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1. Author Correction: The power of genetic diversity in genome-wide association studies of lipids (Nature, (2021), 600, 7890, (675-679), 10.1038/s41586-021-04064-3)

Author

Graham, S.E. Clarke, S.L. Wu, K.-H.H. Kanoni, S. Zajac, G.J.M. Ramdas, S. Surakka, I. Ntalla, I. Vedantam, S. Winkler, T.W. Locke, A.E. Marouli, E. Hwang, M.Y. Han, S. Narita, A. Choudhury, A. Bentley, A.R. Ekoru, K. Verma, A. Trivedi, B. Martin, H.C. Hunt, K.A. Hui, Q. Klarin, D. Zhu, X. Thorleifsson, G. Helgadottir, A. Gudbjartsson, D.F. Holm, H. Olafsson, I. Akiyama, M. Sakaue, S. Terao, C. Kanai, M. Zhou, W. Brumpton, B.M. Rasheed, H. Ruotsalainen, S.E. Havulinna, A.S. Veturi, Y. Feng, Q.P. Rosenthal, E.A. Lingren, T. Pacheco, J.A. Pendergrass, S.A. Haessler, J. Giulianini, F. Bradford, Y. Miller, J.E. Campbell, A. Lin, K. Millwood, I.Y. Hindy, G. Rasheed, A. Faul, J.D. Zhao, W. Weir, D.R. Turman, C. Huang, H. Graff, M. Mahajan, A. Brown, M.R. Zhang, W. Yu, K. Schmidt, E.M. Pandit, A. Gustafsson, S. Yin, X. Luan, J. Zhao, J.-H. Matsuda, F. Jang, H.-M. Yoon, K. Medina-Gomez, C. Pitsillides, A. Hottenga, J.J. Willemsen, G. Wood, A.R. Ji, Y. Gao, Z. Haworth, S. Mitchell, R.E. Chai, J.F. Aadahl, M. Yao, J. Manichaikul, A. Warren, H.R. Ramirez, J. Bork-Jensen, J. Kårhus, L.L. Goel, A. Sabater-Lleal, M. Noordam, R. Sidore, C. Fiorillo, E. McDaid, A.F. Marques-Vidal, P. Wielscher, M. Trompet, S. Sattar, N. Møllehave, L.T. Thuesen, B.H. Munz, M. Zeng, L. Huang, J. Yang, B. Poveda, A. Kurbasic, A. Lamina, C. Forer, L. Scholz, M. Galesloot, T.E. Bradfield, J.P. Daw, E.W. Zmuda, J.M. Mitchell, J.S. Fuchsberger, C. Christensen, H. Brody, J.A. Feitosa, M.F. Wojczynski, M.K. Preuss, M. Mangino, M. Christofidou, P. Verweij, N. Benjamins, J.W. Engmann, J. Kember, R.L. Slieker, R.C. Lo, K.S. Zilhao, N.R. Le, P. Kleber, M.E. Delgado, G.E. Huo, S. Ikeda, D.D. Iha, H. Yang, J. Liu, J. Leonard, H.L. Marten, J. Schmidt, B. Arendt, M. Smyth, L.J. Cañadas-Garre, M. Wang, C. Nakatochi, M. Wong, A. Hutri-Kähönen, N. Sim, X. Xia, R. Huerta-Chagoya, A. Fernandez-Lopez, J.C. Lyssenko, V. Ahmed, M. Jackson, A.U. Yousri, N.A. Irvin, M.R. Oldmeadow, C. Kim, H.