Browsing by Subject "Gene-environment interactions"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Collaborative meta-analysis finds no evidence of a strong interaction between stress and 5-HTTLPR genotype contributing to the development of depression
    (Nature Publishing Group, 2018-01) Culverhouse, Robert C.; Saccone, Nancy L.; Horton, Amy C.; Ma, Yinjiao; Anstey, Kaarin J.; Banaschewski, Tobias; Burmeister, Margit; Cohen-Woods, Sarah; Etain, Bruno; Fisher, Helen L.; Goldman, Noreen; Guillaume, Sébastien; Horwood, John; Juhasz, Gabriella; Lester, Kathryn J.; Mandelli, Laura; Middeldorp, Christel M.; Olié, Emilie; Villafuerte, Sandra; Air, Tracy M.; Araya, Ricardo; Bowes, Lucy; Burns, Richard; Byrne, Enda M.; Coffey, Carolyn; Coventry, William L.; Gawronski, Katerina; Glei, Dana; Hatzimanolis, Alex; Hottenga, Jouke-Jan; Jaussent, Isabelle; Jawahar, Catharine; Jennen-Steinmetz, Christine; Kramer, John R.; Lajnef, Mohamed; Little, Keriann; zu Schwabedissen, Henriette Meyer; Nauck, Matthias; Nederhof, Esther; Petschner, Peter; Peyrot, Wouter J.; Schwahn, Christian; Sinnamon, Grant; Stacey, David; Tian, Yan; Toben, Catherine; Auwera, Sandra Van der; Wainwright, Nick; Wang, Jen-Chyong; Willemsen, Gonneke; Anderson, Ian M.; Arolt, Volker; Åslund, Cecilia; Bagdy, Gyorgy; Baune, Bernhard T.; Bellivier, Frank; Boomsma, Dorret I.; Courtet, Philippe; Dannlowski, Udo; de Geus, Eco J.C.; Deakin, John F. W.; Easteal, Simon; Eley, Thalia; Fergusson, David M.; Goate, Alison M.; Gonda, Xenia; Grabe, Hans J.; Holzman, Claudia; Johnson, Eric O.; Kennedy, Martin; Laucht, Manfred; Martin, Nicholas G.; Munafò, Marcus; Nilsson, Kent W.; Oldehinkel, Albertine J.; Olsson, Craig; Ormel, Johan; Otte, Christian; Patton, George C.; Penninx, Brenda W.J.H.; Ritchie, Karen; Sarchiapone, Marco; Scheid, JM; Serretti, Alessandro; Smit, Johannes H.; Stefanis, Nicholas C.; Surtees, Paul G.; Völzke, Henry; Weinstein, Maxine; Whooley, Mary; Nurnberger, John I., Jr.; Breslau, Naomi; Bierut, Laura J.; Psychiatry, School of Medicine
    The hypothesis that the S allele of the 5-HTTLPR serotonin transporter promoter region is associated with increased risk of depression, but only in individuals exposed to stressful situations, has generated much interest, research, and controversy since first proposed in 2003. Multiple meta-analyses combining results from heterogeneous analyses have not settled the issue. To determine the magnitude of the interaction and the conditions under which it might be observed, we performed new analyses on 31 datasets containing 38 802 European-ancestry subjects genotyped for 5-HTTLPR and assessed for depression and childhood maltreatment or other stressful life events, and meta-analyzed the results. Analyses targeted two stressors (narrow, broad) and two depression outcomes (current, lifetime). All groups that published on this topic prior to the initiation of our study and met the assessment and sample size criteria were invited to participate. Additional groups, identified by consortium members or self-identified in response to our protocol (published prior to the start of analysis1) with qualifying unpublished data were also invited to participate. A uniform data analysis script implementing the protocol was executed by each of the consortium members. Our findings do not support the interaction hypothesis. We found no subgroups or variable definitions for which an interaction between stress and 5-HTTLPR genotype was statistically significant. In contrast, our findings for the main effects of life stressors (strong risk factor) and 5-HTTLPR genotype (no impact on risk) are strikingly consistent across our contributing studies, the original study reporting the interaction, and subsequent meta-analyses. Our conclusion is that if an interaction exists in which the S allele of 5-HTTLPR increases risk of depression only in stressed individuals, then it is not broadly generalizable, but must be of modest effect size and only observable in limited situations.
  • Thumbnail Image
    Item
    Robust Bayesian variable selection for gene-environment interactions
    (Wiley, 2022-06) Ren, Jie; Zhou, Fei; Li, Xiaoxi; Ma, Shuangge; Jiang, Yu; Wu, Cen; Biostatistics and Health Data Science, School of Medicine
    Gene–environment (G× E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G× E studies have been commonly encountered, leading to the development of a broad spectrum of robust regularization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a fully Bayesian robust variable selection method for G× E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, for the robust sparse group selection, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects robustly. An efficient Gibbs sampler has been developed to facilitate fast computation. Extensive simulation studies, analysis of diabetes data with single-nucleotide polymorphism measurements from the Nurses' Health Study, and The Cancer Genome Atlas melanoma data with gene expression measurements demonstrate the superior performance of the proposed method over multiple competing alternatives.
  • Thumbnail Image
    Item
    Robust Bayesian variable selection for gene–environment interactions
    (Oxford University Press, 2023) Ren, Jie; Zhou, Fei; Li, Xiaoxi; Ma, Shuangge; Jiang, Yu; Wu, Cen; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Gene-environment (G× E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G× E studies have been commonly encountered, leading to the development of a broad spectrum of robust regularization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a fully Bayesian robust variable selection method for G× E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, for the robust sparse group selection, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects robustly. An efficient Gibbs sampler has been developed to facilitate fast computation. Extensive simulation studies, analysis of diabetes data with single-nucleotide polymorphism measurements from the Nurses' Health Study, and The Cancer Genome Atlas melanoma data with gene expression measurements demonstrate the superior performance of the proposed method over multiple competing alternatives.