Discordant associations of educational attainment with ASD and ADHD implicate a polygenic form of pleiotropy

Abstract

Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) are complex co-occurring neurodevelopmental conditions. Their genetic architectures reveal striking similarities but also differences, including strong, discordant polygenic associations with educational attainment (EA). To study genetic mechanisms that present as ASD-related positive and ADHD-related negative genetic correlations with EA, we carry out multivariable regression analyses using genome-wide summary statistics (N = 10,610-766,345). Our results show that EA-related genetic variation is shared across ASD and ADHD architectures, involving identical marker alleles. However, the polygenic association profile with EA, across shared marker alleles, is discordant for ASD versus ADHD risk, indicating independent effects. At the single-variant level, our results suggest either biological pleiotropy or co-localisation of different risk variants, implicating MIR19A/19B microRNA mechanisms. At the polygenic level, they point to a polygenic form of pleiotropy that contributes to the detectable genome-wide correlation between ASD and ADHD and is consistent with effect cancellation across EA-related regions.

Fig. 1. Candidate mechanisms underlying discordant genetic association patterns with educational attainment.

Discordant associations with educational attainment, such as observed for ASD versus ADHD risk, may arise due to different mechanisms. Scenario I: Independent markers tag independent ASD and ADHD risk alleles, either (a) at the same gene locus in regions with low linkage disequilibrium (LD) or (b) at different loci. (c) Scenario II: Ascertainment bias during the recruitment of cases may lead to an artificial association of ASD with higher socio-economic status (SES) and ADHD with lower SES (non-testable). Scenario III: Opposite alleles at the same marker may tag opposite ASD and ADHD risk alleles at (d) a single risk variant, or independent ASD and ADHD risk alleles within (e) the same gene or (f) different genes in high-LD regions. Scenario IV: Identical marker alleles tag independent ASD and ADHD risk alleles, either (g) within the same gene or (h) at different genes in high-LD regions. (i) Scenario V: Identical marker alleles tag identical ASD/ADHD risk alleles (biological pleiotropy). Within each subfigure, one or more observed marker allele is shown in linkage disequilibrium with one or more ASD and ADHD risk allele. ADHD Attention-Deficit/Hyperactivity Disorder, ASD Autism Spectrum Disorder, GWAS genome-wide association study, LD, linkage disequilibrium, SES socio-economic status.

Fig. 2. Multivariable regression models.

Acyclic graphs illustrating the multivariable regression (MVR) design for (a) a set of independent ASD-related variants G_i (ASD-MVR) and (b) a set of independent ADHD-related variants G_j (ADHD-MVR). For both MVR models, increaser marker alleles (G_i or G_j) were aligned to ASD ($\widehat{\beta }$_ASD), ADHD ($\widehat{\beta }$_ADHD) and EA ($\widehat{\beta }$_EA) GWAS SNP estimates, capturing genetic association at the single-variant level. Using a bidirectional MVR framework (ASD-MVR; ADHD-MVR), the aggregate association effect with EA across all variants was simultaneously estimated for ASD risk ($\theta$_ASD; $\theta$_#ASD) and ADHD risk ($\theta$_ADHD; $\theta$_*ADHD), including a regression intercept ($\theta$_0*; $\theta$_0#). *Estimation of polygenic risk effects using alleles that were selected to increase liability for ASD, but are shared, by position, with ADHD risk. ^#Estimation of polygenic risk effects using alleles that were selected to increase liability for ADHD, but are shared, by position, with ASD risk. (c) ASD-MVR ($\widehat{\theta }$_ASD; $\widehat{\theta }$_*ADHD) and ADHD-MVR ($\widehat{\theta }$_ADHD; $\widehat{\theta }$_#ASD) effects as change in years of schooling per increase in log odds of ASD or ADHD liability. Multivariate inverse-variance-weighted regression estimates and corresponding 95% confidence intervals (bars) are shown. Individual effect estimates, standard errors and corresponding P-values (t-statistic, two-sided test) are provided in Supplementary Table 3. All tests passed the multiple-testing threshold of P < 0.0023. G_i was selected from ASD(iPSYCH, woADHD) and G_j from ADHD(iPSYCH), both at P_thr < 0.0015 and P_thr < 0.05. ASD ($\widehat{\beta }$_ASD), ADHD ($\widehat{\beta }$_ADHD) and EA ($\widehat{\beta }$_EA) SNP estimates were extracted from ASD(iPSYCH, woADHD; N = 32,985), ADHD(iPSYCH; N = 37,076) and EA(SSGAC; N = 766,345) GWAS statistics respectively. 3D scatter plot of (d) ASD-MVR (G_i; P_thr < 0.0015) and (e) ADHD-MVR (G_j; P_thr < 0.0015), as shown in (c). The regression plane reflects the estimated MVR effects for ASD-MVR ($\widehat{\theta }$_ASD; $\widehat{\theta }$_*ADHD) and ADHD-MVR ($\widehat{\theta }$_ADHD; $\widehat{\theta }$_#ASD), respectively. Source data are provided as a Source data file. ADHD Attention-Deficit/Hyperactivity Disorder, ASD Autism Spectrum Disorder, EA educational attainment, MVR multivariable regression, P_thr, P-value threshold, SNP single-nucleotide polymorphism.

