ArticleGenetic epidemiology2010
SNP selection in genome-wide and candidate gene studies via penalized logistic regression.
Article in Genetic epidemiology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 103 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
103 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Selecting instruments for Mendelian randomization in the wake of genome-wide association studies.International journal of epidemiology · 2016Pooled it
- Insulin sensitivity and cardiovascular risk after revascularization: associations with remodeling and long-term outcomes.Frontiers in cardiovascular medicine · 2026Article
- Development and Validation of Multi-Locus GWAS-Based KASP Markers for MaizePlants (Basel, Switzerland) · 2025Article
- Using machine learning and single nucleotide polymorphisms for improving rheumatoid arthritis risk Prediction in postmenopausal women.PLOS digital health · 2025Article
- Modulatory Neurotransmitter Genotypes Shape Dynamic Functional Connectome Reconfigurations.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Hierarchical joint analysis of marginal summary statistics-Part I: Multipopulation fine mapping and credible set construction.Genetic epidemiology · 2024Article
- Article
- Article
- Genes, exposures, and interactions on preterm birth risk: an exploratory study in an Argentine population.Journal of community genetics · 2022Article
- What predicts people's belief in COVID-19 misinformation? A retrospective study using a nationwide online survey among adults residing in the United States.BMC public health · 2022Article
- A scalable hierarchical lasso for gene-environment interactions.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2022Article
- Efficient feature extraction from highly sparse binary genotype data for cancer prognosis prediction using an auto-encoder.Frontiers in oncology · 2022Article
- Genome-Wide Association Study Statistical Models: A Review.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Review of Mendelian Randomization Studies on Endometrial Cancer.Frontiers in endocrinology · 2022Review
- Smooth-threshold multivariate genetic prediction incorporating gene-environment interactions.G3 (Bethesda, Md.) · 2021Article
- New adaptive lasso approaches for variable selection in automated pharmacovigilance signal detection.BMC medical research methodology · 2021Article
- Boosting and lassoing new prostate cancer SNP risk factors and their connection to selenium.Scientific reports · 2021Article
- Genome-wide association study and its applications in the non-model crop Sesamum indicum.BMC plant biology · 2021Review
- Predictions from algorithmic modeling result in better decisions than from data modeling for soybean iron deficiency chlorosis.PloS one · 2021Article
- An Efficient Algorithm for the Detection of Outliers in Mislabeled Omics Data.Computational and mathematical methods in medicine · 2021Article
43 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Penalized regression methods offer an attractive alternative to single marker testing in genetic association analysis. Penalized regression methods shrink down to zero the coefficient of markers that have little apparent effect on the trait of interest, resulting in a parsimonious subset of what we hope are true pertinent predictors. Here we explore the performance of penalization in selecting SNPs as predictors in genetic association studies. The strength of the penalty can be chosen either to select a good predictive model (via methods such as computationally expensive cross validation), through maximum likelihood-based model selection criterion (such as the BIC), or to select a model that controls for type I error, as done here. We have investigated the performance of several penalized logistic regression approaches, simulating data under a variety of disease locus effect size and linkage disequilibrium patterns. We compared several penalties, including the elastic net, ridge, Lasso, MCP and the normal-exponential-γ shrinkage prior implemented in the hyperlasso software, to standard single locus analysis and simple forward stepwise regression. We examined how markers enter the model as penalties and P-value thresholds are varied, and report the sensitivity and specificity of each of the methods. Results show that penalized methods outperform single marker analysis, with the main difference being that penalized methods allow the simultaneous inclusion of a number of markers, and generally do not allow correlated variables to enter the model, producing a sparse model in which most of the identified explanatory markers are accounted for.
Indexed as
Identifiers
What OpenQuestion holds
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.