt Resistance in Common Bean Core Collection.International journal of molecular sciences · 2023Article
- Harnessing multivariate, penalized regression methods for genomic prediction and QTL detection of drought-related traits in grapevine.G3 (Bethesda, Md.) · 2021Article
- Genome-Wide Identification of Candidate Genes for Milk Production Traits in Korean Holstein Cattle.Animals : an open access journal from MDPI · 2021Article
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- Prioritizing genetic variants in GWAS with lasso using permutation-assisted tuning.Bioinformatics (Oxford, England) · 2020Article
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- Learning the optimal scale for GWAS through hierarchical SNP aggregation.BMC bioinformatics · 2018Article
- A novel linkage-disequilibrium corrected genomic relationship matrix for SNP-heritability estimation and genomic prediction.Heredity · 2018Article
- Biosignature Discovery for Substance Use Disorders Using Statistical Learning.Trends in molecular medicine · 2018Review
- Weighted pseudolikelihood for SNP set analysis with multiple secondary outcomes in case-control genetic association studies.Biometrics · 2017Article
- Association of a 3' untranslated region polymorphism in proprotein convertase subtilisin/kexin type 9 with HIV viral load and CD4+ levels in HIV/hepatitis C virus coinfected women.AIDS (London, England) · 2017Article
- Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models.Nature communications · 2017Article
- Resampling-based tests for Lasso in genome-wide association studies.BMC genetics · 2017Article
- Article
- Controlling the Rate of GWAS False Discoveries.Genetics · 2017Article
- Genetic Variant Selection: Learning Across Traits and Sites.Genetics · 2016Article
- Prediction of complex human diseases from pathway-focused candidate markers by joint estimation of marker effects: case of chronic fatigue syndrome.Human genomics · 2015Article
- Performance of a blockwise approach in variable selection using linkage disequilibrium information.BMC bioinformatics · 2015Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.