Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps

UK10K Consortium

doi:10.1038/ng.3668

Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps

UK10K Consortium

Wellcome Trust
Wellcome Sanger Institute
Boston VA Research Institute
Lady Davis Institute for Medical Research
McGill University and Génome Québec Innovation Centre
University of Washington
Istituto Scientifico San Raffaele
University of Bristol
University of Cambridge
Institute for Maternal and Child Health-IRCCS ''burlo Garofolo''- Trieste
University of Trieste Via A
University Medical Center Groningen
Harokopio University
Karolinska Institutet
Erasmus University Medical Center
Harvard T.H. Chan School of Public Health
Heidelberg University
King's College London
University of Cambridge School of Clinical Medicine
University of Verona
Barts and The London School of Medicine and Dentistry
University of Oxford
University of Verona
Imperial College London
Università Cattolica del Sacro Cuore
Experimental Genetics Division
National Heart and Lung Institute
Fred Hutchinson Cancer Research Center
Medical University of Graz
SYNLAB MVZ Humangenetik Mannheim
University of Oxford Medical Sciences Division
NYU-Langone Medical Center
University of Washington School of Public Health and Community Medicine
University of Wisconsin-Milwaukee

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

56 Scopus citations

Abstract

Large-scale whole-genome sequence data sets offer novel opportunities to identify genetic variation underlying human traits. Here we apply genotype imputation based on whole-genome sequence data from the UK10K and 1000 Genomes Project into 35,981 study participants of European ancestry, followed by association analysis with 20 quantitative cardiometabolic and hematological traits. We describe 17 new associations, including 6 rare (minor allele frequency (MAF) < 1%) or low-frequency (1% < MAF < 5%) variants with platelet count (PLT), red blood cell indices (MCH and MCV) and HDL cholesterol. Applying fine-mapping analysis to 233 known and new loci associated with the 20 traits, we resolve the associations of 59 loci to credible sets of 20 or fewer variants and describe trait enrichments within regions of predicted regulatory function. These findings improve understanding of the allelic architecture of risk factors for cardiometabolic and hematological diseases and provide additional functional insights with the identification of potentially novel biological targets.

Original language	English
Pages (from-to)	1303-1312
Number of pages	10
Journal	Nature Genetics
Volume	48
Issue number	11
DOIs	https://doi.org/10.1038/ng.3668
State	Published - 1 Nov 2016
Externally published	Yes

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

SDG 3 Good Health and Well-being

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10.1038/ng.3668

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@article{38be2c0650b34087bd72665115c53996,

title = "Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps",

abstract = "Large-scale whole-genome sequence data sets offer novel opportunities to identify genetic variation underlying human traits. Here we apply genotype imputation based on whole-genome sequence data from the UK10K and 1000 Genomes Project into 35,981 study participants of European ancestry, followed by association analysis with 20 quantitative cardiometabolic and hematological traits. We describe 17 new associations, including 6 rare (minor allele frequency (MAF) < 1\%) or low-frequency (1\% < MAF < 5\%) variants with platelet count (PLT), red blood cell indices (MCH and MCV) and HDL cholesterol. Applying fine-mapping analysis to 233 known and new loci associated with the 20 traits, we resolve the associations of 59 loci to credible sets of 20 or fewer variants and describe trait enrichments within regions of predicted regulatory function. These findings improve understanding of the allelic architecture of risk factors for cardiometabolic and hematological diseases and provide additional functional insights with the identification of potentially novel biological targets.",

author = "\{UK10K Consortium\} and Valentina Iotchkova and Jie Huang and Morris, \{John A.\} and Deepti Jain and Caterina Barbieri and Klaudia Walter and Min, \{Josine L.\} and Lu Chen and William Astle and Massimilian Cocca and Patrick Deelen and Heather Elding and Farmaki, \{Aliki Eleni\} and Franklin, \{Christopher S.\} and Mattias Franberg and Gaunt, \{Tom R.\} and Albert Hofman and Tao Jiang and Kleber, \{Marcus E.\} and Genevieve Lachance and Jian'An Luan and Giovanni Malerba and Angela Matchan and Daniel Mead and Yasin Memari and Ioanna Ntalla and Kalliope Panoutsopoulou and Raha Pazoki and Perry, \{John R.B.\} and Fernando Rivadeneira and Maria Sabater-Lleal and Bengt Sennblad and Shin, \{So Youn\} and Lorraine Southam and Michela Traglia and \{Van Dijk\}, Freerk and \{Van Leeuwen\}, \{Elisabeth M.\} and Gianluigi Zaza and Weihua Zhang and Najaf Amin and Adam Butterworth and Chambers, \{John C.\} and George Dedoussis and Abbas Dehghan and Franco, \{Oscar H.\} and Lude Franke and Mattia Frontini and Giovanni Gambaro and Paolo Gasparini and Eleftheria Zeggini",

