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Article(id=1322590015167771058, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322589954702688372, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202407352, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1721404800000, receivedDateStr=2024-07-20, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793259632150, onlineDateStr=2026-10-29, pubDate=1736438400000, pubDateStr=2025-01-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793259632150, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793259632150, creator=13701087609, updateTime=1793259632150, updator=13701087609, issue=Issue{id=1322589954702688372, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='1', pageStart='1', pageEnd='192', issueExtLink='null', onlineDate='null', pubDate='1736438400000', pubDateStr='2025-01-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793259617734, creator='13701087609', updateTime=1793260023378, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322591656147276425, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322589954702688372, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322591656147276426, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322589954702688372, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=45, endPage=50, ext={EN=ArticleExt(id=1322590015968883123, articleId=1322590015167771058, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Research on the shared genetic structure of autoimmune diseases and psychiatric disorders based on genome structural equation modeling, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the shared genetic structure between six autoimmune diseases (AIDs) and ten psychiatric disorders (PDs) using Genome Structural Equation Modeling (GSEM) and Mendelian randomization (MR).

Methods

The research employed linkage disequilibrium score regression to assess the genetic correlations between diseases. GSEM was introduced for exploratory factor analysis on odd-numbered chromosomes and confirmatory factor analysis on even-numbered chromosomes for cross-validation, along with path analysis to establish a three-factor model and hierarchical model.Additionally, a multivariable genome-wide association study was conducted on latent factors to test the bidirectional causal relationships between mediating variables and factors, thereby revealing the complex genetic relationships among the diseases.

Results

GSEM results indicated that the selected 16 diseases could be explained by a three-factor model consisting of three independent factors, which fitted the research data best (χ2=865.291, AIC=929.291, CFI=0.940, SRMR=0.097). In this model, Factor 1 and Factor 3 exhibited characteristics closely related to autoimmune diseases, while Factor 2 primarily encompassed psychiatric disorders, with scleroderma serving as a mediating variable linking the two types of diseases. MR results revealed a bidirectional causal effect between scleroderma and Factor 2 and Factor 3.

Conclusion

GSEM and MR can be utilized to uncover the shared genetic structure and causal effects between AIDs and PDs, thereby highlighting their interactions and providing an important methodological tool for understanding the genetic relationships among complex diseases.

, authors=Liu-fei DENG1, Shu-ting XUE1, Zi-wei YAO2, Xiao-mai ZHANG2, Yan-chao WEN1, Long LIU1, 3, authorsList=Liu-fei DENG, Shu-ting XUE, Zi-wei YAO, Xiao-mai ZHANG, Yan-chao WEN, Long LIU, authorCompany=null, correspAuthors=Long LIU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322590023946449365, articleId=1322590015167771058, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=基于基因组结构方程模型的自身免疫性疾病和精神类疾病的共享遗传结构研究, columnId=1228016567632462653, journalTitle=现代预防医学, columnName=流行病与统计方法, runingTitle=null, highlight=null, articleAbstract=
目的

应用基因组结构方程模型（genome structural equation modeling，GSEM）和孟德尔随机化（Mendelian randomization, MR）研究六种自身免疫性疾病（autoimmune diseases，AID）和十种精神类疾病（psychiatric disorders，PD）之间的共享遗传结构。

方法

本研究运用连锁不平衡分数回归评估疾病之间的遗传相关性；引入GSEM，在奇数染色体进行探索性因素分析、偶数染色体进行验证性因素分析来交叉验证，并结合路径分析建立三因子模型和层次模型。此外，对潜在因子（Factor）进行多变量全基因组关联研究，测试中介变量和Factor之间的双向因果关系。从而揭示各疾病之间复杂的遗传关系。

结果

GSEM结果显示所选取的16种疾病可以被3个独立的因子所构成的三因子模型所解释，且在拟合研究数据时表现最佳（χ2=865.291，AIC=929.291，CFI=0.940，SRMR=0.097），模型中，Factor1和Factor3均显示出与AID密切相关的特征；Factor2主要包含精神类疾病，硬皮病作为中介变量联系两类疾病；MR结果显示硬皮病和Factor2、Factor3之间存在双向因果效应。

