Beth Israel Deaconess Medical Center, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Department of Medicine and Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA.
Trends Genet. 2021 Dec;37(12):1081-1094. doi: 10.1016/j.tig.2021.07.005. Epub 2021 Jul 24.
Human large-scale genetic association studies have identified sequence variations at thousands of genetic risk loci that are more common in patients with diverse metabolic disease compared with healthy controls. While these genetic associations have been replicated in multiple large cohorts and sometimes can explain up to 50% of heritability, the molecular and cellular mechanisms affected by common genetic variation associated with metabolic disease remains mostly unknown. A variety of new genome-wide data types, in conjunction with novel biostatistical and computational analytical methodologies and foundational experimental technologies, are paving the way for a principled approach to systematic variant-to-function (V2F) studies for metabolic diseases, turning associated regions into causal variants, cell types and states of action, effector genes, and cellular and physiological mechanisms. Identification of new target genes and cellular programs for metabolic risk loci will improve mechanistic understanding of disease biology and identification of novel therapeutic strategies.
人类大规模遗传关联研究已经确定了数千个遗传风险位点的序列变异,与健康对照相比,这些变异在患有各种代谢性疾病的患者中更为常见。虽然这些遗传关联已经在多个大型队列中得到了复制,有时甚至可以解释高达 50%的遗传性,但与代谢性疾病相关的常见遗传变异所影响的分子和细胞机制在很大程度上仍然未知。各种新的全基因组数据类型,结合新颖的生物统计学和计算分析方法以及基础实验技术,为代谢性疾病的系统变体到功能(V2F)研究铺平了道路,将相关区域转化为因果变异、作用的细胞类型和状态、效应基因以及细胞和生理机制。鉴定代谢风险位点的新靶基因和细胞程序将提高对疾病生物学的机制理解,并确定新的治疗策略。