Statistical Approaches to Gene X Environment Interactions for Complex Phenotypes
Diverse methodological and statistical approaches for investigating the role of gene-environment interactions in a range of complex diseases and traits.
Findings from the Human Genome Project and from Genome-Wide Association (GWA) studies indicate that many diseases and traits manifest a more complex genomic pattern than previously assumed. These findings, and advances in high-throughput sequencing, suggest that there are many sources of influence--genetic, epigenetic, and environmental. This volume investigates the role of the interactions of genes and environment (G E) in diseases and traits (referred to by the contributors as complex phenotypes) including depression, diabetes, obesity, and substance use.
The contributors first present different statistical approaches or strategies to address G E and G G interactions with high-throughput sequenced data, including two-stage procedures to identify G E and G G interactions, marker-set approaches to assessing interactions at the gene level, and the use of a partial-least square (PLS) approach. The contributors then turn to specific complex phenotypes, research designs, or combined methods that may advance the study of G E interactions, considering such topics as randomized clinical trials in obesity research, longitudinal research designs and statistical models, and the development of polygenic scores to investigate G E interactions.
Contributors
Fatima Umber Ahmed, Yin-Hsiu Chen, James Y. Dai, Caroline Y. Doyle, Zihuai He, Li Hsu, Shuo Jiao, Erin Loraine Kinnally, Yi-An Ko, Charles Kooperberg, Seunggeun Lee, Arnab Maity, Jeanne M. McCaffery, Bhramar Mukherjee, Sung Kyun Park, Duncan C. Thomas, Alexandre Todorov, Jung-Ying Tzeng, Tao Wang, Michael Windle, Min Zhang
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