结论

GSEM和MR可以用于揭示AID和PD之间的共享遗传结构和因果效应，从而发现两者的相互作用，为理解复杂疾病之间的遗传关系提供了一个重要的方法学工具。

, authors=邓刘飞1, 薛舒婷1, 姚紫微2, 张潇迈2, 温艳超1, 刘龙1, 3, authorsList=邓刘飞, 薛舒婷, 姚紫微, 张潇迈, 温艳超, 刘龙, authorCompany=null, correspAuthors=刘龙, authorNote=

邓刘飞（1997—），男，硕士在读，研究方向：复杂疾病风险预测模型构建方法

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刘龙，E-mail:
, copyrightStatement=本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点，除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=roolMyjOGVr7twQlKBS3/A==, magXml=uKxMhVWyk4etO8x40i41lA==, pdfUrl=null, pdf=pebJ+QGzXb9aWtkQX3SUNA==, pdfFileSize=1136853, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=ESsT670qL6UcsTAMXoHllQ==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=/t9F33Iik39/+MGGSf9hAg==, mapNumber=null, fund=null)}, authors=[Author(id=1322590026521752030, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590015167771058, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1322590026614026720, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590015167771058, authorId=1322590026521752030, language=EN, stringName=Liu-fei DENG, firstName=Liu-fei, middleName=null, lastName=DENG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, address=Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi 030001, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1322590026945376737, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590015167771058, authorId=1322590026521752030, language=CN, stringName=邓刘飞, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, address=1.山西医科大学公共卫生学院卫生统计学教研室，山西 太原 030001, bio={"content":"

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注：深灰色大椭圆代表潜在的未观察到的因子，白色的圆圈指观察的性状，灰色的圆圈代表每个性状的独特方差；单向箭头描绘了从因子到疾病的回归系数，因子之间的弯曲双箭头表示因子之间的相关性，变量与自身连接起来的双箭头表示变量与自身的方差；括号中的数字表示每个测量值的标准误差。

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注：深灰色大椭圆代表潜在的未观察到的因子，白色的圆圈指观察的性状，灰色的圆圈代表每个性状的独特方差；单向箭头描绘了从因子到疾病的回归系数，因子之间的弯曲双箭头表示因子之间的相关性，变量与自身连接起来的双箭头表示变量与自身的方差；括号中的数字表示每个测量值的标准误差。

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Data information and heritability estimates based on univariate SNP analysis

, figureFileSmall=null, figureFileBig=null, tableContent=
疾病样本量（病例/对照）基于SNP的总遗传力（σ）遗传力Z评分χ2值λGC协方差截距(SE)数据来源
ADHD55 374(20 183/35 191)0.244(0.017)14.7171.3081.2631.024(0.012)PMC6481311
AN72 517(16 992/55 525)0.181(0.012)14.7241.3051.2601.020(0.011)PMC6779477
ANX329 077(21 519/307 558)0.027(0.002)11.9131.2601.2491.064(0.011)FinnGen
TS14 307(4 819/9 488)0.371(0.045)8.3341.1271.1211.012(0.009)PMC6677250
SCZ336 412(6 280/330 132)0.010(0.002)6.7331.0981.1041.025(0.008)FinnGen
ASD46 351(18 382/27 969)0.199(0.017)11.6201.2111.1811.006(0.011)PMC6454898
BD413 466(41 917/371 549)0.069(0.003)21.4381.6631.5071.047(0.014)PMC8192451
INS453 379(NA/NA)0.063(0.003)24.0771.6291.4921.014(0.012)PMC6415688
DEP500 199(170 756/329 443)0.089(0.004)22.3251.6571.5080.996(0.013)PMC6522363
OCD9 725(2 688/7 037)0.419(0.067)6.2171.0541.0500.993(0.007)PMC6660151
CD326 438(2 364/324 074)0.016(0.003)5.9631.1941.1341.063(0.015)PMC8262258
IBD377 277(7 625/369 652)0.024(0.003)7.6451.2791.1991.084(0.018)FinnGen
MG38 243(1 873/36 370)0.070(0.016)4.2561.0771.0631.022(0.009)PMC8812681
RA94 173(22 350/74 823)0.134(0.018)7.2461.3251.2111.061(0.014)PMC10165422
SSc368 028(2 495/365 533)0.007(0.002)4.0621.0621.0491.011(0.009)FinnGen
UC376 564(5 034/371 530)0.017(0.003)6.6401.1931.1381.064(0.013)FinnGen
), ArticleFig(id=1322590032486052367, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590015167771058, language=CN, label=表1, caption=

