Literature DB >> 22199029

Invited commentary: GE-Whiz! Ratcheting gene-environment studies up to the whole genome and the whole exposome.

Duncan C Thomas1, Juan Pablo Lewinger, Cassandra E Murcray, W James Gauderman.   

Abstract

One goal in the post-genome-wide association study era is characterizing gene-environment interactions, including scanning for interactions with all available polymorphisms, not just those showing significant main effects. In recent years, several approaches to such "gene-environment-wide interaction studies" have been proposed. Two contributions in this issue of the American Journal of Epidemiology provide systematic comparisons of the performance of these various approaches, one based on simulation and one based on application to 2 real genome-wide association study scans for type 2 diabetes. The authors discuss some of the broader issues raised by these contributions, including the plausibility of the gene-environment independence assumption that some of these approaches rely upon, the need for replication, and various generalizations of these approaches.

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Mesh:

Year:  2011        PMID: 22199029      PMCID: PMC3261438          DOI: 10.1093/aje/kwr365

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  36 in total

1.  Gene-environment interactions in genome-wide association studies: a comparative study of tests applied to empirical studies of type 2 diabetes.

Authors:  Marilyn C Cornelis; Eric J Tchetgen Tchetgen; Liming Liang; Lu Qi; Nilanjan Chatterjee; Frank B Hu; Peter Kraft
Journal:  Am J Epidemiol       Date:  2011-12-22       Impact factor: 4.897

2.  Testing gene-environment interaction in large-scale case-control association studies: possible choices and comparisons.

Authors:  Bhramar Mukherjee; Jaeil Ahn; Stephen B Gruber; Nilanjan Chatterjee
Journal:  Am J Epidemiol       Date:  2011-12-22       Impact factor: 4.897

3.  Efficient genome-wide association testing of gene-environment interaction in case-parent trios.

Authors:  W James Gauderman; Duncan C Thomas; Cassandra E Murcray; David Conti; Dalin Li; Juan Pablo Lewinger
Journal:  Am J Epidemiol       Date:  2010-06-11       Impact factor: 4.897

4.  Genotype-based association mapping of complex diseases: gene-environment interactions with multiple genetic markers and measurement error in environmental exposures.

Authors:  Iryna Lobach; Ruzong Fan; Raymond J Carroll
Journal:  Genet Epidemiol       Date:  2010-12       Impact factor: 2.135

5.  Gene-environment interaction in genome-wide association studies.

Authors:  Cassandra E Murcray; Juan Pablo Lewinger; W James Gauderman
Journal:  Am J Epidemiol       Date:  2008-11-20       Impact factor: 4.897

6.  Using principal components of genetic variation for robust and powerful detection of gene-gene interactions in case-control and case-only studies.

Authors:  Samsiddhi Bhattacharjee; Zhaoming Wang; Julia Ciampa; Peter Kraft; Stephen Chanock; Kai Yu; Nilanjan Chatterjee
Journal:  Am J Hum Genet       Date:  2010-03-04       Impact factor: 11.025

7.  On the robustness of tests of genetic associations incorporating gene-environment interaction when the environmental exposure is misspecified.

Authors:  Eric J Tchetgen Tchetgen; Peter Kraft
Journal:  Epidemiology       Date:  2011-03       Impact factor: 4.822

8.  Annotation: the analysis of variance and the analysis of causes.

Authors:  R C Lewontin
Journal:  Am J Hum Genet       Date:  1974-05       Impact factor: 11.025

Review 9.  Implications of the exposome for exposure science.