-N. Ryu, S. Timmers, P.R.H.J. Arbe and Graham, S.E. Clarke, S.L. Wu, K.-H.H. Kanoni, S. Zajac, G.J.M. Ramdas, S. Surakka, I. Ntalla, I. Vedantam, S. Winkler, T.W. Locke, A.E. Marouli, E. Hwang, M.Y. Han, S. Narita, A. Choudhury, A. Bentley, A.R. Ekoru, K. Verma, A. Trivedi, B. Martin, H.C. Hunt, K.A. Hui, Q. Klarin, D. Zhu, X. Thorleifsson, G. Helgadottir, A. Gudbjartsson, D.F. Holm, H. Olafsson, I. Akiyama, M. Sakaue, S. Terao, C. Kanai, M. Zhou, W. Brumpton, B.M. Rasheed, H. Ruotsalainen, S.E. Havulinna, A.S. Veturi, Y. Feng, Q.P. Rosenthal, E.A. Lingren, T. Pacheco, J.A. Pendergrass, S.A. Haessler, J. Giulianini, F. Bradford, Y. Miller, J.E. Campbell, A. Lin, K. Millwood, I.Y. Hindy, G. Rasheed, A. Faul, J.D. Zhao, W. Weir, D.R. Turman, C. Huang, H. Graff, M. Mahajan, A. Brown, M.R. Zhang, W. Yu, K. Schmidt, E.M. Pandit, A. Gustafsson, S. Yin, X. Luan, J. Zhao, J.-H. Matsuda, F. Jang, H.-M. Yoon, K. Medina-Gomez, C. Pitsillides, A. Hottenga, J.J. Willemsen, G. Wood, A.R. Ji, Y. Gao, Z. Haworth, S. Mitchell, R.E. Chai, J.F. Aadahl, M. Yao, J. Manichaikul, A. Warren, H.R. Ramirez, J. Bork-Jensen, J. Kårhus, L.L. Goel, A. Sabater-Lleal, M. Noordam, R. Sidore, C. Fiorillo, E. McDaid, A.F. Marques-Vidal, P. Wielscher, M. Trompet, S. Sattar, N. Møllehave, L.T. Thuesen, B.H. Munz, M. Zeng, L. Huang, J. Yang, B. Poveda, A. Kurbasic, A. Lamina, C. Forer, L. Scholz, M. Galesloot, T.E. Bradfield, J.P. Daw, E.W. Zmuda, J.M. Mitchell, J.S. Fuchsberger, C. Christensen, H. Brody, J.A. Feitosa, M.F. Wojczynski, M.K. Preuss, M. Mangino, M. Christofidou, P. Verweij, N. Benjamins, J.W. Engmann, J. Kember, R.L. Slieker, R.C. Lo, K.S. Zilhao, N.R. Le, P. Kleber, M.E. Delgado, G.E. Huo, S. Ikeda, D.D. Iha, H. Yang, J. Liu, J. Leonard, H.L. Marten, J. Schmidt, B. Arendt, M. Smyth, L.J. Cañadas-Garre, M. Wang, C. Nakatochi, M. Wong, A. Hutri-Kähönen, N. Sim, X. Xia, R. Huerta-Chagoya, A. Fernandez-Lopez, J.C. Lyssenko, V. Ahmed, M. Jackson, A.U. Yousri, N.A. Irvin, M.R. Oldmeadow, C. Kim, H.-N. Ryu, S. Timmers, P.R.H.J. Arbe

