Motsinger Alison A, Brassat David, Caillier Stacy J, Erlich Henry A, Walker Karen, Steiner Lori L, Barcellos Lisa F, Pericak-Vance Margaret A, Schmidt Silke, Gregory Simon, Hauser Stephen L, Haines Jonathan L, Oksenberg Jorge R, Ritchie Marylyn D
Center for Human Genetics Research, Department of Molecular Physiology and Biophysics, 519 Light Hall, Vanderbilt University Medical School, Nashville, TN 37232-0700, USA.
Neurogenetics. 2007 Jan;8(1):11-20. doi: 10.1007/s10048-006-0058-9. Epub 2006 Sep 22.
The complex inheritance involved in multiple sclerosis (MS) risk has been extensively investigated, but our understanding of MS genetics remains rudimentary. In this study, we explore 51 single nucleotide polymorphisms (SNPs) in 36 candidate genes from the inflammatory pathway and test for gene-gene interactions using complementary case-control, discordant sibling pair, and trio family study designs. We used a sample of 421 carefully diagnosed MS cases and 96 unrelated, healthy controls; discordant sibling pairs from 146 multiplex families; and 275 trio families. We used multifactor dimensionality reduction to explore gene-gene interactions. Based on our analyses, we have identified several statistically significant models including both main effect models and two-locus, three-locus, and four-locus epistasis models that predict MS disease risk with between approximately 61% and 85% accuracy. These results suggest that significant epistasis, or gene-gene interactions, may exist even in the absence of statistically significant individual main effects.
多发性硬化症（MS）风险所涉及的复杂遗传机制已得到广泛研究，但我们对MS遗传学的理解仍处于初级阶段。在本研究中，我们探究了来自炎症途径的36个候选基因中的51个单核苷酸多态性（SNP），并使用互补病例对照、不一致同胞对和三联体家系研究设计来检测基因-基因相互作用。我们使用了一个样本，其中包括421例经过仔细诊断的MS病例和96名无亲缘关系的健康对照；来自146个多病例家系的不一致同胞对；以及275个三联体家系。我们使用多因素降维法来探究基因-基因相互作用。基于我们的分析，我们确定了几个具有统计学意义的模型，包括主效应模型以及两位点、三位点和四位点上位性模型，这些模型预测MS疾病风险的准确率约在61%至85%之间。这些结果表明，即使在没有统计学显著的个体主效应的情况下，也可能存在显著的上位性，即基因-基因相互作用。
