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A saturated map of common genetic variants associated with human height

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Jer-Yuarn and Xu, Huichun and Yajnik, Chittaranjan S and Yokota, Mitsuhiro and Yuan, Jian-Min and Zeggini, Eleftheria and Zemel, Babette S and Zheng, Wei and Zhu, Xiaofeng and Zmuda, Joseph M and Zonderman, Alan B and Zwart, John-Anker and Partida, Gabriel Cuellar and Sun, Yan and Croteau-Chonka, Damien and Vonk, Judith M and Chanock, Stephen and Le Marchand, Loic and Chasman, Daniel I and Cho, Yoon Shin and Heid, Iris M and McCarthy, Mark I and Ng, Maggie CY and O’Donnell, Christopher J and Rivadeneira, Fernando and Thorsteinsdottir, Unnur and Sun, Yan V and Tai, E Shyong and Boehnke, Michael and Deloukas, Panos and Justice, Anne E and Lindgren, Cecilia M and Loos, Ruth JF and Mohlke, Karen L and North, Kari E and Stefansson, Kari and Walters, Robin G and Winkler, Thomas W and Young, Kristin L and Loh, Po-Ru and Yang, Jian and Esko, Tõnu and Assimes, Themistocles L and Auton, Adam and Abecasis, Goncalo R and Willer, Cristen J and Locke, Adam E and Berndt, Sonja I and Lettre, Guillaume and Frayling, Timothy M and Okada, Yukinori and Wood, Andrew R and Visscher, Peter M and Hirschhorn, Joel N (2022) 'A saturated map of common genetic variants associated with human height.' Nature, 610 (7933). pp. 704-712. ISSN 0028-0836

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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<jats:sup>1</jats:sup>. 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<jats:sup>2</jats:sup>) 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.

Item Type: Article
Uncontrolled Keywords: 23andMe Research Team; VA Million Veteran Program; DiscovEHR (DiscovEHR and MyCode Community Health Initiative); eMERGE (Electronic Medical Records and Genomics Network); Lifelines Cohort Study; PRACTICAL Consortium; Understanding Society Scientific Group; Humans; Body Height; Sample Size; Chromosome Mapping; Gene Frequency; Haplotypes; Linkage Disequilibrium; Phenotype; Polymorphism, Single Nucleotide; Genome, Human; Europe; Genome-Wide Association Study
Divisions: Faculty of Social Sciences
Faculty of Social Sciences > Institute for Social and Economic Research
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 17 Nov 2022 13:02
Last Modified: 17 Nov 2022 13:04
URI: http://repository.essex.ac.uk/id/eprint/33924

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