Wang Maggie Haitian, Sun Rui, Guo Junfeng, Weng Haoyi, Lee Jack, Hu Inchi, Sham Pak Chung, Zee Benny Chung-Ying
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
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.
Nucleic Acids Res. 2016 Jul 8;44(12):e115. doi: 10.1093/nar/gkw347. Epub 2016 Apr 25.
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.
上位性在复杂疾病的发展中起着至关重要的作用。交互作用方法面临着在持续功效、模型复杂性、计算效率和所识别生物标志物的有效性之间寻求平衡这一共同挑战。我们引入了一种新颖的W检验来识别成对的上位性效应，该检验通过组合对数优势比来衡量病例组和对照组之间的分布差异。该检验无模型、速度快，并且继承了具有数据自适应自由度的卡方分布。无需进行排列即可获得P值。模拟研究表明，在低频变异环境中，W检验比其他方法（卡方检验、逻辑回归和多因素降维法（MDR））更具功效。在两个独立的真实双相情感障碍全基因组关联（GWAS）数据集中，W检验识别出了可重复的显著相互作用对，包括SLIT3 - CENPN、SLIT3 - TMEM132D、CNTNAP2 - NDST4和CNTCAP2 - RTN4R。这些对中的基因在神经传递和突触形成中起核心作用。大多数所识别的位点通过主效应无法发现，并且是低频变异。所提出的方法为绘制复杂疾病潜在的遗传谜题提供了一个强大的替代工具。
