ArticleGenetic epidemiology2015
Multiple SNP Set Analysis for Genome-Wide Association Studies Through Bayesian Latent Variable Selection.
Article in Genetic epidemiology, 2015. 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.
- Spike and Slab Regression for Nonstationary Gaussian Linear Mixed Effects Modeling of Rapid Disease Progression.Environmetrics · 2025Article
- Better safe than sorry-Whole-genome sequencing indicates that missense variants are significant in susceptibility to COVID-19.PloS one · 2023Article
- Article
- Views on GWAS statistical analysis.Bioinformation · 2020Article
- Brain Imaging Genomics: Integrated Analysis and Machine Learning.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2020Article
- Model-based clustering for identifying disease-associated SNPs in case-control genome-wide association studies.Scientific reports · 2019Article
- Structured Genome-Wide Association Studies with Bayesian Hierarchical Variable Selection.Genetics · 2019Article
- Genome-Wide Association Study Identifies Genomic Loci Affecting Filet Firmness and Protein Content in Rainbow Trout.Frontiers in genetics · 2019Article
- Group spike-and-slab lasso generalized linear models for disease prediction and associated genes detection by incorporating pathway information.Bioinformatics (Oxford, England) · 2018Article
- Joint Analysis of Strain and Parent-of-Origin Effects for Recombinant Inbred Intercrosses Generated from Multiparent Populations with the Collaborative Cross as an Example.G3 (Bethesda, Md.) · 2018Article
- The SNP-set based association study identifies ITGA1 as a susceptibility gene of attention-deficit/hyperactivity disorder in Han Chinese.Translational psychiatry · 2017Article
- The Spike-and-Slab Lasso Generalized Linear Models for Prediction and Associated Genes Detection.Genetics · 2017Article
- An algorithm for direct causal learning of influences on patient outcomes.Artificial intelligence in medicine · 2017Article
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Authors and funding
7 authors.
Funding
Abstract
The power of genome-wide association studies (GWAS) for mapping complex traits with single-SNP analysis (where SNP is single-nucleotide polymorphism) may be undermined by modest SNP effect sizes, unobserved causal SNPs, correlation among adjacent SNPs, and SNP-SNP interactions. Alternative approaches for testing the association between a single SNP set and individual phenotypes have been shown to be promising for improving the power of GWAS. We propose a Bayesian latent variable selection (BLVS) method to simultaneously model the joint association mapping between a large number of SNP sets and complex traits. Compared with single SNP set analysis, such joint association mapping not only accounts for the correlation among SNP sets but also is capable of detecting causal SNP sets that are marginally uncorrelated with traits. The spike-and-slab prior assigned to the effects of SNP sets can greatly reduce the dimension of effective SNP sets, while speeding up computation. An efficient Markov chain Monte Carlo algorithm is developed. Simulations demonstrate that BLVS outperforms several competing variable selection methods in some important scenarios.
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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.