Fig. 3. Genetic correlations with educational attainment for ASD + ADHD cross-disorder meta-analyses.

Genetic correlations (r_g) of educational attainment (EA) with ASD, ADHD and combined ASD + ADHD risk were estimated using unconstrained Linkage Disequilibrium Score correlation. Genetic correlations with EA(SSGAC; N = 766,345) were estimated for (a) ASD(iPSYCH, woADHD; N = 32,985), ADHD(iPSYCH; N = 37,076) and a combination of these summary statistics (cross-disorder meta-analysis), and, analogously, for (b) ASD(PGC; N = 10,610), ADHD(iPSYCH, N = 37,076) and a combination of these summary statistics (cross-disorder meta-analysis). Cross-disorder meta-analyses were conducted with METACARPA, allowing for sample overlap. LDSC correlation estimates with 95% confidence intervals (bars) are shown. Individual genetic correlation estimates, standard errors and corresponding P-values (Z-statistic, two-sided test) are provided in Supplementary Table 6. All tests passed the multiple-testing threshold of P < 0.002. Source data are provided as a Source data file. ADHD Attention-Deficit/Hhyperactivity Disorder, ASD Autism Spectrum Disorder, iPSYCH The Lundbeck Foundation Initiative for Integrative Psychiatric Research, PGC psychiatric genetics consortium, r_g genetic correlation, SSGAC Social Science Genetic Association Consortium, woADHD without ADHD.

Fig. 4. Identification of single variants using conditional P-value thresholding.

Acyclic graphs illustrating the multivariable regression (MVR) design for (a) a set of independent ASD-related variants G_i, given joint association with ADHD variant set G_j (ASD-MVR with G_i|j) and (b) a set of independent ADHD-related variants G_j, given joint association with ASD variant set G_i (ADHD-MVR with G_j|i). Here, G_i|j and G_j|i are illustrated as subsets (concentric circles) of G_i and G_j across a grid of six P-values thresholds (0.0015 < P_thr < 0.5), based on ASD(iPSYCH, woADHD) and ADHD(iPSYCH) summary statistics. (c) ASD-MVR ($\widehat{\theta }$_ASD; $\widehat{\theta }$_*ADHD) and ADHD-MVR ($\widehat{\theta }$_ADHD; $\widehat{\theta }$_#ASD) effects as change in years of schooling per increase in log odds of ASD or ADHD liability (for the definition of $\widehat{\theta }$_*ADHD and $\widehat{\theta }$_#ASD, see Fig. 2). SNP sets G_i|j and G_j|i were selected from ASD(iPSYCH, woADHD) and ADHD(iPSYCH), as shown in (a, b). SNP estimates for ASD ($\widehat{\beta }$_ASD), ADHD ($\widehat{\beta }$_ADHD) and EA ($\widehat{\beta }$_EA) were extracted from ASD(iPSYCH,woADHD; N = 32,985), ADHD(iPSYCH; N = 37,076) and EA(SSGAC; N = 766,345) GWAS statistics respectively. Multivariate inverse-variance-weighted regression estimates and corresponding 95% confidence intervals (bars) are shown. Individual effect estimates, standard errors and corresponding P-values (t-statistic, two-sided test) are provided in Supplementary Tables 10–11. All MVR effects passed the multiple-testing threshold of P < 0.0023, except for ADHD effects estimated with ADHD-MVR (G_j|i: ADHD P_thr < 0.0015; ASD P_thr < 0.05), which were present as trend (P = 0.01). Source data are provided as a Source data file. ADHD Attention-Deficit/Hyperactivity Disorder, ASD Autism Spectrum Disorder, EA educational attainment, MVR multivariable regression; P_thr P-value threshold, SNP single-nucleotide polymorphism, woADHD without ADHD.

Fig. 5. Characterisation of loci (N = 83) contributing to polygenic pleiotropy.

(a) Chromosomal position and (b) functional enrichment for micro-RNA targets of variants selected at a joint P-value threshold for both ASD and ADHD (P_thr < 0.0015). For enrichment analyses variants were mapped to 52 genes, and, of those, 45 were aligned to unique Ensembl IDs (v92) and subjected to gene-set enrichment analysis (requesting at least 5 overlapping genes) within the Molecular Signature Database (v7.0), WikiPathways (v20191010) and reported genes from the GWAS Catalog (e96_2019-09-24) using FUMA software (v1.3.6a). Evidence for enrichment was assessed using competitive gene-set analysis as implemented in MAGMA (v1.08) using a one-sided hypergeometric test. The false discovery rate (FDR) was controlled using the Benjamini–Hochberg procedure (FUMA, v1.3.6a). The strongest evidence for enrichment was found for micro RNA target TTTGCAC_MIR19A_MIR19B (Enrichment TTTGCAC_MIR19A_MIR19B P_FDR-adjusted = 7.7 × 10⁻⁴, out of 515 genes). Among other categories, enrichment was also found for ACCAAAG_MIR9 (Enrichment ACCAAAG_MIR9 P_FDR-adjusted = 0.028, out of 500 genes). Source data are provided as a Source data file. ADHD Attention-Deficit/Hyperactivity Disorder, ASD, Autism Spectrum Disorder, GWAS genome-wide association study, MVR multivariable regression, P_thr P-value threshold; SNP, single-nucleotide polymorphism.