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Implicating genes, pleiotropy and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis
Stavroula Kanoni, Sarah E. Graham, Yuxuan Wang, Ida Surakka, Shweta Ramdas, Xiang Zhu, Shoa L. Clarke, Konain Fatima Bhatti, Sailaja Vedantam, Thomas W. Winkler, Adam E. Locke, Eirini Marouli, Greg J.M. Zajac, Kuan-Han H. Wu, Ιωάννα Ντάλλα, Qin Hui, Derek Klarin, Austin T. Hilliard, Zeyuan Wang, Chao Xue, Guðmar Þorleifsson, Anna Helgadóttir, Daníel F. Guðbjartsson, Hilma Hólm, Ísleifur Ólafsson, Mi Yeong Hwang, Sohee Han, Masato Akiyama, Saori Sakaue, Chikashi Terao, Masahiro Kanai, Wei Zhou, Ben Brumpton, Humaira Rasheed, Aki S. Havulinna, Yogasudha Veturi, Jennifer A. Pacheco, Elisabeth A. Rosenthal, Todd Lingren, QiPing Feng, Iftikhar J. Kullo, Akira Narita, Jun Takayama, Hilary C. Martin, Karen A. Hunt, Bhavi Trivedi, Jeffrey Haessler, Franco Giulianini, Yuki Bradford, Jason E. Miller, Archie Campbell, Kuang Lin, Iona Y. Millwood, Asif Rasheed, George Hindy, Jessica D. Faul, Wei Zhao, David R. 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Wang, Shih-Yi Lin, Jun‐Sing Wang, Christian Couture, Leo‐Pekka Lyytikäinen, Kjell Nikus, Gabriel Cuéllar-Partida, Henrik Vestergaard, Bertha Hidalgo, Olga Giannakopoulou, Qiuyin Cai, Morgan O. Obura, Jessica van Setten, Jingjing Liang, Hua Tang, Natalie Terzikhan, Jae Hun Shin, Rebecca D. Jackson, Alex P. Reiner, Lisa W. Martin, Zhengming Chen, Liming Li, Takahisa Kawaguchi, Joachim Thiery, Lenore J. Launer, Huaixing Li, Mike A. Nalls, Olli T. Raitakari, Sahoko Ichihara, Sarah H. Wild, Christopher P. Nelson, Harry Campbell, Susanne Jäger, Toru Nabika, Fahd Al‐Mulla, Harri Niinikoski, Peter S. Braund, Ivana Kolčić, Péter Kovács, Tota Giardoglou, Tomohiro Katsuya, Dominique P.V. de Kleijn, Gert J. de Borst, Eung Kweon Kim, Hieab H.H. Adams, M. Arfan Ikram, Xiaofeng Zhu, Folkert W. Asselbergs, Adriaan O. Kraaijeveld, Joline W. J. Beulens, Xiao‐Ou Shu, Lοukianos S. Rallidis, Oluf Pedersen, Torben Hansen, Paul Mitchell, Alex W. 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Aguilar‐Salinas, Linda S. Adair, Sonny Augustin Bechayda, H. Janaka de Silva, Ananda R. Wickremasinghe, Ronald M. Krauss, Jer‐Yuarn Wu, Wei Zheng, Anneke I. den Hollander, Dwaipayan Bharadwaj, Adolfo Correa, James G. Wilson, Lars Lind, Chew‐Kiat Heng, Amanda E. Nelson, Yvonne M. Golightly, James F. Wilson, Brenda W.J.H. Penninx, Hyung‐Lae Kim, John Attia, Rodney J. Scott, D. C. Rao, Donna K. Arnett, Mark Walker, Heikki A. Koistinen, Giriraj R. Chandak, Josep M. Mercader, Clicerio González Villalpando, Lorena Orozco, Myriam Fornage, E Shyong Tai, Rob M. van Dam, Terho Lehtimäki, Nish Chaturvedi, Mitsuhiro Yokota, Jianjun Liu, Dermot F. Reilly, Amy Jayne McKnight, Frank Kee, Karl‐Heinz Jöckel, Mark I. McCarthy, Véronique Vitart, Caroline Hayward, Eleanor M. Simonsick, Cornelia M. van Duijn, Zi‐Bing Jin, Jia Qu, Haretsugu Hishigaki, Lin Xu, Winfried März, Vilmundur Guðnason, Jean‐Claude Tardif, Guillaume Lettre, Leen M. ‘t Hart, Petra J. M. Elders, Daniel J. Rader, Scott M. Damrauer, Meena Kumari, Mika Kivimäki, Pim van der Harst, Tim D. Spector, Ruth J. F. Loos, Michael A. Province, Esteban J. Parra, Miguel Cruz, Bruce