ArticleStatistical analysis and data mining2013
Penalized Regression and Risk Prediction in Genome-Wide Association Studies.
Article in Statistical analysis and data mining, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- A penalized regression framework for building polygenic risk models based on summary statistics from genome-wide association studies and incorporating external information.Journal of the American Statistical Association · 2021Article
- Detecting PCOS susceptibility loci from genome-wide association studies via iterative trend correlation based feature screening.BMC bioinformatics · 2020Article
- Multiple-kernel learning for genomic data mining and prediction.BMC bioinformatics · 2019Article
- Evaluation of polygenic risk scores for ovarian cancer risk prediction in a prospective cohort study.Journal of medical genetics · 2018Article
- A method combining a random forest-based technique with the modeling of linkage disequilibrium through latent variables, to run multilocus genome-wide association studies.BMC bioinformatics · 2018Article
- Genetic risk models: Influence of model size on risk estimates and precision.Genetic epidemiology · 2017Article
- Prediction of Quantitative Traits Using Common Genetic Variants: Application to Body Mass Index.Genomics & informatics · 2016Article
- Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies.Scientific reports · 2016Article
- Review and evaluation of penalised regression methods for risk prediction in low-dimensional data with few events.Statistics in medicine · 2016Review
- Exploiting Linkage Disequilibrium for Ultrahigh-Dimensional Genome-Wide Data with an Integrated Statistical Approach.Genetics · 2016Article
- Generic Feature Selection with Short Fat Data.Journal of the Indian Society of Agricultural Statistics. Indian Society of Agricultural Statistics · 2014Article
- Penalized regression approaches to testing for quantitative trait-rare variant association.Frontiers in genetics · 2014Article
- Large sample size, wide variant spectrum, and advanced machine-learning technique boost risk prediction for inflammatory bowel disease.American journal of human genetics · 2013Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
An important task in personalized medicine is to predict disease risk based on a person's genome, e.g. on a large number of single-nucleotide polymorphisms (SNPs). Genome-wide association studies (GWAS) make SNP and phenotype data available to researchers. A critical question for researchers is how to best predict disease risk. Penalized regression equipped with variable selection, such as LASSO and SCAD, is deemed to be promising in this setting. However, the sparsity assumption taken by the LASSO, SCAD and many other penalized regression techniques may not be applicable here: it is now hypothesized that many common diseases are associated with many SNPs with small to moderate effects. In this article, we use the GWAS data from the Wellcome Trust Case Control Consortium (WTCCC) to investigate the performance of various unpenalized and penalized regression approaches under true sparse or non-sparse models. We find that in general penalized regression outperformed unpenalized regression; SCAD, TLP and LASSO performed best for sparse models, while elastic net regression was the winner, followed by ridge, TLP and LASSO, for non-sparse models.
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Registered trials
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.