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An exposure‐weighted score test for genetic associations integrating environmental risk factors
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| Zeitschriftentitel: | Biometrics |
|---|---|
| Personen und Körperschaften: | , , , , , |
| In: | Biometrics, 71, 2015, 3, S. 596-605 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Oxford University Press (OUP)
|
| Schlagwörter: |
| author_facet |
Han, Summer S. Rosenberg, Philip S. Ghosh, Arpita Landi, Maria Teresa Caporaso, Neil E. Chatterjee, Nilanjan Han, Summer S. Rosenberg, Philip S. Ghosh, Arpita Landi, Maria Teresa Caporaso, Neil E. Chatterjee, Nilanjan |
|---|---|
| author |
Han, Summer S. Rosenberg, Philip S. Ghosh, Arpita Landi, Maria Teresa Caporaso, Neil E. Chatterjee, Nilanjan |
| spellingShingle |
Han, Summer S. Rosenberg, Philip S. Ghosh, Arpita Landi, Maria Teresa Caporaso, Neil E. Chatterjee, Nilanjan Biometrics An exposure‐weighted score test for genetic associations integrating environmental risk factors Applied Mathematics General Agricultural and Biological Sciences General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology General Medicine Statistics and Probability |
| author_sort |
han, summer s. |
| spelling |
Han, Summer S. Rosenberg, Philip S. Ghosh, Arpita Landi, Maria Teresa Caporaso, Neil E. Chatterjee, Nilanjan 0006-341X 1541-0420 Oxford University Press (OUP) Applied Mathematics General Agricultural and Biological Sciences General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology General Medicine Statistics and Probability http://dx.doi.org/10.1111/biom.12328 <jats:title>Summary</jats:title><jats:sec><jats:label /><jats:p>Current methods for detecting genetic associations lack full consideration of the background effects of environmental exposures. Recently proposed methods to account for environmental exposures have focused on logistic regressions with gene–environment interactions. In this report, we developed a test for genetic association, encompassing a broad range of risk models, including linear, logistic and probit, for specifying joint effects of genetic and environmental exposures. We obtained the test statistics by maximizing over a class of score tests, each of which involves modified standard tests of genetic association through a weight function. This weight function reflects the potential heterogeneity of the genetic effects by levels of environmental exposures under a particular model. Simulation studies demonstrate the robust power of these methods for detecting genetic associations under a wide range of scenarios. Applications of these methods are further illustrated using data from genome‐wide association studies of type 2 diabetes with body mass index and of lung cancer risk with smoking.</jats:p></jats:sec> An exposure‐weighted score test for genetic associations integrating environmental risk factors Biometrics |
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An exposure‐weighted score test for genetic associations integrating environmental risk factors |
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An exposure‐weighted score test for genetic associations integrating environmental risk factors |
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An exposure‐weighted score test for genetic associations integrating environmental risk factors |
| title_fullStr |
An exposure‐weighted score test for genetic associations integrating environmental risk factors |
| title_full_unstemmed |
An exposure‐weighted score test for genetic associations integrating environmental risk factors |
| title_short |
An exposure‐weighted score test for genetic associations integrating environmental risk factors |
| title_sort |
an exposure‐weighted score test for genetic associations integrating environmental risk factors |
| topic |
Applied Mathematics General Agricultural and Biological Sciences General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology General Medicine Statistics and Probability |
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http://dx.doi.org/10.1111/biom.12328 |
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2015 |
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<jats:title>Summary</jats:title><jats:sec><jats:label /><jats:p>Current methods for detecting genetic associations lack full consideration of the background effects of environmental exposures. Recently proposed methods to account for environmental exposures have focused on logistic regressions with gene–environment interactions. In this report, we developed a test for genetic association, encompassing a broad range of risk models, including linear, logistic and probit, for specifying joint effects of genetic and environmental exposures. We obtained the test statistics by maximizing over a class of score tests, each of which involves modified standard tests of genetic association through a weight function. This weight function reflects the potential heterogeneity of the genetic effects by levels of environmental exposures under a particular model. Simulation studies demonstrate the robust power of these methods for detecting genetic associations under a wide range of scenarios. Applications of these methods are further illustrated using data from genome‐wide association studies of type 2 diabetes with body mass index and of lung cancer risk with smoking.</jats:p></jats:sec> |
