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

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 disease. 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 (R_g = -0.22, P = 5.5 × 10^-13), T2D (R_g = -0.27, P = 1.1 × 10^-6) and coronary artery disease (R_g = -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.

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 birth weight.

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) is plotted against the genomic position (NCBI Build 37; x axis). Association signals that reached genome-wide significance (P<5x10^-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 signals. 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<5x10^-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 birth weight loci, ZBTB7B (a), HMGA1 (b) and PTCH1 (c).

Regional plots for each locus 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 are plotted with their association P-values (on a -log₁₀ scale) as a function of genomic position (NCBI Build 37). Estimated recombination rates (blue lines) are plotted to reflect the local LD structure around the index SNPs and their correlated proxies. SNPs are coloured in reference to LD 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 foetal effect sizes and maternal effect sizes at 60 known and novel birth weight loci (continues to Extended Data Figure 5).

For each BW locus, the following six effect sizes (with 95% CI) are shown, all aligned to the same BW-raising allele: foetal_GWAS = foetal allelic effect on BW (from European ancestry meta-analysis of up to n=143,677 individuals); foetal_unadjusted = foetal allelic effect on BW (unconditioned in n=12,909 mother-child pairs); foetal_adjusted = foetal 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); maternal_unadjusted = maternal allelic effect on offspring BW (unconditioned, n=12,909); maternal_adjusted = maternal effect (conditioned on foetal genotype, n=12,909). The 60 BW loci are ordered by chromosome and position (Supplementary Tables 10, 11). These plots illustrate that in large GWAS of BW, foetal effect size estimates are larger than those of maternal at 55/60 identified loci (binomial P=1x10^-11), suggesting that most of the associations are driven by the foetal genotype. In conditional analyses that modelled the effects of both maternal and foetal genotypes (n=12,909 mother-child pairs), confidence intervals around the estimates were wide, precluding inference about the likely contribution of maternal vs. foetal genotype at individual loci.

Extended Data Figure 5. Comparison of foetal effect sizes and maternal effect sizes at 60 known and novel birth weight loci.

a, Continued from Extended Data Figure 4. b, The scatterplot illustrates the difference between the foetal (x axis) and maternal (y axis) effect sizes in the overall maternal vs. foetal GWAS results.

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

a, Illustrates the largest global component of birth weight (BW) PPI network containing 13 modules. 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 depicted. c, Illustrates a heatmap which takes the top 50 biological processes over-represented in the global BW PPI network (listed at the right of the plot), and displays 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 type 2 diabetes (T2D)/fasting glucose (FG). Red nodes are those that are 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 300kb from the lead SNP), and have 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 (a) variance comparison between heterozygotes and homozygotes analysis in 57,715 UK Biobank samples and (b) parent-of-origin specific analysis in 4,908 ALSPAC mother-child pairs at 59 autosomal birth weight loci plus DLK1.

a, QQ plot from the Quicktest analysis 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 vs. 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=5.6x10^-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 systolic blood pressure (SBP, a), coronary artery disease (CAD, b, e), type 2 diabetes (T2D, c, f) and adult height (d) and their effect on birth weight.

a-d, Effect sizes (left y axis) of previously reported 30 SBP loci,, 45 CAD loci, 84 T2D loci and 422 adult height loci are plotted against effects on BW (x axis). Effect sizes are aligned to the adult trait-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 are 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 BP association. f, Effect sizes (with 95% CI) on BW of 32 known T2D loci are plotted, subdivided by previously reported categories derived from detailed adult physiological data. Heterogeneity in BW effect sizes between five T2D loci groups with different mechanistic categories was substantial (P_het=1.2x10^-9). In pairwise comparisons, the “beta cell” group of variants differed from the other four groups: fasting hyperglycaemia (P_het =3x10^-11), insulin resistance (P_het =0.002), proinsulin (P_het =0.78) and unclassified (P_het =0.02) groups. All of the BW effect sizes plotted in the forest plots are aligned to the trait (or risk)-raising allele.

Figure 1. Genome-wide genetic correlation between birth weight and a range of traits and diseases in later life.

Genetic correlation (r_g) and corresponding standard error between BW and the traits displayed on the x axis are estimated using LD Score regression. The genetic correlation estimates (r_g) are colour coded according to their intensity and direction (red for positive and blue for inverse correlation). WHRadjBMI=waist-hip ratio adjusted for body mass index, HOMA-B/IR=homeostatic model assessment of beta-cell function/insulin resistance, HbA1c=Hemoglobin A1c, BMD=bone mineral density, ADHD=attention deficit hyperactivity disorder. See Supplementary Table 12 for references for each of the traits and diseases displayed.

Figure 2. Hierarchical clustering of birth weight loci based on similarity of overlap with adult diseases, metabolic and anthropometric traits.

For the lead SNP at each BW locus (x-axis), z-scores (aligned to BW-raising allele) were obtained from publicly available GWAS for various traits (y-axis; see Supplementary Table 17). A positive z-score (red) indicates a positive association between the BW-raising allele and the outcome trait, while a negative z-score (blue) indicates an inverse association. BW loci and traits are clustered according to the Euclidean distance amongst z-scores (see Methods). Squares are outlined with a solid black line if the BW locus is significantly (P<5x10^-8) associated with the trait in publicly available GWAS, or with a dashed line if reported significant elsewhere. WHRadjBMI=waist-hip ratio adjusted for body mass index.