Statistical Approaches to Gene x Environment Interactions for Complex Phenotypes
Nonfiction, Science & Nature, Science, Biological Sciences, Genetics, Health & Well Being, Psychology, Pathological Psychology, Computers
| 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
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
