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Genome-wide associations for birth weight and correlations with adult disease

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Abstract

Birth weight (BW) has been shown to be influenced by both fetal and maternal factors and in observational studies is reproducibly associated with future risk of adult metabolic diseases including type 2 diabetes (T2D) and cardiovascular disease1. These life-course associations have often been attributed to the impact of an adverse early life environment. Here, we performed a multi-ancestry genome-wide association study (GWAS) meta-analysis of BW in 153,781 individuals, identifying 60 loci where fetal genotype was associated with BW (P < 5 × 10−8). Overall, approximately 15% of variance in BW was captured by assays of fetal genetic variation. Using genetic association alone, we found strong inverse genetic correlations between BW and systolic blood pressure (Rg = −0.22, P = 5.5 × 10−13), T2D (Rg = −0.27, P = 1.1 × 10−6) and coronary artery disease (Rg = −0.30, P = 6.5 × 10−9). In addition, using large -cohort datasets, we demonstrated that genetic factors were the major contributor to the negative covariance between BW and future cardiometabolic risk. Pathway analyses indicated that the protein products of genes within BW-associated regions were enriched for diverse processes including insulin signalling, glucose homeostasis, glycogen biosynthesis and chromatin remodelling. There was also enrichment of associations with BW in known imprinted regions (P = 1.9 × 10−4). We demonstrate that life-course associations between early growth phenotypes and adult cardiometabolic disease are in part the result of shared genetic effects and identify some of the pathways through which these causal genetic effects are mediated.

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Figure 1: Genome-wide genetic correlation between BW and a range of traits and diseases in later life.
Figure 2: Hierarchical clustering of BW loci based on similarity of overlap with adult diseases, metabolic and anthropometric traits.

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  • 03 October 2016

    The Supplementary Information pdf was replaced and the description updated.

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Acknowledgements

Full acknowledgements and supporting grant details can be found in the Supplementary Information.

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Contributions

Core analyses and writing: M.H., R.N.B., F.R.D., N.M.W., M.N.K., J.F.-T., N.R.v.Z., K.J.G., A.P.M., K.K.O., J.F.F., N.J.T., J.R.P., D.M.E., M.I.M., R.M.F. Statistical analysis in individual studies: M.H., R.N.B., F.R.D., N.M.W., M.N.K., B.F., N.G., J.P.B., D.P.S., R.L.-G., T.S.A., E.K., R.R., L.-P.L., D.L.C., Y.W., E.T., C.A.W., C.T.H., J.-J.H., N.V.-T., P.K.J., E.T.H.B., I.N., N.P., A.M., E.M.v.L., R.J., V.La., M.N., J.M.M., S.E.J., P.-R.L., K.S.R., M.A.T., J.T., A.R.W., H.Y., D.M.S., I.P., K.Pan., X.W., L.C., F.G., K.E.S., M.Mu., E.V.R.A., Z.K., S.B.-G., F.S., D.T., J.W., C.M.-G., N.R.R., E.Z., G.V.D., Y.-Y.T., H.N.K., A.P.M., J.F.F., N.J.T., J.R.P., D.M.E., R.M.F. GWAS look-up in unpublished datasets: K.T.Z., N.R., D.R.N., R.C.W.M., C.H.T.T., W.H.T., S.K.G., F.J.v.R. Sample collection and data generation in individual studies: F.R.D., M.N.K., B.F., N.G., J.P.B., D.P.S., R.L.-G., R.R., L.-P.L., J.-J.H., I.N., E.M.v.L., M.B., P.M.-V., A.J.B., L.P., P.K., M.A., S.M.W., F.G., C.E.v.B., G.W., E.V.R.A., C.E.F., C.T., C.M.T., M.Sta., Z.K., D.M.H., M.V.H., H.G.d.H., F.R.R., C.M.-G., S.M.R., G.H., G.M., N.R.R., C.J.G., C.L., J.L., R.A.S., J.H.Z., F.D.M., W.L.L.Jr, A.T., M.Stu., V.Li., T.A.L., C.M.v.D., A.K., T.I.S., H.N., K.Pah., O.T.R., E.Z., G.V.D., S.-M.S., M.Me., H.C., J.F.W., M.V., J.-C.H., T.H., S.S., L.J.B., J.P.N., C.E.P., L.S.A., J.B.B., K.L.M., J.G.E., E.E.W., M.K., J.S.V., T.L., P.V., K.B., H.B., D.O.M.-K., F.R., A.G.U., C.Pi., O.P., N.J.W., H.H., V.W.J., S.F.G., A.A.V., D.A.L., G.D.S., K.K.O., J.F.F., N.J.T., J.R.P., M.I.M. Functional follow-up experiment: L.A.D., S.M.M., R.M.R., E.D., B.R.W. Individual study design and principal investigators: J.P.B., I.N., M.A., F.D.M., W.L.L.Jr, A.T., M.Stu., V.Li., T.A.L., C.M.v.D., W.K., A.K., T.I.S., H.N., K.Pah., O.T.R., G.V.D., Y.-Y.T., S.-M.S., M.Me., H.C., J.F.W., M.V., E.J.d.G., D.I.B., H.N.K., J.-C.H., T.H., A.T.H., L.J.B., J.P.N., C.E.P., J.H., L.S.A., J.B.B., K.L.M., J.G.E., E.E.W., M.K., J.S.V., T.L., P.V., K.B., H.B., D.O.M-K., A.H., F.R., A.G.U., C.Pi., O.P., C.Po., E.H., N.J.W., H.H., V.W.J., M.-R.J., S.F.G., A.A.V., T.M.F., A.P.M., K.K.O., N.J.T., J.R.P., M.I.M., R.M.F.

