Abstract
BACKGROUND: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. RESULTS: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. CONCLUSIONS: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.
| Original language | English |
|---|---|
| Article number | 268 |
| Pages (from-to) | 268 |
| Number of pages | 1 |
| Journal | Genome Biology |
| Volume | 23 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 27 Dec 2022 |
Bibliographical note
FundingGMP, PN, and CW are supported by NHLBI R01HL127564. GMP and PN are supported by R01HL142711. AG acknowledge
support from the Wellcome Trust (201543/B/16/Z), European Union Seventh Framework Programme FP7/2007–2013
under grant agreement no. HEALTH-F2-2013–601456 (CVGenes@Target) & the TriPartite Immunometabolism Consortium
[TrIC]-Novo Nordisk Foundation’s Grant number NNF15CC0018486. JMM is supported by American Diabetes Associa‑
tion Innovative and Clinical Translational Award 1–19-ICTS-068. SR was supported by the Academy of Finland Center
of Excellence in Complex Disease Genetics (Grant No 312062), the Finnish Foundation for Cardiovascular Research, the
Sigrid Juselius Foundation, and University of Helsinki HiLIFE Fellow and Grand Challenge grants. EW was supported by
the Finnish innovation fund Sitra (EW) and Finska Läkaresällskapet. CNS was supported by American Heart Association
Postdoctoral Fellowships 15POST24470131 and 17POST33650016. Charles N Rotimi is supported by Z01HG200362.
Zhe Wang, Michael H Preuss, and Ruth JF Loos are supported by R01HL142302. NJT is a Wellcome Trust Investiga‑
tor (202802/Z/16/Z), is the PI of the Avon Longitudinal Study of Parents and Children (MRC & WT 217065/Z/19/Z), is
supported by the University of Bristol NIHR Biomedical Research Centre (BRC-1215–2001) and the MRC Integrative
Epidemiology Unit (MC_UU_00011), and works within the CRUK Integrative Cancer Epidemiology Programme (C18281/
A19169). Ruth E Mitchell is a member of the MRC Integrative Epidemiology Unit at the University of Bristol funded by the
MRC (MC_UU_00011/1). Simon Haworth is supported by the UK National Institute for Health Research Academic Clinical
Fellowship. Paul S. de Vries was supported by American Heart Association grant number 18CDA34110116. Julia Ramierz
acknowledges support by the People Programme of the European Union’s Seventh Framework Programme grant n°
608765 and Marie Sklodowska-Curie grant n° 786833. Maria Sabater-Lleal is supported by a Miguel Servet contract from
the ISCIII Spanish Health Institute (CP17/00142) and co-fnanced by the European Social Fund. Jian Yang is funded by the
Westlake Education Foundation. Olga Giannakopoulou has received funding from the British Heart Foundation (BHF)
(FS/14/66/3129). CHARGE Consortium cohorts were supported by R01HL105756. Study-specifc acknowledgements are
available in the Additional fle 32: Supplementary Note. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart,
Publisher Copyright:
© 2022. The Author(s).
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This output contributes to the following UN Sustainable Development Goals (SDGs)
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In: Genome Biology, Vol. 23, No. 1, 268, 27.12.2022, p. 268.
Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis
AU - Kanoni, Stavroula
AU - Graham, Sarah E.
AU - Wang, Yuxuan
AU - Surakka, Ida
AU - Ramdas, Shweta
AU - Zhu, Xiang
AU - Clarke, Shoa L.
AU - Bhatti, Konain Fatima
AU - Vedantam, Sailaja
AU - Winkler, Thomas W.
AU - Locke, Adam E.
AU - Marouli, Eirini
AU - Zajac, Greg J.M.
AU - Wu, Kuan Han H.
AU - Ntalla, Ioanna
AU - Hui, Qin
AU - Klarin, Derek
AU - Hilliard, Austin T.
AU - Wang, Zeyuan
AU - Xue, Chao
AU - Thorleifsson, Gudmar
AU - Helgadottir, Anna
AU - Gudbjartsson, Daniel F.
AU - Holm, Hilma
AU - Olafsson, Isleifur
AU - Hwang, Mi Yeong
AU - Han, Sohee
AU - Akiyama, Masato
AU - Sakaue, Saori
AU - Terao, Chikashi
AU - Kanai, Masahiro
AU - Zhou, Wei
AU - Brumpton, Ben M.
AU - Rasheed, Humaira
AU - Havulinna, Aki S.
AU - Veturi, Yogasudha
AU - Pacheco, Jennifer Allen
AU - Rosenthal, Elisabeth A.
AU - Lingren, Todd
AU - Feng, Qi Ping
AU - Kullo, Iftikhar J.
