دورية أكاديمية
Does accounting for gene-environment interactions help uncover association between rare variants and complex diseases?
| العنوان: | Does accounting for gene-environment interactions help uncover association between rare variants and complex diseases? |
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| المؤلفون: | Kazma R; Department of Epidemiology and Biostatistics and Institute for Human Genetics, University of California, San Francisco, CA, USA., Cardin NJ, Witte JS |
| المصدر: | Human heredity [Hum Hered] 2012; Vol. 74 (3-4), pp. 205-14. Date of Electronic Publication: 2013 Apr 11. |
| نوع المنشور: | Journal Article; Research Support, N.I.H., Extramural |
| اللغة: | English |
| بيانات الدورية: | Publisher: Karger Country of Publication: Switzerland NLM ID: 0200525 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1423-0062 (Electronic) Linking ISSN: 00015652 NLM ISO Abbreviation: Hum Hered Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Basel, New York, Karger. |
| مواضيع طبية MeSH: | Gene-Environment Interaction* , Genetic Predisposition to Disease* , Genetic Variation*, Humans ; Models, Genetic ; Models, Statistical |
| مستخلص: | Objective: To determine whether accounting for gene-environment (G×E) interactions improves the power to detect associations between rare variants and a disease, we have extended three statistical methods and compared their power under various simulated disease models. Methods: To test for association of a group of rare variants with a disease, Min-P uses the lowest p value within the group of variants, CAST (Cohort Allelic Sums Test) uses an indicator variable to quantify the rare alleles within the group of variants, and SKAT (Sequence Kernel Association Test) uses a logistic regression based on kernel machine. For each method, we incorporate a term for the G×E interaction and test for association and interaction jointly. Results: When testing for disease association with a set of rare variants, accounting for G×E interactions can improve power in specific situations (pure interaction or high proportion of causal variants interacting with the environment). However, the power of this approach can decrease, in particular in the presence of main genetic or environmental effects. Among the methods compared, the optimized and weighted SKAT performed best, whether to test for genetic association or to test it jointly with G×E interactions. Conclusion: This approach can be used in specific situations but is not appropriate for a primary analysis. (Copyright © 2013 S. Karger AG, Basel.) |
| معلومات مُعتمدة: | R25 CA112355 United States CA NCI NIH HHS; R01 CA88164 United States CA NCI NIH HHS; U01 CA127298 United States CA NCI NIH HHS |
| تواريخ الأحداث: | Date Created: 20130419 Date Completed: 20131017 Latest Revision: 20211021 |
| رمز التحديث: | 20221213 |
| DOI: | 10.1159/000346825 |
| PMID: | 23594498 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1423-0062 |
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| DOI: | 10.1159/000346825 |