Abstract

Correction to: Nature Published online 9 December 2021 In the version of this article initially published, Noha A. Yousri (Department of Genetic Medicine, Weill Cornell Medicine-Qatar, Doha, Qatar and Department of Computer and Systems Engineering, Alexandria University, Egypt) and Steven C. Hunt (Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA and Department of Genetic Medicine, Weill Cornell Medicine-Qatar, Doha, Qatar) were not included in the author list. In addition, Hieab H. H. Adams (Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands and Department of Clinical Genetics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands) was shown with an incorrect second affiliation in the HTML and PDF versions of the article. Finally, in the HTML version, Cristen J. Willer was mistakenly listed with an extra affiliation (Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah, Saudi Arabia). The authors and affiliations have been corrected in the HTML and PDF versions of the article. © 2023, The Author(s), under exclusive licence to Springer Nature Limited.

Published
2023

2. A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

Author

Ramdas, S. Judd, J. Graham, S.E. Kanoni, S. Wang, Y. Surakka, I. Wenz, B. Clarke, S.L. Chesi, A. Wells, A. Bhatti, K.F. Vedantam, S. Winkler, T.W. Locke, A.E. Marouli, E. Zajac, G.J.M. Wu, K.-H.H. Ntalla, I. Hui, Q. Klarin, D. Hilliard, A.T. Wang, Z. Xue, C. Thorleifsson, G. Helgadottir, A. Gudbjartsson, D.F. Holm, H. Olafsson, I. Hwang, M.Y. Han, S. Akiyama, M. Sakaue, S. Terao, C. Kanai, M. Zhou, W. Brumpton, B.M. Rasheed, H. Havulinna, A.S. Veturi, Y. Pacheco, J.A. Rosenthal, E.A. Lingren, T. Feng, Q. Kullo, I.J. Narita, A. Takayama, J. Martin, H.C. Hunt, K.A. Trivedi, B. Haessler, J. Giulianini, F. Bradford, Y. Miller, J.E. Campbell, A. Lin, K. Millwood, I.Y. Rasheed, A. Hindy, G. Faul, J.D. Zhao, W. Weir, D.R. Turman, C. Huang, H. Graff, M. Choudhury, A. Sengupta, D. Mahajan, A. Brown, M.R. Zhang, W. Yu, K. Schmidt, E.M. Pandit, A. Gustafsson, S. Yin, X. Luan, J. Zhao, J.-H. Matsuda, F. Jang, H.-M. Yoon, K. Medina-Gomez, C. Pitsillides, A. Hottenga, J.J. Wood, A.R. Ji, Y. Gao, Z. Haworth, S. Mitchell, R.E. Chai, J.F. Aadahl, M. Bjerregaard, A.A. Yao, J. Manichaikul, A. Lee, W.