The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou, China.
The University of New South Wales, Sydney, New South Wales, Australia.
J Cell Mol Med. 2024 Sep;28(17):e70045. doi: 10.1111/jcmm.70045.
This study offers insights into the genetic and biological connections between nine common metabolic diseases using data from genome-wide association studies. Our goal is to unravel the genetic interactions and biological pathways of these complex diseases, enhancing our understanding of their genetic architecture. We employed a range of advanced analytical techniques to explore the genetic correlations and shared genetic variants of these diseases. These methods include Linked Disequilibrium Score Regression, High-Definition Likelihood (HDL), genetic analysis combining multiplicity and annotation (GPA), two-sample Mendelian randomization analyses, analysis under the multiplicity-complex null hypothesis (PLACO), and Functional mapping and annotation of genetic associations (FUMA). Additionally, Bayesian co-localization analyses were used to examine associations of specific loci across traits. Our study discovered significant genomic correlations and shared loci, indicating complex genetic interactions among these metabolic diseases. We found several shared single nucleotide variants and risk loci, notably highlighting the role of the immune system and endocrine pathways in these diseases. Particularly, rs2476601 and its associated gene PTPN22 appear to play a crucial role in the connection between type 2 diabetes mellitus, hypothyroidism/mucous oedema and hypoglycaemia. These findings enhance our understanding of the genetic underpinnings of these diseases and open new potential avenues for targeted therapeutic and preventive strategies. The results underscore the importance of considering pleiotropic effects in deciphering the genetic architecture of complex diseases, especially metabolic ones.
本研究利用全基因组关联研究的数据，深入探讨了九种常见代谢性疾病之间的遗传和生物学联系。我们的目标是揭示这些复杂疾病的遗传相互作用和生物学途径，加深我们对其遗传结构的理解。我们采用了一系列先进的分析技术来探索这些疾病的遗传相关性和共享遗传变异。这些方法包括连锁不平衡得分回归、高分辨率似然（HDL）、多变量和注释综合分析（GPA）、两样本孟德尔随机化分析、多效性复杂零假设下的分析（PLACO）以及遗传关联的功能映射和注释（FUMA）。此外，贝叶斯共定位分析用于研究特定基因座在不同性状之间的关联。我们的研究发现了显著的基因组相关性和共享基因座，表明这些代谢性疾病之间存在复杂的遗传相互作用。我们发现了一些共享的单核苷酸变异和风险基因座，特别强调了免疫系统和内分泌途径在这些疾病中的作用。特别是 rs2476601 及其相关基因 PTPN22 似乎在 2 型糖尿病、甲状腺功能减退症/黏液水肿和低血糖之间的联系中发挥关键作用。这些发现增进了我们对这些疾病遗传基础的理解，并为靶向治疗和预防策略开辟了新的潜在途径。研究结果强调了在解析复杂疾病（尤其是代谢性疾病）的遗传结构时，考虑多效性影响的重要性。
