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Rare and low-frequency coding variants alter human adult height

Abstract

Height is a highly heritable, classic polygenic trait with approximately 700 common associated variants identified through genome-wide association studies so far. Here, we report 83 height-associated coding variants with lower minor-allele frequencies (in the range of 0.1–4.8%) and effects of up to 2 centimetres per allele (such as those in IHH, STC2, AR and CRISPLD2), greater than ten times the average effect of common variants. In functional follow-up studies, rare height-increasing alleles of STC2 (giving an increase of 1–2 centimetres per allele) compromised proteolytic inhibition of PAPP-A and increased cleavage of IGFBP-4 in vitro, resulting in higher bioavailability of insulin-like growth factors. These 83 height-associated variants overlap genes that are mutated in monogenic growth disorders and highlight new biological candidates (such as ADAMTS3, IL11RA and NOX4) and pathways (such as proteoglycan and glycosaminoglycan synthesis) involved in growth. Our results demonstrate that sufficiently large sample sizes can uncover rare and low-frequency variants of moderate-to-large effect associated with polygenic human phenotypes, and that these variants implicate relevant genes and pathways.

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Figure 1: Variants with a larger effect size on height variation tend to be rarer.
Figure 2: Heat map showing subset of DEPICT gene set enrichment results.
Figure 3: STC2 mutants p.Arg44Leu (R44L) and p.Met86Ile (M86I) show compromised proteolytic inhibition of PAPP-A.

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Acknowledgements

A full list of acknowledgments appears in the Supplementary Information. Part of this work was conducted using the UK Biobank resource.

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Contributions

Writing group (wrote and edited manuscript): P.D., T.M.F., M.Gr., J.N.H., G.L., K.S.L., Y.Lu., E.M., C.M.-G., F.Ri. All authors contributed and discussed the results, and commented on the manuscript. Data preparation group (checked and prepared data from contributing cohorts for meta-analyses and replication): T.Es., M.Gr., H.M.H., A.E.J., T.Ka., K.S.L., A.E.L., Y.Lu., E.M., N.G.D.M., C. M.-G., P.Mu., M.C.Y.N., M.A.R., C.S., K.St., V.T., S.V., T.W.W., K.L.Y. This work was done under the auspices of the GIANT, CHARGE, BBMRI, UK ExomeChip, and GOT2D consortia. Height meta-analyses (discovery and replication, single-variant and gene-based): P.D., T.M.F., M.Gr., J.N.H., G.L., D.J.L., K.S.L., Y.Lu, E.M., C.M.-G., F.Ri., A.R.W. UK Biobank-based integration of height association signals group and heritability analyses: P.D., T.M.F., G.L., Z.K., K.S.L., E.M., S.R., A.R.W. Pleiotropy working group: G.A., M. Bo., J.P.C., P.D., F.D., J.C.F., H.M.H., S. Kat., C.M.L., D.J.L., R.J.F.L., A.Ma., E.M., M.I.M., P.B.M., G.M.P., J.R.B.P., K.S.R., C.J.W. Biological and clinical enrichment and pathway analyses: R.S.F., J.N.H., Z.K., D.L., G.L., K.S.L., T.H.P. Functional characterization of STC2: T.R.K., C.O.

Corresponding authors

Correspondence to Joel N. Hirschhorn, Panos Deloukas or Guillaume Lettre.

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The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks J. Barrett, D. Hinds and D. Hunter for their contribution to the peer review of this work.

A list of members and their affiliations appears in the Supplementary Information.

Extended data figures and tables

Extended Data Figure 1 Flowchart of the GIANT ExomeChip height study design.

Extended Data Figure 2 Height ExomeChip association results.

a, Quantile–quantile plot of ExomeChip variants and their association to adult height under an additive genetic model in individuals of European ancestry. We stratified results on the basis of allele frequency. b, Manhattan plot of all ExomeChip variants and their association to adult height under an additive genetic model in individuals of European ancestry with a focus on the 553 independent SNPs, of which 469 have a MAF > 5% (grey), 55 have MAF between 1–5% (green), and 29 have a MAF < 1% (blue). c, Linkage disequilibrium (LD) score regression analysis for the height association results in European-ancestry studies. In the plot, each point represents a linkage disequilibrium score quantile, where the x axis of the point is the mean linkage disequilibrium score of variants in that quantile and the y axis is the mean χ2 statistic of variants in that quantile. The linkage disequilibrium score regression slope of the black line is calculated using equation 1 in ref. 34, which is estimated upwards owing to the small number of common variants (n = 15,848) and the design of the ExomeChip. The linkage disequilibrium score regression intercept is 1.4, the λGC is 2.7, the mean χ2 is 7.0, and the ratio statistic of (intercept − 1)/(mean χ2 − 1) is 0.067 (s.e.m. = 0.012). d, Scatter plot comparison of the effect sizes for all variants that reached significance in the European-ancestry-discovery results (n = 381,625) and results including only studies with sample sizes of more than 5,000 individuals (n = 241,453).