-J. Hsiung, C.A. Warren, H.R. Ramirez, J. Bork-Jensen, J. Kårhus, L.L. Goel, A. Sabater-Lleal, M. Noordam, R. Mauro, P. Matteo, F. McDaid, A.F. Marques-Vidal, P. Wielscher, M. Trompet, S. Sattar, N. Møllehave, L.T. Munz, M. Zeng, L. Huang, J. Yang, B. Poveda, A. Kurbasic, A. Schönherr, S. Forer, L. Scholz, M. Galesloot, T.E. Bradfield, J.P. Ruotsalainen, S.E. Daw, E.W. Zmuda, J.M. Mitchell, J.S. Fuchsberger, C. Christensen, H. Brody, J.A. Le, P. Feitosa, M.F. Wojczynski, M.K. Hemerich, D. Preuss, M. Mangino, M. Christofidou, P. Verweij, N. Benjamins, J.W. Engmann, J. Noah, T.L. Verma, A. Slieker, R.C. Lo, K.S. Zilhao, N.R. Kleber, M.E. Delgado, G.E. Huo, S. Ikeda, D.D. Iha, H. Yang, J. Liu, J. Demirkan, A. Leonard, H.L. Marten, J. Emmel, C. Schmidt, B. Smyth, L.J. Cañadas-Garre, M. Wang, C. Nakatochi, M. Wong, A. Hutri-Kähönen, N. Sim, X. Xia, R. Huerta-Chagoya, A. Fernandez-Lopez, J.C. Ly and Ramdas, S. Judd, J. Graham, S.E. Kanoni, S. Wang, Y. Surakka, I. Wenz, B. Clarke, S.L. Chesi, A. Wells, A. Bhatti, K.F. Vedantam, S. Winkler, T.W. Locke, A.E. Marouli, E. Zajac, G.J.M. Wu, K.-H.H. Ntalla, I. Hui, Q. Klarin, D. Hilliard, A.T. Wang, Z. Xue, C. Thorleifsson, G. Helgadottir, A. Gudbjartsson, D.F. Holm, H. Olafsson, I. Hwang, M.Y. Han, S. Akiyama, M. Sakaue, S. Terao, C. Kanai, M. Zhou, W. Brumpton, B.M. Rasheed, H. Havulinna, A.S. Veturi, Y. Pacheco, J.A. Rosenthal, E.A. Lingren, T. Feng, Q. Kullo, I.J. Narita, A. Takayama, J. Martin, H.C. Hunt, K.A. Trivedi, B. Haessler, J. Giulianini, F. Bradford, Y. Miller, J.E. Campbell, A. Lin, K. Millwood, I.Y. Rasheed, A. Hindy, G. Faul, J.D. Zhao, W. Weir, D.R. Turman, C. Huang, H. Graff, M. Choudhury, A. Sengupta, D. Mahajan, A. Brown, M.R. Zhang, W. Yu, K. Schmidt, E.M. Pandit, A. Gustafsson, S. Yin, X. Luan, J. Zhao, J.-H. Matsuda, F. Jang, H.-M. Yoon, K. Medina-Gomez, C. Pitsillides, A. Hottenga, J.J. Wood, A.R. Ji, Y. Gao, Z. Haworth, S. Mitchell, R.E. Chai, J.F. Aadahl, M. Bjerregaard, A.A. Yao, J. Manichaikul, A. Lee, W.-J. Hsiung, C.A. Warren, H.R. Ramirez, J. Bork-Jensen, J. Kårhus, L.L. Goel, A. Sabater-Lleal, M. Noordam, R. Mauro, P. Matteo, F. McDaid, A.F. Marques-Vidal, P. Wielscher, M. Trompet, S. Sattar, N. Møllehave, L.T. Munz, M. Zeng, L. Huang, J. Yang, B. Poveda, A. Kurbasic, A. Schönherr, S. Forer, L. Scholz, M. Galesloot, T.E. Bradfield, J.P. Ruotsalainen, S.E. Daw, E.W. Zmuda, J.M. Mitchell, J.S. Fuchsberger, C. Christensen, H. Brody, J.A. Le, P. Feitosa, M.F. Wojczynski, M.K. Hemerich, D. Preuss, M. Mangino, M. Christofidou, P. Verweij, N. Benjamins, J.W. Engmann, J. Noah, T.L. Verma, A. Slieker, R.C. Lo, K.S. Zilhao, N.R. Kleber, M.E. Delgado, G.E. Huo, S. Ikeda, D.D. Iha, H. Yang, J. Liu, J. Demirkan, A. Leonard, H.L. Marten, J. Emmel, C. Schmidt, B. Smyth, L.J. Cañadas-Garre, M. Wang, C. Nakatochi, M. Wong, A. Hutri-Kähönen, N. Sim, X. Xia, R. Huerta-Chagoya, A. Fernandez-Lopez, J.C. Ly