Corresponding authors

Correspondence to Mark I. McCarthy or Rachel M. Freathy.

Ethics declarations

Competing interests

K.Z. has a scientific collaboration with Bayer HealthCare Ltd. and Population Diagnostics Inc.

Additional information

Summary statistics from the meta-analyses are available at http://egg-consortium.org/.

Reviewer Information Nature thanks J. Whitfield and the other anonymous reviewer(s) for their contribution to the peer review of this work.

A list of consortium members appears in the Supplementary Information.

A list of consortium members appears in the Supplementary Information.

Extended data figures and tables

Extended Data Figure 1 Flow chart of the study design.

Extended Data Figure 2 Manhattan and quantile–quantile (QQ) plots of the trans-ancestry meta-analysis for BW.

a, Manhattan (main panel) and QQ (top right) plots of genome-wide association results for BW from trans-ancestry meta-analysis of up to 153,781 individuals. The association P value (on −log10 scale) for each of up to 22,434,434 SNPs (y axis) was plotted against the genomic position (NCBI Build 37; x axis). Association signals that reached genome-wide significance (P < 5 × 10−8) are shown in green if novel and pink if previously reported. In the QQ plot, the black dots represent observed P values and the grey line represents expected P values under the null distribution. The red dots represent observed P values after excluding the previously identified signals5. b, Manhattan (main panel) and QQ (top right) plots of trans-ethnic GWAS meta-analysis for BW highlighting the reported imprinted regions described in Supplementary Table 14. Novel association signals that reached genome-wide significance (P < 5 × 10−8) and mapped to imprinted regions are shown in green. Genomic regions outside imprinted regions are shaded in grey. SNPs in the imprinted regions are shown in light blue or dark blue, depending on chromosome number (odd or even). In the QQ plot, the black dots represent observed P values and the grey lines represent expected P values and their 95% confidence intervals under the null distribution for the SNPs within the imprinted regions.

Extended Data Figure 3 Regional plots for multiple distinct signals at three BW loci.

