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  • Published: 14 October 2020

Genome-wide association study reveals new insights into the heritability and genetic correlates of developmental dyslexia

  • Alessandro Gialluisi   ORCID: orcid.org/0000-0002-7388-4463 1 , 2 , 3 ,
  • Till F. M. Andlauer   ORCID: orcid.org/0000-0002-2917-5889 1 , 2 , 4   na1 ,
  • Nazanin Mirza-Schreiber   ORCID: orcid.org/0000-0003-0836-8267 1 , 5   na1 ,
  • Kristina Moll 6   na1 ,
  • Jessica Becker 7 ,
  • Per Hoffmann   ORCID: orcid.org/0000-0002-6573-983X 7 ,
  • Kerstin U. Ludwig   ORCID: orcid.org/0000-0002-8541-2519 7 ,
  • Darina Czamara   ORCID: orcid.org/0000-0001-7381-904X 1 ,
  • Beate St Pourcain   ORCID: orcid.org/0000-0002-4680-3517 8 , 9 ,
  • Ferenc Honbolygó 10 ,
  • Dénes Tóth 10 ,
  • Valéria Csépe 10 ,
  • Guillaume Huguet   ORCID: orcid.org/0000-0002-4746-6030 11 ,
  • Yves Chaix 12 , 13 ,
  • Stephanie Iannuzzi 13 ,
  • Jean-Francois Demonet 14 ,
  • Andrew P. Morris 15 , 16 , 17 ,
  • Jacqueline Hulslander 18 ,
  • Erik G. Willcutt 18 ,
  • John C. DeFries 18 ,
  • Richard K. Olson 18 ,
  • Shelley D. Smith 19 ,
  • Bruce F. Pennington 20 ,
  • Anniek Vaessen 21 ,
  • Urs Maurer   ORCID: orcid.org/0000-0002-4156-8597 22 ,
  • Heikki Lyytinen 23 ,
  • Myriam Peyrard-Janvid 24 ,
  • Paavo H. T. Leppänen 23 ,
  • Daniel Brandeis 25 , 26 , 27 , 28 ,
  • Milene Bonte 21 ,
  • John F. Stein   ORCID: orcid.org/0000-0001-5843-8986 29 ,
  • Joel B. Talcott   ORCID: orcid.org/0000-0001-7958-8369 30 ,
  • Fabien Fauchereau 11 ,
  • Arndt Wilcke 31 ,
  • Holger Kirsten   ORCID: orcid.org/0000-0002-3126-7950 31 , 32 ,
  • Bent Müller 31 ,
  • Clyde Francks   ORCID: orcid.org/0000-0002-9098-890X 8 ,
  • Thomas Bourgeron 11 ,
  • Anthony P. Monaco   ORCID: orcid.org/0000-0001-7480-3197 17 , 33 ,
  • Franck Ramus   ORCID: orcid.org/0000-0002-1122-5913 34 ,
  • Karin Landerl 35 ,
  • Juha Kere   ORCID: orcid.org/0000-0003-1974-0271 24 , 36 ,
  • Thomas S. Scerri 17 , 37 ,
  • Silvia Paracchini   ORCID: orcid.org/0000-0001-9934-8602 38 ,
  • Simon E. Fisher   ORCID: orcid.org/0000-0002-3132-1996 8 ,
  • Johannes Schumacher 7   na2 ,
  • Markus M. Nöthen 7   na2 ,
  • Bertram Müller-Myhsok 1 , 2 , 39   na2 &
  • Gerd Schulte-Körne   ORCID: orcid.org/0000-0002-9960-1572 6   na2  

Molecular Psychiatry volume  26 ,  pages 3004–3017 ( 2021 ) Cite this article

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  • Psychiatric disorders

Developmental dyslexia (DD) is a learning disorder affecting the ability to read, with a heritability of 40–60%. A notable part of this heritability remains unexplained, and large genetic studies are warranted to identify new susceptibility genes and clarify the genetic bases of dyslexia. We carried out a genome-wide association study (GWAS) on 2274 dyslexia cases and 6272 controls, testing associations at the single variant, gene, and pathway level, and estimating heritability using single-nucleotide polymorphism (SNP) data. We also calculated polygenic scores (PGSs) based on large-scale GWAS data for different neuropsychiatric disorders and cortical brain measures, educational attainment, and fluid intelligence, testing them for association with dyslexia status in our sample. We observed statistically significant ( p   < 2.8 × 10 −6 ) enrichment of associations at the gene level, for LOC388780 (20p13; uncharacterized gene), and for VEPH1 (3q25), a gene implicated in brain development. We estimated an SNP-based heritability of 20–25% for DD, and observed significant associations of dyslexia risk with PGSs for attention deficit hyperactivity disorder (at p T  = 0.05 in the training GWAS: OR = 1.23[1.16; 1.30] per standard deviation increase; p   = 8 × 10 −13 ), bipolar disorder (1.53[1.44; 1.63]; p  = 1 × 10 −43 ), schizophrenia (1.36[1.28; 1.45]; p  = 4 × 10 −22 ), psychiatric cross-disorder susceptibility (1.23[1.16; 1.30]; p  = 3 × 10 −12 ), cortical thickness of the transverse temporal gyrus (0.90[0.86; 0.96]; p  = 5 × 10 −4 ), educational attainment (0.86[0.82; 0.91]; p  = 2 × 10 −7 ), and intelligence (0.72[0.68; 0.76]; p  = 9 × 10 −29 ). This study suggests an important contribution of common genetic variants to dyslexia risk, and novel genomic overlaps with psychiatric conditions like bipolar disorder, schizophrenia, and cross-disorder susceptibility. Moreover, it revealed the presence of shared genetic foundations with a neural correlate previously implicated in dyslexia by neuroimaging evidence.