数据信息及基于单变量SNP的遗传力估计结果

, figureFileSmall=null, figureFileBig=null, tableContent=
疾病样本量（病例/对照）基于SNP的总遗传力（σ）遗传力Z评分χ2值λGC协方差截距(SE)数据来源
ADHD55 374(20 183/35 191)0.244(0.017)14.7171.3081.2631.024(0.012)PMC6481311
AN72 517(16 992/55 525)0.181(0.012)14.7241.3051.2601.020(0.011)PMC6779477
ANX329 077(21 519/307 558)0.027(0.002)11.9131.2601.2491.064(0.011)FinnGen
TS14 307(4 819/9 488)0.371(0.045)8.3341.1271.1211.012(0.009)PMC6677250
SCZ336 412(6 280/330 132)0.010(0.002)6.7331.0981.1041.025(0.008)FinnGen
ASD46 351(18 382/27 969)0.199(0.017)11.6201.2111.1811.006(0.011)PMC6454898
BD413 466(41 917/371 549)0.069(0.003)21.4381.6631.5071.047(0.014)PMC8192451
INS453 379(NA/NA)0.063(0.003)24.0771.6291.4921.014(0.012)PMC6415688
DEP500 199(170 756/329 443)0.089(0.004)22.3251.6571.5080.996(0.013)PMC6522363
OCD9 725(2 688/7 037)0.419(0.067)6.2171.0541.0500.993(0.007)PMC6660151
CD326 438(2 364/324 074)0.016(0.003)5.9631.1941.1341.063(0.015)PMC8262258
IBD377 277(7 625/369 652)0.024(0.003)7.6451.2791.1991.084(0.018)FinnGen
MG38 243(1 873/36 370)0.070(0.016)4.2561.0771.0631.022(0.009)PMC8812681
RA94 173(22 350/74 823)0.134(0.018)7.2461.3251.2111.061(0.014)PMC10165422
SSc368 028(2 495/365 533)0.007(0.002)4.0621.0621.0491.011(0.009)FinnGen
UC376 564(5 034/371 530)0.017(0.003)6.6401.1931.1381.064(0.013)FinnGen
), ArticleFig(id=1322590032574132752, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590015167771058, language=EN, label=Table 2, caption=

Model fit statistics for GSEM models

, figureFileSmall=null, figureFileBig=null, tableContent=
染色体EFA染色体模型负载截断值交叉载荷
(Y/N)		负载荷(Y/N)自由度P值AICCFISRMR
EvenOddCFA0.2YY72510.1143.25×10-67576.1140.9550.110
EvenOddCFA0.2YN73509.6631.06×10-66573.6630.9550.110
EvenOddCFA0.2NY73599.4912.65×10-22663.4910.9460.128
EvenOddCFA0.2NN74612.6858.79×10-86674.6850.9450.127
ALLOddCFA0.2YN73865.2918.58×10-136929.2910.9400.097
ALLOddhierarchal0.2YN73865.2948.56×10-136929.2940.9400.097
), ArticleFig(id=1322590032645435921, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590015167771058, language=CN, label=表2, caption=

