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.
Highlights
Metabolic diseases are becoming a major growing public health challenge and causes of morbidity and mortality in the world
High-depth whole genome sequencing (WGS) dataset of the ChinaMAP The ChinaMAP is based on three large-scale cohorts: The China Noncommunicable Disease Surveillance 2010, a nationally representative study with 150,000 participants;14 the Risk Evaluation of cAncers in Chinese diabeTic Individuals: a lONgitudinal (REACTION) study with 250,000 participants15 and the Communitybased Cardiovascular Risk During Urbanization in Shanghai with 50,000 participants
The ChinaMAP obtained a more massive Chinese genomic dataset compared to the lowdepth genome data from non-invasive prenatal testing and SG10K study
Summary
Metabolic diseases are becoming a major growing public health challenge and causes of morbidity and mortality in the world. The most common and important metabolic diseases, type 2 diabetes and obesity, are comprised of different subtypes requiring specific diagnosis and treatments. Understanding the genetic architecture of metabolic traits is crucial for individual risk assessment, prevention, and treatment of metabolic diseases. Applying a comprehensive genetic analysis of massive cohorts can provide a systematic approach and effective strategy for the discovery of novel markers and targets. The variant spectrum of coding and non-coding regions from population genomics promotes a further understanding of the genetic basis of complex metabolic traits and diseases. The findings from the genome-wide association studies (GWAS) and population genome sequencing projects construct the knowledge of variants associated with metabolic traits.
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