M. Psaty, Ivan Brandslund, Peter P. Pramstaller, Charles N. Rotimi, Kaare Christensen, Samuli Ripatti, Elisabeth Widén, Håkon Håkonarson, Struan F.A. Grant, Lambertus A. Kiemeney, Jacqueline de Graaf, Markus Loeffler, Florian Kronenberg, Dongfeng Gu, Jeanette Erdmann, Heribert Schunkert, Paul W. Franks, Allan Linneberg, J. Wouter Jukema, Amit V. Khera, Minna Männikkö, Marjo‐Riitta Järvelin, Zoltán Kutalik, Cucca Francesco, Dennis O. Mook‐Kanamori, Ko Willems van Dijk, Hugh Watkins, David P. Strachan, Niels Grarup, Peter Sever, Neil R Poulter, Lee‐Ming Chuang, Jerome I. Rotter, Thomas Meinertz Dantoft, Fredrik Karpe, Matt J. Neville, Nicholas J. Timpson, Ching‐Yu Cheng, Tien Yin Wong, Chiea Chuen Khor, Hengtong Li, Charumathi Sabanayagam, Annette Peters, Christian Gieger, Andrew T. Hattersley, Nancy L. Pedersen, Patrik K. E. 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Ritchie, Pekka Jousilahti, Veikko Salomaa, Kristian Hveem, Bjørn Olav Åsvold, Michiaki Kubo, Yukinori Okada, Yoshinori Murakami, Bong-Jo Kim, Unnur Þorsteinsdóttir, Kāri Stefánsson, Jifeng Zhang, Y Eugene Chen, Yuk‐Lam Ho, Julie A. Lynch, Philip S. Tsao, Kyong‐Mi Chang, Kelly Cho, Christopher J. O’Donnell, John Michael Gaziano, Peter W.F. Wilson, Timothy M. Frayling, Joel N. Hirschhorn, Sekar Kathiresan, Karen L. Mohlke, Yan V. Sun, Andrew P. Morris, Michael Boehnke, Christopher D. Brown, Pradeep Natarajan, Panos Deloukas, Cristen J. Willer, Themistocles L. Assimes, Gina M. Peloso
Bibliographic record
VenuemedRxiv · 2021
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsUniversity of TorontoCentre for Global Health ResearchUniversité de MontréalUniversité LavalMontreal Heart Institute
FundersNational Heart, Lung, and Blood InstituteMedical Research CouncilSydäntutkimussäätiöNovo Nordisk FondenCancer Research UKAcademy of FinlandUniversity of BristolEuropean CommissionNovo NordiskNational Institute for Health and Care ResearchWellcome TrustHelsingin YliopistoFinska LäkaresällskapetFoundation for Cardiovascular ResearchHelsinki Institute of Life Science, Helsingin YliopistoAmerican Heart AssociationAmerican Diabetes Association
KeywordsBiologyPleiotropyGeneticsGeneGenome-wide association studyLipid metabolismGenetic associationPhenomePhenotypeCandidate geneBioinformaticsEvolutionary biologySingle-nucleotide polymorphismGenotypeEndocrinology
Abstract
ABSTRACT Genetic variants within nearly 1,000 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. 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 2,286 lipid associations by combining six gene prediction methods and assigning a confidence score. We assign, most confidently, 118 candidate causal genes and identify potential drug targets including bona-fide ( PCSK9 ) and putative ( PNLIP and ARF6 ) genes. 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. Taken together, our findings provide insights into the mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.075
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.014 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
Opus teacher head0.094
GPT teacher head0.331
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".
Quick stats
Citations9
Published2021
Admission routes1
Has abstractyes