GSEM模型的模型拟合统计指标

, figureFileSmall=null, figureFileBig=null, tableContent=
染色体EFA染色体模型负载截断值交叉载荷
(Y/N)		负载荷(Y/N)自由度P值AICCFISRMR
EvenOddCFA0.2YY72510.1143.25×10-67576.1140.9550.110
EvenOddCFA0.2YN73509.6631.06×10-66573.6630.9550.110
EvenOddCFA0.2NY73599.4912.65×10-22663.4910.9460.128
EvenOddCFA0.2NN74612.6858.79×10-86674.6850.9450.127
ALLOddCFA0.2YN73865.2918.58×10-136929.2910.9400.097
ALLOddhierarchal0.2YN73865.2948.56×10-136929.2940.9400.097
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基于基因组结构方程模型的自身免疫性疾病和精神类疾病的共享遗传结构研究
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邓刘飞 1 , 薛舒婷 1 , 姚紫微 2 , 张潇迈 2 , 温艳超 1 , 刘龙 1, 3
现代预防医学 | 流行病与统计方法 2025,52(1): 45-50
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现代预防医学 |流行病与统计方法 2025 , 52 (1) : 45 -50
基于基因组结构方程模型的自身免疫性疾病和精神类疾病的共享遗传结构研究
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邓刘飞1, 薛舒婷1, 姚紫微2, 张潇迈2, 温艳超1, 刘龙1, 3
作者信息
  • 1.山西医科大学公共卫生学院卫生统计学教研室，山西 太原 030001
  • 2.山西医科大学医学科学院卫生统计教研室
  • 3.重大疾病风险评估山西省重点实验室，山西 太原 030001
通讯作者:
刘龙，E-mail:
作者简介:

邓刘飞（1997—），男，硕士在读，研究方向：复杂疾病风险预测模型构建方法

Research on the shared genetic structure of autoimmune diseases and psychiatric disorders based on genome structural equation modeling
Liu-fei DENG1, Shu-ting XUE1, Zi-wei YAO2, Xiao-mai ZHANG2, Yan-chao WEN1, Long LIU1, 3
Affiliations
  • Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi 030001, China
出版时间: 2025-01-10 doi: 10.20043/j.cnki.MPM.202407352
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摘要
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目的

应用基因组结构方程模型（genome structural equation modeling，GSEM）和孟德尔随机化（Mendelian randomization, MR）研究六种自身免疫性疾病（autoimmune diseases，AID）和十种精神类疾病（psychiatric disorders，PD）之间的共享遗传结构。

方法

本研究运用连锁不平衡分数回归评估疾病之间的遗传相关性；引入GSEM，在奇数染色体进行探索性因素分析、偶数染色体进行验证性因素分析来交叉验证，并结合路径分析建立三因子模型和层次模型。此外，对潜在因子（Factor）进行多变量全基因组关联研究，测试中介变量和Factor之间的双向因果关系。从而揭示各疾病之间复杂的遗传关系。

结果

GSEM结果显示所选取的16种疾病可以被3个独立的因子所构成的三因子模型所解释，且在拟合研究数据时表现最佳（χ2=865.291，AIC=929.291，CFI=0.940，SRMR=0.097），模型中，Factor1和Factor3均显示出与AID密切相关的特征；Factor2主要包含精神类疾病，硬皮病作为中介变量联系两类疾病；MR结果显示硬皮病和Factor2、Factor3之间存在双向因果效应。

结论

GSEM和MR可以用于揭示AID和PD之间的共享遗传结构和因果效应，从而发现两者的相互作用，为理解复杂疾病之间的遗传关系提供了一个重要的方法学工具。

基因组结构方程模型  /  因子分析  /  孟德尔随机化  /  自身免疫性疾病  /  精神类疾病
Objective

To investigate the shared genetic structure between six autoimmune diseases (AIDs) and ten psychiatric disorders (PDs) using Genome Structural Equation Modeling (GSEM) and Mendelian randomization (MR).

Methods

The research employed linkage disequilibrium score regression to assess the genetic correlations between diseases. GSEM was introduced for exploratory factor analysis on odd-numbered chromosomes and confirmatory factor analysis on even-numbered chromosomes for cross-validation, along with path analysis to establish a three-factor model and hierarchical model.Additionally, a multivariable genome-wide association study was conducted on latent factors to test the bidirectional causal relationships between mediating variables and factors, thereby revealing the complex genetic relationships among the diseases.

