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Li M, Wei C, Wen Y, et al. Detecting Gene-Gene Interactions Associated with Multiple Complex Traits with U-Statistics. Curr Genomics. 2016;17(5):403-415doi: 10.2174/1389202917666160513100946.
Li, M., Wei, C., Wen, Y., Wang, T., & Lu, Q. (2016). Detecting Gene-Gene Interactions Associated with Multiple Complex Traits with U-Statistics. Current genomics, 17(5), 403-415. https://doi.org/10.2174/1389202917666160513100946
Li, Ming, et al. "Detecting Gene-Gene Interactions Associated with Multiple Complex Traits with U-Statistics." Current genomics vol. 17,5 (2016): 403-415. doi: https://doi.org/10.2174/1389202917666160513100946
Li M, Wei C, Wen Y, Wang T, Lu Q. Detecting Gene-Gene Interactions Associated with Multiple Complex Traits with U-Statistics. Curr Genomics. 2016 Oct;17(5):403-415. doi: 10.2174/1389202917666160513100946. PMID: 28479869; PMCID: PMC5320542.
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Curr Genomics. 2016 Oct;17(5):403-415. doi: 10.2174/1389202917666160513100946.

Detecting Gene-Gene Interactions Associated with Multiple Complex Traits with U-Statistics.

Current genomics

Ming Li, Changshuai Wei, Yalu Wen, Tong Wang, Qing Lu

Affiliations

  1. 1Department of Epidemiology and Biostatistics, Indiana University at Bloomington, Bloomington, IN 47405, U.S.A; 2Department of Epidemiology and Biostatistics, University of North Texas Health Science Center, Fort Worth, TX 76107, U.S.A; 3Department of Statistics, University of Auckland, Auckland 1010, New Zealand; 4Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi 030001, P.R. China; 5Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, U.S.A.

PMID: 28479869 PMCID: PMC5320542 DOI: 10.2174/1389202917666160513100946

Abstract

Many complex diseases, such as psychiatric and behavioral disorders, are commonly characterized through various measurements that reflect physical, behavioral and psychological aspects of diseases. While it remains a great challenge to find a unified measurement to characterize a disease, the available multiple phenotypes can be analyzed jointly in the genetic association study. Simultaneously testing these phenotypes has many advantages, including considering different aspects of the disease in the analysis, and utilizing correlated phenotypes to improve the power of detecting disease-associated variants. Furthermore, complex diseases are likely caused by the interplay of multiple genetic variants through complicated mechanisms. Considering gene-gene interactions in the joint association analysis of complex diseases could further increase our ability to discover genetic variants involving complex disease pathways. In this article, we propose a stepwise U-test for joint association analysis of multiple loci and multiple phenotypes. Through simulations, we demonstrated that testing multiple phenotypes simultaneously could attain higher power than testing one single phenotype at a time, especially when there are shared genes contributing to multiple phenotypes. We also illustrated the proposed method with an application to Nicotine Dependence (ND), using datasets from the Study of Addition, Genetics and Environment (SAGE). The joint analysis of three ND phenotypes identified two SNPs, rs10508649 and rs2491397, and reached a nominal

Keywords: Nicotine dependence; Pleiotropy; Population-based association studies

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