Design and analysis issues in gene and environment studies
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| Vydáno v: | Environmental Health vol. 11 (2012), p. n/a |
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| Hlavní autor: | |
| Další autoři: | , , , |
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Springer Nature B.V.
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| Témata: | |
| On-line přístup: | Citation/Abstract Full Text Full Text - PDF |
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| 100 | 1 | |a Liu, Chen-yu | |
| 245 | 1 | |a Design and analysis issues in gene and environment studies | |
| 260 | |b Springer Nature B.V. |c 2012 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Doc number: 93 Abstract: Both nurture (environmental) and nature (genetic factors) play an important role in human disease etiology. Traditionally, these effects have been thought of as independent. This perspective is ill informed for non-mendelian complex disorders which result as an interaction between genetics and environment. To understand health and disease we must study how nature and nurture interact. Recent advances in human genomics and high-throughput biotechnology make it possible to study large numbers of genetic markers and gene products simultaneously to explore their interactions with environment. The purpose of this review is to discuss design and analytic issues for gene-environment interaction studies in the "-omics" era, with a focus on environmental and genetic epidemiological studies. We present an expanded environmental genomic disease paradigm. We discuss several study design issues for gene-environmental interaction studies, including confounding and selection bias, measurement of exposures and genotypes. We discuss statistical issues in studying gene-environment interactions in different study designs, such as choices of statistical models, assumptions regarding biological factors, and power and sample size considerations, especially in genome-wide gene-environment studies. Future research directions are also discussed. Both nurture (environmental) and nature (genetic factors) play an important role in human disease etiology. Traditionally, these effects have been thought of as independent. This perspective is ill informed for non-mendelian complex disorders which result as an interaction between genetics and environment. To understand health and disease we must study how nature and nurture interact. Recent advances in human genomics and high-throughput biotechnology make it possible to study large numbers of genetic markers and gene products simultaneously to explore their interactions with environment. The purpose of this review is to discuss design and analytic issues for gene-environment interaction studies in the "-omics" era, with a focus on environmental and genetic epidemiological studies. We present an expanded environmental genomic disease paradigm. We discuss several study design issues for gene-environmental interaction studies, including confounding and selection bias, measurement of exposures and genotypes. We discuss statistical issues in studying gene-environment interactions in different study designs, such as choices of statistical models, assumptions regarding biological factors, and power and sample size considerations, especially in genome-wide gene-environment studies. Future research directions are also discussed. | |
| 610 | 4 | |a Harvard School of Public Health | |
| 650 | 2 | 2 | |a Confounding Factors (Epidemiology) |
| 650 | 2 | 2 | |a Environmental Exposure |
| 650 | 1 | 2 | |a Epidemiologic Research Design |
| 650 | 2 | 2 | |a Epidemiologic Studies |
| 650 | 2 | 2 | |a Epigenesis, Genetic |
| 650 | 1 | 2 | |a Gene-Environment Interaction |
| 650 | 2 | 2 | |a Humans |
| 650 | 2 | 2 | |a Selection Bias |
| 653 | |a Studies | ||
| 653 | |a Epigenetics | ||
| 653 | |a Genetics | ||
| 653 | |a Public health | ||
| 653 | |a Statistical methods | ||
| 653 | |a Disease | ||
| 653 | |a Genomics | ||
| 653 | |a Drug dosages | ||
| 653 | |a DNA methylation | ||
| 653 | |a Biomarkers | ||
| 653 | |a Epidemiology | ||
| 653 | |a Health care | ||
| 653 | |a Preventive medicine | ||
| 653 | |a Gene expression | ||
| 653 | |a Environmental health | ||
| 653 | |a Health sciences | ||
| 653 | |a Genotype-environment interactions | ||
| 653 | |a Statistical models | ||
| 653 | |a Design | ||
| 653 | |a Genotypes | ||
| 653 | |a Genetic factors | ||
| 653 | |a Biotechnology | ||
| 653 | |a Genetic markers | ||
| 653 | |a Social | ||
| 700 | 1 | |a Maity, Arnab | |
| 700 | 1 | |a Lin, Xihong | |
| 700 | 1 | |a Wright, Robert O | |
| 700 | 1 | |a Christiani, David C | |
| 773 | 0 | |t Environmental Health |g vol. 11 (2012), p. n/a | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/1271860053/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/1271860053/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/1271860053/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |