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

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 re...

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Autores principales: Cao, Yanan, Li, Lin, Xu, Min, Feng, Zhimin, Sun, Xiaohui, Lu, Jieli, Xu, Yu, Du, Peina, Wang, Tiange, Hu, Ruying, Ye, Zhen, Shi, Lixin, Tang, Xulei, Yan, Li, Gao, Zhengnan, Chen, Gang, Zhang, Yinfei, Chen, Lulu, Ning, Guang, Bi, Yufang, Wang, Weiqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Singapore 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7609296/
https://www.ncbi.nlm.nih.gov/pubmed/32355288
http://dx.doi.org/10.1038/s41422-020-0322-9
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author Cao, Yanan
Li, Lin
Xu, Min
Feng, Zhimin
Sun, Xiaohui
Lu, Jieli
Xu, Yu
Du, Peina
Wang, Tiange
Hu, Ruying
Ye, Zhen
Shi, Lixin
Tang, Xulei
Yan, Li
Gao, Zhengnan
Chen, Gang
Zhang, Yinfei
Chen, Lulu
Ning, Guang
Bi, Yufang
Wang, Weiqing
author_facet Cao, Yanan
Li, Lin
Xu, Min
Feng, Zhimin
Sun, Xiaohui
Lu, Jieli
Xu, Yu
Du, Peina
Wang, Tiange
Hu, Ruying
Ye, Zhen
Shi, Lixin
Tang, Xulei
Yan, Li
Gao, Zhengnan
Chen, Gang
Zhang, Yinfei
Chen, Lulu
Ning, Guang
Bi, Yufang
Wang, Weiqing
author_sort Cao, Yanan
collection PubMed
description 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.
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spelling pubmed-76092962020-11-05 The ChinaMAP analytics of deep whole genome sequences in 10,588 individuals Cao, Yanan Li, Lin Xu, Min Feng, Zhimin Sun, Xiaohui Lu, Jieli Xu, Yu Du, Peina Wang, Tiange Hu, Ruying Ye, Zhen Shi, Lixin Tang, Xulei Yan, Li Gao, Zhengnan Chen, Gang Zhang, Yinfei Chen, Lulu Ning, Guang Bi, Yufang Wang, Weiqing Cell Res Article 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. Springer Singapore 2020-04-30 2020-09 /pmc/articles/PMC7609296/ /pubmed/32355288 http://dx.doi.org/10.1038/s41422-020-0322-9 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Cao, Yanan
Li, Lin
Xu, Min
Feng, Zhimin
Sun, Xiaohui
Lu, Jieli
Xu, Yu
Du, Peina
Wang, Tiange
Hu, Ruying
Ye, Zhen
Shi, Lixin
Tang, Xulei
Yan, Li
Gao, Zhengnan
Chen, Gang
Zhang, Yinfei
Chen, Lulu
Ning, Guang
Bi, Yufang
Wang, Weiqing
The ChinaMAP analytics of deep whole genome sequences in 10,588 individuals
title The ChinaMAP analytics of deep whole genome sequences in 10,588 individuals
title_full The ChinaMAP analytics of deep whole genome sequences in 10,588 individuals
title_fullStr The ChinaMAP analytics of deep whole genome sequences in 10,588 individuals
title_full_unstemmed The ChinaMAP analytics of deep whole genome sequences in 10,588 individuals
title_short The ChinaMAP analytics of deep whole genome sequences in 10,588 individuals
title_sort chinamap analytics of deep whole genome sequences in 10,588 individuals
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7609296/
https://www.ncbi.nlm.nih.gov/pubmed/32355288
http://dx.doi.org/10.1038/s41422-020-0322-9
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