Major histocompatibility complex harbors widespread genotypic variability of non-additive risk of rheumatoid arthritis including epistasis.
Major histocompatibility complex harbors widespread genotypic variability of non-additive risk of rheumatoid arthritis including epistasis.
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DOI：
10.1038/srep25014
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发表时间：
2016-04-25
影响因子：
4.6
通讯作者：
Eyre S
中科院分区：
文献类型：
--
作者：
Wei WH;Bowes J;Plant D;Viatte S;Yarwood A;Massey J;Worthington J;Eyre S
基于基因型变异的全基因组关联研究 (vGWAS) 可以在不事先了解相互作用因素的情况下识别潜在的相互作用位点。我们报告了一种使 vGWAS 适用于疾病的两阶段方法：首先使用混合模型方法将二分表型划分为责任量表上的加性风险和非加性环境残差，其次使用 Levene（Brown-Forsythe）检验来评估每个标记的基因型组之间残差方差的相等性。我们发现在所有三个类风湿性关节炎研究队列的主要组织相容性复合体 (MHC) 内都存在广泛的显着 (P< 2.5e-05) vGWAS 信号。我们进一步确定了 vGWAS 信号之间的 10 种上位相互作用，与 MHC 加性效应无关，每种效应均较弱，但共同解释了 1.9% 的表型方差。 PTPN22也在发现队列中被发现，但仅在一个独立队列中复制。将这三个队列结合起来增强了 vGWAS 的能力，并另外鉴定了 TYK2 和 ANKRD55。 PTPN22 和 TYK2 都有其他地方报道的相互作用的证据。我们的结论是，vGWAS 可以帮助发现复杂疾病的相互作用位点，但需要大量样本才能找到额外的信号。
Genotypic variability based genome-wide association studies (vGWASs) can identify potentially interacting loci without prior knowledge of the interacting factors. We report a two-stage approach to make vGWAS applicable to diseases: firstly using a mixed model approach to partition dichotomous phenotypes into additive risk and non-additive environmental residuals on the liability scale and secondly using the Levene’s (Brown-Forsythe) test to assess equality of the residual variances across genotype groups per marker. We found widespread significant (P < 2.5e-05) vGWAS signals within the major histocompatibility complex (MHC) across all three study cohorts of rheumatoid arthritis. We further identified 10 epistatic interactions between the vGWAS signals independent of the MHC additive effects, each with a weak effect but jointly explained 1.9% of phenotypic variance. PTPN22 was also identified in the discovery cohort but replicated in only one independent cohort. Combining the three cohorts boosted power of vGWAS and additionally identified TYK2 and ANKRD55. Both PTPN22 and TYK2 had evidence of interactions reported elsewhere. We conclude that vGWAS can help discover interacting loci for complex diseases but require large samples to find additional signals.