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

V Iotchkova; J Huang; JA Morris; D Jain; C Barbieri; K Walter; JL Min; L Chen; W Astle; M Cocca; P Deelen; H Elding; AE Farmaki; CS Franklin; M Franberg; TR Gaunt; Bert Hofman; T Jiang; ME Kleber; G Lachance; J Luan; G Malerba; A Matchan; D Mead; Y Memari; I Ntalla; K Panoutsopoulou; Raha Pazoki; JRB Perry; Fernando Rivadeneira; M Sabater-Lleal; B Sennblad; SY Shin; L Southam; M Traglia; F van Dijk; Elisa Leeuwen; G Zaza; WH Zhang; Najaf Amin; A Butterworth; JC Chambers; G Dedoussis; Abbas Dehghan; OH Franco Duran; L Franke; M Frontini; G Gambaro; P Gasparini; A Hamsten; Aaron Isaacs; JS Kooner; C Kooperberg; C Langenberg; W Marz; RA Scott; MA Swertz; D Toniolo; André Uitterlinden; Cornelia Duijn; H Watkins; E Zeggini; MT Maurano; NJ Timpson; AP Reiner; PL Auer; N Soranzo

doi:10.1038/ng.3668

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

V Iotchkova, J Huang, JA Morris, D Jain, C Barbieri, K Walter, JL Min, L Chen, W Astle, M Cocca, P Deelen, H Elding, AE Farmaki, CS Franklin, M Franberg, TR Gaunt, Bert Hofman, T Jiang, ME Kleber, G LachanceJ Luan, G Malerba, A Matchan, D Mead, Y Memari, I Ntalla, K Panoutsopoulou, Raha Pazoki, JRB Perry, Fernando Rivadeneira, M Sabater-Lleal, B Sennblad, SY Shin, L Southam, M Traglia, F van Dijk, Elisa Leeuwen, G Zaza, WH Zhang, Najaf Amin, A Butterworth, JC Chambers, G Dedoussis, Abbas Dehghan, OH Franco Duran, L Franke, M Frontini, G Gambaro, P Gasparini, A Hamsten, Aaron Isaacs, JS Kooner, C Kooperberg, C Langenberg, W Marz, RA Scott, MA Swertz, D Toniolo, André Uitterlinden, Cornelia Duijn, H Watkins, E Zeggini, MT Maurano, NJ Timpson, AP Reiner, PL Auer, N Soranzo

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

55 Citations (Scopus)

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 UK1OK 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	Undefined/Unknown
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
Publication status	Published - 2016

Research programs

EMC MM-01-39-09-A
EMC NIHES-01-64-01

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

Access to Document

10.1038/ng.3668

http://hdl.handle.net/1765/96565

Cite this

Iotchkova, V., Huang, J., Morris, JA., Jain, D., Barbieri, C., Walter, K., Min, JL., Chen, L., Astle, W., Cocca, M., Deelen, P., Elding, H., Farmaki, AE., Franklin, CS., Franberg, M., Gaunt, TR., Hofman, B., Jiang, T., Kleber, ME., ... Soranzo, N. (2016). Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps. Nature Genetics, 48(11), 1303-1312. https://doi.org/10.1038/ng.3668

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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 UK1OK 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 = "V Iotchkova and J Huang and JA Morris and D Jain and C Barbieri and K Walter and JL Min and L Chen and W Astle and M Cocca and P Deelen and H Elding and AE Farmaki and CS Franklin and M Franberg and TR Gaunt and Bert Hofman and T Jiang and ME Kleber and G Lachance and J Luan and G Malerba and A Matchan and D Mead and Y Memari and I Ntalla and K Panoutsopoulou and Raha Pazoki and JRB Perry and Fernando Rivadeneira and M Sabater-Lleal and B Sennblad and SY Shin and L Southam and M Traglia and {van Dijk}, F and Elisa Leeuwen and G Zaza and WH Zhang and Najaf Amin and A Butterworth and JC Chambers and G Dedoussis and Abbas Dehghan and {Franco Duran}, OH and L Franke and M Frontini and G Gambaro and P Gasparini and A Hamsten and Aaron Isaacs and JS Kooner and C Kooperberg and C Langenberg and W Marz and RA Scott and MA Swertz and D Toniolo and Andr{\'e} Uitterlinden and Cornelia Duijn and H Watkins and E Zeggini and MT Maurano and NJ Timpson and AP Reiner and PL Auer and N Soranzo",

