Authors

Loïc Yengo, The University of Queensland
Sailaja Vedantam, Boston Children's Hospital
Eirini Marouli, Barts and The London School of Medicine and Dentistry
Julia Sidorenko, The University of Queensland
Eric Bartell, Boston Children's Hospital
Saori Sakaue, Broad Institute
Marielisa Graff, UNC Gillings School of Global Public Health
Anders U. Eliasen, Københavns Universitet
Yunxuan Jiang, 23andMe Inc.
Sridharan Raghavan, U.S. Department of Veterans Affairs
Jenkai Miao, Boston Children's Hospital
Joshua D. Arias, National Cancer Institute (NCI)
Sarah E. Graham, University of Michigan Medical School
Ronen E. Mukamel, Broad Institute
Cassandra N. Spracklen, UNC School of Medicine
Xianyong Yin, University of Michigan School of Public Health
Shyh Huei Chen, Wake Forest University School of Medicine
Teresa Ferreira, Nuffield Department of Medicine
Heather H. Highland, UNC Gillings School of Global Public Health
Yingjie Ji, College of Medicine and Health
Tugce Karaderi, Det Sundhedsvidenskabelige Fakultet
Kuang Lin, University of Oxford Medical Sciences Division
Kreete Lüll, Tartu Ülikooli Genoomika Instituut
Deborah E. Malden, University of Oxford Medical Sciences Division
Carolina Medina-Gomez, Erasmus MC
Moara Machado, National Cancer Institute (NCI)
Amy Moore, RTI International
Sina Rüeger, Université de Lausanne (UNIL)
Xueling Sim, National University of Singapore
Scott Vrieze, University of Minnesota Twin Cities
Tarunveer S. Ahluwalia, Steno Diabetes Center Copenhagen
Masato Akiyama, RIKEN Center for Integrative Medical Sciences
Matthew A. Allison, University of California, San Diego

Document Type

Article

Publication Date

10-27-2022

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.

Publication Source (Journal or Book title)

Nature

First Page

704

Last Page

712

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