Title

A saturated map of common genetic variants associated with human height

Authors

Loïc Yengo, Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia. l.yengo@imb.uq.edu.au.
Sailaja Vedantam, Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA.
Eirini Marouli, William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
Julia Sidorenko, Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.
Eric Bartell, Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA.
Saori Sakaue, Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Marielisa Graff, Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Anders U. Eliasen, COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark.
Yunxuan Jiang, 23andMe, Sunnyvale, CA, USA.
Sridharan Raghavan, Department of Veterans Affairs, Eastern Colorado Healthcare System, Aurora, CO, USA.
Jenkai Miao, Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA.
Joshua D. Arias, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Sarah E. Graham, Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, USA.
Ronen E. Mukamel, Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Cassandra N. Spracklen, Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Xianyong Yin, Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
Shyh-Huei Chen, Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA.
Teresa Ferreira, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Heather H. Highland, Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Yingjie Ji, Genetics of Complex Traits, College of Medicine and Health, University of Exeter, Exeter, UK.
Tugce Karaderi, Center for Health Data Science, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Kuang Lin, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Kreete Lüll, Institute of Genomics, Estonian Genome Centre, University of Tartu, Tartu, Estonia.
Deborah E. Malden, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Carolina Medina-Gomez, Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
Moara Machado, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Amy Moore, Division of Biostatistics and Epidemiology, RTI International, Durham, NC, USA.
Sina Rüeger, Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland.
Xueling Sim, Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.
Scott Vrieze, Department of Psychology, University of Minnesota, Minneapolis, MN, USA.
Tarunveer S. Ahluwalia, Steno Diabetes Center Copenhagen, Herlev, Denmark.
Masato Akiyama, Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.

Document Type

Journal Article

Publication Date

10-1-2022

Journal

Nature

Volume

610

Issue

7933

DOI

10.1038/s41586-022-05275-y

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 sizes. 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 panel) 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.

Department

Exercise and Nutrition Sciences

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