Regional plots for each locus, ZBTB7B (a), HMGA1 (b) and PTCH1 (c), are displayed from: the unconditional European-specific meta-analysis of up to 143,677 individuals (left); the approximate conditional meta-analysis for the primary signal after adjustment for the index variant for the secondary signal (middle); and the approximate conditional meta-analysis for the secondary signal after adjustment for the index variant for the primary signal (right). Directly genotyped or imputed SNPs were plotted with their association P values (on a −log10 scale) as a function of genomic position (NCBI Build 37). Estimated recombination rates (blue lines) were plotted to reflect the local linkage-disequilibrium structure around the index SNPs and their correlated proxies. SNPs were coloured in reference to linkage-disequilibrium with the particular index SNP according to a blue to red scale from R2 = 0 to 1, based on pairwise R2 values estimated from a reference of 5,000 individuals of white British origin, randomly selected from the UK Biobank.

Extended Data Figure 4 Comparison of fetal effect sizes and maternal effect sizes at 60 known and novel birth weight loci, for the first 24 loci.

The remaining loci are shown in Extended Data Fig. 5a. For each BW locus, the following six effect sizes (with 95% CI) are shown, all aligned to the same BW-raising allele: fetal_GWAS, fetal allelic effect on BW (from European ancestry meta-analysis of up to n = 143,677 individuals); fetal_unadjusted, fetal allelic effect on BW (unconditioned in n = 12,909 mother–child pairs); fetal_adjusted, fetal effect (conditioned on maternal genotype, n = 12,909); maternal_GWAS, maternal allelic effect on offspring BW (from meta-analysis of up to n = 68,254 European mothers)7; maternal_unadjusted, maternal allelic effect on offspring BW (unconditioned, n = 12,909); maternal_adjusted, maternal effect (conditioned on fetal genotype, n = 12,909). The 60 BW loci were ordered by chromosome and position (Supplementary Tables 10, 11). These plots illustrate that, in large GWAS of BW, fetal effect size estimates are larger than those of maternal at 55 out of 60 identified loci (binomial P = 1 × 10−11), suggesting that most of the associations are driven by the fetal genotype. In conditional analyses that modelled the effects of both maternal and fetal genotypes (n = 12,909 mother–child pairs), confidence intervals around the estimates were wide, precluding inference about the likely contribution of maternal versus fetal genotype at individual loci.

Extended Data Figure 5 Comparison of fetal effect sizes and maternal effect sizes at 60 known and novel birth weight loci, for the remaining 36 loci.

a, Continued from Extended Data Fig. 4. b, The scatter plot illustrates the difference between the fetal (x axis) and maternal (y axis) effect sizes in the overall maternal versus fetal GWAS results.

Extended Data Figure 6 Protein–protein Interaction (PPI) Network analysis.

a, The largest global component of BW PPI network containing 13 modules is shown. b, The histogram shows the null distribution of Z scores of BW PPI networks based on 10,000 random networks, and where the Z scores for the 13 BW modules (M1–13) lie. For each module, the two most significant GO terms are shown. c, A heat map is shown, which takes the top 50 biological processes over-represented in the global BW PPI network (listed at the right of the plot), and displays the extent of enrichment for the various trait-specific “point of contact“ (PoC) PPI networks. d, e, Trait-specific PoC PPI networks composed of proteins that are shared in both the global BW PPI network and networks generated using the same pipeline for each of the adult traits: d, canonical Wnt signalling pathway enriched for PoC PPI between BW and blood pressure (BP)-related phenotypes; and e, regulation of insulin secretion pathway enriched for PoC between BW and T2D/fasting glucose (FG). Red nodes indicate those present in PoC for BW and traits of interest; blue nodes correspond to the pathway nodes; purple nodes are those present in both the pathway and PoC; orange nodes are genes in BW loci that overlap with both the pathway and PoC. Large nodes correspond to genes in BW loci (within 300 kb from the lead SNP), and have a black border if they, amongst all BW loci, have a stronger (top 5) association with at least one of the pairing adult traits.