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Introduction.

Developmental dyslexia (DD) is a specific learning disorder affecting the ability to read that is not better accounted for by intellectual disabilities, uncorrected visual or auditory acuity, other mental or neurological disorders, or inadequate educational instruction [ 1 ]. People with dyslexia show difficulties in accurate and/or fluent word recognition, decoding, spelling, and/or reading comprehension [ 2 ]. The prevalence of DD is reported to be around 5–10% among school-aged children, depending on the criteria used for diagnosis [ 3 ]. DD tends to recur in families [ 4 , 5 ] and most twin studies have reported a heritability ( h 2 ) between 40 and 60% [ 2 , 6 ]. A similar range of heritability has been reported for several cognitive skills representing/underlying reading ability, such as word reading, spelling, and phoneme awareness ( h 2 ~40–70%) [ 7 , 8 , 9 ]. Of note, a large proportion of this heritability remains unexplained, and DD shows a complex architecture, with multiple genetic and environmental factors playing a role in its aetiology [ 10 ].

Linkage and candidate gene association studies have identified a small number of candidate susceptibility genes, most of which have been associated not only with dyslexia, but also with continuous interindividual variation in relevant cognitive skills like word reading, spelling, and others (as reviewed in [ 11 , 12 , 13 ]). The most robust candidate genes identified so far include DYX1C1 (15q21) [ 14 ], DCDC2 and KIAA0319 (6p22.3) [ 15 , 16 , 17 , 18 ], GCFC2 and MRPL19 (2p12) [ 19 ], and ROBO1 (3p12.3-p12.3) [ 20 , 21 , 22 ]. DD and reading-related cognitive traits have also been investigated via genome-wide association studies (GWAS), which involve analyses of many single-nucleotide polymorphisms (SNPs) spread across the genome. A few such studies have been reported, using either a case-control design [ 23 , 24 , 25 ] or a continuous trait analysis approach [ 26 , 27 , 28 , 29 , 30 ]. However, only two of these studies identified associations that met criteria for genome-wide significance [ 27 , 28 ]. The first was a GWAS of multiple cognitive skills related to reading ability, which revealed a genome-wide significant association at rs17663182 ( MIR924HG ; 18q12.2) with rapid automatized naming (RAN), in nine cohorts of reading-impaired and typically developing subjects of European ancestry (maximum N  = 3468) [ 28 ]. More recently, in a north-American cohort of non-European ancestry ( N  = 1331), Truong et al. [ 27 ] identified a genome-wide significant multivariate association of rs1555839 (10q23.31; upstream from the RPL7P34 gene) with RAN and rapid alternating stimulus, and replicated the association with RAN in an independent cohort of European ancestry [ 27 ].

Here, we carried out a case-control GWAS meta-analysis involving 2274 dyslexia cases and 6272 controls from nine different countries that partly overlap with those from the prior Gialluisi et al. study of continuous traits (≤2500 overlapping samples) [ 28 ]. We performed association testing at the single variant, gene, and pathway level, and estimated SNP-based heritability. Moreover, we analyzed associations of polygenic scores (PGS) derived from large-scale GWAS data from other related neuropsychiatric disorders, as well as intelligence, educational attainment, and cortical brain measures.

Subjects and methods

The datasets involved in the present study were collected in nine different populations of European ancestry, with six different languages (see Table  1 ). Subsets have already been tested for association with continuous reading-related traits [ 28 ]. Ethical approval was obtained for each cohort at the local level, and written informed consent was obtained for all the participants or their parents.

Unrelated DD cases and controls with IQ in the normal range were recruited in Austria ( N  = 374), Finland ( N  = 336), France ( N  = 165), Germany ( N  = 1454), Hungary ( N  = 243), The Netherlands ( N  = 311), and Switzerland ( N  = 67) (see Table  1 ). DD cases were defined as participants showing low performance on tests of word reading (standardized score ≤−1.25), with the exception of 148 German cases, which were defined based on a ≥1.5 standard deviation discrepancy between the observed and expected spelling score based on their IQ (see [ 31 , 32 ] and Supplementary methods ). Controls were defined as individuals with standardized word reading scores >−0.85 [ 33 , 34 ].