Results

GSEM results indicated that the selected 16 diseases could be explained by a three-factor model consisting of three independent factors, which fitted the research data best (χ2=865.291, AIC=929.291, CFI=0.940, SRMR=0.097). In this model, Factor 1 and Factor 3 exhibited characteristics closely related to autoimmune diseases, while Factor 2 primarily encompassed psychiatric disorders, with scleroderma serving as a mediating variable linking the two types of diseases. MR results revealed a bidirectional causal effect between scleroderma and Factor 2 and Factor 3.

Conclusion

GSEM and MR can be utilized to uncover the shared genetic structure and causal effects between AIDs and PDs, thereby highlighting their interactions and providing an important methodological tool for understanding the genetic relationships among complex diseases.

Genome structural equation modeling  /  Factor analysis  /  Mendelian randomization  /  Autoimmune diseases  /  Psychiatric disorders
邓刘飞, 薛舒婷, 姚紫微, 张潇迈, 温艳超, 刘龙. 基于基因组结构方程模型的自身免疫性疾病和精神类疾病的共享遗传结构研究. 现代预防医学, 2025 , 52 (1) : 45 -50 . DOI: 10.20043/j.cnki.MPM.202407352
Liu-fei DENG, Shu-ting XUE, Zi-wei YAO, Xiao-mai ZHANG, Yan-chao WEN, Long LIU. Research on the shared genetic structure of autoimmune diseases and psychiatric disorders based on genome structural equation modeling[J]. Modern Preventive Medicine, 2025 , 52 (1) : 45 -50 . DOI: 10.20043/j.cnki.MPM.202407352
正文
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自身免疫性疾病（autoimmune diseases，AID）是一组相对罕见的疾病，多为慢性炎症，其特点是免疫系统对身体自身组织产生全身性或局部的自身反应，患者容易出现反复发作或病情加重的情况，增加了医疗和生活负担，通常具有遗传易感性[1-2]。且该类疾病常伴有精神异常，如焦虑、抑郁、精神病和认知功能障碍[3]。故免疫功能紊乱与精神类疾病（psychiatric disorders，PD）的病理生理学越来越受到关注，但其潜在的遗传机制在很大程度上未知。全基因组关联性研究已经识别出一系列与AID和PD相关的基因变异，表明这两类疾病可能在遗传基础上有某种联系。本文旨在运用基因组结构方程模型（genome structural equation modeling，GSEM）探究AID和PD之间的相互作用和共享遗传结构，利用孟德尔随机化（Mendelian randomization, MR）揭示疾病间的因果关系，以提高对这两类疾病遗传学基础的全面理解，从而促进对发病机制的认识。
1　资料与方法
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1.1　资料来源