Abstract

A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology. © 2022 American Society of Human Genetics

Published
2022

3. Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Author

Kanoni, S. Graham, S.E. Wang, Y. Surakka, I. Ramdas, S. Zhu, X. Clarke, S.L. Bhatti, K.F. Vedantam, S. Winkler, T.W. Locke, A.E. Marouli, E. Zajac, G.J.M. Wu, K.-H.H. Ntalla, I. Hui, Q. Klarin, D. Hilliard, A.T. Wang, Z. Xue, C. Thorleifsson, G. Helgadottir, A. Gudbjartsson, D.F. Holm, H. Olafsson, I. Hwang, M.Y. Han, S. Akiyama, M. Sakaue, S. Terao, C. Kanai, M. Zhou, W. Brumpton, B.M. Rasheed, H. Havulinna, A.S. Veturi, Y. Pacheco, J.A. Rosenthal, E.A. Lingren, T. Feng, Q.P. Kullo, I.J. Narita, A. Takayama, J. Martin, H.C. Hunt, K.A. Trivedi, B. Haessler, J. Giulianini, F. Bradford, Y. Miller, J.E. Campbell, A. Lin, K. Millwood, I.Y. Rasheed, A. Hindy, G. Faul, J.D. Zhao, W. Weir, D.R. Turman, C. Huang, H. Graff, M. Choudhury, A. Sengupta, D. Mahajan, A. Brown, M.R. Zhang, W. Yu, K. Schmidt, E.M. Pandit, A. Gustafsson, S. Yin, X. Luan, J. Zhao, J.-H. Matsuda, F. Jang, H.-M. Yoon, K. Medina-Gomez, C. Pitsillides, A. Hottenga, J.J. Wood, A.R. Ji, Y. Gao, Z. Haworth, S. Yousri, N.A. Mitchell, R.E. Chai, J.F. Aadahl, M. Bjerregaard, A.A. Yao, J. Manichaikul, A. Hwu, C.-M. Hung, Y.-J. Warren, H.R. Ramirez, J. Bork-Jensen, J. Kårhus, L.L. Goel, A. Sabater-Lleal, M. Noordam, R. Mauro, P. Matteo, F. McDaid, A.F. Marques-Vidal, P. Wielscher, M. Trompet, S. Sattar, N. Møllehave, L.T. Munz, M. Zeng, L. Huang, J. Yang, B. Poveda, A. Kurbasic, A. Lamina, C. Forer, L. Scholz, M. Galesloot, T.E. Bradfield, J.P. Ruotsalainen, S.E. Daw, E.W. Zmuda, J.M. Mitchell, J.S. Fuchsberger, C. Christensen, H. Brody, J.A. Vazquez-Moreno, M. Feitosa, M.F. Wojczynski, M.K. Wang, Z. Preuss, M.H. Mangino, M. Christofidou, P. Verweij, N. Benjamins, J.W. Engmann, J. Tsao, N.L. Verma, A. Slieker, R.C. Lo, K.S. Zilhao, N.R. Le, P. Kleber, M.E. Delgado, G.E. Huo, S. Ikeda, D.D. Iha, H. Yang, J. Liu, J. Demirkan, A. Leonard, H.L. Marten, J. Frank, M. Schmidt, B. Smyth, L.J. Cañadas-Garre, M. Wang, C. Nakatochi, M. Wong, A. Hutri-Kähönen, N. Sim, X. Xia, R. Huerta-Chagoya, A. Fernandez-Lopez, J.C. Lyss and Kanoni, S. Graham, S.E. Wang, Y. Surakka, I. Ramdas, S. Zhu, X. Clarke, S.L. Bhatti, K.F. Vedantam, S. Winkler, T.W. Locke, A.E. Marouli, E. Zajac, G.J.M. Wu, K.-H.H. Ntalla, I. Hui, Q. Klarin, D. Hilliard, A.T. Wang, Z. Xue, C. Thorleifsson, G. Helgadottir, A. Gudbjartsson, D.F. Holm, H. Olafsson, I. Hwang, M.Y. Han, S. Akiyama, M. Sakaue, S. Terao, C. Kanai, M. Zhou, W. Brumpton, B.M. Rasheed, H. Havulinna, A.S. Veturi, Y. Pacheco, J.A. Rosenthal, E.A. Lingren, T. Feng, Q.P. Kullo, I.J. Narita, A. Takayama, J. Martin, H.C. Hunt, K.A. Trivedi, B. Haessler, J. Giulianini, F. Bradford, Y. Miller, J.E. Campbell, A. Lin, K. Millwood, I.Y. Rasheed, A. Hindy, G. Faul, J.D. Zhao, W. Weir, D.R. Turman, C. Huang, H. Graff, M. Choudhury, A. Sengupta, D. Mahajan, A. Brown, M.R. Zhang, W. Yu, K. Schmidt, E.M. Pandit, A. Gustafsson, S. Yin, X. Luan, J. Zhao, J.-H. Matsuda, F. Jang, H.-M. Yoon, K. Medina-Gomez, C. Pitsillides, A. Hottenga, J.J. Wood, A.R. Ji, Y. Gao, Z. Haworth, S. Yousri, N.A. Mitchell, R.E. Chai, J.F. Aadahl, M. Bjerregaard, A.A. Yao, J. Manichaikul, A. Hwu, C.-M. Hung, Y.-J. Warren, H.R. Ramirez, J. Bork-Jensen, J. Kårhus, L.L. Goel, A. Sabater-Lleal, M. Noordam, R. Mauro, P. Matteo, F. McDaid, A.F. Marques-Vidal, P. Wielscher, M. Trompet, S. Sattar, N. Møllehave, L.T. Munz, M. Zeng, L. Huang, J. Yang, B. Poveda, A. Kurbasic, A. Lamina, C. Forer, L. Scholz, M. Galesloot, T.E. Bradfield, J.P. Ruotsalainen, S.E. Daw, E.W. Zmuda, J.M. Mitchell, J.S. Fuchsberger, C. Christensen, H. Brody, J.A. Vazquez-Moreno, M. Feitosa, M.F. Wojczynski, M.K. Wang, Z. Preuss, M.H. Mangino, M. Christofidou, P. Verweij, N. Benjamins, J.W. Engmann, J. Tsao, N.L. Verma, A. Slieker, R.C. Lo, K.S. Zilhao, N.R. Le, P. Kleber, M.E. Delgado, G.E. Huo, S. Ikeda, D.D. Iha, H. Yang, J. Liu, J. Demirkan, A. Leonard, H.L. Marten, J. Frank, M. Schmidt, B. Smyth, L.J. Cañadas-Garre, M. Wang, C. Nakatochi, M. Wong, A. Hutri-Kähönen, N. Sim, X. Xia, R. Huerta-Chagoya, A. Fernandez-Lopez, J.C. Lyss

