Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits

Ioanna Tachmazidou; Dániel Süveges; Josine L Min; Graham R S Ritchie; Julia Steinberg; Klaudia Walter; Valentina Iotchkova; Jeremy Schwartzentruber; Jie Huang; Yasin Memari; Shane McCarthy; Andrew J. Crawford; Cristina Bombieri; Massimiliano Cocca; Aliki-Eleni Farmaki; Tom R Gaunt; Pekka Jousilahti; Marjolein N Kooijman; Benjamin Lehne; Giovanni Malerba; Satu Männistö; Angela Matchan; Carolina Medina-Gomez; Sarah J Metrustry; Abhishek Nag; Ioanna Ntalla; Lavinia Paternoster; Nigel W Rayner; Cinzia Sala; William R Scott; Hashem A. Shihab; Lorraine Southam; Beate St Pourcain; Michela Traglia; Katerina Trajanoska; Gialuigi Zaza; Weihua Zhang; Maria Soler Artigas; Narinder Bansal; Marianne Benn; Zhongsheng Chen; Petr Danecek; Wei-Yu Lin; Adam E Locke; Jian'an Luan; Alisa K Manning; Antonella Mulas; Carlo Sidore; Anne Tybjaerg-Hansen; Børge G Nordestgaard; SpiroMeta Consortium

doi:10.1016/j.ajhg.2017.04.014

Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits

Ioanna Tachmazidou, Dániel Süveges, Josine L Min, Graham R S Ritchie, Julia Steinberg, Klaudia Walter, Valentina Iotchkova, Jeremy Schwartzentruber, Jie Huang, Yasin Memari, Shane McCarthy, Andrew J. Crawford, Cristina Bombieri, Massimiliano Cocca, Aliki-Eleni Farmaki, Tom R Gaunt, Pekka Jousilahti, Marjolein N Kooijman, Benjamin Lehne, Giovanni MalerbaSatu Männistö, Angela Matchan, Carolina Medina-Gomez, Sarah J Metrustry, Abhishek Nag, Ioanna Ntalla, Lavinia Paternoster, Nigel W Rayner, Cinzia Sala, William R Scott, Hashem A. Shihab, Lorraine Southam, Beate St Pourcain, Michela Traglia, Katerina Trajanoska, Gialuigi Zaza, Weihua Zhang, Maria Soler Artigas, Narinder Bansal, Marianne Benn, Zhongsheng Chen, Petr Danecek, Wei-Yu Lin, Adam E Locke, Jian'an Luan, Alisa K Manning, Antonella Mulas, Carlo Sidore, Anne Tybjaerg-Hansen, Børge G Nordestgaard, SpiroMeta Consortium

Department of Clinical Medicine

47 Citations (Scopus)

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Abstract

Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common- and low-frequency spectra. We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader allelic architecture of 12 anthropometric traits associated with height, body mass, and fat distribution in up to 267,616 individuals. We report 106 genome-wide significant signals that have not been previously identified, including 9 low-frequency variants pointing to functional candidates. Of the 106 signals, 6 are in genomic regions that have not been implicated with related traits before, 28 are independent signals at previously reported regions, and 72 represent previously reported signals for a different anthropometric trait. 71% of signals reside within genes and fine mapping resolves 23 signals to one or two likely causal variants. We confirm genetic overlap between human monogenic and polygenic anthropometric traits and find signal enrichment in cis expression QTLs in relevant tissues. Our results highlight the potential of WGS strategies to enhance biologically relevant discoveries across the frequency spectrum.

Original language	English
Journal	American Journal of Human Genetics
Volume	100
Issue number	6
Pages (from-to)	865-884
Number of pages	20
ISSN	0002-9297
DOIs	https://doi.org/10.1016/j.ajhg.2017.04.014
Publication status	Published - 1 Jun 2017

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Tachmazidou, I., Süveges, D., Min, J. L., Ritchie, G. R. S., Steinberg, J., Walter, K., Iotchkova, V., Schwartzentruber, J., Huang, J., Memari, Y., McCarthy, S., Crawford, A. J., Bombieri, C., Cocca, M., Farmaki, A.-E., Gaunt, T. R., Jousilahti, P., Kooijman, M. N., Lehne, B., ... SpiroMeta Consortium (2017). Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits. American Journal of Human Genetics, 100(6), 865-884. https://doi.org/10.1016/j.ajhg.2017.04.014

Tachmazidou, I, Süveges, D, Min, JL, Ritchie, GRS, Steinberg, J, Walter, K, Iotchkova, V, Schwartzentruber, J, Huang, J, Memari, Y, McCarthy, S, Crawford, AJ, Bombieri, C, Cocca, M, Farmaki, A-E, Gaunt, TR, Jousilahti, P, Kooijman, MN, Lehne, B, Malerba, G, Männistö, S, Matchan, A, Medina-Gomez, C, Metrustry, SJ, Nag, A, Ntalla, I, Paternoster, L, Rayner, NW, Sala, C, Scott, WR, Shihab, HA, Southam, L, St Pourcain, B, Traglia, M, Trajanoska, K, Zaza, G, Zhang, W, Artigas, MS, Bansal, N, Benn, M, Chen, Z, Danecek, P, Lin, W-Y, Locke, AE, Luan, J, Manning, AK, Mulas, A, Sidore, C, Tybjaerg-Hansen, A , Nordestgaard, BG & SpiroMeta Consortium 2017, 'Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits', American Journal of Human Genetics, vol. 100, no. 6, pp. 865-884. https://doi.org/10.1016/j.ajhg.2017.04.014