Samples from Austria, Germany, and Switzerland were merged together into a single dataset (hereafter called AGS), since they shared language and genetic ancestry [ 28 ]. Two additional datasets were included in the study, made up of native English speakers. One of them consisted of DD cases selected from two sibling-based cohorts, namely the Colorado Reading Disability Cohort [ 26 , 35 ] and an independent cohort from Oxford, UK [ 28 , 36 ]. These cases were merged to form a single case-control dataset with unscreened controls from the Wellcome Trust Case Control Consortium 2 (WTCCC2) 1958 British birth cohort (WTCCC2_1958), a sample of sequential live births in the UK during 1 week in 1958 [ 37 ]. The other English-speaking dataset consisted of unrelated DD cases recruited in Cardiff, UK and the WTCCC2 National Blood Service (WTCCC2_NBS) cohort, a collection of subjects who have donated blood to the UK blood service. These datasets, hereafter called ENall1 ( N  = 3531) and ENall2 ( N  = 2947), met the same word reading-based inclusion criteria as above for cases, while controls were unscreened, as in other prominent studies [ 38 , 39 ].

Genotype quality control (QC) and imputation

Genotyping array platforms used for the different datasets are reported in Table  S1a . These included Illumina HumanHap 300k, 550k, 660k, OmniExpress Human CoreExome and BeadChips, and Illumina 1.2 M chips. Genotype QC was carried out, as previously described [ 28 ], in PLINK v1.90b3s [ 40 ] and QCTOOL v1.4 (see URLs). Briefly, SNPs were excluded if they showed a variant call rate <98%, a minor allele frequency (MAF) <5%, or a Hardy–Weinberg equilibrium (HWE) exact test p value < 10 −6 . Samples showing a genotyping rate <98%, mismatches between genetic and pedigree-based sex, cryptic relatedness (in datasets of unrelated subjects), or identity-by-descent not corresponding to the available pedigree information (in datasets including related cases) were also discarded. Similarly, we discarded genetic ancestry outliers detected in a multidimensional scaling (MDS) analysis of pairwise genetic distance and samples with extreme genome-wide heterozygosity values (see Table  S1b ).

For imputation, genotypes of autosomal SNPs were aligned to the 1000 Genomes phase I v3 reference panel (ALL populations, June 2014 release) [ 41 ] and pre-phased using SHAPEIT v2 (r837) [ 42 ]. Imputation was then performed using IMPUTE2 v2.3.2 [ 43 ] in 5 Mb chunks with 500 kb buffers, filtering out variants that were monomorphic in the 1000 Genomes EUR (European) samples. Chunks with <51 genotyped variants or concordance rates <92% were fused with neighbouring chunks and re-imputed. Finally, imputed variants (genotype probabilities) were filtered for IMPUTE2 INFO metric ≥0.8, as well as MAF and HWE thresholds as above. We re-evaluated genetic ancestry and genome-wide heterozygosity outliers after imputation and observed substantial concordance with pre-imputation QC. After QC, 2274 dyslexia cases and 6272 controls were left for analysis (see Tables  S1c, d for a power computation).

Genetic association test and meta-analysis

After genotype QC and imputation, we tested autosomal variant allelic dosages for association with case-control status within each dataset. In all the datasets except ENall1, we ran association tests through logistic regression in PLINK, using the first ten genetic ancestry (MDS) components as covariates. To account for the genetic relationship among related subjects in ENall1, we modelled a generalized linear mixed-effects model association test through FastLMM v2.07 [ 44 ], using a genetic relationship matrix as a random effect, while disabling normalization to unit variance for tested SNPs. Then we combined the results of the association tests in the different datasets through a fixed-effects sample size-based meta-analysis in METAL v25-03-2011 (“Stouffer” method) [ 45 ]. This was done in order to overcome the heterogeneity of scales of the association tests used in the different datasets. The genome-wide significance threshold was set to α  = 5 × 10 −8 . To obtain an estimate of the odds ratio (OR) for the top association identified, we performed a Wald association test in the ENall1 dataset through a logistic mixed model approach in GMMAT [ 46 ], which was not possible to perform at the genome-wide level due to the high computational load implied. We then meta-analyzed the resulting association statistics across datasets, through a fixed-effects inverse variance-weighted method in METAL [ 45 ].

Gene- and pathway-based enrichment tests

We performed a gene-based association analysis on the results of the GWAS meta-analysis in MAGMA v1.06 [ 47 ]. First, we assigned genetic variants to protein-coding genes based on their position according to the NCBI 37.3 ( hg19 ) build, extending the region of annotation to 10 kb from the 3′-/5′-UTR (untranslated region). In total, 18,013 genes (out of 19,427 genes available) included at least one variant that passed internal QC and were thus tested for enrichment of single-variant associations, using default settings. For this analysis, we set a genome-wide significance threshold α  = 2.8 × 10 −6 , correcting for 18,013 genes tested.

We used the results of the gene-based association analysis to carry out a pathway-based enrichment test for associations with DD, through a competitive gene-set analysis in MAGMA v1.06. We tested for enrichment 1329 canonical pathways (i.e., classical representations of biological processes compiled by domain experts) from the Molecular Signatures Database website (MSigDB v5.2, collection C2, subcollection CP; see URLs). To correct enrichment statistics for testing of multiple pathways, we used an adaptive permutation procedure with default settings (up to a maximum of 10,000 permutations). Hence, in this analysis we set the significance threshold to α  = 0.05.

