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Bispebjerg Hospital - en del af Københavns Universitetshospital
Udgivet

The trans-ancestral genomic architecture of glycemic traits

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

DOI

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  • Ji Chen
  • Cassandra N Spracklen
  • Gaëlle Marenne
  • Arushi Varshney
  • Laura J Corbin
  • Jian'an Luan
  • Sara M Willems
  • Ying Wu
  • Xiaoshuai Zhang
  • Momoko Horikoshi
  • Thibaud S Boutin
  • Reedik Mägi
  • Johannes Waage
  • Ruifang Li-Gao
  • Kei Hang Katie Chan
  • Jie Yao
  • Mila D Anasanti
  • Audrey Y Chu
  • Annique Claringbould
  • Jani Heikkinen
  • Jaeyoung Hong
  • Jouke-Jan Hottenga
  • Shaofeng Huo
  • Marika A Kaakinen
  • Tin Louie
  • Winfried März
  • Hortensia Moreno-Macias
  • Anne Ndungu
  • Sarah C Nelson
  • Ilja M Nolte
  • Kari E North
  • Chelsea K Raulerson
  • Debashree Ray
  • Rebecca Rohde
  • Denis Rybin
  • Claudia Schurmann
  • Xueling Sim
  • Tarunveer S Ahluwalia
  • Thomas Sparsø
  • Arne Astrup
  • Marit E Jørgensen
  • Allan Linneberg
  • Betina Thuesen
  • Henrik Vestergaard
  • Hans Bisgaard
  • Klaus Bønnelykke
  • Torben Hansen
  • Peter Schwarz
  • Thorkild I A Sørensen
  • Nick J Wareham
  • LifeLines Cohort study
Vis graf over relationer

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10-8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.

OriginalsprogEngelsk
TidsskriftNature Genetics
Vol/bind53
Udgave nummer6
Sider (fra-til)840-860
Antal sider21
ISSN1061-4036
DOI
StatusUdgivet - jun. 2021

ID: 67442756