ArticleAmerican journal of human genetics2007
Enriching the analysis of genomewide association studies with hierarchical modeling.
Article in American journal of human genetics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 2 of them syntheses that pooled it.
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Who cites it
57 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Identification of lung cancer histology-specific variants applying Bayesian framework variant prioritization approaches within the TRICL and ILCCO consortia.Carcinogenesis · 2015Pooled it
- Cross Cancer Genomic Investigation of Inflammation Pathway for Five Common Cancers: Lung, Ovary, Prostate, Breast, and Colorectal Cancer.Journal of the National Cancer Institute · 2015Pooled it
- A regularized Cox hierarchical model for incorporating annotation information in predictive omic studies.BioData mining · 2024Article
- Hierarchical joint analysis of marginal summary statistics-Part II: High-dimensional instrumental analysis of omics data.Genetic epidemiology · 2024Article
- A scalable Bayesian functional GWAS method accounting for multivariate quantitative functional annotations with applications for studying Alzheimer disease.HGG advances · 2022Article
- Estimating Genetic Similarity Matrices Using Phylogenies.Journal of computational biology : a journal of computational molecular cell biology · 2021Article
- A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis.American journal of epidemiology · 2021Article
- Population genetic models of GERP scores suggest pervasive turnover of constrained sites across mammalian evolution.PLoS genetics · 2020Article
- A multiple coefficient of determination-based method for parsing SNPs that correlate with mRNA expression.Scientific reports · 2019Article
- Development of a tissue augmented Bayesian model for expression quantitative trait loci analysis.Mathematical biosciences and engineering : MBE · 2019Article
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- Using local multiplicity to improve effect estimation from a hypothesis-generating pharmacogenetics study.The pharmacogenomics journal · 2016Article
- A Cross-Cancer Genetic Association Analysis of the DNA Repair and DNA Damage Signaling Pathways for Lung, Ovary, Prostate, Breast, and Colorectal Cancer.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2016Article
- Leveraging Multi-ethnic Evidence for Mapping Complex Traits in Minority Populations: An Empirical Bayes Approach.American journal of human genetics · 2015Article
- Article
- Joint analysis of functional genomic data and genome-wide association studies of 18 human traits.American journal of human genetics · 2014Article
- Two novel pathway analysis methods based on a hierarchical model.Bioinformatics (Oxford, England) · 2014Article
- Replication of breast cancer susceptibility loci in whites and African Americans using a Bayesian approach.American journal of epidemiology · 2014Article
- Two-phase and family-based designs for next-generation sequencing studies.Frontiers in genetics · 2013Review
- A unified mixed-effects model for rare-variant association in sequencing studies.Genetic epidemiology · 2013Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Genomewide association studies (GWAs) initially investigate hundreds of thousands of single-nucleotide polymorphisms (SNPs), and the most promising SNPs are further evaluated with additional subjects, for replication or a joint analysis. Deciding which SNPs merit follow-up is one of the most crucial aspects of these studies. We present here an approach for selecting the most-promising SNPs that incorporates into a hierarchical model both conventional results and other existing information about the SNPs. The model is developed for general use, its potential value is shown by application, and tools are provided for undertaking hierarchical modeling. By quantitatively harnessing all available information in GWAs, hierarchical modeling may more clearly distinguish true causal variants from noise.
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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.