本研究采用大型联盟和全基因组关联研究（genome-wide association study，GWAS）汇总数据进行研究，采集其中6个AID及10个PD公开可用的数据，AID包括：乳糜泻（celiac sprue，CD）、炎症性肠炎（inflammatory bowel disease，IBD）、重症肌无力（myasthenia gravis，MG）、类风湿性关节炎（rheumatoid arthritis，RA）、硬皮病（scleroderma，SSc）、溃疡性结肠炎（ulcerative colitis，UC）；PD包括：注意缺陷多动障碍（attention-deficit/hyperactivity disorder，ADHD）、神经性厌食症（anorexia nervosa，AN）、焦虑（anxiety，ANX）、自闭症谱系障碍（autism spectrum disorder，ASD）、躁郁症（bipolar disorder，BD）、抑郁（depression，DEP）、失眠（insomnia，INS）、强迫症（obsessive- compulsive disorder，OCD）、精神分裂症（schizophrenia，SCZ）、图雷特综合征（Tourette syndrome，TS）。16种疾病的数据信息见表1。所选用的数据样本量均大于5 000。
1.2　原理与方法
1.2.1　数据质控
筛选16种疾病的单核苷酸多态性（single nucleotide polymorphism，SNP）数据时，我们保留了次要等位基因频率大于0.01%且归因得分大于0.9的SNPs。主要组织相容性复合体（major histocompatibility complex，MHC）区域含有一个复杂的基因网络，通常伴有高度连锁不平衡（linkage disequilibrium，LD）分数和与效应不相关的SNPs，可能导致假阳性结果。因此，本研究排除了MHC区域的SNPs数据，以确保结果的准确性和可靠性。
1.2.2　遗传相关性
使用连锁不平衡评分回归（linkage disequilibrium score regression，LDSC），我们计算了每种疾病的遗传力、遗传力Z评分，并选择了Z评分大于4的疾病。同时，我们测量了所有疾病之间的LD分数，以确保存在足够的遗传相关性，适合应用GSEM[4-5]。
1.2.3　GSEM
GSEM是一种复杂的统计方法，结合了因子分析和路径分析的原理[6-7]，旨在探索遗传信息与多个表型指标之间的关系。这种方法允许研究者在一个统一的框架中建立结构模型，并通过两阶段流程进行参数估计。第一阶段，估计遗传协方差矩阵：使用LDSC估计遗传协方差矩阵S和样本协方差矩阵VSLDSC；第二阶段，对S中的元素进行加权处理来拟合遗传协方差矩阵VSLDSC。
在第一阶段，使用LDSC估计遗传协方差矩阵S和其对应的样本协方差矩阵VSLDSC。遗传协方差矩阵S是一个k×k的对称矩阵，对角线上是SNP的遗传力量，非对角线上是表型之间的遗传协方差σgi, gj。具体而言，表型i和表型j之间的遗传协方差可以计算为，其中rgi, gj是遗传相关系数，和是两个表型的遗传力量。
在第二阶段，使用斜权最小二乘法（diagonally weighted least squares，DWLS）来估计模型参数，以使模型隐含协方差矩阵∑(θ)尽可能与观测到的S一致。
建立结构模型，描述遗传信息η和观测到的表型Y之间的线性关系，其中Λ为因子载荷矩阵，ε为残差，公式如下：
构建模型隐含协方差矩阵∑(θ)：∑(θ)是基于估计参数θ计算得到的，包括潜在变量η之间的协方差矩阵Φ，以及残差协方差矩阵Θ，公式如下：
应用DWLS进行参数估计，将以下函数最小化：
其中，s是向量化的S，σ(θ)是向量化的∑σ(θ)，D是VSLDSC的对角矩阵，用于对协方差矩阵进行加权处理。
标准误和检验统计量的修正，使用“三明治”校正法修正参数估计的标准误Vθ和检验统计量，公式如下：
其中，是模型导数在估计值处的矩阵，г是估计方法（DWLS）下的权重矩阵，Vs是通过多变量LDSC获得的S的抽样协方差矩阵。
由于所纳入疾病之间的潜在结构未知，且所有疾病不是来自于同一全基因组关联研究数据，故本研究在奇常染色体中进行探索性因子分析（exploratory factor analysis，EFA），在偶常染色体中进行验证性因子分析（confirmatory factor analysis，CFA），以此作为交叉验证，防止模型的过拟合。
此外，为了更全面地理解和量化全基因组数据中的常见遗传变异，本研究测试了层次模型。这些模型的应用使得对总体上遗传变异的认识更加深入，有助于揭示基因组中不同部分之间的复杂关系。
1.3　模型评估
使用拟合指数（comparative fit index，CFI），标准化均方根残差（standardized root mean square residual，SRMR），赤池信息量准则（Akaike information criterion，AIC），χ2统计量评估模型拟合效果。