Estimation of heritability

We used the summary statistics from the DD case-control GWAS to compute SNP-based heritability of the disorder, through LD score regression [ 48 , 49 ]. For this analysis, we used only common SNPs tested in the GWAS and present in the HapMap 3 reference panel [ 50 ], excluding the MHC region, since these variants show a good imputation quality ( r 2  > 0.9) in most studies. All the analyses presented below were performed on these variants (1,025,494 SNPs), using LD information based on the 1000 G phase 1 v3 EUR panel (see URLs).

We first computed the proportion of genetic variance explained by all SNPs mentioned above on the observed scale, and then repeated the analysis using a liability threshold model, i.e., assuming that the binary trait that we use is determined by an unobserved normally distributed liability threshold [ 48 , 49 ]. This analysis requires specification of the proportion of cases in the GWAS (27%), and the estimated prevalence of the disorder in the reference population, which has been reported to be 5–10% among school-aged children [ 3 ]. Hence, we carried out the analysis using the limits of this prevalence range, namely 0.05 and 0.10, respectively.

To extrapolate biological information from our GWAS summary statistics, we computed partitioned heritability for 53 overlapping functional annotation categories identified in the genome [ 51 ], irrespective of the cell types analyzed (baseline model). These annotations include DNase I hypersensitivity sites, coding regions, untranslated regions, enhancers, promoters and several histone marks as defined by different public resources (see “Results” section and [ 51 ] for a complete list). Similarly, we carried out a stratified LD score regression using only central nervous system (CNS) cell-specific annotations of four histone marks—H3K4me1, H3K4me3, H3K9ac, and H3K27ac—to identify a specific enrichment of functional elements associated with transcriptional activity in these cells. We performed this analysis both for all the CNS cells pooled together and singularly for each cell type available in brain tissues, while correcting for the contribution of all functional annotation categories previously tested in the baseline model, as suggested by the developer [ 51 ]. Thereby, we could identify the contribution of common variants annotated to histone marks which are specifically enriched in nervous cells. Finally, we computed partitioned heritability for diverse sets of genes whose expression is specifically enriched in 13 different brain regions, based on RNA-seq data from the Genotype-Tissue Expression portal (GTEx v6) [ 52 , 53 ]. The brain regions available included amygdala, anterior cingulate cortex, caudate nucleus, cerebellar hemispheres, cerebellum, cortex, frontal cortex, hippocampus, hypothalamus, nucleus accumbens, putamen, spinal cord, and substantia nigra.

Polygenic score (PGS) analyses

Genetic liability to neuropsychiatric disorders, intelligence and education.

We investigated potential genetic links between dyslexia and related and/or comorbid neuropsychiatric disorders, including attention deficit hyperactivity disorder (ADHD) [ 54 , 55 , 56 ], autism spectrum disorder (ASD) [ 57 ], major depressive disorder (MDD) [ 58 ], bipolar disorder (BD) [ 59 ], and schizophrenia (SCZ) [ 60 ], as well as with genetic liability shared across different neuropsychiatric disorders, including ADHD, ASD, BD, MDD, SCZ, anorexia nervosa, obsessive-compulsive disorder and Tourette syndrome [ 61 ]. Moreover, we tested association with fluid intelligence [ 62 ] and educational attainment (years of education completed, EduYears) [ 63 ], which are phenotypically correlated with reading ability [ 64 , 65 ]. To this end, we performed a PGS analysis in our sample using summary statistics available from previous independent GWAS studies of the other traits of interest (hereafter called training GWAS) [ 61 , 62 , 63 , 66 , 67 , 68 , 69 , 70 ]. PGSs were computed with PRSice-2 v2.2.11 [ 71 ], using only summary statistics based on samples of European ancestry in the training GWAS and quality controlled variants in a random extraction of one individual per family from our dyslexia (target) GWAS (MAF ≥ 5%; HWE p  ≥ 10 −6 ; variant call rate ≥95%; N  = 8456). We further pruned SNPs through LD-clumping (pairwise r 2  < 0.05 within sliding 300 kb windows) and removed those variants with discordant coordinates/alleles between the training and the target GWAS. We then computed average (default) PGS using only variants with association p value < 0.05 in the training GWAS (as in [ 28 , 72 , 73 ]), since this represents a reasonable trade-off between goodness-of-fit of the PGS and the risk of introducing noise in the model by including genetic variants meeting more lenient association thresholds. We then built generalized linear models (glm) of dyslexia vs PGS adjusted for sex and genetic ancestry (10 MDS components) in the same set of unrelated subjects used above (2184 cases and 6272 controls). To check for robustness of our findings, we repeated the analysis at different association significance thresholds in each training GWAS (with p  < 5 × 10 −8 , 1 × 10 −5 , 0.001, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 and 1.0). A Bonferroni-corrected significance threshold was set to α  = 4.5 × 10 −4 for this analysis, conservatively correcting for eight (six binary neuropsychiatric and two continuous) training traits, and 14 significance thresholds tested.

