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Exome Chip Analysis of 14,026 Koreans Reveals Known and Newly Discovered Genetic Loci Associated with Type 2 Diabetes Mellitus.

Authors
  • Cho, Seong Beom1
  • Jang, Jin Hwa1
  • Chung, Myung Guen1
  • Kim, Sang Cheol1
  • 1 Division of Biomedical Informatics, Center for Genome Science, National Institute of Health, Korea Center for Disease Control and Prevention, Cheongju, Korea. , (North Korea)
Type
Published Article
Journal
Diabetes & metabolism journal
Publication Date
Mar 01, 2021
Volume
45
Issue
2
Pages
231–240
Identifiers
DOI: 10.4093/dmj.2019.0163
PMID: 32794382
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Most loci associated with type 2 diabetes mellitus (T2DM) discovered to date are within noncoding regions of unknown functional significance. By contrast, exonic regions have advantages for biological interpretation. We analyzed the association of exome array data from 14,026 Koreans to identify susceptible exonic loci for T2DM. We used genotype information of 50,543 variants using the Illumina exome array platform. In total, 7 loci were significant with a Bonferroni adjusted P=1.03×10-6. rs2233580 in paired box gene 4 (PAX4) showed the highest odds ratio of 1.48 (P=1.60×10-10). rs11960799 in membrane associated ring-CH-type finger 3 (MARCH3) and rs75680863 in transcobalamin 2 (TCN2) were newly identified loci. When we built a model to predict the incidence of diabetes with the 7 loci and clinical variables, area under the curve (AUC) of the model improved significantly (AUC=0.72, P<0.05), but marginally in its magnitude, compared with the model using clinical variables (AUC=0.71, P<0.05). When we divided the entire population into three groups-normal body mass index (BMI; <25 kg/m2), overweight (25≤ BMI <30 kg/m2), and obese (BMI ≥30 kg/m2) individuals-the predictive performance of the 7 loci was greatest in the group of obese individuals, where the net reclassification improvement was highly significant (0.51; P=8.00×10-5). We found exonic loci having a susceptibility for T2DM. We found that such genetic information is advantageous for predicting T2DM in a subgroup of obese individuals.

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