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. © 2022, The Author(s).

Published
2022

4. Genome-wide analysis of 102,084 migraine cases identifies 123 risk loci and subtype-specific risk alleles

Author

Hautakangas, H., Winsvold, B.S., Ruotsalainen, S.E., Bjornsdottir, G., Harder, A.V.E., Kogelman, L.J.A., Thomas, L.F., Noordam, R., Benner, C., Gormley, P., Artto, V., Banasik, K., Bjornsdottir, A., Boomsma, D.I., Brumpton, B., Burgdorf, K.S., Buring, J.E., Chalmer, M.A., Boer, I. de, Dichgans, M., Erikstrup, C., Farkkila, M., Garbrielsen, M.E., Ghanbari, M., Hagen, K., Happola, P., Hottenga, J.J., Hrafnsdottir, M.G., Hveem, K., Johnsen, M.B., Kahonen, M., Kristoffersen, E.S., Kurth, T., Lehtimaki, T., Lighart, L., Magnusson, S.H., Malik, R., Pedersen, O.B., Pelzer, N., Penninx, B.W.J.H., Ran, C., Ridker, P.M., Rosendaal, F.R., Sigurdardottir, G.R., Skogholt, A.H., Sveinsson, O.A., Thorgeirsson, T.E., Ullum, H., Vijfhuizen, L.S., Widen, E., Dijk, K.W. van, Aromaa, A., Belin, A.C., Freilinger, T., Ikram, M.A., Jarvelin, M.R., Raitakari, O.T., Terwindt, G.M., Kallela, M., Wessman, M., Olesen, J., Chasman, D.I., Nyholt, D.R., Stefansson, H., Stefansson, K., Maagdenberg, A.M.J.M. van den, Hansen, T.F., Ripatti, S., Zwart, J.A., Palotie, A., Pirinen, M., Int Headache Genetics Consortium, HUNT All-in Headache, Danish Blood Donor Study Genomic C, Biological Psychology, APH - Mental Health, APH - Methodology, APH - Health Behaviors & Chronic Diseases, APH - Personalized Medicine, Sociology and Social Gerontology, Institute for Molecular Medicine Finland, Statistical and population genetics, Complex Disease Genetics, Clinicum, HUS Helsinki and Uusimaa Hospital District, HUS Neurocenter, Neurologian yksikkö, Genomics of Neurological and Neuropsychiatric Disorders, Centre of Excellence in Complex Disease Genetics, Genomic Discoveries and Clinical Translation, Biosciences, Faculty Common Matters (Faculty of Social Sciences), Department of Public Health, Biostatistics Helsinki, Research Programs Unit, Research Programme of Molecular Medicine, Department of Mathematics and Statistics, Helsinki Institute for Information Technology, Tampere University, Department of Clinical Physiology and Nuclear Medicine, Clinical Medicine, Department of Clinical Chemistry, Epidemiology, Psychiatry, Amsterdam Neuroscience - Complex Trait Genetics, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, and APH - Digital Health