Polygenic scores of brain cortical measures

We carried out an exploratory analysis to test associations with PGSs influencing the surface area (SA) and thickness (T) of 34 brain cortical regions (June 2020 release), recently analyzed in a GWAS involving 33,992 participants of European ancestry [ 74 ]. We tested PGSs (at p  < 0.05 in the training GWAS) of both SA and T of all the cortical regions adjusted for global measures (total SA and average T, respectively), both separately and jointly in a multivariable setting. This choice was motivated by the fact that different structural alterations have been described in dyslexic subjects [ 75 ] and a complex brain network of different structures is thought to underlie dyslexia phenotypes and related skills [ 76 ]. To insure against potential overfitting bias in a conventional ordinary least squares regression with a high number of predictors, we applied two alternative multivariable models. First, a stepwise regression through the stepaic() function of the MASS package, which retains only variables associated with a decrease in the Akaike information criterion, representing a trade-off between goodness-of-fit and parsimony of the model. Then, an elastic net regression, using the glmnet and caret packages, as in [ 77 ]. To this end, we divided our dataset into a random training and test set (80:20 ratio), then trained the elastic net and carried out hyperparameter ( α and λ ) tuning in the training set, with tenfold cross-validation. Finally, we tested the performance of the optimized model, assessing classification accuracy in the independent test set ( N  = 1690). All the models involving cortical PGSs were adjusted for MDS components and sex, as explained above. For this analysis, we considered associations as statistically robust only if they showed significant and similar effect sizes across the different models tested (using a significance threshold α  = 8.3 × 10 −4 , correcting for 60 independent cortical measures, as in [ 74 ]).

Single-variant genome-wide associations

No single-variant association with DD reached genome-wide significance (Figs.  1 and S1 ). The strongest single-variant associations detected in the GWAS are reported in Table  2 ( p  < 5 × 10 −7 ) and, more extensively, in Table  S2a ( p  < 10 −5 ). The top hit was detected at rs6035856 (G/T, MAF = 0.45; p  = 9.9 × 10 −8 ), an intronic variant located within the gene LOC388780 (chr20p13; Fig.  2a ). Following logistic mixed modelling and inverse variance-based meta-analysis of the rs6035856 association, we computed an OR [confidence interval] of 1.27[1.16; 1.39] for the major allele G ( p  = 3.2 × 10 −7 ). In all datasets, the major allele G was associated with increased DD risk (Fig.  2b and Table  S2b ). Although this SNP was not directly genotyped, it showed high quality imputation statistics across datasets (INFO metric in the range 0.89–0.95). Other SNPs in the vicinity of rs6035856 were also associated with DD (see Table  2 ) and were all in moderate/high LD with the top hit ( r 2  > 0.6; see Fig.  2a ).

figure 1

The blue and red line represent the genome-wide ( α  = 5 × 10 −8 ) and suggestive significance ( α  = 1 × 10 −5 ) threshold.

figure 2

a Local association and b forest plot of the genome-wide top variant (rs6035856). The forest plot shows the odds ratio (OR) and 95% confidence intervals (CI) on the x -axis, by dataset and for the pooled analysis. Detailed OR statistics can be found in Table  S2b . Note to forest plot: the sibling-based dataset ENall1 was analyzed genome-wide through linear mixed modelling (in FastLMM) for computational reasons, while its OR, as shown here, was computed via a Wald test in a logistic mixed model (GMMAT), to make it comparable to the other ORs produced through logistic regression (PLINK). Hence, the result of the pooled analysis—which here was performed through the inverse variance-based method—is slightly discrepant from the original genome-wide analysis (see Table  2 ).

Gene- and pathway-based enrichment analyses

Gene-level analysis of genome-wide single-variant association signals with DD revealed two significant enrichments, after correcting for the 18,013 genes tested across the genome ( p  < 2.8 × 10 −6 ; Table  S2c ). These enrichments were observed for the gene VEPH1 ( ventricular zone expressed PH domain-containing 1 ; 3q25; Z  = 5.63; permutation-based p  = 8 × 10 −8 ) and for the gene LOC388780 , where top GWAS variant mapped to ( Z  = 5.26; p  = 1.7 × 10 −7 ). However, the analysis of 1329 canonical pathways from the MSigDB website did not reveal any significant enrichment (Table  S2d ).

SNP-based heritability

We computed the SNP-based heritability ( h 2 SNP ) of DD through LD score regression, using the summary statistics of HapMap 3 SNPs analysed in the GWAS. This analysis yielded an estimate of h 2 SNP (SE) = 0.19(0.06) on the observed scale, while, on the liability scale, we observed h 2 SNP (SE) of 0.20(0.06) (assuming a dyslexia prevalence of 0.05) and of 0.25(0.08) (for prevalence 0.1; see Table  S3a ).

