目的:即使考虑到已知的心血管危险因素,1型糖尿病也与冠心病(CAD)的过度风险相关。改变白细胞产生的造血的遗传扰动是CAD风险的新的独立调节剂。我们检查了1型糖尿病之间是否存在共同的遗传决定因素和因果关系,CAD和白细胞计数。
方法:使用全基因组关联研究摘要统计进行配对连锁不平衡评分回归和摘要统计(ρ-HESS)的遗传力估计,分别估计全基因组和局部遗传相关性,和两个孟德尔随机样本来估计白细胞计数之间的因果关系(335,855名健康个体),1型糖尿病(18,942例,501,638名对照个体)和CAD(122,733例,424,528名对照个人)。进行潜在因果变量(LCV)模型以估计1型糖尿病和CAD之间遗传相关性的遗传因果关系比例。
结果:1型糖尿病和CAD之间存在显着的全基因组遗传相关性(rg)(rg=0.088,p=8.60×10-3),并且两种疾病都具有显着的全基因组遗传决定因素,嗜酸性粒细胞计数(1型糖尿病的rg[rg(T1D)]=0.093,p=7.20×10-3,CAD的rg[rg=16个独立基因座在白细胞计数之间显示出严格的Bonferroni显着的局部遗传相关性,1型糖尿病和/或CAD。1型糖尿病和CAD之间共享基因座内基因表达水平的顺式遗传调控与两种疾病以及白细胞计数相关。包括SH2B3,CTSH,MORF4L1、CTRB1、CTRB2、CFDP1和IFIH1。遗传预测的淋巴细胞,中性粒细胞和嗜酸性粒细胞计数与1型糖尿病和CAD相关(1型糖尿病的淋巴细胞OR[ORT1D]=0.67,p=2.02-19,ORCAD=1.09,p=2.67×10-6;中性粒细胞ORT1D=0.82,p=5.63×10-5,ORCAD=1.17,p=5.02×10-14;嗜酸性粒细胞ORT1D=1.45,p=2.07×1型糖尿病与CAD之间的遗传因果关系比例为0.36±0.16(pLCV=1.30×10-2)。提出了一个可能的中介因果变量。
结论:这项研究揭示了1型糖尿病和CAD共同的遗传机制,这可能有助于通过调节基因表达和白细胞计数,并确定细胞和分子靶标,以进一步研究疾病预测和潜在的药物发现。
OBJECTIVE: Type 1 diabetes is associated with excess coronary artery disease (CAD) risk even when known cardiovascular risk factors are accounted for. Genetic perturbation of haematopoiesis that alters leukocyte production is a novel independent modifier of CAD risk. We examined whether there are shared genetic determinants and causal relationships between type 1 diabetes, CAD and leukocyte counts.
METHODS: Genome-wide association study summary statistics were used to perform pairwise linkage disequilibrium score regression and heritability estimation from summary statistics (ρ-HESS) to respectively estimate the genome-wide and local genetic correlations, and two-sample Mendelian randomisation to estimate the causal relationships between leukocyte counts (335,855 healthy individuals), type 1 diabetes (18,942 cases, 501,638 control individuals) and CAD (122,733 cases, 424,528 control individuals). A latent causal variable (LCV) model was performed to estimate the genetic causality proportion of the genetic correlation between type 1 diabetes and CAD.
RESULTS: There was significant genome-wide genetic correlation (rg) between type 1 diabetes and CAD (rg=0.088, p=8.60 × 10-3) and both diseases shared significant genome-wide genetic determinants with eosinophil count (rg for type 1 diabetes [rg(T1D)]=0.093, p=7.20 × 10-3, rg for CAD [rg(CAD)]=0.092, p=3.68 × 10-6) and lymphocyte count (rg(T1D)=-0.052, p=2.76 × 10-2, rg(CAD)=0.176, p=1.82 × 10-15). Sixteen independent loci showed stringent Bonferroni significant local genetic correlations between leukocyte counts, type 1 diabetes and/or CAD. Cis-genetic regulation of the expression levels of genes within shared loci between type 1 diabetes and CAD was associated with both diseases as well as leukocyte counts, including SH2B3, CTSH, MORF4L1, CTRB1, CTRB2, CFDP1 and IFIH1. Genetically predicted lymphocyte, neutrophil and eosinophil counts were associated with type 1 diabetes and CAD (lymphocyte OR for type 1 diabetes [ORT1D]=0.67, p=2.02-19, ORCAD=1.09, p=2.67 × 10-6; neutrophil ORT1D=0.82, p=5.63 × 10-5, ORCAD=1.17, p=5.02 × 10-14; and eosinophil ORT1D=1.67, p=5.45 × 10-25, ORCAD=1.07, p=2.03 × 10-4. The genetic causality proportion between type 1 diabetes and CAD was 0.36 ± 0.16 (pLCV=1.30 × 10-2), suggesting a possible intermediary causal variable.
CONCLUSIONS: This study sheds light on shared genetic mechanisms underlying type 1 diabetes and CAD, which may contribute to their co-occurrence through regulation of gene expression and leukocyte counts and identifies cellular and molecular targets for further investigation for disease prediction and potential drug discovery.