Loucoubar Cheikh, Grant Audrey V, Bureau Jean-François, Casademont Isabelle, Bar Ndjido Ardo, Bar-Hen Avner, Diop Mamadou, Faye Joseph, Sarr Fatoumata Diene, Badiane Abdoulaye, Tall Adama, Trape Jean-François, Cliquet Freddy, Schwikowski Benno, Lathrop Mark, Paul Richard Edward, Sakuntabhai Anavaj
Brief Bioinform. 2017 May 1;18(3):394-402. doi: 10.1093/bib/bbw039.
The era of genome-wide association studies (GWAS) has led to the discovery of numerous genetic variants associated with disease. Better understanding of whether these or other variants interact leading to differential risk compared with individual marker effects will increase our understanding of the genetic architecture of disease, which may be investigated using the family-based study design. We present M-TDT (the multi-locus transmission disequilibrium test), a tool for detecting family-based multi-locus multi-allelic effects for qualitative or quantitative traits, extended from the original transmission disequilibrium test (TDT). Tests to handle the comparison between additive and epistatic models, lack of independence between markers and multiple offspring are described. Performance of M-TDT is compared with a multifactor dimensionality reduction (MDR) approach designed for investigating families in the hypothesis-free genome-wide setting (the multifactor dimensionality reduction pedigree disequilibrium test, MDR-PDT). Other methods derived from the TDT or MDR to investigate genetic interaction in the family-based design are also discussed. The case of three independent biallelic loci is illustrated using simulations for one- to three-locus alternative hypotheses. M-TDT identified joint-locus effects and distinguished effectively between additive and epistatic models. We showed a practical example of M-TDT based on three genes already known to be implicated in malaria susceptibility. Our findings demonstrate the value of M-TDT in a hypothesis-driven context to test for multi-way epistasis underlying common disease etiology, whereas MDR-PDT-based methods are more appropriate in a hypothesis-free genome-wide setting.
全基因组关联研究(GWAS)时代已促成了众多与疾病相关的基因变异的发现。相较于单个标记效应,更好地理解这些变异或其他变异之间是否相互作用从而导致不同的疾病风险,将增进我们对疾病遗传结构的认识,而这可通过基于家系的研究设计来进行探究。我们提出了M-TDT(多位点传递不平衡检验),这是一种从原始传递不平衡检验(TDT)扩展而来的、用于检测基于家系的定性或定量性状的多位点多等位基因效应的工具。文中描述了用于处理加性模型和上位性模型之间比较、标记间缺乏独立性以及多个后代情况的检验方法。将M-TDT的性能与一种为在无假设的全基因组背景下研究家系而设计的多因素降维方法(多因素降维家系不平衡检验,MDR-PDT)进行了比较。还讨论了从TDT或MDR衍生而来的、用于在基于家系的设计中研究基因相互作用的其他方法。针对一至三位点替代假设,通过模拟说明了三个独立双等位基因位点的情况。M-TDT识别出了联合位点效应,并有效地区分了加性模型和上位性模型。我们展示了一个基于三个已知与疟疾易感性有关的基因的M-TDT实际例子。我们的研究结果表明,在假设驱动的背景下,M-TDT对于检验常见疾病病因背后的多位点上位性具有价值,而基于MDR-PDT的方法在无假设的全基因组背景下更为适用。