1.4　MR
MR是一种利用全基因组分析结果进行因果推断的方法[8]，依赖于使用遗传工具变量来估计暴露对结果的因果效应，我们对三因子模型中建模的潜在因子（Factor）进行了多变量全基因组关联研究。随后，将Factor上的SNP估计值作为双向MR分析的输入，测试SSc和Factor之间的因果关系。本研究使用R中的TwoSampleMR包[9]，包括LD独立（10 000 kb簇窗口，r2<0.001）基因组范围内显著的变异（p<5×10-6）作为遗传工具变量。采用逆方差加权（inverse variance weighted，IVW）MR方法来获得结果估计值，从而得出暴露与结果之间的因果关系。
Var（）表示第i个遗传变异效应估计的方差，ωi是第之个变异的权重，等于方差的倒数，是暴露因素对结果的因果效应估计是第i个变异的效应估计。
使用Cochran Q评估异质性，并使用MR-Egger回归截距项检查水平多效性。
2　结果
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2.1　估计遗传相关性
本研究使用LDSC测量了16个疾病的遗传力和遗传力Z评分，见表1。结果显示Z评分均大于4，遗传力显著，且所有单变量GWAS的LD得分回归截距约为1，表明人群分层对结果的影响较小，结果相对可靠。图1显示了所有疾病之间的遗传相关性，表明疾病之间存在复杂的遗传网络结构，适合进行GSEM分析。特别是某些疾病（如UC和IBD、DEP和ANX等）之间的相关性较强，有利于应用GSEM来探索这些疾病的共同遗传基础。
2.2　基因组结构方程模型
我们使用GSEM对16种疾病的遗传协方差矩阵进行了建模。我们在EFA中纳入了奇数常染色体，分别测试了二因子、三因子和四因子模型，以评估测试的结果是否与观察到的遗传协方差矩阵接近。由于二因子模型解释方差较小而四因子模型中出现海伍德现象（模型过拟合），所以本研究认为三因子模型拟合较好。该模型中，Factor1和Factor3均显示出与AID密切相关的特征。Factor1主要集中于炎性肠病（UC和IBD），而Factor3则主要涵盖免疫相关疾病（如MG、RA和SSc）；Factor2主要包含PD，模型区分良好。之后我们在偶数染色体上分析，选用拟合效果最好的三因子及其潜在结构进行CFA建模。最优的CFA模型［χ2（73）=509.663，AIC=573.663，CFI=0.955，SRMR=0.110］包括了交叉载荷，但不包括负载荷，见表2。故剔除在三个因子中都显示出负载荷的SCZ和CD。将全染色体数据代入该模型中，结果显示模型性能依旧良好［χ2（73）= 865.291，AIC=929.291，CFI=0.940，SRMR=0.097］，见图2。
此外，本研究测试了层次模型，见图3。高阶因子Factor4加载在三个一级因子Factor1、Factor2、Factor3之上，而一级因子则直接加载在具体的疾病上。每个一级因子代表了一组在遗传上具有相似性的疾病，这与上述三因子结果相同，而高阶因子Factor4捕捉了这些疾病组之间的共同变异。其中高阶因子Factor4与Factor2之间存在一个较强的正向关系，即Factor4可能在较大程度上解释了Factor2的变异（β=0.65，σ=0.182）。
通过对三因子模型和层次模型的比较，发现三因子模型的拟合效果略优于层次模型［χ2（73）=865.291，
2.3　双向MR
图4展示了Factor2和Factor3与SSc的MR分析结果。结果表明，Factor2和Factor3可能与SSc的发病有因果关系，其中Factor3对SSc OR=7.39，表明这些因子可能会显著增加患SSc的风险。并且正向结果表明SSc对Factor2、Factor3也存在因果效应，进一步解释了AID和PD可能存在遗传结构之间的交互作用。
3　讨论
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本研究利用GWAS汇总数据，采用GSEM对AID和PD之间的共享遗传结构进行建模和探索。研究聚焦于揭示两者在遗传层面上的相互关系，识别中介变量，并利用MR分析中介变量与Factor之间的因果效应。这一方法整合了复杂的遗传互动，揭示了这些疾病可能通过共享的遗传途径相互影响。