We next computed partitioned heritability for different functional categories in the genome, through stratified LD score regression. The analysis of 53 overlapping functional annotation categories in the baseline model (i.e., including functional annotations irrespective of the cell type) revealed no statistically significant enrichments of heritability for such general annotation classes (see Table  S3b ). Similarly, the stratified LD score regression applied to annotations specific to CNS cell types detected no significant contribution to SNP-based heritability of the four histone marks tested (H3K4me1, H3K4me3, H3K9ac, and H3K27ac; Table  S3c ). When we analysed partitioned heritability by sets of specifically overexpressed genes in 13 different brain regions available in the GTEx database (see “Methods” section), we observed no significant contributions to h 2 SNP surviving correction for multiple testing (Table  S3d ).

Polygenic scores and dyslexia risk

We report in Table  3 the results of the main PGS analysis on neuropsychiatric disorders, intelligence and educational attainment, including only variants with association p  < 0.05 in the training GWAS (i.e. at p T  = 0.05), while the results at the different association significance ( p T ) thresholds tested are reported in Table  S4a–h . At p T  = 0.05, glm logistic regressions revealed that standardized PGS of EDUyears and fluid intelligence were significantly associated with dyslexia risk in our sample, surviving correction for multiple testing, with OR = 0.86[0.82; 0.91] ( R 2  = 0.39%; p  = 1.95 × 10 −7 ) and 0.72[0.68; 0.76] (1.79%; 9.40 × 10 −29 ), respectively. Also, we observed significant associations with dyslexia risk for three of the neuropsychiatric disorders analyzed: ADHD (1.23[1.16; 1.3]; 0.73%; 7.66 × 10 −13 ), BD (1.53[1.44; 1.63]; 2.80%; 1.33 × 10 −43 ) and SCZ (1.36[1.28; 1.45]; 1.35%; 3.65 × 10 −22 ). Similarly, we identified a significant association with common genetic liability shared across different psychiatric disorders (1.23 [1.16; 1.30]; 0.69%; 3.12 × 10 −12 ). These associations were concordant across all tested significance thresholds, and p values decreased when more inclusive criteria were used (i.e., for p T ranging between 0.1 and 1, see Table  S4a–h ).

The analysis of PGS for SA and T of 34 brain cortical regions revealed an association of the transverse temporal gyrus T with prevalent DD risk, which remained significant after correction for multiple testing (OR = 0.90[0.86; 0.96]; p  = 4.53 × 10 −4 ; Table  S4i ). This association was confirmed in a multivariable setting, both in stepwise (0.90[0.85; 0.95]; p  = 2.45 × 10 −4 ; Table  S4j ) and in elastic net regression (OR = 0.92; Table  S4k ). However, the variance explained by this PGS was low (0.17% in univariate regression) and all the cortical PGS selected in elastic net regression jointly conferred only a modest gain in dyslexic classification accuracy, compared to the null model including only covariates (0.4%).

To the best of our knowledge, the present work reports the largest case-control GWAS study conducted on dyslexia to date, involving 2274 DD cases and 6272 controls from nine different populations of European ancestry, speaking six different languages.

We identified a suggestive association at rs6035856 (p~10 −8 ), an intronic variant located within the gene LOC388780 (20p13), ~400 bp downstream of exon 1. This small (~6 kb) gene encodes a non-coding RNA which has not been functionally characterized yet, but is expressed in different organs, including the CNS [ 52 ]. Gene-based association testing supported the implication of LOC388780 in DD genetic risk, showing a genome-wide significant enrichment of associations for this gene. Based on the Roadmap Epigenome 25-state model using 12 imputed marks, this region is classified as a Promoter Upstream Transcription Start Site ( 2_PromU chromatin state) in several brain cell types, including those from middle hippocampus, anterior caudate, cingulate gyrus, inferior frontal lobe, and dorsolateral prefrontal cortex [ 78 ], suggesting potential roles in transcriptional regulation.

Gene-based analysis also detected significant evidence of enrichment for the gene VEPH1 ( ventricular zone expressed PH domain-containing 1 ; 3q25), coding for a partly characterized protein which promotes brain development [ 79 ], probably through regulation of the TGF-β signalling pathway [ 80 ]. However, we did not observe any significant enrichment of associations for TGF-β-related pathways.

The analysis of SNP-based heritability indicated that 20–25% of the total variance in DD could be explained by common variants in our dataset. This estimate is lower than typical heritability estimates for dyslexia provided by twin studies (40–60%) [ 2 ]. As with other complex traits, the discrepancy between twin- and SNP-based heritability suggests that part of dyslexia risk may be due to the genetic effects of variants other than SNPs, such as common copy number (CNVs) and rare variants. Although the relationship of CNVs and rare variants with DD and reading-related traits has not been extensively investigated to date, this hypothesis is partly supported by some recent findings. First, rare CNVs have often been implicated in familial forms of dyslexia [ 81 ] and the candidate gene DYX1C1 was first identified through a rare chromosomal rearrangement which co-segregated with dyslexia in a Finnish family [ 82 ]. Similarly, a targeted high-throughput sequencing study of 96 reading-impaired subjects reported an excess of putatively damaging rare variants in the candidate susceptibility loci DYX2 and CCDC136/FLNC [ 83 ]. Second, CNVs associated with neuropsychiatric disorders showed a significant influence on different cognitive traits in a large Icelandic population-based sample ( N ~102,000) [ 84 ]. In particular, a recurrent deletion of 15q11.2 was associated with a history of dyslexia and dyscalculia [ 84 ] and, in a later study, with cognitive, structural, and functional correlates of these impairments [ 85 ]. Third, a study reported >50% of the heritability of general cognition (IQ) and educational attainment (EduYears) to be explained by genetic variants in low LD with SNPs commonly genotyped on microarrays, especially rare variants. Indeed, SNP-based heritability of these traits approached the total heritability estimates from previous studies, when including also rare variants [ 86 ]. This suggests a substantial contribution of rare genetic variants to individual differences in intelligence and education, which may also extend to correlated cognitive traits such as reading ability.

