ArticleGenetic epidemiology2011
Incorporating model uncertainty in detecting rare variants: the Bayesian risk index.
Article in Genetic epidemiology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- JOINT IDENTIFICATION OF SPATIALLY VARIABLE GENES VIA A NETWORK-ASSISTED BAYESIAN REGULARIZATION APPROACH.The annals of applied statistics · 2025Article
- Article
- Testing for association with rare variants in the coding and non-coding genome: RAVA-FIRST, a new approach based on CADD deleteriousness score.PLoS genetics · 2022Article
- Rare Variants in the DNA Repair Pathway and the Risk of Colorectal Cancer.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2021Article
- Rare variant association testing in the non-coding genome.Human genetics · 2020Review
- From genome-wide associations to candidate causal variants by statistical fine-mapping.Nature reviews. Genetics · 2018Review
- Rare germline variants in DNA repair genes and the angiogenesis pathway predispose prostate cancer patients to develop metastatic disease.British journal of cancer · 2018Article
- A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases.American journal of human genetics · 2017Article
- Illustrating, Quantifying, and Correcting for Bias in Post-hoc Analysis of Gene-Based Rare Variant Tests of Association.Frontiers in genetics · 2017Article
- Post hoc Analysis for Detecting Individual Rare Variant Risk Associations Using Probit Regression Bayesian Variable Selection Methods in Case-Control Sequencing Studies.Genetic epidemiology · 2016Article
- Genetic Variant Selection: Learning Across Traits and Sites.Genetics · 2016Article
- Identifying rare and common variants with Bayesian variable selection.BMC proceedings · 2016Article
- Fine Mapping Causal Variants with an Approximate Bayesian Method Using Marginal Test Statistics.Genetics · 2015Article
- False discovery rates for rare variants from sequenced data.Genetic epidemiology · 2015Article
- A Bayesian approach to identify genes and gene-level SNP aggregates in a genetic analysis of cancer data.Statistics and its interface · 2015Article
- Rare-variant association analysis: study designs and statistical tests.American journal of human genetics · 2014Review
- Replication of breast cancer susceptibility loci in whites and African Americans using a Bayesian approach.American journal of epidemiology · 2014Article
- A method to incorporate prior information into score test for genetic association studies.BMC bioinformatics · 2014Article
- Family-based Bayesian collapsing method for rare-variant association study.BMC proceedings · 2014Article
- A variational Bayes discrete mixture test for rare variant association.Genetic epidemiology · 2014Article
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4 authors.
Funding
Abstract
We are interested in investigating the involvement of multiple rare variants within a given region by conducting analyses of individual regions with two goals: (1) to determine if regional rare variation in aggregate is associated with risk; and (2) conditional upon the region being associated, to identify specific genetic variants within the region that are driving the association. In particular, we seek a formal integrated analysis that achieves both of our goals. For rare variants with low minor allele frequencies, there is very little power to statistically test the null hypothesis of equal allele or genotype counts for each variant. Thus, genetic association studies are often limited to detecting association within a subset of the common genetic markers. However, it is very likely that associations exist for the rare variants that may not be captured by the set of common markers. Our framework aims at constructing a risk index based on multiple rare variants within a region. Our analytical strategy is novel in that we use a Bayesian approach to incorporate model uncertainty in the selection of variants to include in the index as well as the direction of the associated effects. Additionally, the approach allows for inference at both the group and variant-specific levels. Using a set of simulations, we show that our methodology has added power over other popular rare variant methods to detect global associations. In addition, we apply the approach to sequence data from the WECARE Study of second primary breast cancers.
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