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中国人群深度全基因组序列的 ChinaMAP 分析。

The ChinaMAP analytics of deep whole genome sequences in 10,588 individuals.

机构信息

National Clinical Research Centre for Metabolic Diseases, State Key Laboratory of Medical Genomics, Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute for Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.

National Research Center for Translational Medicine, National Key Scientific Infrastructure for Translational Medicine (Shanghai), Shanghai Jiao Tong University, Shanghai, 200240, China.

出版信息

Cell Res. 2020 Sep;30(9):717-731. doi: 10.1038/s41422-020-0322-9. Epub 2020 Apr 30.

Abstract

Metabolic diseases are the most common and rapidly growing health issues worldwide. The massive population-based human genetics is crucial for the precise prevention and intervention of metabolic disorders. The China Metabolic Analytics Project (ChinaMAP) is based on cohort studies across diverse regions and ethnic groups with metabolic phenotypic data in China. Here, we describe the centralized analysis of the deep whole genome sequencing data and the genetic bases of metabolic traits in 10,588 individuals from the ChinaMAP. The frequency spectrum of variants, population structure, pathogenic variants and novel genomic characteristics were analyzed. The individual genetic evaluations of Mendelian diseases, nutrition and drug metabolism, and traits of blood glucose and BMI were integrated. Our study establishes a large-scale and deep resource for the genetics of East Asians and provides opportunities for novel genetic discoveries of metabolic characteristics and disorders.

摘要

代谢性疾病是全球最常见且增长最快的健康问题。大规模基于人群的人类遗传学对于代谢紊乱的精准预防和干预至关重要。中国代谢分析计划(ChinaMAP)是基于中国不同地区和不同种族的队列研究,具有代谢表型数据。在这里,我们描述了对来自 ChinaMAP 的 10588 个人的全基因组深度测序数据和代谢特征的遗传基础的集中分析。分析了变异的频谱、群体结构、致病性变异和新的基因组特征。个体的孟德尔疾病、营养和药物代谢以及血糖和 BMI 特征的遗传评估进行了整合。我们的研究为东亚人群的遗传学建立了一个大规模和深入的资源,并为代谢特征和疾病的新的遗传发现提供了机会。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f210/7642290/285a1f766164/41422_2020_322_Fig1_HTML.jpg

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