Volume 30, No 9, Sep 2020

ISSN: 1001-0602 
EISSN: 1748-7838 2018 
impact factor 17.848* 
(Clarivate Analytics, 2019)

Volume 30 Issue 9, September 2020: 717-731   |  Open Access

ORIGINAL ARTICLES

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

Yanan Cao^1,2,† , Lin Li^1,2,† , Min Xu^1,† , Zhimin Feng^1,† , Xiaohui Sun^1,† , Jieli Lu^1,† , Yu Xu^1,† , Peina Du^1,† , Tiange Wang^1,† , Ruying Hu³ , Zhen Ye³ , Lixin Shi⁴ , Xulei Tang⁵ , Li Yan⁶ , Zhengnan Gao⁷ , Gang Chen⁸ , Yinfei Zhang⁹ , Lulu Chen¹⁰ , Guang Ning^1,* , Yufang Bi^1,* , Weiqing Wang^1,* , The ChinaMAP Consortium

¹National 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;
²National Research Center for Translational Medicine, National Key Scientific Infrastructure for Translational Medicine (Shanghai), Shanghai Jiao Tong University, Shanghai 200240, China;
³Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310006 Zhejiang, China;
⁴Affiliated Hospital of Guiyang Medical College, Guiyang 550004 Guizhou, China;
⁵The First Hospital of Lanzhou University, Lanzhou 730000 Gansu, China;
⁶Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120 Guangdong, China;
⁷Dalian Municipal Central Hospital, Dalian 116033 Liaoning, China;
⁸Fujian Provincial Hospital, Fujian Medical University, Fuzhou 350001 Fujian, China;
⁹Central Hospital of Shanghai Jiading District, Shanghai 201800, China
¹⁰Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022 Hubei, China
^†These authors contributed equally
Correspondence: Guang Ning(gning@sibs.ac.cn)Yufang Bi(byf10784@rjh.com.cn)Weiqing Wang(wqingw61@163.com)

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.

https://doi.org/10.1038/s41422-020-0322-9

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