Michael Windle (ed.)
- Published in print:
- 2016
- Published Online:
- May 2017
- ISBN:
- 9780262034685
- eISBN:
- 9780262335522
- Item type:
- book
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262034685.001.0001
- Subject:
- Biology, Biomathematics / Statistics and Data Analysis / Complexity Studies
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 ...
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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 ZhangLess
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
Michael Windle
- Published in print:
- 2016
- Published Online:
- May 2017
- ISBN:
- 9780262034685
- eISBN:
- 9780262335522
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262034685.003.0001
- Subject:
- Biology, Biomathematics / Statistics and Data Analysis / Complexity Studies
This chapter provides an introduction and overview of important issues that served as motivations for this book. For many complex phenotypes (e.g., depression, diabetes, obesity, substance use), ...
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This chapter provides an introduction and overview of important issues that served as motivations for this book. For many complex phenotypes (e.g., depression, diabetes, obesity, substance use), there is substantial evidence that while genetic influences are important, so are environmental influences; moreover, there is substantial evidence from both behavior genetic studies (e.g., twin and adoptee studies) and molecular genetic studies (both human and infrahuman) that genes commonly interact with environmental factors in predicting complex phenotypes. The fields of genomics and other –omics (e.g., proteomics, metabolomics) provide exciting opportunities to advance science and foster the goals of public health and a more individualized intervention approach (e.g., precision medicine). The goals of these more individualized approaches would benefit greatly not only by advances in genomics and other –omics, but also by incorporating information both on environments and their interactions with genomic and other biological material and regulatory processes (e.g., environmental signal to biological pathway responses). Such findings would thereby offer more flexible guidance to a broader range of prevention, intervention, and treatment targets, and facilitate more tailored programs based on a fuller complement of G and E influences.Less
This chapter provides an introduction and overview of important issues that served as motivations for this book. For many complex phenotypes (e.g., depression, diabetes, obesity, substance use), there is substantial evidence that while genetic influences are important, so are environmental influences; moreover, there is substantial evidence from both behavior genetic studies (e.g., twin and adoptee studies) and molecular genetic studies (both human and infrahuman) that genes commonly interact with environmental factors in predicting complex phenotypes. The fields of genomics and other –omics (e.g., proteomics, metabolomics) provide exciting opportunities to advance science and foster the goals of public health and a more individualized intervention approach (e.g., precision medicine). The goals of these more individualized approaches would benefit greatly not only by advances in genomics and other –omics, but also by incorporating information both on environments and their interactions with genomic and other biological material and regulatory processes (e.g., environmental signal to biological pathway responses). Such findings would thereby offer more flexible guidance to a broader range of prevention, intervention, and treatment targets, and facilitate more tailored programs based on a fuller complement of G and E influences.
Ophélie Ronce and Jean Clobert
- Published in print:
- 2012
- Published Online:
- December 2013
- ISBN:
- 9780199608898
- eISBN:
- 9780191774560
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199608898.003.0010
- Subject:
- Biology, Ecology, Evolutionary Biology / Genetics
This chapter focuses on dispersal syndromes and how they describe patterns of covariation of morphological, behavioural, and/or life-history traits associated with dispersal. Covariation is a ...
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This chapter focuses on dispersal syndromes and how they describe patterns of covariation of morphological, behavioural, and/or life-history traits associated with dispersal. Covariation is a continuous measure of statistical association between traits composing a complex phenotype. There are four main reasons why this chapter is interested in covariation: firstly, syndromes may help us predict a priori, from the observation of dispersal phenotypes, the intensity, nature, and modalities of movement. Secondly, they have the potential to provide information regarding the mechanistic determinants of dispersal and the constraints associated with movement. Thirdly, they may provide information about the proximate motivations and ultimate causes of dispersal. Lastly, and most convincingly, the patterns of covariation between traits associated with dispersal will critically affect both the demographic and genetic consequences of movement.Less
This chapter focuses on dispersal syndromes and how they describe patterns of covariation of morphological, behavioural, and/or life-history traits associated with dispersal. Covariation is a continuous measure of statistical association between traits composing a complex phenotype. There are four main reasons why this chapter is interested in covariation: firstly, syndromes may help us predict a priori, from the observation of dispersal phenotypes, the intensity, nature, and modalities of movement. Secondly, they have the potential to provide information regarding the mechanistic determinants of dispersal and the constraints associated with movement. Thirdly, they may provide information about the proximate motivations and ultimate causes of dispersal. Lastly, and most convincingly, the patterns of covariation between traits associated with dispersal will critically affect both the demographic and genetic consequences of movement.
