基于孟德尔随机化法的皮肤黏膜病变为特征的病毒感染与皮肌炎的因果关系

doi:10.3969/j.issn.1004-583X.2025.09.006

摘要/Abstract

摘要：

目的应用孟德尔随机化法(Mendelian randomization,MR),以单核苷酸多态性(single nucleotide polymorphism, SNP)作为工具变量,探讨皮肤和黏膜病变为特征的病毒感染与皮肌炎(dermatomyositis, DM)的因果关系。方法从IEU Open GWAS project网站中获得近3a、样本量最大的皮肤和黏膜病变为特征的病毒感染(finn-b-AB1_VIRAL_SKIN_MUCOUS_MEMBRANE)及DM(finn-b-M13_DERMATOPOLY)的全基因组关联研究(genome-wide association study,GWAS)数据。从finn-b-AB1_VIRAL_SKIN_MUCOUS_MEMBRANE中筛选与皮肤和黏膜病变为特征的病毒感染高度相关的SNP(以 $P$ <5×10^-8作为筛选条件,连锁不平衡区域宽度为10 000 kb,连锁不平衡系数 $r 2$ 为0.001)。从finn-b-M13_DERMATOPOLY中提取与皮肤和黏膜病变相关的高度连锁性SNP,最小 $r 2$ 值>0.8。汇集两个数据集,去除与DM直接相关的SNP,将包括rs10051884、rs9438624、rs62194265等位点的73个SNP作为工具变量,采用MR-Egger回归、随机效应逆方差加权法(inverse-variance weighted, IVW)及加权中位数法回归模型,分析皮肤和黏膜病变为特征的病毒感染与DM的因果联系。结果 GWAS数据均来源于欧洲人群,不限男女。MR-Egger 的回归截距项是-0.022( $P$ =0.644),这提示筛选出的SNP同DM间不存在基因多效性。MR-Egger回归、IVW、加权中位数法的 $O R$ (95% $C I$ )分别为1.676(0.808~3.480)、1.362(0.865~2.145)、1.439(1.021~2.029);由于存在异质性( $Q$ =93.823, $P$ =0.036),故关注随机效应IVW的结果。结论皮肤和黏膜病变为特征的病毒感染是DM的危险因素。

关键词: 皮肌炎, 皮肤和黏膜病变为特征的病毒感染, 孟德尔随机化

Abstract:

Objective To investigate the causal relationship between viral infection characterized by skin and mucosal lesions and dermatomyositis (DM) using Mendelian randomization (MR) with single nucleotide polymorphism (SNP) as instrumental variable. Methods Genome-wide association study (GWAS) data of viral infection (Finn-B-Ab1_Viral_Skin_Mucous_Membrane) and DM (finn-b-M13_DERMATOPOLY) with the largest sample size in recent three years were obtained from the IEU Open GWAS project website. Single nucleotide polymorphisms (SNPs) with a high correlation with virus infection characterized by skin and mucous membrane were screened from Finn-b-ab1 _ viral _ skin _ mucous_membrane (threshold: $P$ <5×10^-8, width of linkage disequilibrium region: 10 000 kb, and the linkage disequilibrium coefficient $r 2$ =0.001). Highly linked SNPs related to skin and mucosal lesions were extracted from finn-b-M13_DERMATOPOLY, and the minimum $r 2$ value was >0.8. Two datasets were pooled to remove SNPs directly related to DM. Seventy-three SNPs were used as instrumental variables, including rs10051884, rs9438624 and rs62194265. MR-Egger regression, random effect inverse variance weighted method (IVW) and weighted median method (WME) regression models were used to analyze the causal relationship between viral infection characterized by skin and mucosal lesions and DM. Results Two groups of GWAS data were sourced from the European population, regardless of gender. The intercept term of MR Egger regression was -0.022 ( $P$ =0.644), indicating no pleiotropy between the screened SNPs and DM. The odds ratio ( $O R$ ) (95% $C I$ ) of MR-Egger regression, IVW, and WME was 1.676(0.808-3.480), 1.362(0.865-2.145), and 1.439 (1.021-2.029), respectively. The results of random effect IVW were of concern due to the presence of heterogeneity ( $Q$ =93.823, $P$ =0.036). Conclusion Virus infection characterized by skin and mucosal lesions is a risk factor for DM.

Key words: dermatomyositis, viral infections characterized by skin and mucosal lesions, Mendelian randomization

中图分类号:

R593.26

李影, 曹婷婷, 王翠翠, 李艳霞, 杨栋梁. 基于孟德尔随机化法的皮肤黏膜病变为特征的病毒感染与皮肌炎的因果关系[J]. 临床荟萃, 2025, 40(9): 811-815.

Li Ying, Cao Tingting, Wang Cuicui, Li Yanxia, Yang Dongliang. Causal relationship between virus infection characterized by skin and mucosal lesions and dermatomyositis based on Mendelian randomization[J]. Clinical Focus, 2025, 40(9): 811-815.

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链接本文: https://www.lchc.cn/CN/10.3969/j.issn.1004-583X.2025.09.006

https://www.lchc.cn/CN/Y2025/V40/I9/811

图/表 4

表1 最终筛选出的部分SNP的信息

Tab.1 Information on partial selected SNPs

SNP	chr	pos	效应等位基因	等位基因频率	β	SE
rs10051884	5	114147730	C	0.1494	0.1006	0.0243
rs10118804	9	135586105	T	0.2654	-0.088	0.0197
rs10146873	14	53195799	A	0.3906	0.0757	0.0177
rs1032185	1	76623827	T	0.6915	0.0791	0.0187
rs77975072	18	67052871	C	0.00944	0.4143	0.0939
rs78144617	6	156751058	G	0.01671	-0.2817	0.0679
rs8077128	17	76281130	G	0.09054	0.1279	0.0307
rs9438624	1	26758044	T	0.772	-0.0871	0.0206

表2 3种方法MR回归结果

Tab.2 MR regression results of three methods

方法	β	SE	$O R$ (95% $C I$ )	$t$ 值	$P$ 值
MR-Egger	0.517	0.373	1.676(0.808~3.480)	1.386	0.170
WME	0.309	0.232	1.362(0.865~2.145)	1.332	0.182
IVW	0.364	0.175	1.439(1.021~2.029)	2.080	0.038

表2 3种方法MR回归结果

Tab.2 MR regression results of three methods

方法	β	SE	$O R$ (95% $C I$ )	$t$ 值	$P$ 值
MR-Egger	0.517	0.373	1.676(0.808~3.480)	1.386	0.170
WME	0.309	0.232	1.362(0.865~2.145)	1.332	0.182
IVW	0.364	0.175	1.439(1.021~2.029)	2.080	0.038

图1 单个SNP的MR效应森林图

Fig.1 Forest map of MR effect for each SNP

图2 MR散点图(左)和漏斗图(右)

Fig.2 Scatter plot (left) and funnel plot (right) of MR

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