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| author | Han, Summer S., Rosenberg, Philip S., Ghosh, Arpita, Landi, Maria Teresa, Caporaso, Neil E., Chatterjee, Nilanjan |
| author_facet | Han, Summer S., Rosenberg, Philip S., Ghosh, Arpita, Landi, Maria Teresa, Caporaso, Neil E., Chatterjee, Nilanjan, Han, Summer S., Rosenberg, Philip S., Ghosh, Arpita, Landi, Maria Teresa, Caporaso, Neil E., Chatterjee, Nilanjan |
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| description | <jats:title>Summary</jats:title><jats:sec><jats:label /><jats:p>Current methods for detecting genetic associations lack full consideration of the background effects of environmental exposures. Recently proposed methods to account for environmental exposures have focused on logistic regressions with gene–environment interactions. In this report, we developed a test for genetic association, encompassing a broad range of risk models, including linear, logistic and probit, for specifying joint effects of genetic and environmental exposures. We obtained the test statistics by maximizing over a class of score tests, each of which involves modified standard tests of genetic association through a weight function. This weight function reflects the potential heterogeneity of the genetic effects by levels of environmental exposures under a particular model. Simulation studies demonstrate the robust power of these methods for detecting genetic associations under a wide range of scenarios. Applications of these methods are further illustrated using data from genome‐wide association studies of type 2 diabetes with body mass index and of lung cancer risk with smoking.</jats:p></jats:sec> |
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| spelling | Han, Summer S. Rosenberg, Philip S. Ghosh, Arpita Landi, Maria Teresa Caporaso, Neil E. Chatterjee, Nilanjan 0006-341X 1541-0420 Oxford University Press (OUP) Applied Mathematics General Agricultural and Biological Sciences General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology General Medicine Statistics and Probability http://dx.doi.org/10.1111/biom.12328 <jats:title>Summary</jats:title><jats:sec><jats:label /><jats:p>Current methods for detecting genetic associations lack full consideration of the background effects of environmental exposures. Recently proposed methods to account for environmental exposures have focused on logistic regressions with gene–environment interactions. In this report, we developed a test for genetic association, encompassing a broad range of risk models, including linear, logistic and probit, for specifying joint effects of genetic and environmental exposures. We obtained the test statistics by maximizing over a class of score tests, each of which involves modified standard tests of genetic association through a weight function. This weight function reflects the potential heterogeneity of the genetic effects by levels of environmental exposures under a particular model. Simulation studies demonstrate the robust power of these methods for detecting genetic associations under a wide range of scenarios. Applications of these methods are further illustrated using data from genome‐wide association studies of type 2 diabetes with body mass index and of lung cancer risk with smoking.</jats:p></jats:sec> An exposure‐weighted score test for genetic associations integrating environmental risk factors Biometrics |
| spellingShingle | Han, Summer S., Rosenberg, Philip S., Ghosh, Arpita, Landi, Maria Teresa, Caporaso, Neil E., Chatterjee, Nilanjan, Biometrics, An exposure‐weighted score test for genetic associations integrating environmental risk factors, Applied Mathematics, General Agricultural and Biological Sciences, General Immunology and Microbiology, General Biochemistry, Genetics and Molecular Biology, General Medicine, Statistics and Probability |
| title | An exposure‐weighted score test for genetic associations integrating environmental risk factors |
| title_full | An exposure‐weighted score test for genetic associations integrating environmental risk factors |
| title_fullStr | An exposure‐weighted score test for genetic associations integrating environmental risk factors |
| title_full_unstemmed | An exposure‐weighted score test for genetic associations integrating environmental risk factors |
| title_short | An exposure‐weighted score test for genetic associations integrating environmental risk factors |
| title_sort | an exposure‐weighted score test for genetic associations integrating environmental risk factors |
| title_unstemmed | An exposure‐weighted score test for genetic associations integrating environmental risk factors |
| topic | Applied Mathematics, General Agricultural and Biological Sciences, General Immunology and Microbiology, General Biochemistry, Genetics and Molecular Biology, General Medicine, Statistics and Probability |
| url | http://dx.doi.org/10.1111/biom.12328 |