The trans-ancestral genomic architecture of glycemic traits

LifeLines Cohort Study; The Meta-Analysis of Glucose and Insulin-related Traits Consortium (MAGIC)

doi:10.1038/s41588-021-00852-9

The trans-ancestral genomic architecture of glycemic traits

LifeLines Cohort Study, The Meta-Analysis of Glucose and Insulin-related Traits Consortium (MAGIC)

Research output: Contribution to journal › Article › peer-review

266 Citations (Scopus)

Abstract

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 x 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.

A trans-ancestry meta-analysis of GWAS of glycemic traits in up to 281,416 individuals identifies 99 novel loci, of which one quarter was found due to the multi-ancestry approach, which also improves fine-mapping of credible variant sets.

Original language	English
Pages (from-to)	840-860
Number of pages	41
Journal	Nature Genetics
Volume	53
Issue number	6
Early online date	31 May 2021
DOIs	https://doi.org/10.1038/s41588-021-00852-9
Publication status	Published - Jun 2021

Bibliographical note

Acknowledgements
We thank all investigators, staff members and study participants for their contribution to all participating studies. The funders had no role in study design, data collection, analysis, decision to publish or preparation of the manuscript. The authors received no specific funding for this work. A full list of funding as well as individual and study acknowledgments appears in the Supplementary Note.

Keywords

WIDE ASSOCIATION
INSULIN-RESISTANCE
GENE-EXPRESSION
DISEASE RISK
VARIANTS
GLUCOSE
LOCI
METAANALYSIS
MECHANISMS
HEMOGLOBIN

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Cite this

@article{95f8080c4df2482c8ecbcf0d0b605f95,

title = "The trans-ancestral genomic architecture of glycemic traits",

abstract = "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 x 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.A trans-ancestry meta-analysis of GWAS of glycemic traits in up to 281,416 individuals identifies 99 novel loci, of which one quarter was found due to the multi-ancestry approach, which also improves fine-mapping of credible variant sets.",

keywords = "WIDE ASSOCIATION, INSULIN-RESISTANCE, GENE-EXPRESSION, DISEASE RISK, VARIANTS, GLUCOSE, LOCI, METAANALYSIS, MECHANISMS, HEMOGLOBIN",

author = "Ji Chen and Spracklen, {Cassandra N.} and Gaelle Marenne and Arushi Varshney and Corbin, {Laura J.} and Jian'an Luan and Willems, {Sara M.} and Ying Wu and Xiaoshuai Zhang and Momoko Horikoshi and Boutin, {Thibaud S.} and Reedik Magi and Johannes Waage and Ruifang Li-Gao and Chan, {Kei Hang Katie} and Jie Yao and Anasanti, {Mila D.} and Chu, {Audrey Y.} and Annique Claringbould and Jani Heikkinen and Jaeyoung Hong and Jouke-Jan Hottenga and Shaofeng Huo and Kaakinen, {Marika A.} and Tin Louie and Winfried Maerz and Hortensia Moreno-Macias and Anne Ndungu and Nelson, {Sarah C.} and Nolte, {Ilja M.} and North, {Kari E.} and Raulerson, {Chelsea K.} and Debashree Ray and Rebecca Rohde and Denis Rybin and Claudia Schurmann and Xueling Sim and Lorraine Southam and Stewart, {Isobel D.} and Wang, {Carol A.} and Yujie Wang and Peitao Wu and Weihua Zhang and Ahluwalia, {Tarunveer S.} and Appel, {Emil V. R.} and Bielak, {Lawrence F.} and Brody, {Jennifer A.} and Burtt, {Noel P.} and Cabrera, {Claudia P.} and Corri Black and {LifeLines Cohort Study} and {The Meta-Analysis of Glucose and Insulin-related Traits Consortium (MAGIC)}",

note = "Acknowledgements We thank all investigators, staff members and study participants for their contribution to all participating studies. The funders had no role in study design, data collection, analysis, decision to publish or preparation of the manuscript. The authors received no specific funding for this work. A full list of funding as well as individual and study acknowledgments appears in the Supplementary Note. ",

year = "2021",

month = jun,

doi = "10.1038/s41588-021-00852-9",

language = "English",

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T1 - The trans-ancestral genomic architecture of glycemic traits

AU - Chen, Ji

AU - Spracklen, Cassandra N.