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abstract = "Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common- and low-frequency spectra. We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader allelic architecture of 12 anthropometric traits associated with height, body mass, and fat distribution in up to 267,616 individuals. We report 106 genome-wide significant signals that have not been previously identified, including 9 low-frequency variants pointing to functional candidates. Of the 106 signals, 6 are in genomic regions that have not been implicated with related traits before, 28 are independent signals at previously reported regions, and 72 represent previously reported signals for a different anthropometric trait. 71% of signals reside within genes and fine mapping resolves 23 signals to one or two likely causal variants. We confirm genetic overlap between human monogenic and polygenic anthropometric traits and find signal enrichment in cis expression QTLs in relevant tissues. Our results highlight the potential of WGS strategies to enhance biologically relevant discoveries across the frequency spectrum.",

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AU - Tachmazidou, Ioanna

AU - Süveges, Dániel

AU - Min, Josine L

AU - Ritchie, Graham R S

AU - Steinberg, Julia

AU - Walter, Klaudia

AU - Iotchkova, Valentina

AU - Schwartzentruber, Jeremy

AU - Huang, Jie

AU - Memari, Yasin

AU - McCarthy, Shane

AU - Crawford, Andrew J.

AU - Bombieri, Cristina

AU - Cocca, Massimiliano

AU - Farmaki, Aliki-Eleni

AU - Gaunt, Tom R

AU - Jousilahti, Pekka

AU - Kooijman, Marjolein N

AU - Lehne, Benjamin

AU - Malerba, Giovanni

AU - Männistö, Satu

AU - Matchan, Angela

AU - Medina-Gomez, Carolina

AU - Metrustry, Sarah J

AU - Nag, Abhishek

AU - Ntalla, Ioanna

AU - Paternoster, Lavinia

AU - Rayner, Nigel W

AU - Sala, Cinzia

AU - Scott, William R

AU - Shihab, Hashem A.

AU - Southam, Lorraine

AU - St Pourcain, Beate

AU - Traglia, Michela

AU - Trajanoska, Katerina

AU - Zaza, Gialuigi

AU - Zhang, Weihua

AU - Artigas, Maria Soler

AU - Bansal, Narinder

AU - Benn, Marianne

AU - Chen, Zhongsheng

AU - Danecek, Petr

AU - Lin, Wei-Yu

AU - Locke, Adam E

AU - Luan, Jian'an

AU - Manning, Alisa K

AU - Mulas, Antonella

AU - Sidore, Carlo

AU - Tybjaerg-Hansen, Anne

AU - Nordestgaard, Børge G

AU - SpiroMeta Consortium

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N2 - Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common- and low-frequency spectra. We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader allelic architecture of 12 anthropometric traits associated with height, body mass, and fat distribution in up to 267,616 individuals. We report 106 genome-wide significant signals that have not been previously identified, including 9 low-frequency variants pointing to functional candidates. Of the 106 signals, 6 are in genomic regions that have not been implicated with related traits before, 28 are independent signals at previously reported regions, and 72 represent previously reported signals for a different anthropometric trait. 71% of signals reside within genes and fine mapping resolves 23 signals to one or two likely causal variants. We confirm genetic overlap between human monogenic and polygenic anthropometric traits and find signal enrichment in cis expression QTLs in relevant tissues. Our results highlight the potential of WGS strategies to enhance biologically relevant discoveries across the frequency spectrum.

AB - Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common- and low-frequency spectra. We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader allelic architecture of 12 anthropometric traits associated with height, body mass, and fat distribution in up to 267,616 individuals. We report 106 genome-wide significant signals that have not been previously identified, including 9 low-frequency variants pointing to functional candidates. Of the 106 signals, 6 are in genomic regions that have not been implicated with related traits before, 28 are independent signals at previously reported regions, and 72 represent previously reported signals for a different anthropometric trait. 71% of signals reside within genes and fine mapping resolves 23 signals to one or two likely causal variants. We confirm genetic overlap between human monogenic and polygenic anthropometric traits and find signal enrichment in cis expression QTLs in relevant tissues. Our results highlight the potential of WGS strategies to enhance biologically relevant discoveries across the frequency spectrum.

KW - Journal Article

U2 - 10.1016/j.ajhg.2017.04.014

DO - 10.1016/j.ajhg.2017.04.014

M3 - Journal article

C2 - 28552196

SN - 0002-9297

VL - 100

SP - 865

EP - 884

JO - American Journal of Human Genetics

JF - American Journal of Human Genetics

IS - 6

ER -

Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits

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