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Iotchkova, V, Huang, J, Morris, JA, Jain, D, Barbieri, C, Walter, K, Min, JL, Chen, L, Astle, W, Cocca, M, Deelen, P, Elding, H, Farmaki, AE, Franklin, CS, Franberg, M, Gaunt, TR, Hofman, B, Jiang, T, Kleber, ME, Lachance, G, Luan, J, Malerba, G, Matchan, A, Mead, D, Memari, Y, Ntalla, I, Panoutsopoulou, K, Pazoki, R, Perry, JRB, Rivadeneira, F, Sabater-Lleal, M, Sennblad, B, Shin, SY, Southam, L, Traglia, M, van Dijk, F, Leeuwen, E, Zaza, G, Zhang, WH, Amin, N, Butterworth, A, Chambers, JC, Dedoussis, G, Dehghan, A, Franco Duran, OH, Franke, L, Frontini, M, Gambaro, G, Gasparini, P, Hamsten, A, Isaacs, A, Kooner, JS, Kooperberg, C, Langenberg, C, Marz, W, Scott, RA, Swertz, MA, Toniolo, D, Uitterlinden, A, Duijn, C, Watkins, H, Zeggini, E, Maurano, MT, Timpson, NJ, Reiner, AP, Auer, PL & Soranzo, N 2016, 'Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps', Nature Genetics, vol. 48, no. 11, pp. 1303-1312. https://doi.org/10.1038/ng.3668

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

AU - Iotchkova, V

AU - Huang, J

AU - Morris, JA

AU - Jain, D

AU - Barbieri, C

AU - Walter, K

AU - Min, JL

AU - Chen, L

AU - Astle, W

AU - Cocca, M

AU - Deelen, P

AU - Elding, H

AU - Farmaki, AE

AU - Franklin, CS

AU - Franberg, M

AU - Gaunt, TR

AU - Hofman, Bert

AU - Jiang, T

AU - Kleber, ME

AU - Lachance, G

AU - Luan, J

AU - Malerba, G

AU - Matchan, A

AU - Mead, D

AU - Memari, Y

AU - Ntalla, I

AU - Panoutsopoulou, K

AU - Pazoki, Raha

AU - Perry, JRB

AU - Rivadeneira, Fernando

AU - Sabater-Lleal, M

AU - Sennblad, B

AU - Shin, SY

AU - Southam, L

AU - Traglia, M

AU - van Dijk, F

AU - Leeuwen, Elisa

AU - Zaza, G

AU - Zhang, WH

AU - Amin, Najaf

AU - Butterworth, A

AU - Chambers, JC

AU - Dedoussis, G

AU - Dehghan, Abbas

AU - Franco Duran, OH

AU - Franke, L

AU - Frontini, M

AU - Gambaro, G

AU - Gasparini, P

AU - Hamsten, A

AU - Isaacs, Aaron

AU - Kooner, JS

AU - Kooperberg, C

AU - Langenberg, C

AU - Marz, W

AU - Scott, RA

AU - Swertz, MA

AU - Toniolo, D

AU - Uitterlinden, André

AU - Duijn, Cornelia

AU - Watkins, H

AU - Zeggini, E

AU - Maurano, MT

AU - Timpson, NJ

AU - Reiner, AP

AU - Auer, PL

AU - Soranzo, N

PY - 2016

Y1 - 2016

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 UK1OK 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 UK1OK 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.

U2 - 10.1038/ng.3668

DO - 10.1038/ng.3668

M3 - Article

C2 - 27668658

SN - 1061-4036

VL - 48

SP - 1303

EP - 1312

JO - Nature Genetics

JF - Nature Genetics

IS - 11

ER -