Authors:  Stephen M Rappaport
Journal:  J Expo Sci Environ Epidemiol       Date:  2010-11-17       Impact factor: 5.563

10.  An Environment-Wide Association Study (EWAS) on type 2 diabetes mellitus.

Authors:  Chirag J Patel; Jayanta Bhattacharya; Atul J Butte
Journal:  PLoS One       Date:  2010-05-20       Impact factor: 3.240

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  33 in total

1.  Next generation analytic tools for large scale genetic epidemiology studies of complex diseases.

Authors:  Leah E Mechanic; Huann-Sheng Chen; Christopher I Amos; Nilanjan Chatterjee; Nancy J Cox; Rao L Divi; Ruzong Fan; Emily L Harris; Kevin Jacobs; Peter Kraft; Suzanne M Leal; Kimberly McAllister; Jason H Moore; Dina N Paltoo; Michael A Province; Erin M Ramos; Marylyn D Ritchie; Kathryn Roeder; Daniel J Schaid; Matthew Stephens; Duncan C Thomas; Clarice R Weinberg; John S Witte; Shunpu Zhang; Sebastian Zöllner; Eric J Feuer; Elizabeth M Gillanders
Journal:  Genet Epidemiol       Date:  2011-12-06       Impact factor: 2.135

2.  Inclusion of gene-gene and gene-environment interactions unlikely to dramatically improve risk prediction for complex diseases.

Authors:  Hugues Aschard; Jinbo Chen; Marilyn C Cornelis; Lori B Chibnik; Elizabeth W Karlson; Peter Kraft
Journal:  Am J Hum Genet       Date:  2012-05-24       Impact factor: 11.025

3.  Tests for Gene-Environment Interactions and Joint Effects With Exposure Misclassification.

Authors:  Philip S Boonstra; Bhramar Mukherjee; Stephen B Gruber; Jaeil Ahn; Stephanie L Schmit; Nilanjan Chatterjee
Journal:  Am J Epidemiol       Date:  2016-01-10       Impact factor: 4.897

Review 4.  Opportunities and Challenges for Environmental Exposure Assessment in Population-Based Studies.

Authors:  Chirag J Patel; Jacqueline Kerr; Duncan C Thomas; Bhramar Mukherjee; Beate Ritz; Nilanjan Chatterjee; Marta Jankowska; Juliette Madan; Margaret R Karagas; Kimberly A McAllister; Leah E Mechanic; M Daniele Fallin; Christine Ladd-Acosta; Ian A Blair; Susan L Teitelbaum; Christopher I Amos
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2017-07-14       Impact factor: 4.254

5.  Comparisons of power of statistical methods for gene-environment interaction analyses.

Authors:  Markus J Ege; David P Strachan
Journal:  Eur J Epidemiol       Date:  2013-09-05       Impact factor: 8.082

6.  Gene-environment interactions in cancer epidemiology: a National Cancer Institute Think Tank report.

Authors:  Carolyn M Hutter; Leah E Mechanic; Nilanjan Chatterjee; Peter Kraft; Elizabeth M Gillanders
Journal:  Genet Epidemiol       Date:  2013-10-05       Impact factor: 2.135

7.  Commentary: Fundamental problems with candidate gene-by-environment interaction studies - reflections on Moore and Thoemmes (2016).

Authors:  Richard Border; Matthew C Keller
Journal:  J Child Psychol Psychiatry       Date:  2017-03       Impact factor: 8.982

Review 8.  Genetic prediction of common diseases. Still no help for the clinical diabetologist!

Authors:  S Prudente; B Dallapiccola; F Pellegrini; A Doria; V Trischitta
Journal:  Nutr Metab Cardiovasc Dis       Date:  2012-07-21       Impact factor: 4.222

9.  Is the gene-environment interaction paradigm relevant to genome-wide studies? The case of education and body mass index.

Authors:  Jason D Boardman; Benjamin W Domingue; Casey L Blalock; Brett C Haberstick; Kathleen Mullan Harris; Matthew B McQueen
Journal:  Demography       Date:  2014-02

Review 10.  Confluence of genes, environment, development, and behavior in a post Genome-Wide Association Study world.

Authors:  Scott I Vrieze; William G Iacono; Matt McGue
Journal:  Dev Psychopathol       Date:  2012-11
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