Subjects
Central Nervous System, SUSCEPTIBILITY LOCI, Migraine Disorders, Migraine with Aura, Quantitative Trait Loci, PATHOPHYSIOLOGY, Genome-wide association studies, Cardiovascular System, Polymorphism, Single Nucleotide, Genetics, Humans, Genetic Predisposition to Disease, TRANSCRIPTOME, AURA, Migraine, Alleles, METAANALYSIS, GENE-EXPRESSION, ARCHITECTURE, MUTATIONS, HERITABILITY, 1184 Genetics, developmental biology, physiology, Molecular Sequence Annotation, ASSOCIATION, 3142 Public health care science, environmental and occupational health, Genetic Loci, Case-Control Studies, 3111 Biomedicine, Genome-Wide Association Study
Abstract

Genome-wide association analyses identify 123 susceptibility loci for migraine and implicate neurovascular mechanisms in its pathophysiology. Subtype analyses highlight risk loci specific for migraine with or without aura in addition to shared risk variants. Migraine affects over a billion individuals worldwide but its genetic underpinning remains largely unknown. Here, we performed a genome-wide association study of 102,084 migraine cases and 771,257 controls and identified 123 loci, of which 86 are previously unknown. These loci provide an opportunity to evaluate shared and distinct genetic components in the two main migraine subtypes: migraine with aura and migraine without aura. Stratification of the risk loci using 29,679 cases with subtype information indicated three risk variants that seem specific for migraine with aura (in HMOX2, CACNA1A and MPPED2), two that seem specific for migraine without aura (near SPINK2 and near FECH) and nine that increase susceptibility for migraine regardless of subtype. The new risk loci include genes encoding recent migraine-specific drug targets, namely calcitonin gene-related peptide (CALCA/CALCB) and serotonin 1F receptor (HTR1F). Overall, genomic annotations among migraine-associated variants were enriched in both vascular and central nervous system tissue/cell types, supporting unequivocally that neurovascular mechanisms underlie migraine pathophysiology.

Published
2022
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5. The power of genetic diversity in genome-wide association studies of lipids