GSEM是一种新的统计遗传技术，在结构方程模型的基础上引入基因数据，综合了遗传相关性和SNP遗传性，用于分析复杂性状之间的遗传结构。这些相关性和SNPs遗传性是从具有不同和未知重叠程度的样本的单个性状的GWAS汇总统计中推断出来的，由于其数据量大，从而提高了研究的统计效能和结果的可靠性[10]。GSEM建立在LDSC的基础上，以拟合遗传关联的多变量模型，使研究人员能够识别多种表型的潜在遗传因子结构[11]，有助于理解基因之间的相互作用、基因与表型之间的关系，能够更好地处理复杂的遗传结构[12]。Grotzinger通过模拟研究证明，GSEM能够有效处理测量误差，确保SNPs效应估计的准确性。即使SNPs效应与因子结构偏离时，GSEM仍能保持较高的检测能力[10]。此外，GSEM允许研究者使用不同的模型结构（如相关因子模型和层次因子模型）来探索SNPs与多个性状之间的关系。例如Foote等人用三因子模型、双因素模型等来探究阿尔茨海默病及其风险因素之间的共享遗传结构，为其生物学机制提供了研究基础。
MR的基本原理基于孟德尔遗传定律，即基因变异在出生时就已确定，不受后天环境因素的影响，因此它们可以作为研究暴露与结果之间因果关系的无偏估计工具[13-14]。与传统的观察性研究相比，MR通过使用遗传变异作为工具变量，可以提高因果关系推断的可信度。本研究旨在通过将多变量全基因组方法与孟德尔随机化相结合，探索它们的共享遗传基础。
本研究使用三因子模型、层次模型探索了AID及PD之间的共享遗传结构。最佳拟合的三因子模型结果显示：Factor1与Factor3中所包含的大多数疾病都是AID，而Factor2中所包含疾病均是PD，表明各Factor中所纳入的疾病共享遗传结构，模型区分疾病类型较好。SSc同时处于两个Factor下，表明SSc在不同维度的遗传影响上都具有显著的作用。这种情况在GSEM中表示一种中介效应：SSc可能通过改变免疫系统的状态，增加PD的风险；PD可能通过神经内分泌途径影响免疫系统功能，进而增加SSc的风险。美国一项关于SSc患者心理健康的研究表明，6%的患者患有抑郁症，而25%的患者被诊断为广泛性焦虑障碍[15]。此外，Amoda等人[16]的研究结果表明，SSc患者可能会发生慢性病、PD等疾病，但无法表明其时序和果关系，而本研究进一步的双向MR更准确地解释和量化这种因果效应。研究表明SSc可以既作为暴露也可以结局，这在遗传学的角度上意味着SSc不仅是其他疾病的潜在结果，也是其潜在原因。正如Thomas等人[17]的研究，与健康人相比，SSc患者在访谈时接受精神病学诊断评估的几率是健康人的4.5倍。除此之外，其他AID与PD之间均有相关关系，这意味着AID和PD之间虽然有各自独特的遗传背景，但也存在一定的共同遗传因素，这为进一步理解这些疾病的遗传基础和潜在机制提供了重要的线索。先前的研究也同样证明该研究结果，例如Samaroo[18]在研究精神分裂症患者中发现的自身抗原组织转谷氨酰胺酶抗体（指示乳糜泻）的人数约为普通人群的五倍，表明两种疾病间存在一些潜在联系。Nielsen[19]的研究结果显示个体自身免疫性疾病与ADHD风险增加相关，发病率比为1.24（95%CI：1.10～1.40），且母体自身免疫性疾病与后代ADHD相关。Eaton[20]的研究结果表明五种自身免疫性疾病同时诊断或延迟诊断为精神分裂症的风险增加，表明这些疾病与精神分裂症有共同的遗传病因。在生理病理学机制上，AID和PD之间的联系可能是抗神经元抗体的存在以及B细胞和T细胞稳态失调导致的免疫系统异常活化[3]。这种异常活化可能影响神经递质的正常调节，进而影响大脑的功能和情绪调控机制，导致精神类疾病的发生。
本研究也存在一些局限性。由于样本仅来自欧洲人群，结果的外推可能受到限制，未来的研究应该扩大样本规模并在不同人群中进行验证。另外，我们的研究未涉及环境途径以及基因与环境之间的交互作用，我们计划下一步将纳入环境与疾病的联系，使这项研究更加全面可靠。

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doi: 10.20043/j.cnki.MPM.202407352
  • 接收时间：2024-07-20
  • 首发时间：2026-10-29
  • 出版时间：2025-01-10
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  • 收稿日期：2024-07-20
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    1.山西医科大学公共卫生学院卫生统计学教研室，山西 太原 030001
    2.山西医科大学医学科学院卫生统计教研室
    3.重大疾病风险评估山西省重点实验室，山西 太原 030001

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刘龙，E-mail:
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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