The Polygenic Nature and Complex Genetic Architecture of Specific Learning Disorder

Abstract

Specific Learning Disorder (SLD) is a multifactorial, neurodevelopmental disorder which may involve persistent difficulties in reading (dyslexia), written expression and/or mathematics. Dyslexia is characterized by difficulties with speed and accuracy of word reading, deficient decoding abilities, and poor spelling. Several studies from different, but complementary, scientific disciplines have investigated possible causal/risk factors for SLD. Biological, neurological, hereditary, cognitive, linguistic-phonological, developmental and environmental factors have been incriminated. Despite worldwide agreement that SLD is highly heritable, its exact biological basis remains elusive. We herein present: (a) an update of studies that have shaped our current knowledge on the disorder's genetic architecture; (b) a discussion on whether this genetic architecture is 'unique' to SLD or, alternatively, whether there is an underlying common genetic background with other neurodevelopmental disorders; and, (c) a brief discussion on whether we are at a position of generating meaningful correlations between genetic findings and anatomical data from neuroimaging studies or specific molecular/cellular pathways. We conclude with open research questions that could drive future research directions.

Keywords: dyscalculia; dyslexia; genetic variants; specific learning disorder (SLD); susceptibility.

Figure 1

Heatmap of RNA-sequencing-based gene expression from the SLD-associated (protein-coding) genes presented in Table 5, generated in GTEx portal for a multi-gene query in seven brain areas (basal ganglia and hypothalamus are excluded) [204]. SLC2A3 on chromosome 12 was included as an indirectly associated gene (potentially being trans-regulated by a directly associated variant on chromosome 4) (see text in Section 3). PCDHG represents a whole gene cluster, thus excluded from the query. TPM: Transcripts per kilobase million (expresses RNA-sequencing reads normalized for gene length and sequencing depth).

Figure 2

Top gene ontology (GO) terms (Biological Processes; y-axis) significantly enriched in the Figure 1. The figure was generated in R using ClusterProfiler. Count: Number of genes per GO term (gene-set), GeneRatio: Number of genes per GO term (gene-set) to the total number of queried genes (n = 36), p.adjust: Adjusted p-value using the Benjamini–Hochberg correction for multiple comparisons (p < 0.05). Data accompanying this figure are available in the Appendix A (Table A1).