AU - Marenne, Gaelle

AU - Varshney, Arushi

AU - Corbin, Laura J.

AU - Luan, Jian'an

AU - Willems, Sara M.

AU - Wu, Ying

AU - Zhang, Xiaoshuai

AU - Horikoshi, Momoko

AU - Boutin, Thibaud S.

AU - Magi, Reedik

AU - Waage, Johannes

AU - Li-Gao, Ruifang

AU - Chan, Kei Hang Katie

AU - Yao, Jie

AU - Anasanti, Mila D.

AU - Chu, Audrey Y.

AU - Claringbould, Annique

AU - Heikkinen, Jani

AU - Hong, Jaeyoung

AU - Hottenga, Jouke-Jan

AU - Huo, Shaofeng

AU - Kaakinen, Marika A.

AU - Louie, Tin

AU - Maerz, Winfried

AU - Moreno-Macias, Hortensia

AU - Ndungu, Anne

AU - Nelson, Sarah C.

AU - Nolte, Ilja M.

AU - North, Kari E.

AU - Raulerson, Chelsea K.

AU - Ray, Debashree

AU - Rohde, Rebecca

AU - Rybin, Denis

AU - Schurmann, Claudia

AU - Sim, Xueling

AU - Southam, Lorraine

AU - Stewart, Isobel D.

AU - Wang, Carol A.

AU - Wang, Yujie

AU - Wu, Peitao

AU - Zhang, Weihua

AU - Ahluwalia, Tarunveer S.

AU - Appel, Emil V. R.

AU - Bielak, Lawrence F.

AU - Brody, Jennifer A.

AU - Burtt, Noel P.

AU - Cabrera, Claudia P.

AU - Black, Corri

AU - LifeLines Cohort Study

AU - The Meta-Analysis of Glucose and Insulin-related Traits Consortium (MAGIC)

N1 - Acknowledgements We thank all investigators, staff members and study participants for their contribution to all participating studies. The funders had no role in study design, data collection, analysis, decision to publish or preparation of the manuscript. The authors received no specific funding for this work. A full list of funding as well as individual and study acknowledgments appears in the Supplementary Note.

PY - 2021/6

Y1 - 2021/6

N2 - 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 x 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.A trans-ancestry meta-analysis of GWAS of glycemic traits in up to 281,416 individuals identifies 99 novel loci, of which one quarter was found due to the multi-ancestry approach, which also improves fine-mapping of credible variant sets.

AB - 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 x 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.A trans-ancestry meta-analysis of GWAS of glycemic traits in up to 281,416 individuals identifies 99 novel loci, of which one quarter was found due to the multi-ancestry approach, which also improves fine-mapping of credible variant sets.

KW - WIDE ASSOCIATION

KW - INSULIN-RESISTANCE

KW - GENE-EXPRESSION

KW - DISEASE RISK

KW - VARIANTS

KW - GLUCOSE

KW - LOCI

KW - METAANALYSIS

KW - MECHANISMS

KW - HEMOGLOBIN

U2 - 10.1038/s41588-021-00852-9

DO - 10.1038/s41588-021-00852-9

M3 - Article

C2 - 34059833

SN - 1061-4036

VL - 53

SP - 840

EP - 860

JO - Nature Genetics

JF - Nature Genetics

IS - 6

ER -

The trans-ancestral genomic architecture of glycemic traits

Abstract

Bibliographical note

Keywords

UN SDGs

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Cite this