A saturated map of common genetic variants associated with human height.

Relationship between frequency and estimated effect sizes of minor alleles.Each dot represents one of the 12,111 quasi-independent GWS SNPs that were identified in our cross-ancestry GWAS meta-analysis. Data underlying this figure are available in Supplementary Table 10. SNP effect estimates (y axis) are expressed in height standard deviation (s.d.) per minor allele as defined in our cross-ancestry GWAS meta-analysis. SNPs were stratified in five classes according to their P value (P) of association. We show two curves representing the theoretical relationship between frequency and expected magnitude of SNP effect detectable at P < 5 × 10−8 with a statistical power of 90%. Statistical power was assessed under two experimental designs with sample sizes equal to n = 0.5 million and n = 5 million.Source data

Brisbane plot showing the genomic density of independent genetic associations with height.Each dot represents one of the 12,111 quasi-independent GWS (P < 5 × 10−8) height-associated SNPs identified using approximate COJO analyses of our cross-ancestry GWAS meta-analysis. Data underlying this figure are available in Supplementary Table 10. GWS SNPs with the largest density on each chromosome were annotated with the closest gene. We highlight 24 of 12,111 associations that are mainly contributed by groups of non-European ancestry (3 from African ancestries, 10 from Hispanic ethnicities or ancestries and 11 from East Asian ancestries). The full list of height-associated SNPs detected in groups of non-European ancestry and independent of associations detected in European ancestry GWASs is reported in Supplementary Table 9. Signal density was calculated for each associated SNP as the number of other independent associations within 100 kb. A density of 1 means that a GWS COJO SNP shares its location with another independent GWS COJO SNP within less than 100 kb. The mean signal density across the genome is 2 and the median signal density is 1 (s.e. 0.14 and 0.0, respectively). The s.e. values were calculated using a leave-one-chromosome-out jackknife approach (LOCO-S.E.). SNPs that did not reach genome-wide significance are not represented on the figure.Source data

Variance of height explained by HM3 SNPs within GWS loci.a, Stratified SNP-based heritability (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}_{{\rm{SNP}}}^{2}$$\end{document}hSNP2) estimates obtained after partitioning the genome into SNPs within 35 kb of a GWS SNP ('GWS loci' label) versus SNPs that are more than 35 kb away from any GWS SNP. Analyses were performed in samples of five different ancestries or ethnic groups: European (EUR: meta-analysis of UK Biobank (UKB) + Lifelines study), African (AFR: meta-analysis of UKB + PAGE study), East Asian (EAS: meta-analysis of UKB + China Kadoorie Biobank), South Asian (SAS: UKB) and Hispanic (HIS: PAGE). Error bars represent standard errors. b, More than 90% of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}_{{\rm{SNP}}}^{2}$$\end{document}hSNP2 in all ancestries is explained by SNPs within GWS loci identified in this study. The cumulative length of non-overlapping GWS loci is around 647 Mb; that is, around 21% of the genome, assuming a genome length of around 3,039 Mb (ref. 26). The proportion of HM3 SNPs in GWS loci is around 27%.Source data

Accuracy of PGSs within families and across ancestries.Prediction accuracy (R2) was measured as the squared correlation between PGS and actual height adjusted for age, sex and 10 genetic principal components. a, Accuracy of PGSs assessed in participants of five different ancestry groups: European (EUR) from the UKB (n = 14,587) and the Lifelines Biobank (n = 14,058); South Asian (SAS; n = 9,257) from UKB; East Asian (EAS; n = 2,246) from UKB; Hispanic (HIS; n = 5,798) from the PAGE study; and admixed African (AFR) from UKB (n = 6,911) and PAGE (n = 8,238). PGSs used for prediction, in a, are based on GWS SNPs or around 1.1 million HM3 SNPs. When using all HapMap 3 SNPs, SNP effects were calculated using the SBayesC method (Methods), whereas PGSs based on GWS SNPs used joint SNP effects estimated using the COJO method (Methods). Both SBayesC and COJO were applied to (1) our cross-ancestry meta-analysis (turquoise bar); (2) our EUR meta-analysis (yellow bar); and (3) each ancestry-specific meta-analysis (red bar). b, Squared correlation of height between EUR participants in UKB and their first-degree relatives, and the accuracy of a predictor combining PGS (denoted PGSGWS, as based on GWS SNPs) and familial information. The accuracies of PGSGWS and PGSHM3 shown in b are the average of the respective accuracies of these PGSs in EUR participants from UKB and the Lifelines Biobank as shown in a. Sibling correlation was calculated in 17,492 independent EUR sibling pairs from the UKB and parent–offspring correlations in 981 EUR unrelated trios (that is, two parents and one child) from the UKB. PA, parental average.Source data