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Testing for Sufficient-Cause Gene-Environment Interactions Under the Assumptions of Independence and Hardy-Weinberg Equilibrium.
Lee WC. Lee WC. Am J Epidemiol. 2015 Jul 1;182(1):9-16. doi: 10.1093/aje/kwv030. Epub 2015 May 29. Am J Epidemiol. 2015. PMID: 26025233
The proposed tests can be tailored to detect a particular type of sufficient-cause gene-environment interaction with greater sensitivity. These tests include testing for autosomal dominant, autosomal recessive, and gene-dosage interactions
The proposed tests can be tailored to detect a particular type of sufficient-cause gene-environment interaction
Boosting for detection of gene-environment interactions.
Pashova H, LeBlanc M, Kooperberg C. Pashova H, et al. Stat Med. 2013 Jan 30;32(2):255-66. doi: 10.1002/sim.5444. Epub 2012 Jul 5. Stat Med. 2013. PMID: 22764060 Free PMC article.
Traditional methods to identify gene-environment interactions typically consider only one measured environmental variable at a time. ...In this paper, we develop a variant of L(2) boosting that is specifically designed to identify combinations of envir …
Traditional methods to identify gene-environment interactions typically consider only one measured environmental variab …
Generalized multifactor dimensionality reduction approaches to identification of genetic interactions underlying ordinal traits.
Hou TT, Lin F, Bai S, Cleves MA, Xu HM, Lou XY. Hou TT, et al. Genet Epidemiol. 2019 Feb;43(1):24-36. doi: 10.1002/gepi.22169. Epub 2018 Nov 2. Genet Epidemiol. 2019. PMID: 30387901
The manifestation of complex traits is influenced by gene-gene and gene-environment interactions, and the identification of multifactor interactions is an important but challenging undertaking for genetic studies. ...In this study, we pro …
The manifestation of complex traits is influenced by gene-gene and gene-environment interactions, and the …
Power Analysis for Population-Based Longitudinal Studies Investigating Gene-Environment Interactions in Chronic Diseases: A Simulation Study.
Ma J, Thabane L, Beyene J, Raina P. Ma J, et al. PLoS One. 2016 Feb 22;11(2):e0149940. doi: 10.1371/journal.pone.0149940. eCollection 2016. PLoS One. 2016. PMID: 26901422 Free PMC article.
The results showed that the statistical power to identify the effect of environmental and genetic risk exposures, and their interaction on a disease was boosted when: (1) the prevalence of the risk exposures increased; (2) the disease of interest is relatively commo …
The results showed that the statistical power to identify the effect of environmental and genetic risk exposures, and their interaction
Lower-order effects adjustment in quantitative traits model-based multifactor dimensionality reduction.
Mahachie John JM, Cattaert T, Lishout FV, Gusareva ES, Steen KV. Mahachie John JM, et al. PLoS One. 2012;7(1):e29594. doi: 10.1371/journal.pone.0029594. Epub 2012 Jan 5. PLoS One. 2012. PMID: 22242176 Free PMC article.
Identifying gene-gene interactions or gene-environment interactions in studies of human complex diseases remains a big challenge in genetic epidemiology. ...Moreover, our interaction study focuses on 2-way SNP-SNP interactions
Identifying gene-gene interactions or gene-environment interactions in studies of human complex di …
Stability of variable importance scores and rankings using statistical learning tools on single-nucleotide polymorphisms and risk factors involved in gene x gene and gene x environment interactions.
Nicodemus KK, Wang W, Shugart YY. Nicodemus KK, et al. BMC Proc. 2007;1 Suppl 1(Suppl 1):S58. doi: 10.1186/1753-6561-1-s1-s58. Epub 2007 Dec 18. BMC Proc. 2007. PMID: 18466558 Free PMC article.
For the simulated data of Problem 3 in the Genetic Analysis Workshop 15 (GAW15), we examined the variability of both rankings and magnitude of variable importance measures using 10 variables simulated to participate in gene x gene and gene x environment
For the simulated data of Problem 3 in the Genetic Analysis Workshop 15 (GAW15), we examined the variability of both rankings and magnitude …
Multivariate dimensionality reduction approaches to identify gene-gene and gene-environment interactions underlying multiple complex traits.
Xu HM, Sun XW, Qi T, Lin WY, Liu N, Lou XY. Xu HM, et al. PLoS One. 2014 Sep 26;9(9):e108103. doi: 10.1371/journal.pone.0108103. eCollection 2014. PLoS One. 2014. PMID: 25259584 Free PMC article.
A multivariate approach for detecting interactions is thus greatly needed on the purposes of handling a multifaceted phenotype and longitudinal data, as well as improving statistical power for multiple significance testing via a two-stage testing procedure that invo …
A multivariate approach for detecting interactions is thus greatly needed on the purposes of handling a multifaceted phenotype …
IGENT: efficient entropy based algorithm for genome-wide gene-gene interaction analysis.
Kwon MS, Park M, Park T. Kwon MS, et al. BMC Med Genomics. 2014;7 Suppl 1(Suppl 1):S6. doi: 10.1186/1755-8794-7-S1-S6. Epub 2014 May 8. BMC Med Genomics. 2014. PMID: 25077411 Free PMC article.
Gene-gene interaction (GGI) analysis is expected to unveil a large portion of unexplained heritability of complex traits. METHODS: In this work, we propose IGENT, Information theory-based GEnome-wide gene-gene iNTeraction method. IGENT is
Gene-gene interaction (GGI) analysis is expected to unveil a large portion of unexplained heritability of complex trait
The case-only test for gene-environment interaction is not uniformly powerful: an empirical example.
Wu C, Chang J, Ma B, Miao X, Zhou Y, Liu Y, Li Y, Wu T, Hu Z, Shen H, Jia W, Zeng Y, Lin D, Kraft P. Wu C, et al. Genet Epidemiol. 2013 May;37(4):402-7. doi: 10.1002/gepi.21713. Epub 2013 Mar 13. Genet Epidemiol. 2013. PMID: 23595356 Free PMC article.
The case-only test has been proposed as a more powerful approach to detect gene-environment (G × E) interactions. This approach assumes that the genetic and environmental factors are independent. ...Although the case-only test yielded the most signific …
The case-only test has been proposed as a more powerful approach to detect gene-environment (G × E) interactions
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