Extended Data Figure 7 Quantile–Quantile (QQ) plots of variance comparison between heterozygotes and homozygotes analysis in 57,715 UK Biobank samples and parent-of-origin specific analysis in 4,908 ALSPAC mother–child pairs at 59 autosomal BW loci plus DLK1.

a, QQ plot from the Quicktest analysis (ref. 77) comparing the BW variance of heterozygotes with homozygotes in 57,715 UK Biobank samples. b, QQ plot from the parent-of-origin specific analysis testing the association between BW and maternally transmitted versus paternally transmitted alleles in 4,908 mother–child pairs from the ALSPAC study (Methods, Supplementary Tables 15, 16). In both panels, the black dots represent lead SNPs at 59 identified autosomal BW loci and a further sub-genome-wide significant signal for BW near DLK1 (rs6575803; P = 5.6 × 10−8). The grey lines represent expected P values and their 95% confidence intervals under the null distribution for the 60 SNPs. Both results show trends in favour of imprinting effects at BW loci; however, despite the large sample size, these analyses were underpowered (see Methods) and much larger sample sizes are required for definitive analysis.

Extended Data Figure 8 Summary of previously reported loci for SBP, CAD, T2D and adult height and their effect on birth weight.

ad, Effect sizes (left y axis) of previously reported 30 SBP loci13,14, 45 CAD loci23, 84 T2D loci24 and 422 adult height loci25 were plotted against effects on BW (x axis). Effect sizes were aligned to the adult trait (or risk)-raising allele. The colour of each dot indicates BW association P value: red, P < 5 × 10−8; orange, 5 × 10−8 ≤ P < 0.001; yellow, 0.001 ≤ P < 0.01; white, P ≥ 0.01. The superimposed grey frequency histogram shows the number of SNPs (right y axis) in each category of BW effect size. e, Effect sizes (with 95% CI) on BW of 45 known CAD loci were plotted arranged in the order of CAD effect size from highest to lowest, separating out the known SBP loci. CAD loci with a larger effect on BW concentrated amongst loci with primary blood pressure association. f, Effect sizes (with 95% CI) on BW of 32 known T2D loci were plotted, subdivided by previously reported categories derived from detailed adult physiological data27. Heterogeneity in BW effect sizes between five T2D loci groups with different mechanistic categories was substantial (Cochran’s Q statistic Phet = 1.2 × 10−9). In pairwise comparisons, the ‘beta cell’ group of variants differed from the other four groups: fasting hyperglycaemia (Phet = 3 × 10−11), insulin resistance (Phet = 0.002), proinsulin (Phet = 0.78) and unclassified (Phet = 0.02) groups. All of the BW effect sizes plotted in the forest plots were aligned to the trait (or risk)-raising allele.

Extended Data Table 1 Sixty loci associated with BW (P < 5 × 10−8) in European ancestry meta-analysis of up to 143,677 individuals and/or trans-ancestry meta-analysis of up to 153,781 individuals
Extended Data Table 2 Gene set enrichment analysis and protein–protein interaction (PPI) analysis

Supplementary information

Supplementary Information

This file contains Supplementary Tables 1-16 and 18-19 (see separate excel file for Supplementary Table 17), Supplementary Figures 1-2, details of grants and funding supports and a list of acknowledgements. This file was updated on 03 October 2016 to include the consortium membership lists. (PDF 2603 kb)

Supplementary Table 17

This file shows the association of BW signals with various adult metabolic and anthropometric traits. (GWAS look-ups). (XLSX 131 kb)

Supplementary Data

This file contains 60 regional plots for birth weight association. (PDF 4509 kb)

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Horikoshi, M., Beaumont, R., Day, F. et al. Genome-wide associations for birth weight and correlations with adult disease. Nature 538, 248–252 (2016). https://doi.org/10.1038/nature19806

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