A fast and powerful W-test for pairwise epistasis testing.
Epistasis plays an essential role in the development of complex diseases. Interaction methods face common challenge of seeking a balance between persistent power, model complexity, computation efficiency, and validity of identified bio-markers. We introduce a novel W-test to identify pairwise epistasis effect, which measures the distributional difference between cases and controls through a combined log odds ratio. The test is model-free, fast, and inherits a Chi-squared distribution with data adaptive degrees of freedom. No permutation is needed to obtain the P-values. Simulation studies demonstrated that the W-test is more powerful in low frequency variants environment than alternative methods, which are the Chi-squared test, logistic regression and multifactor-dimensionality reduction (MDR). In two independent real bipolar disorder genome-wide associations (GWAS) datasets, the W-test identified significant interactions pairs that can be replicated, including SLIT3-CENPN, SLIT3-TMEM132D, CNTNAP2-NDST4 and CNTCAP2-RTN4R The genes in the pairs play central roles in neurotransmission and synapse formation. A majority of the identified loci are undiscoverable by main effect and are low frequency variants. The proposed method offers a powerful alternative tool for mapping the genetic puzzle underlying complex disorders.
Division of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China CUHK Shenzhen Research Institute, Shenzhen, China maggiew@cuhk.edu.hk.
Maggie Haitian
MH
Wang
Division of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China CUHK Shenzhen Research Institute, Shenzhen, China.
Rui
R
Sun
The Australian National University, Canberra, Australia.
Junfeng
J
Guo
Division of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China CUHK Shenzhen Research Institute, Shenzhen, China.
Haoyi
H
Weng
Division of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China.
Jack
J
Lee
ISOM Department and Biomedical Engineering Division, the Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China.
Inchi
I
Hu
Department of Psychiatry; Centre for Genomic Sciences, the University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China.
Pak Chung
PC
Sham
Division of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China CUHK Shenzhen Research Institute, Shenzhen, China.
Benny Chung-Ying
BC
Zee
D000465:Algorithms; D003198:Computer Simulation; D004843:Epistasis, Genetic; D006801:Humans; D016015:Logistic Models; D008957:Models, Genetic; D058011:Multifactor Dimensionality Reduction; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*