Abstract
Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.
Original language | English |
---|---|
Pages (from-to) | 704-712 |
Journal | Nature |
Volume | 610 |
Issue number | 7933 |
Early online date | 12 Oct 2022 |
DOIs | |
Publication status | E-pub ahead of print - 12 Oct 2022 |
Keywords / Materials (for Non-textual outputs)
- Gene Frequency/genetics
- Genome
- Genome-Wide Association Study
- Humans
- Linkage Disequilibrium/genetics
- Polymorphism, Single Nucleotide/genetics
Access to Document
- AAM_Yengo2022_MainText_01Nov2021Accepted author manuscript, 1.07 MBLicence: Creative Commons: Attribution (CC-BY)
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In: Nature, Vol. 610, No. 7933, 12.10.2022, p. 704-712.
Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - A saturated map of common genetic variants associated with human height
AU - 23andMe Research Team
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AU - Marouli, Eirini
AU - Sidorenko, Julia
AU - Bartell, Eric
AU - Sakaue, Saori
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AU - Jiang, Yunxuan
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N1 - Funding Information: Y. Jiang is employed by and holds stock or stock options in 23andMe. T.S.A. is a shareholder in Zealand Pharma A/S and Novo Nordisk A/S. G.C.-P. is an employee of 23andMe. M.E.K. is employed by SYNLAB Holding Deutschland GmbH. H.L.L. receives support from a consulting contract between Data Tecnica International and the National Institute on Aging (NIA), National Institutes of Health (NIH). As of January 2020, A. Mahajan is an employee of Genentech, and a holder of Roche stock. I.N. is an employee and stock owner of Gilead Sciences; this work was conducted before employment by Gilead Sciences. J. Shi is employed by and holds stock or stock options in 23andMe. C. Sidore is an employee of Regeneron. V. Steinthorsdottir is employed by deCODE Genetics/Amgen. Since completing the work contributed to this paper, D.J.T. has left the University of Cambridge and is now employed by Genomics PLC. G.T. is employed by deCODE Genetics/Amgen. S.W.v.d.L. has received Roche funding for unrelated work. H.B. has consultant arrangements with Chiesi Pharmaceuticals and Boehringer Ingelheim. M. J. Caulfield is Chief Scientist for Genomics England, a UK Government company. M. J. Cutler has served on the advisory board or consulted for Biosense Webster, Janssen Scientific Affairs and Johnson & Johnson. S.M.D. receives research support from RenalytixAI and personal consulting fees from Calico Labs, outside the scope of the current research. P.T.E. receives sponsored research support from Bayer AG and IBM Health, and he has served on advisory boards or consulted for Bayer AG, Quest Diagnostics, MyoKardia and Novartis. P. Kirchhof has received support from several drug and device companies active in atrial fibrillation, and has received honoraria from several such companies in the past, but not in the last three years. P. Kirchhof is listed as inventor on two patents held by University of Birmingham (Atrial Fibrillation Therapy WO 2015140571, Markers for Atrial Fibrillation WO 2016012783). G.D.K. has given talks, attended conferences and participated in trials sponsored by Amgen, MSD, Lilly, Vianex and Sanofi, and has also accepted travel support to conferences from Amgen, Sanofi, MSD and Elpen. S. A. Lubitz previously received sponsored research support from Bristol Myers Squibb, Pfizer, Bayer AG, Boehringer Ingelheim, Fitbit and IBM, and has consulted for Bristol Myers Squibb, Pfizer, Bayer AG and Blackstone Life Sciences. S. A. Lubitz is a current employee of Novartis Institute of Biomedical Research. W.M. reports grants and personal fees from AMGEN, BASF, Sanofi, Siemens Diagnostics, Aegerion Pharmaceuticals, Astrazeneca, Danone Research, Numares, Pfizer and Hoffmann LaRoche; personal fees from MSD and Alexion; and grants from Abbott Diagnostics, all outside the submitted work. W.M. is employed with Synlab Holding Deutschland. M.A.N. receives support from a consulting contract between Data Tecnica International and the National Institute on Aging (NIA), National Institutes of Health (NIH). S.N. is a scientific advisor to Circle software, ADAS software, CardioSolv and ImriCor and receives grant support from Biosense Webster, ADAS software and ImriCor. H. Schunkert has received honoraria for consulting from AstraZeneca, MSD, Merck, Daiichi, Servier, Amgen and Takeda Pharma. He has further received honoraria for lectures and/or chairs from AstraZeneca, BayerVital, BRAHMS, Daiichi, Medtronic, Novartis, Sanofi and Servier. P.S. has received research awards from Pfizer. The members of the 23andMe Research Team are employed by and hold stock or stock options in 23andMe. The views expressed in this article are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. M. I. McCarthy has served on advisory panels for Pfizer, Novo Nordisk and Zoe Global, and has received honoraria from Merck, Pfizer, Novo Nordisk and Eli Lilly and research funding from Abbvie, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, Novo Nordisk, Pfizer, Roche, Sanofi Aventis, Servier and Takeda. As of June 2019, M. I. McCarthy is an employee of Genentech, and a holder of Roche stock. C.J.O. is a current employee of Novartis Institute of Biomedical Research. U.T. is employed by deCODE Genetics (Amgen). K.S. is employed by deCODE Genetics (Amgen). A. Auton is employed by and holds stock or stock options in 23andMe. G.R.A. is an employee of Regeneron Pharmaceuticals and owns stock and stock options for Regeneron Pharmaceuticals. C.J.W.'s spouse is employed by Regeneron. A.E.L. is currently employed by and holds stock in Regeneron Pharmaceuticals. J.N.H. holds equity in Camp4 Therapeutics. The remaining authors declare no competing interests. Publisher Copyright: © 2022, The Author(s).
PY - 2022/10/12
Y1 - 2022/10/12
N2 - Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.
AB - Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.
KW - Gene Frequency/genetics
KW - Genome
KW - Genome-Wide Association Study
KW - Humans
KW - Linkage Disequilibrium/genetics
KW - Polymorphism, Single Nucleotide/genetics
U2 - 10.1038/s41586-022-05275-y
DO - 10.1038/s41586-022-05275-y
M3 - Article
C2 - 36224396
SN - 0028-0836
VL - 610
SP - 704
EP - 712
JO - Nature
JF - Nature
IS - 7933
ER -