year = "2016",

month = nov,

day = "1",

doi = "10.1038/ng.3668",

language = "English",

volume = "48",

pages = "1303--1312",

journal = "Nature Genetics",

issn = "1061-4036",

publisher = "Nature Research",

number = "11",

}

TY - JOUR

T1 - Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps

AU - UK10K Consortium

AU - Iotchkova, Valentina

AU - Huang, Jie

AU - Morris, John A.

AU - Jain, Deepti

AU - Barbieri, Caterina

AU - Walter, Klaudia

AU - Min, Josine L.

AU - Chen, Lu

AU - Astle, William

AU - Cocca, Massimilian

AU - Deelen, Patrick

AU - Elding, Heather

AU - Farmaki, Aliki Eleni

AU - Franklin, Christopher S.

AU - Franberg, Mattias

AU - Gaunt, Tom R.

AU - Hofman, Albert

AU - Jiang, Tao

AU - Kleber, Marcus E.

AU - Lachance, Genevieve

AU - Luan, Jian'An

AU - Malerba, Giovanni

AU - Matchan, Angela

AU - Mead, Daniel

AU - Memari, Yasin

AU - Ntalla, Ioanna

AU - Panoutsopoulou, Kalliope

AU - Pazoki, Raha

AU - Perry, John R.B.

AU - Rivadeneira, Fernando

AU - Sabater-Lleal, Maria

AU - Sennblad, Bengt

AU - Shin, So Youn

AU - Southam, Lorraine

AU - Traglia, Michela

AU - Van Dijk, Freerk

AU - Van Leeuwen, Elisabeth M.

AU - Zaza, Gianluigi

AU - Zhang, Weihua

AU - Amin, Najaf

AU - Butterworth, Adam

AU - Chambers, John C.

AU - Dedoussis, George

AU - Dehghan, Abbas

AU - Franco, Oscar H.

AU - Franke, Lude

AU - Frontini, Mattia

AU - Gambaro, Giovanni

AU - Gasparini, Paolo

AU - Zeggini, Eleftheria

PY - 2016/11/1

Y1 - 2016/11/1

N2 - Large-scale whole-genome sequence data sets offer novel opportunities to identify genetic variation underlying human traits. Here we apply genotype imputation based on whole-genome sequence data from the UK10K and 1000 Genomes Project into 35,981 study participants of European ancestry, followed by association analysis with 20 quantitative cardiometabolic and hematological traits. We describe 17 new associations, including 6 rare (minor allele frequency (MAF) < 1%) or low-frequency (1% < MAF < 5%) variants with platelet count (PLT), red blood cell indices (MCH and MCV) and HDL cholesterol. Applying fine-mapping analysis to 233 known and new loci associated with the 20 traits, we resolve the associations of 59 loci to credible sets of 20 or fewer variants and describe trait enrichments within regions of predicted regulatory function. These findings improve understanding of the allelic architecture of risk factors for cardiometabolic and hematological diseases and provide additional functional insights with the identification of potentially novel biological targets.

AB - Large-scale whole-genome sequence data sets offer novel opportunities to identify genetic variation underlying human traits. Here we apply genotype imputation based on whole-genome sequence data from the UK10K and 1000 Genomes Project into 35,981 study participants of European ancestry, followed by association analysis with 20 quantitative cardiometabolic and hematological traits. We describe 17 new associations, including 6 rare (minor allele frequency (MAF) < 1%) or low-frequency (1% < MAF < 5%) variants with platelet count (PLT), red blood cell indices (MCH and MCV) and HDL cholesterol. Applying fine-mapping analysis to 233 known and new loci associated with the 20 traits, we resolve the associations of 59 loci to credible sets of 20 or fewer variants and describe trait enrichments within regions of predicted regulatory function. These findings improve understanding of the allelic architecture of risk factors for cardiometabolic and hematological diseases and provide additional functional insights with the identification of potentially novel biological targets.

UR - https://www.scopus.com/pages/publications/84988713790

U2 - 10.1038/ng.3668

DO - 10.1038/ng.3668

M3 - Article

C2 - 27668658

AN - SCOPUS:84988713790

SN - 1061-4036

VL - 48

SP - 1303

EP - 1312

JO - Nature Genetics

JF - Nature Genetics

IS - 11

ER -

Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps

Abstract

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