Author

Graham, S.E., Clarke, S.L., Wu, K.H., Kanoni, S., Zajac, G.J.M., Ramdas, S., Surakka, I., Ntalla, I., Vedantam, S., Winkler, T.W., Locke, A.E., Marouli, E., Hwang, M.Y., Han, S., Narita, A., Choudhury, A., Bentley, A.R., Ekoru, K., Verma, A., Trivedi, B., Martin, H.C., Hunt, K.A., Hui, Q., Klarin, D., Zhu, X, Thorleifsson, G., Helgadottir, A., Gudbjartsson, D.F., Holm, H., Olafsson, I., Akiyama, M., Sakaue, S., Terao, C., Kanai, M., Zhou, W., Brumpton, B.M., Rasheed, H., Ruotsalainen, S.E., Havulinna, A.S., Veturi, Y., Feng, Q., Rosenthal, E.A., Lingren, T., Pacheco, J.A., Pendergrass, S.A., Haessler, J., Giulianini, F., Bradford, Y., Miller, J.E., Campbell, A., Lin, K., Millwood, I.Y., Hindy, G., Rasheed, A., Faul, J.D., Zhao, W., Weir, D.R., Turman, C., Huang, H., Graff, M, Mahajan, A., Brown, M.R., Zhang, W., Yu, K., Schmidt, E.M., Pandit, A., Gustafsson, S., Yin, X., Luan, J., Zhao, J.H., Matsuda, F., Jang, H.M., Yoon, K., Medina-Gomez, C., Pitsillides, A., Hottenga, J.J., Willemsen, G., Wood, A.R., Ji, Y., Gao, Z, Haworth, S., Mitchell, R.E., Chai, J.F., Aadahl, M., Yao, J., Manichaikul, A., Warren, H.R., Ramirez, J., Bork-Jensen, J., Kårhus, L.L., Goel, A., Sabater-Lleal, M., Noordam, R., Sidore, C., Fiorillo, E., McDaid, A.F., Marques-Vidal, P., Wielscher, M., Trompet, S., Sattar, N., Galesloot, T.E., Kiemeney, B., Graaf, J. de, Lores de Motta, L., Pauper, M., Hollander, A.I. den, Sun, Y.V., Willer, C.J., Graham, S.E., Clarke, S.L., Wu, K.H., Kanoni, S., Zajac, G.J.M., Ramdas, S., Surakka, I., Ntalla, I., Vedantam, S., Winkler, T.W., Locke, A.E., Marouli, E., Hwang, M.Y., Han, S., Narita, A., Choudhury, A., Bentley, A.R., Ekoru, K., Verma, A., Trivedi, B., Martin, H.C., Hunt, K.A., Hui, Q., Klarin, D., Zhu, X, Thorleifsson, G., Helgadottir, A., Gudbjartsson, D.F., Holm, H., Olafsson, I., Akiyama, M., Sakaue, S., Terao, C., Kanai, M., Zhou, W., Brumpton, B.M., Rasheed, H., Ruotsalainen, S.E., Havulinna, A.S., Veturi, Y., Feng, Q., Rosenthal, E.A., Lingren, T., Pacheco, J.A., Pendergrass, S.A., Haessler, J., Giulianini, F., Bradford, Y., Miller, J.E., Campbell, A., Lin, K., Millwood, I.Y., Hindy, G., Rasheed, A., Faul, J.D., Zhao, W., Weir, D.R., Turman, C., Huang, H., Graff, M, Mahajan, A., Brown, M.R., Zhang, W., Yu, K., Schmidt, E.M., Pandit, A., Gustafsson, S., Yin, X., Luan, J., Zhao, J.H., Matsuda, F., Jang, H.M., Yoon, K., Medina-Gomez, C., Pitsillides, A., Hottenga, J.J., Willemsen, G., Wood, A.R., Ji, Y., Gao, Z, Haworth, S., Mitchell, R.E., Chai, J.F., Aadahl, M., Yao, J., Manichaikul, A., Warren, H.R., Ramirez, J., Bork-Jensen, J., Kårhus, L.L., Goel, A., Sabater-Lleal, M., Noordam, R., Sidore, C., Fiorillo, E., McDaid, A.F., Marques-Vidal, P., Wielscher, M., Trompet, S., Sattar, N., Galesloot, T.E., Kiemeney, B., Graaf, J. de, Lores de Motta, L., Pauper, M., Hollander, A.I. den, Sun, Y.V., and Willer, C.J.

Abstract

Item does not contain fulltext, Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use(1). Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels(2), heart disease remains the leading cause of death worldwide(3). Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS(4-23) have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns(24). Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine(25), we anticipate that increased diversity of participants will lead to more accurate and equitable(26) application of polygenic scores in clinical practice.

Published
2021

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