AU - Narita, Akira
AU - Takayama, Jun
AU - Martin, Hilary C.
AU - Hunt, Karen A.
AU - Trivedi, Bhavi
AU - Haessler, Jeffrey
AU - Giulianini, Franco
AU - Bradford, Yuki
AU - Miller, Jason E.
AU - Campbell, Archie
AU - Lin, Kuang
AU - Millwood, Iona Y.
AU - Rasheed, Asif
AU - Hindy, George
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AU - Weir, David R.
AU - Turman, Constance
AU - Huang, Hongyan
AU - Graff, Mariaelisa
AU - Choudhury, Ananyo
AU - Sengupta, Dhriti
AU - Mahajan, Anubha
AU - Brown, Michael R.
AU - Zhang, Weihua
AU - Yu, Ketian
AU - Schmidt, Ellen M.
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AU - Gustafsson, Stefan
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AU - Luan, Jian'an
AU - Zhao, Jing Hua
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AU - Jang, Hye Mi
AU - Yoon, Kyungheon
AU - Medina-Gomez, Carolina
AU - Pitsillides, Achilleas
AU - Hottenga, Jouke Jan
AU - Wood, Andrew R.
AU - Ji, Yingji
AU - Gao, Zishan
AU - Haworth, Simon
AU - Yousri, Noha A.
AU - Mitchell, Ruth E.
AU - Chai, Jin Fang
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AU - Yao, Jie
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AU - Wang, Jun Sing
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N1 - Funding GMP, PN, and CW are supported by NHLBI R01HL127564. GMP and PN are supported by R01HL142711. AG acknowledge support from the Wellcome Trust (201543/B/16/Z), European Union Seventh Framework Programme FP7/2007–2013 under grant agreement no. HEALTH-F2-2013–601456 (CVGenes@Target) & the TriPartite Immunometabolism Consortium [TrIC]-Novo Nordisk Foundation’s Grant number NNF15CC0018486. JMM is supported by American Diabetes Associa‑ tion Innovative and Clinical Translational Award 1–19-ICTS-068. SR was supported by the Academy of Finland Center of Excellence in Complex Disease Genetics (Grant No 312062), the Finnish Foundation for Cardiovascular Research, the Sigrid Juselius Foundation, and University of Helsinki HiLIFE Fellow and Grand Challenge grants. EW was supported by the Finnish innovation fund Sitra (EW) and Finska Läkaresällskapet. CNS was supported by American Heart Association Postdoctoral Fellowships 15POST24470131 and 17POST33650016. Charles N Rotimi is supported by Z01HG200362. Zhe Wang, Michael H Preuss, and Ruth JF Loos are supported by R01HL142302. NJT is a Wellcome Trust Investiga‑ tor (202802/Z/16/Z), is the PI of the Avon Longitudinal Study of Parents and Children (MRC & WT 217065/Z/19/Z), is supported by the University of Bristol NIHR Biomedical Research Centre (BRC-1215–2001) and the MRC Integrative Epidemiology Unit (MC_UU_00011), and works within the CRUK Integrative Cancer Epidemiology Programme (C18281/ A19169). Ruth E Mitchell is a member of the MRC Integrative Epidemiology Unit at the University of Bristol funded by the MRC (MC_UU_00011/1). Simon Haworth is supported by the UK National Institute for Health Research Academic Clinical Fellowship. Paul S. de Vries was supported by American Heart Association grant number 18CDA34110116. Julia Ramierz acknowledges support by the People Programme of the European Union’s Seventh Framework Programme grant n° 608765 and Marie Sklodowska-Curie grant n° 786833. Maria Sabater-Lleal is supported by a Miguel Servet contract from the ISCIII Spanish Health Institute (CP17/00142) and co-fnanced by the European Social Fund. Jian Yang is funded by the Westlake Education Foundation. Olga Giannakopoulou has received funding from the British Heart Foundation (BHF) (FS/14/66/3129). CHARGE Consortium cohorts were supported by R01HL105756. Study-specifc acknowledgements are available in the Additional fle 32: Supplementary Note. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Publisher Copyright: © 2022. The Author(s).
PY - 2022/12/27
Y1 - 2022/12/27
N2 - BACKGROUND: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. RESULTS: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. CONCLUSIONS: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.
AB - BACKGROUND: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. RESULTS: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. CONCLUSIONS: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.
UR - https://www.scopus.com/pages/publications/85144774123
U2 - 10.1186/s13059-022-02837-1
DO - 10.1186/s13059-022-02837-1
M3 - Article
C2 - 36575460
AN - SCOPUS:85144774123
SN - 1474-7596
VL - 23
SP - 268
JO - Genome Biology
JF - Genome Biology
IS - 1
M1 - 268
ER -