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. Statistical Approaches to Gene × Environment Interactions for Complex Phenotypes 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.
Michael Windle is Professor of Public Health in the Department of Behavioral Sciences and Health Education at Emory University.
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