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Get Free AccessBackground Genome-wide association studies for glycemic traits have identified hundreds of loci associated with these biomarkers of glucose homeostasis. Despite this success, the challenge remains to link variant associations to genes, and underlying biological pathways. Methods To identify coding variant associations which may pinpoint effector genes at both novel and previously established genome-wide association loci, we performed meta-analyses of exome-array studies for four glycemic traits: glycated hemoglobin (HbA1c, up to 144,060 participants), fasting glucose (FG, up to 129,665 participants), fasting insulin (FI, up to 104,140) and 2hr glucose post-oral glucose challenge (2hGlu, up to 57,878). In addition, we performed network and pathway analyses. Results Single-variant and gene-based association analyses identified coding variant associations at more than 60 genes, which when combined with other datasets may be useful to nominate effector genes. Network and pathway analyses identified pathways related to insulin secretion, zinc transport and fatty acid metabolism. HbA1c associations were strongly enriched in pathways related to blood cell biology. Conclusions Our results provided novel glycemic trait associations and highlighted pathways implicated in glycemic regulation. Exome-array summary statistic results are being made available to the scientific community to enable further discoveries.
Sara M. Willems, Natasha Hui Jin Ng, Juan Fernandez, Rebecca S. Fine, Eleanor Wheeler, Jennifer Wessel, Hidetoshi Kitajima, Gaëlle Marenne, Xueling Sim, Hanieh Yaghootkar, Shuai Wang, Sai Chen, Yuning Chen, Yii‐Der Ida Chen, Niels Grarup, Ruifang Li‐Gao, Tibor V. Varga, Jennifer L. Asimit, Shuang Feng, Rona J. Strawbridge, Erica L. Kleinbrink, Tarunveer S. Ahluwalia, Ping An, Emil V. R. Appel, Dan E. Arking, Juha Auvinen, Lawrence F. Bielak, Nathan A. Bihlmeyer, Jette Bork‐Jensen, Jennifer A. Brody, Archie Campbell, Audrey Y. Chu, Gail Davies, Ayşe Demirkan, James S. Floyd, Franco Giulianini, Xiuqing Guo, Stefan Gustafsson, Anne Jackson, Jóhanna Jakobsdóttir, Paul M Ridker, Richard A. Jensen, Stavroula Kanoni, Sirkka Keinänen‐Kiukaanniemi, Man Li, Yingchang Lu, Jian’an Luan, Alisa K. Manning, Jonathan Marten, Karina Meidtner, Dennis O. Mook‐Kanamori, Taulant Muka, Giorgio Pistis, Bram P. Prins, Kenneth Rice, Serena Sanna, Albert V. Smith, Jennifer A. Smith, Lorraine Southam, Heather M. Stringham, Vinicius Tragante, Sander W. van der Laan, Helen R. Warren, Jie Yao, Andrianos M. Yiorkas, Weihua Zhang, Wei Zhao, Mariaelisa Graff, Heather M. Highland, Anne E. Justice, Eirini Marouli, Carolina Medina‐Gómez, Saima Afaq, Wesam A. Alhejily, Najaf Amin, Folkert W. Asselbergs, Lori L. Bonnycastle, Michiel L. Bots, Ivan Brandslund, Ji Chen, John Danesh, Renée de Mutsert, Abbas Dehghan, Tapani Ebeling, Paul Elliott, Aliki‐Eleni Farmaki, Jessica D. Faul, Paul W. Franks, Steve Franks, Andreas Fritsche, Anette P. Gjesing, Mark O. Goodarzi, Vilmundur Guðnason, Göran Hallmans, Tamara B. Harris, Karl‐Heinz Herzig, Marie‐France Hivert, Torben Jørgensen, Marit E. Jørgensen, Pekka Jousilahti (2023). Large-scale exome array summary statistics resources for glycemic traits to aid effector gene prioritization. , 8, DOI: https://doi.org/10.12688/wellcomeopenres.18754.1.
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Type
Preprint
Year
2023
Authors
100
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.12688/wellcomeopenres.18754.1
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