[特邀报告]基于孟德尔随机化(MR)构建糖脂代谢性状的因果网络

基于孟德尔随机化(MR)构建糖脂代谢性状的因果网络
编号:98 访问权限:仅限参会人 更新:2022-07-12 13:06:50 浏览:451次 特邀报告

报告开始:2022年07月23日 15:25 (Asia/Shanghai)

报告时间:20min

所在会议:[S4] 分会场4 » [S4-1] 群体遗传学与微生物组学

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摘要
We systematically investigated the bidirectional causality between high-density-lipoprotein cholesterol (HDL-C), low-density-lipoprotein cholesterol (LDL-C), triglycerides (TG), fasting insulin (FI), and glycated hemoglobin A1c (HbA1c) based on genome-wide association summary statistics of Europeans (sample size n = 1,320,016 for lipids, 151,013 for FI, and 344,182 for HbA1c). We applied multivariable Mendelian randomization (MR) to account for the correlation between different traits, and constructed a causal graph with 13 significant causal effects after adjusting for multiple testing (P < 0.05/20). Remarkably, we found the effects of lipids on glycemic traits were through FI from TG (β = 0.06 [95% CI: 0.03, 0.08] in unit of 1-SD for each trait) and HDL-C (β = -0.02 [-0.03, -0.01]). On the other hand, FI had strong a negative effect on HDL-C (β = -0.15 [-0.21, -0.09]) and positive effects on TG (β = 0.22 [0.14, 0.31]) and HbA1c (β = 0.15 [0.12, 0.19]), while HbA1c could raise LDL-C (β = 0.06 [0.03, 0.08]) and TG (β = 0.08 [0.06, 0.10]). These estimates derived from the inverse-variance weighting method were robust when using different MR methods. Our results suggested that elevated FI was a strong causal factor of high TG and low HDL-C, which in turn would further increase FI. Therefore, early control of insulin resistance is critical to reduce the risk of type 2 diabetes, dyslipidemia, and cardiovascular complications. 
关键字
孟德尔随机化; 血脂; 空腹胰岛素;糖化血红蛋白;因果网络
报告人
王超龙
教授 华中科技大学

王超龙,华中科技大学公共卫生学院教授、副院长,国家级青年人才。围绕疾病精准防控的重大需求,通过群体遗传学、流行病学、统计学和生物信息学等多学科交叉,在揭示亚洲人群遗传多样性、阐明复杂疾病遗传机制和流行特征、优化疾病风险评估模型等取得一系列创新成果,发表论文50余篇,包括近5年作为通讯作者在Cell(封面)、Nature(封面)、Genome Medicine、Diabetes、Molecular Biology and Evolution、Briefings in Bioinformatics等一流期刊发表论文十余篇,获2020全国十大生物信息学进展等荣誉。
 

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