Journal article

Post hoc Analysis for Detecting Individual Rare Variant Risk Associations Using Probit Regression Bayesian Variable Selection Methods in Case-Control Sequencing Studies

Nicholas B Larson, Shannon McDonnell, Lisa Cannon Albright, Craig Teerlink, Janet Stanford, Elaine A Ostrander, William B Isaacs, Jianfeng Xu, Kathleen A Cooney, Ethan Lange, Johanna Schleutker, John D Carpten, Isaac Powell, Joan Bailey-Wilson, Olivier Cussenot, Geraldine Cancel-Tassin, Graham Giles, Robert MacInnis, Christiane Maier, Alice S Whittemore Show all

Genetic Epidemiology | WILEY | Published : 2016

Abstract

Rare variants (RVs) have been shown to be significant contributors to complex disease risk. By definition, these variants have very low minor allele frequencies and traditional single-marker methods for statistical analysis are underpowered for typical sequencing study sample sizes. Multimarker burden-type approaches attempt to identify aggregation of RVs across case-control status by analyzing relatively small partitions of the genome, such as genes. However, it is generally the case that the aggregative measure would be a mixture of causal and neutral variants, and these omnibus tests do not directly provide any indication of which RVs may be driving a given association. Recently, Bayesian..

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Grants

Awarded by US Public Health Service, National Institutes of Health (NIH)


Awarded by National Cancer Institute


Awarded by Cancer Research UK


Awarded by NATIONAL CANCER INSTITUTE


Awarded by NATIONAL HUMAN GENOME RESEARCH INSTITUTE


Awarded by NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES


Funding Acknowledgements

This research was supported by the US Public Health Service, National Institutes of Health (NIH), contract grant number GM065450 (D.J.S.) and National Cancer Institute, grant number U01 CA 89600 (S.N.T.). There are no conflicts of interest to declare.