Extended Data Figure 3 Height ExomeChip association results in African-ancestry populations.

Among the all-ancestry results, we found eight variants for which the genetic association with height is mostly driven by individuals of African ancestry. The MAF of these variants is <1% (or monomorphic) in all ancestries except African ancestry. In individuals of African ancestry, the variants had allele frequencies between 9 and 40%.

Extended Data Figure 4 Concordance between direct conditional effect sizes using UK Biobank (x axis) and conditional analysis performed using a combination of imputation-based methodology and approximate conditional analysis (SSimp, y axis).

The Pearson’s correlation coefficient is r = 0.85. The dashed line indicates the identity line. The 95% confidence interval is indicated in both directions. Red, SNPs with Pcond > 0.05 in the UK Biobank; green, SNPs with Pcond ≤ 0.05 in the UK Biobank.

Extended Data Figure 5 Heritability estimated for all known height variants in the first release of the UK Biobank dataset.

a, We observed a weak but significant positive trend between MAF and heritability (P = 0.012). b, Average heritability explained per variant when stratifying the analyses by allele frequency or genomic annotation. For heritability estimations in UKBB, variants were pruned to r2 < 0.2 in the 1000 Genomes Project dataset, and the heritability figures are based on h2 = 80% for height.

Extended Data Figure 6 Comparison of DEPICT gene set enrichment results based on coding variation from ExomeChip or non-coding variation from GWAS data.

The x axis indicates the P value for enrichment of a given gene set using DEPICT adapted for ExomeChip (EC) data, where the input to DEPICT is the genes implicated by coding ExomeChip variants that are independent of known GWAS signals. The y axis indicates the P value for gene set enrichment using DEPICT, using as input the GWAS loci that do not overlap the coding signals. Each point represents a meta-gene set and the best P value for any gene set within the meta-gene set is shown. Only significant (false discovery rate < 0.01) gene set enrichment results are plotted. Colours correspond to whether the meta-gene set was significant for ExomeChip only (blue), GWAS only (green), both but more significant for ExomeChip (purple), or both but more significant for GWAS (orange), and the most significant gene sets within each category are labelled. A line is drawn at x = y for ease of comparison.

Extended Data Figure 7 Heat map showing entire DEPICT gene set enrichment results.

This figure is analagous to Fig. 2. For any given square, the colour indicates how strongly the corresponding gene (shown on the x axis) is predicted to belong to the reconstituted gene set (y axis). This value is based on the Z score of the gene for gene set inclusion in DEPICT’s reconstituted gene sets, where red indicates a higher Z score and blue indicates a lower one. The proteoglycan-binding pathway was uniquely implicated by coding variants (as opposed to common variants) by both DEPICT and the Pascal method. To visually reduce redundancy and increase clarity, we chose one representative ‘meta-gene set’ for each group of highly correlated gene sets based on affinity propagation clustering (see Methods and Supplementary Information). Heat map intensity and DEPICT P values correspond to the most significantly enriched gene set within the meta-gene set; meta-gene sets are listed with their database source. Annotations for the genes indicate whether the gene has OMIM annotation as underlying a disorder of skeletal growth (black and grey) and the MAF of the significant ExomeChip variant (shades of blue; if multiple variants, the lowest-frequency variant was kept). Annotations for the gene sets indicate if the gene set was also found significant for ExomeChip by the Pascal method (yellow and grey) and if the gene set was found significant by DEPICT for ExomeChip only or for both ExomeChip and GWAS (purple and green). GO, Gene Ontology; KEGG, Kyoto encyclopaedia of genes and genomes; MP, mouse phenotype in the Mouse Genetics Initiative; PPI, protein–protein interaction in the InWeb database.

Extended Data Figure 8 Coding height variants are pleiotropic.

a, b, Heat maps showing associations of the height variants to other complex traits; –log10(P values) are oriented with beta effect direction for the alternate allele, white are missing values, yellow are non-significant (P > 0.05), green to blue shading for hits with positive beta in the other trait and P values between 0.05 and <2 × 10−7 and orange to red shading for hits with negative beta in the other trait and P values between 0.05 and <2 × 10−7. Short and tall labels are given for the minor alleles. Clustering is done by the complete linkage method with Euclidean distance measure for the loci. Clusters highlight SNPs that are more significantly associated with the same set of traits. a shows variants for which the minor allele is the height-decreasing allele. b shows variants for which the minor allele is the height-increasing allele.

Extended Data Table 1 Rare variants associated with adult height
Extended Data Table 2 Low-frequency variants associated with adult height

Supplementary information

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Marouli, E., Graff, M., Medina-Gomez, C. et al. Rare and low-frequency coding variants alter human adult height. Nature 542, 186–190 (2017). https://doi.org/10.1038/nature21039

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