PGS analyses revealed several significant associations between dyslexia risk and genetic liability to psychiatric disorders and other correlates.

First, we observed that PGSs for educational attainment and fluid intelligence were significantly associated with DD in our sample, in line with previous studies [ 30 , 72 , 87 ]. Luciano et al. [ 87 ] observed that PGSs of word reading, nonword repetition, and reading–spelling from GWAS studies of ~6600 children from UK and Australia showed significant positive associations with both verbal-numerical reasoning and educational attainment (college or university degree) in the UK Biobank cohort. Similarly, a PGS based on EduYears accounted for 2–5% of the variance in reading efficiency and comprehension in an independent UK sample ( N  = 5825) [ 72 ]. In the same study, Selzam et al. reported a PGSs of childhood general cognitive ability and adult verbal-numerical reasoning to explain a small but significant proportion (0.1–1.1%) of the variance in reading efficiency and comprehension at several developmental stages [ 72 ]. We later replicated these findings in a GWAS of reading-related cognitive skills partly overlapping with the present study ( N max  = 3468), extending the evidence of genetic overlap to cognitive predictors of dyslexia risk like phoneme awareness and digit span [ 28 ]. More recently, a GWAS of word reading in 4430 US children presenting in hospitals/clinics provided a further replication, reporting higher fractions of variance explained by EduYears (18%) and intelligence PGSs (7%) [ 30 ]. Together, the various PGS-based studies strongly support the existence of shared genetic factors influencing educational attainment, general cognition, and more specialized abilities like reading [ 88 ].

Second, genetic liability to ADHD was significantly associated with an increased DD risk, explaining 0.73% of its variance (at p T  = 0.05). This finding is in line with the hypothesis of shared genetic bases between these disorders, initially suggested by twin studies [ 54 , 56 ], and with evidence of genomic overlap reported for ADHD and the key cognitive features of dyslexia in our previous GWAS [ 28 ]. Recently, Price et al. [ 30 ] replicated the inverse association between ADHD-PGS and word reading in US children, as did Verhoef et al. [ 89 ] in a British longitudinal cohort ( N max  = 5919) for reading accuracy/comprehension at age 7, reading and spelling accuracy at age 9.

Third, we detected genetic links between two other neuropsychiatric disorders—BD and SCZ—and dyslexia. Standardized BD- and SCZ-PGS were associated with an increased DD risk, explaining 2.8% and 1.4% of its variance, respectively. Comorbidity of DD with a number of psychiatric disorders—including also BD and SCZ—has been previously reported [ 59 , 60 ], and siblings of dyslexic subjects showed a high relative risk of being affected by ADHD, BD, SCZ, depression and autism, among others [ 59 ]. Although no significant associations between a SCZ-PGS and continuous reading-related traits were observed in a smaller independent dataset [ 87 ], the association between SCZ genetic risk and DD is in line with the reported genetic influence of SCZ risk variants on reading problems in the general population [ 84 ]. To the best of our knowledge, no evidence of a common genetic basis for BD and reading difficulties has been reported so far, although shared familial (and potentially genetic) risks have been previously suggested [ 59 , 90 ]. Of note, we detected no significant associations between MDD-/ASD-PGS and dyslexia, but we did observe this for psychiatric cross-disorder genetic liability. These findings open up new scenarios in psychiatric genetics, suggesting a shared genetic and biological foundation across many different neurodevelopmental and neuropsychiatric conditions of phenotypically and clinically different nature.

Finally, the analysis of PGS influencing different brain cortical regions revealed a small, but robust and significant, protective effect against DD risk for a PGS increasing thickness of the transverse temporal gyrus. This region, also known as Heschl’s gyrus, is located within the primary auditory cortex—which is fundamental for auditory discrimination and speech perception [ 91 ]—and has been previously implicated in dyslexia by neuroimaging evidence, although not always consistently across studies [ 92 , 93 , 94 , 95 ]. Moreover, it overlaps with the left perisylvian regions where Galaburda et al. detected neuronal ectopias in four post-mortem dyslexic brains [ 96 ]. Here, we provide evidence of a genetic overlap between dyslexia risk and potential brain structural features proposed from non-genetic studies, although caution is suggested in the interpretation of these findings due to the inconsistencies across neuroimaging studies and to the potential role of regional brain asymmetries in the measures analyzed, which here were not taken into account due to the unavailability of GWAS summary statistics for separate hemispheres [ 75 ].

