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Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes

Author

Listed:
  • Angli Xue

    (The University of Queensland)

  • Yang Wu

    (The University of Queensland)

  • Zhihong Zhu

    (The University of Queensland)

  • Futao Zhang

    (The University of Queensland)

  • Kathryn E. Kemper

    (The University of Queensland)

  • Zhili Zheng

    (The University of Queensland
    Wenzhou Medical University)

  • Loic Yengo

    (The University of Queensland)

  • Luke R. Lloyd-Jones

    (The University of Queensland)

  • Julia Sidorenko

    (The University of Queensland
    Institute of Genomics, University of Tartu)

  • Yeda Wu

    (The University of Queensland)

  • Allan F. McRae

    (The University of Queensland
    The University of Queensland)

  • Peter M. Visscher

    (The University of Queensland
    The University of Queensland)

  • Jian Zeng

    (The University of Queensland)

  • Jian Yang

    (The University of Queensland
    Wenzhou Medical University
    The University of Queensland)

Abstract

Type 2 diabetes (T2D) is a very common disease in humans. Here we conduct a meta-analysis of genome-wide association studies (GWAS) with ~16 million genetic variants in 62,892 T2D cases and 596,424 controls of European ancestry. We identify 139 common and 4 rare variants associated with T2D, 42 of which (39 common and 3 rare variants) are independent of the known variants. Integration of the gene expression data from blood (n = 14,115 and 2765) with the GWAS results identifies 33 putative functional genes for T2D, 3 of which were targeted by approved drugs. A further integration of DNA methylation (n = 1980) and epigenomic annotation data highlight 3 genes (CAMK1D, TP53INP1, and ATP5G1) with plausible regulatory mechanisms, whereby a genetic variant exerts an effect on T2D through epigenetic regulation of gene expression. Our study uncovers additional loci, proposes putative genetic regulatory mechanisms for T2D, and provides evidence of purifying selection for T2D-associated variants.

Suggested Citation

  • Angli Xue & Yang Wu & Zhihong Zhu & Futao Zhang & Kathryn E. Kemper & Zhili Zheng & Loic Yengo & Luke R. Lloyd-Jones & Julia Sidorenko & Yeda Wu & Allan F. McRae & Peter M. Visscher & Jian Zeng & Jian, 2018. "Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes," Nature Communications, Nature, vol. 9(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-04951-w
    DOI: 10.1038/s41467-018-04951-w
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    Cited by:

    1. Parker C. Wilson & Yoshiharu Muto & Haojia Wu & Anil Karihaloo & Sushrut S. Waikar & Benjamin D. Humphreys, 2022. "Multimodal single cell sequencing implicates chromatin accessibility and genetic background in diabetic kidney disease progression," Nature Communications, Nature, vol. 13(1), pages 1-20, December.
    2. Qing Cheng & Xiao Zhang & Lin S. Chen & Jin Liu, 2022. "Mendelian randomization accounting for complex correlated horizontal pleiotropy while elucidating shared genetic etiology," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    3. Amil M. Shah & Peder L. Myhre & Victoria Arthur & Pranav Dorbala & Humaira Rasheed & Leo F. Buckley & Brian Claggett & Guning Liu & Jianzhong Ma & Ngoc Quynh Nguyen & Kunihiro Matsushita & Chiadi Ndum, 2024. "Large scale plasma proteomics identifies novel proteins and protein networks associated with heart failure development," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    4. Zhen Qiao & Julia Sidorenko & Joana A. Revez & Angli Xue & Xueling Lu & Katri Pärna & Harold Snieder & Peter M. Visscher & Naomi R. Wray & Loic Yengo, 2023. "Estimation and implications of the genetic architecture of fasting and non-fasting blood glucose," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    5. Gianluca Ursini & Pasquale Di Carlo & Sreya Mukherjee & Qiang Chen & Shizhong Han & Jiyoung Kim & Maya Deyssenroth & Carmen J. Marsit & Jia Chen & Ke Hao & Giovanna Punzi & Daniel R. Weinberger, 2023. "Prioritization of potential causative genes for schizophrenia in placenta," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    6. Danielle Rasooly & Gina M. Peloso & Alexandre C. Pereira & Hesam Dashti & Claudia Giambartolomei & Eleanor Wheeler & Nay Aung & Brian R. Ferolito & Maik Pietzner & Eric H. Farber-Eger & Quinn Stanton , 2023. "Genome-wide association analysis and Mendelian randomization proteomics identify drug targets for heart failure," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    7. Léon Gurp & Leon Fodoulian & Daniel Oropeza & Kenichiro Furuyama & Eva Bru-Tari & Anh Nguyet Vu & John S. Kaddis & Iván Rodríguez & Fabrizio Thorel & Pedro L. Herrera, 2022. "Generation of human islet cell type-specific identity genesets," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    8. Liam McAllan & Damir Baranasic & Sergio Villicaña & Scarlett Brown & Weihua Zhang & Benjamin Lehne & Marco Adamo & Andrew Jenkinson & Mohamed Elkalaawy & Borzoueh Mohammadi & Majid Hashemi & Nadia Fer, 2023. "Integrative genomic analyses in adipocytes implicate DNA methylation in human obesity and diabetes," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    9. Fengzhe Xu & Evan Yi-Wen Yu & Xue Cai & Liang Yue & Li-peng Jing & Xinxiu Liang & Yuanqing Fu & Zelei Miao & Min Yang & Menglei Shuai & Wanglong Gou & Congmei Xiao & Zhangzhi Xue & Yuting Xie & Sainan, 2023. "Genome-wide genotype-serum proteome mapping provides insights into the cross-ancestry differences in cardiometabolic disease susceptibility," Nature Communications, Nature, vol. 14(1), pages 1-12, December.

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