Genetic neurodevelopmental clustering and dyslexia

Abstract

Dyslexia is a learning difficulty with neurodevelopmental origins, manifesting as reduced accuracy and speed in reading and spelling. It is substantially heritable and frequently co-occurs with other neurodevelopmental conditions, particularly attention deficit-hyperactivity disorder (ADHD). Here, we investigate the genetic structure underlying dyslexia and a range of psychiatric traits using results from genome-wide association studies of dyslexia, ADHD, autism, anorexia nervosa, anxiety, bipolar disorder, major depressive disorder, obsessive compulsive disorder, schizophrenia, and Tourette syndrome. Genomic Structural Equation Modelling (GenomicSEM) showed heightened support for a model consisting of five correlated latent genomic factors described as: F1) compulsive disorders (including obsessive-compulsive disorder, anorexia nervosa, Tourette syndrome), F2) psychotic disorders (including bipolar disorder, schizophrenia), F3) internalising disorders (including anxiety disorder, major depressive disorder), F4) neurodevelopmental traits (including autism, ADHD), and F5) attention and learning difficulties (including ADHD, dyslexia). ADHD loaded more strongly on the attention and learning difficulties latent factor (F5) than on the neurodevelopmental traits latent factor (F4). The attention and learning difficulties latent factor (F5) was positively correlated with internalising disorders (.40), neurodevelopmental traits (.25) and psychotic disorders (.17) latent factors, and negatively correlated with the compulsive disorders (-.16) latent factor. These factor correlations are mirrored in genetic correlations observed between the attention and learning difficulties latent factor and other cognitive, psychological and wellbeing traits. We further investigated genetic variants underlying both dyslexia and ADHD, which implicated 49 loci (40 not previously found in GWAS of the individual traits) mapping to 174 genes (121 not found in GWAS of individual traits) as potential pleiotropic variants. Our study confirms the increased genetic relation between dyslexia and ADHD versus other psychiatric traits and uncovers novel pleiotropic variants affecting both traits. In future, analyses including additional co-occurring traits such as dyscalculia and dyspraxia will allow a clearer definition of the attention and learning difficulties latent factor, yielding further insights into factor structure and pleiotropic effects.

Fig. 1. Genetic relationships between ten neurodevelopmental and psychiatric disorders.

a Pairwise genetic correlations detected using LDSC. Colour intensity scales with correlation coefficient (r_g), radius of circles scales with significance of p-values. Asterisks denote statistically significant (p ≤ 0.001) correlations after Bonferroni correction. b Path diagram of genetic correlations. Each edge connecting two phenotype nodes represents genetic correlation between those traits. Width and colour intensity of edges scale with correlation coefficient (r_g). Only pairs where r_g > 0.3 and correlation is statistically significant (p ≤ 0.05) after Bonferroni correction are displayed. c Regression of effective sample size on estimated genetic correlation for each pair of traits. Selected pairs where r_g > 0.3, or r_g < 0.1, or effective sample size >100,000 are labelled.

Fig. 2. Structural models of 10 neurodevelopmental and psychiatric disorders.

Each genetic factor (F1–F5) represents shared genetic liability. Single-headed arrows represent standardised loading parameters, which indicate covariance of the latent factor with a given parameter. Standard errors are given in parentheses. Double-headed arrows connecting factors represent pairwise correlation. Double-headed arrows connecting a component to itself represent residual variance, i.e., variability that is unexplained by factor loading. Factor residuals are fixed for scaling. a Confirmatory model based on exploratory factor analysis. b Modified confirmatory model that separates DYX and ADHD into a separate cluster of learning difficulties, based on observed genetic correlations.

Fig. 3. Genomic correlations between the learning difficulties latent factor and other traits.

Only 66 out of 330 significant correlations after Bonferroni correction are shown. Traits marked with an asterisk are traits used in this study for GenomicSEM modelling. Error bars represent SEM.

Fig. 4. Investigating pleiotropic genomic loci influencing dyslexia and ADHD.

a Manhattan plot of pleiotropy effect size p-values across SNPs shared between dyslexia and ADHD GWAS datasets. Dotted line represents Bonferroni-significant p-value (3.081 × 10^–6). b Quantile-quantile plot displaying the observed vs expected statistics under the null hypothesis. c MAGMA tissue expression analysis of all SNPs using GTEx v8 dataset with 30 general tissue types. Dotted line represents Bonferroni-significant p-value. d Venn diagram of significantly associated genomic loci identified in single phenotype GWAS studies and in the pleiotropy analysis. e Venn diagram of genes mapped to significantly associated SNPs in single phenotype GWAS studies and in the pleiotropy analysis.