Gene-gene interaction of BLK, TNFSF4, TRAF1, TNFAIP3, and REL in systemic lupus erythematosus.

Gene-gene interaction of BLK, TNFSF4, TRAF1, TNFAIP3, and REL in systemic lupus erythematosus.
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DOI:
10.1002/art.33318
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发表时间:
2012-01
影响因子:
--
通讯作者:
Zhang, Hong
Zhang, Hong
中科院分区:
其他
文献类型:
--
作者:
Zhou, Xu-jie;Lu, Xiao-lan;Nath, Swapan K.;Lv, Ji-cheng;Zhu, Sai-nan;Yang, Hai-zhen;Qin, Lian-xiang;Zhao, Ming-hui;Su, Yin;Shen, Nan;Li, Zhan-guo;Zhang, Hong

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尽管令人信服地建立的与系统性红斑狼疮 (SLE) 的遗传关联的数量在过去几年中急剧增加,但需要对这些关联进行细化,并且需要进一步研究它们在基因-基因相互作用中的潜在作用。最近 SLE 的全基因组关联研究 (GWAS) 重新引起了人们对 B 细胞/T 细胞反应和 NF-κB 信号通路的兴趣。本研究的目的是使用基于信号通路作用的方法,基于已识别的单核苷酸多态性 (SNP) 寻找可能的基因间相互作用。复制 BLK、TNFSF4、TRAF1、TNFAIP3 和 REL 中的 SNP,以评估与 SLE 的遗传关联。对 804 名中国 SLE 患者和 722 名匹配的对照受试者进行了 TaqMan 基因分型。使用多元逻辑回归模型来估计 SNP 的乘性相互作用效应,并通过 2 × 2 因子设计分析加性相互作用。来自国际系统性红斑狼疮遗传学联盟先前发表的 GWAS 数据用于比较和验证。单标志物分析验证了 BLK rs2736340 (P = 4.25 × 10–6) 以及 TNFSF4 rs2205960 (P = 2.82 × 10–5) 和 TNFAIP3 rs5029939 (P = 1.92 × 10–3) 与中国人 SLE 易感性的关联。乘法交互分析表明,BLK 与 TNFSF4 在中国 SLE 患者中具有交互作用(P = 6.57 × 10-4)。加性相互作用分析揭示了中国人 (P = 2.18 × 10–3) 和白种人 (P = 2.86 × 10–4) 中 TRAF1 和 TNFAIP3 之间的相互作用。此外,还观察到了多种相互作用的趋势,并且随着风险基因型数量的增加,观察到了累加效应。本研究的结果提供了 BLK、TNFSF4、TRAF1、TNFAIP3 和 REL 在 SLE 中可能存在的基因-基因相互作用的证据,这可能代表 T 细胞和 B 细胞通过 NF-κB 途径在确定免疫异常方面发挥协同作用。
Although the number of convincingly established genetic associations with systemic lupus erythematosus (SLE) has increased sharply over the last few years, refinement of these associations is required, and their potential roles in gene–gene interactions need to be further investigated. Recent genome-wide association studies (GWAS) in SLE have produced renewed interest in B cell/T cell responses and the NF-κB signaling pathway. The aim of this study was to search for possible gene–gene interactions based on identified single-nucleotide polymorphisms (SNPs), in using an approach based on the role of signaling pathways. The SNPs in BLK, TNFSF4, TRAF1, TNFAIP3, and REL were replicated in order to evaluate genetic associations with SLE. TaqMan genotyping was conducted in 804 Chinese patients with SLE and 722 matched control subjects. A multiple logistic regression model was used to estimate the multiplicative interaction effect of the SNPs, and additive interactions were analyzed by 2 × 2 factorial designs. Data from a previously published GWAS conducted by the International Consortium on the Genetics of Systemic Lupus Erythematosus were derived for comparison and validation. Single-marker analysis validated the association of BLK rs2736340 (P = 4.25 × 10–6) as well as TNFSF4 rs2205960 (P = 2.82 × 10–5) and TNFAIP3 rs5029939 (P = 1.92 × 10–3) with SLE susceptibility in Chinese. Multiplicative interaction analysis indicated that BLK had an interactive effect with TNFSF4 in Chinese patients with SLE (P = 6.57 × 10–4). Additive interaction analysis revealed interactions between TRAF1 and TNFAIP3 in both Chinese (P = 2.18 × 10–3) and Caucasians (P = 2.86 × 10–4). In addition, multiple tendencies toward interactions were observed, and an additive effect was observed as the number of risk genotypes increased. The results of this study provide evidence of the possible gene–gene interactions of BLK, TNFSF4, TRAF1, TNFAIP3, and REL in SLE, which may represent a synergic effect of T cells and B cells through the NF-κB pathway in determining immunologic aberration.
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