Statistical Approaches to Gene x Environment Interactions for Complex Phenotypes

Nonfiction, Science & Nature, Science, Biological Sciences, Genetics, Health & Well Being, Psychology, Pathological Psychology, Computers
Cover of the book Statistical Approaches to Gene x Environment Interactions for Complex Phenotypes by Charles Kooperberg, James Y. Dai, Li Hsu, Jung-Ying Tzeng, Arnab Maity, Shuo Jiao, Tao Wang, Alexandre Todorov, Jeanne M. McCaffery, Caroline Y. Doyle, Bhramar Mukherjee, Yin Hsui Chen, Yi-An Ko, Zihuai He, Seunggeun Lee, Min Zhang, Sung Kyun Park, Fatima Umber Ahmed, Erin Loraine Kinnally, Duncan C. Thomas, Michael Windle, The MIT Press
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Author: Charles Kooperberg, James Y. Dai, Li Hsu, Jung-Ying Tzeng, Arnab Maity, Shuo Jiao, Tao Wang, Alexandre Todorov, Jeanne M. McCaffery, Caroline Y. Doyle, Bhramar Mukherjee, Yin Hsui Chen, Yi-An Ko, Zihuai He, Seunggeun Lee, Min Zhang, Sung Kyun Park, Fatima Umber Ahmed, Erin Loraine Kinnally, Duncan C. Thomas, Michael Windle ISBN: 9780262335515
Publisher: The MIT Press Publication: July 8, 2016
Imprint: The MIT Press Language: English
Author: Charles Kooperberg, James Y. Dai, Li Hsu, Jung-Ying Tzeng, Arnab Maity, Shuo Jiao, Tao Wang, Alexandre Todorov, Jeanne M. McCaffery, Caroline Y. Doyle, Bhramar Mukherjee, Yin Hsui Chen, Yi-An Ko, Zihuai He, Seunggeun Lee, Min Zhang, Sung Kyun Park, Fatima Umber Ahmed, Erin Loraine Kinnally, Duncan C. Thomas, Michael Windle
ISBN: 9780262335515
Publisher: The MIT Press
Publication: July 8, 2016
Imprint: The MIT Press
Language: English

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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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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