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Genetic variants linked to type 2 diabetes identified in Chinese populations

Tests of one intergenic variant associated with higher blood sugar levels show how it decreases gene activity

PLOS

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IMAGE: Principal components analysis of allele frequency for 8,403 subjects in the China Health and Nutrition Survey. Dots representing each subject are colored by the province in which they reside. view more 

Credit: Cassandra N. Spracklen, Jinxiu Shi and colleagues

Researchers investigated genomes from diverse Chinese populations to identify new and known genetic variants that contribute to a person's blood sugar level and risk of Type 2 diabetes. Karen Mohlke at the University of North Carolina at Chapel Hill, Wei Huang at the Chinese National Human Genome Center and Shanghai Industrial Technology Institute, and their colleagues report these findings in a new study published April 5th, 2018 in PLOS Genetics.

Type 2 diabetes affects more than 422 million people worldwide and at least 30% of these cases occur in East Asian populations. A person's risk of Type 2 diabetes, as well as the levels of blood sugar, insulin and HbA1c, which gives an average of recent blood sugar levels, are all inherited traits. The genetic variants that contribute to these traits can vary between populations, so researchers conducted genome-wide association analyses to identify these variants in up to 7,178 Chinese individuals from nine provinces who participated in the China Health and Nutrition Survey (CHNS). The study identified new variants and confirmed 32 previously described variants believed to contribute to Type 2 diabetes and blood sugar level, which vary in frequency across the population. The researchers also performed laboratory assays to show that one variant located in a gene regulatory element between the SIX2 and SIX3 genes reduces transcriptional activity and gene expression in pancreatic islets, leading to elevated blood sugar. "We compared variants linked to glucose level in East Asians with variants linked to islet gene expression levels in Europeans,"Dr. Mohlke explained. "This cross-ancestry comparison helped define a molecular mechanism that supports, in humans, a role for the SIX3 and/or SIX2 transcription factors affecting insulin secretion."

A next step in this work will be to investigate further the function of other genetic variants identified in the study to better understand how they contribute to blood sugar levels and risk of Type 2 diabetes. This work also highlights the usefulness of the diverse population within the CHNS for performing genetic studies. As researchers conduct more genome-wide meta-analyses across genetically diverse populations, they will likely identify additional genetic variants that will better explain the levels of heritability of complex traits like diabetes.

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In your coverage please use this URL to provide access to the freely available article in PLOS Genetics:

http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1007275

Citation: Spracklen CN, Shi J, Vadlamudi S, Wu Y, Zou M, Raulerson CK, et al. (2018) Identification and functional analysis of glycemic trait loci in the China Health and Nutrition Survey. PLoS Genet 14(4): e1007275. https://doi.org/10.1371/journal.pgen.1007275

Image Credit: Cassandra N. Spracklen, Jinxiu Shi and colleagues

Image Caption: Principal components analysis of allele frequency for 8,403 subjects in the China Health and Nutrition Survey. Dots representing each subject are colored by the province in which they reside.

Funding: This research uses data from the China Health and Nutrition Survey (CHNS). We thank the National Institute for Nutrition and Health, China Center for Disease Control and Prevention, Carolina Population Center (P2C HD050924, T32 HD007168), the University of North Carolina at Chapel Hill, the NIH (R01-HD30880, DK056350, R24 HD050924, and R01-HD38700), and the NIH Fogarty International Center (D43 TW009077, D43 TW007709) for financial support for the CHNS data collection and analysis. We also thank the China-Japan Friendship Hospital, Ministry of Health, the Chinese National Human Genome Center at Shanghai, and the Beijing Municipal Center for Disease Prevention and Control for CHNS support. Statistical and molecular analyses were supported by NIH R01 DK072193 and U01 DK105561. We also thank for the following organizations for individual support: the American Heart Association Postdoctoral Fellowship 15POST24470131 and 17POST33650016 (CNS); .the National Institutes of Health T32-HL129982 (JPD); the National Institutes of Health T32GM67553(CKR); and the University of North Carolina at Chapel Hill Department of Genetics' Summer of Learning and Research (SOLAR), Spelman College's Research Initiative for Science Enhancement (RISE) programs, and the National Institutes of Health 4R25GM060500-16 (KJ).

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Competing Interests: The authors have declared that no competing interests exist.

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