A saturated map of common genetic variants associated with human height

Summary, in English

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. © 2022, The Author(s).

• Author: L. Yengo; G. Hindy; A. Kurbasic; V. Lyssenko; A. Poveda; S. Elmståhl; P.W. Franks; J. Gustav Smith; T. Tuomi; The PRACTICAL Consortium; Understanding Society Scientific Group; Joel N. Hirschhorn
• Department/s: Diabetes - Cardiovascular Disease; Cardiovascular Research - Hypertension; Genetic and Molecular Epidemiology; Translational Muscle Research; EpiHealth: Epidemiology for Health; EXODIAB: Excellence of Diabetes Research in Sweden; Geriatrics; eSSENCE: The e-Science Collaboration; WCMM-Wallenberg Centre for Molecular Medicine; Heart Failure and Mechanical Support; Cardiovascular Epigenetics; Molecular Epidemiology and Cardiology; Diabetic Complications
• Publishing year: 2022
• Language: English
• Pages: 704-712
• Publication/Series: Nature
• Links: http://dx.doi.org/10.1038/s41586-022-05275-y
• Document type: Journal article
• Publisher: Nature Publishing Group
• Topic: Medical Genetics
• Keywords: adult; allele; article; effect size; female; gene frequency; gene linkage disequilibrium; genetic association; genetic variability; genome-wide association study; haplotype map; heritability; human; human experiment; major clinical study; male; prediction; sample size; single nucleotide polymorphism; genetics; genome; Gene Frequency; Genome; Genome-Wide Association Study; Humans; Linkage Disequilibrium; Polymorphism, Single Nucleotide
• Status: Published
• Research group: Diabetes - Cardiovascular Disease; Cardiovascular Research - Hypertension; Genetic and Molecular Epidemiology; Genomics, Diabetes and Endocrinology; Geriatrics; Heart Failure and Mechanical Support; Cardiovascular Epigenetics; Molecular Epidemiology and Cardiology; Diabetic Complications
• ISBN/ISSN/Other: ISSN: 0028-0836