Literature DB >> 9433637

Genetic analysis with hierarchical models.

J S Witte1.   

Abstract

During the Genetic Analysis Workshop 9 presentations [Goldin et al., 1995] a brief discussion took place about the value of empirical-Bayes methods in genetic analysis. Due to the informal nature of this discussion, the improvements available for analyzing data with this approach--and with the broader class of hierarchical models--were not clearly presented. As a methodologic contribution, I further explore how one can use this potentially valuable technique in analysis of genetic data, including data similar to those given in GAW10.

Mesh:

Year:  1997        PMID: 9433637     DOI: 10.1002/(SICI)1098-2272(1997)14:6<1137::AID-GEPI96>3.0.CO;2-H

Source DB:  PubMed          Journal:  Genet Epidemiol        ISSN: 0741-0395            Impact factor:   2.135


  13 in total

1.  Hierarchical modeling of linkage disequilibrium: genetic structure and spatial relations.

Authors:  David V Conti; John S Witte
Journal:  Am J Hum Genet       Date:  2003-01-13       Impact factor: 11.025

2.  Localizing putative markers in genetic association studies by incorporating linkage disequilibrium into bayesian hierarchical models.

Authors:  Brooke L Fridley; Gregory D Jenkins
Journal:  Hum Hered       Date:  2010-06-10       Impact factor: 0.444

3.  Recommendations and proposed guidelines for assessing the cumulative evidence on joint effects of genes and environments on cancer occurrence in humans.

Authors:  Paolo Boffetta; Deborah M Winn; John P Ioannidis; Duncan C Thomas; Julian Little; George Davey Smith; Vincent J Cogliano; Stephen S Hecht; Daniela Seminara; Paolo Vineis; Muin J Khoury
Journal:  Int J Epidemiol       Date:  2012-05-16       Impact factor: 7.196

4.  An empirical Bayes method for updating inferences in analysis of quantitative trait loci using information from related genome scans.

Authors:  Kui Zhang; Howard Wiener; Mark Beasley; Varghese George; Christopher I Amos; David B Allison
Journal:  Genetics       Date:  2006-06-04       Impact factor: 4.562

5.  Enriching the analysis of genomewide association studies with hierarchical modeling.

Authors:  Gary K Chen; John S Witte
Journal:  Am J Hum Genet       Date:  2007-06-26       Impact factor: 11.025

6.  A powerful and flexible multilocus association test for quantitative traits.

Authors:  Lydia Coulter Kwee; Dawei Liu; Xihong Lin; Debashis Ghosh; Michael P Epstein
Journal:  Am J Hum Genet       Date:  2008-02       Impact factor: 11.025

7.  Rare genetic variants and treatment response: sample size and analysis issues.

Authors:  John S Witte
Journal:  Stat Med       Date:  2012-06-27       Impact factor: 2.373

Review 8.  Methods for investigating gene-environment interactions in candidate pathway and genome-wide association studies.

Authors:  Duncan Thomas
Journal:  Annu Rev Public Health       Date:  2010       Impact factor: 21.981

9.  Peroxisome proliferator-activated receptor-alpha (PPARA) genetic polymorphisms and breast cancer risk: a Long Island ancillary study.

Authors:  Amanda K Golembesky; Marilie D Gammon; Kari E North; Jeannette T Bensen; Jane C Schroeder; Susan L Teitelbaum; Alfred I Neugut; Regina M Santella
Journal:  Carcinogenesis       Date:  2008-06-26       Impact factor: 4.944

10.  Hierarchical modeling identifies novel lung cancer susceptibility variants in inflammation pathways among 10,140 cases and 11,012 controls.

Authors:  Darren R Brenner; Paul Brennan; Paolo Boffetta; Christopher I Amos; Margaret R Spitz; Chu Chen; Gary Goodman; Joachim Heinrich; Heike Bickeböller; Albert Rosenberger; Angela Risch; Thomas Muley; John R McLaughlin; Simone Benhamou; Christine Bouchardy; Juan Pablo Lewinger; John S Witte; Gary Chen; Shelley Bull; Rayjean J Hung
Journal:  Hum Genet       Date:  2013-02-01       Impact factor: 4.132

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