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多种共病模式下 14 种常见病症/疾病的遗传效应和因果关联分析。

Genetic effects and causal association analyses of 14 common conditions/diseases in multimorbidity patterns.

机构信息

Collaborative Innovation Center for Bone and Immunology between Sihong Hospital and Soochow University, Center for Genetic Epidemiology and Genomics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou, Jiangsu P. R. China.

Department of Orthopedics, Sihong Hospital, Suzhou, Jiangsu, P. R. China.

出版信息

PLoS One. 2024 May 16;19(5):e0300740. doi: 10.1371/journal.pone.0300740. eCollection 2024.

Abstract

BACKGROUND

Multimorbidity has become an important health challenge in the aging population. Accumulated evidence has shown that multimorbidity has complex association patterns, but the further mechanisms underlying the association patterns are largely unknown.

METHODS

Summary statistics of 14 conditions/diseases were available from the genome-wide association study (GWAS). Linkage disequilibrium score regression analysis (LDSC) was applied to estimate the genetic correlations. Pleiotropic SNPs between two genetically correlated traits were detected using pleiotropic analysis under the composite null hypothesis (PLACO). PLACO-identified SNPs were mapped to genes by Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA), and gene set enrichment analysis and tissue differential expression were performed for the pleiotropic genes. Two-sample Mendelian randomization analyses assessed the bidirectional causality between conditions/diseases.

RESULTS

LDSC analyses revealed the genetic correlations for 20 pairs based on different two-disease combinations of 14 conditions/diseases, and genetic correlations for 10 pairs were significant after Bonferroni adjustment (P<0.05/91 = 5.49E-04). Significant pleiotropic SNPs were detected for 11 pairs of correlated conditions/diseases. The corresponding pleiotropic genes were differentially expressed in the brain, nerves, heart, and blood vessels and enriched in gluconeogenesis and drug metabolism, biotransformation, and neurons. Comprehensive causal analyses showed strong causality between hypertension, stroke, and high cholesterol, which drive the development of multiple diseases.

CONCLUSIONS

This study highlighted the complex mechanisms underlying the association patterns that include the shared genetic components and causal effects among the 14 conditions/diseases. These findings have important implications for guiding the early diagnosis, management, and treatment of comorbidities.

摘要

背景

多病共存已成为老龄化人口中的一个重要健康挑战。已有大量证据表明,多病共存存在复杂的关联模式,但关联模式背后的进一步机制在很大程度上尚不清楚。

方法

全基因组关联研究(GWAS)提供了 14 种疾病/状况的汇总统计数据。应用连锁不平衡得分回归分析(LDSC)估计遗传相关性。在复合零假设下(PLACO)使用多效性分析检测两种遗传相关性状之间的多效性 SNP。PLACO 鉴定的 SNP 通过全基因组关联研究的功能映射和注释(FUMA)映射到基因上,并对多效性基因进行基因集富集分析和组织差异表达分析。两样本 Mendelian 随机化分析评估了疾病之间的双向因果关系。

结果

LDSC 分析基于 14 种疾病/状况的两种不同两疾病组合,揭示了 20 对的遗传相关性,在 Bonferroni 调整后,10 对的遗传相关性具有统计学意义(P<0.05/91=5.49E-04)。检测到 11 对相关疾病/状况存在显著的多效性 SNP。对应的多效性基因在大脑、神经、心脏和血管中表达不同,并在糖异生和药物代谢、生物转化和神经元中富集。综合因果分析表明,高血压、中风和高胆固醇之间存在很强的因果关系,这些因素驱动了多种疾病的发展。

结论

本研究强调了 14 种疾病/状况关联模式背后的复杂机制,包括共享的遗传成分和因果效应。这些发现对于指导多种疾病的早期诊断、管理和治疗具有重要意义。

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