In spite of strengths like the wealth of cohorts and languages analyzed, and a relative homogeneity of recruitment, phenotypic assessment, and QC procedures, the present study also shows some limitations. In particular, there was a non-optimal case:control ratio in some datasets and a lack of properly screened controls in the English-speaking datasets. Although we acknowledge these would be preferred to improve power, the use of unscreened population controls is common where large numbers are needed, and has been exploited elsewhere [ 38 , 39 , 97 ]. Indeed, while for very common diseases the use of unscreened controls may notably affect power, for less common disorders/statuses (with prevalence <0.2) the loss of power is reduced and counterbalanced by the larger sample size which can be achieved through the use of unscreened populations [ 98 ]. Also, the PGS approach is based on the assumption that population structure and other possible confounds are well controlled in the training and target GWAS, which we implemented by adjusting all analyses for sex and genetic ancestry. However, independent replication of these results is warranted to substantiate the novel findings coming from the PGS analysis. Moreover, although the present study represents to our knowledge the largest GWAS on dyslexia to date [ 98 ], its sample size is relatively low compared to other studies in the neuropsychiatric field [ 66 , 67 , 68 , 69 , 70 ], which limited the power of analyses. Larger collaborative efforts are being implemented to improve these aspects to further enlighten the genetic epidemiology of dyslexia.

Wellcome Trust Case Control Consortium 2: https://www.wtccc.org.uk/ccc2/

PLINK: https://www.cog-genomics.org/plink2

QCTOOL: http://www.well.ox.ac.uk/~gav/qctool/

METAL: http://www.sph.umich.edu/csg/abecasis/Metal/index.html

MAGMA: http://ctg.cncr.nl/software/magma

MSigDB: http://software.broadinstitute.org/gsea/msigdb ;

LD score regression: https://github.com/bulik/ldsc

Per-variant LD scores: https://data.broadinstitute.org/alkesgroup/LDSCORE/

Genotype-Tissue Expression portal (GTEx): http://www.gtexportal.org/home/

Brain eQTL Almanac (Braineac): http://www.braineac.org/

LocusZoom: http://www.locuszoom.org/

The R Project: https://www.r-project.org/

MASS package: https://cran.r-project.org/web/packages/MASS/index.html

Glmnet package: https://cran.r-project.org/web/packages/glmnet/index.html

Caret package: https://cran.r-project.org/web/packages/caret/index.html

Data availability

Summary statistics data supporting the findings of the present study are available upon request to the corresponding authors.

Code availability

Bioinformatic codes supporting the findings of the present study are available upon request to the corresponding authors.

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Acknowledgements

AG and TFMA were supported by the Munich Cluster for Systems Neurology (SyNergy). AG was supported by Fondazione Umberto Veronesi. SP is a Royal Society University Research fellow. BMM, CF, BSP and SEF are supported by the Max Planck Society. AW, BM and HK were funded by the Fraunhofer Society and the Max Planck Society within the “Pakt für Forschung und Innovation”. HK was also supported by LIFE—Leipzig Research Center for Civilization Diseases funded by means of the European Union; the European Regional Development Fund (ERDF); and the Free State of Saxony within the excellence initiative. FR is supported by Agence Nationale de la Recherche (ANR-06-NEURO-019-01, ANR-17-EURE-0017 IEC, ANR-10-IDEX-0001-02 PSL, ANR-11-BSV4-014-01), European Commission (LSHM-CT-2005-018696). TFMA was supported by the B.M.B.F. through the DIFUTURE consortium of the Medical Informatics Initiative Germany (grant 01ZZ1804A) and by the European Union’s Horizon 2020 Research and Innovation Programme (grant MultipleMS, EU RIA 733161). We would also like to acknowledge our project partners Catherine Billard, Caroline Bogliotti, Vanessa Bongiovanni, Laure Bricout, Camille Chabernaud, Isabelle Comte-Gervais, Florence Delteil-Pinton, Florence George, Christophe-Loïc Gérard, Marie Lageat, Marie-France Leheuzey, Marie-Thérèse Lenormand, Marion Liébert, Emilie Longeras, Emilie Racaud, Isabelle Soares-Boucaud, Sylviane Valdois, Nadège Villiermet, and Johannes Ziegler. This study makes use of data generated by the WTCCC. A full list of the investigators who contributed to the generation of the data is available at www.wtccc.org.uk . Funding for the WTCCC project was provided by the Wellcome Trust under awards 076113 and 085475. Open Access funding enabled and organized by Projekt DEAL.

Author information

These authors contributed equally: Till F. M. Andlauer, Nazanin Mirza-Schreiber, Kristina Moll

These authors contributed equally: Johannes Schumacher, Markus M. Nöthen, Bertram Müller-Myhsok, Gerd Schulte-Körne

Authors and Affiliations

Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany

Alessandro Gialluisi, Till F. M. Andlauer, Nazanin Mirza-Schreiber, Darina Czamara & Bertram Müller-Myhsok

Munich Cluster for Systems Neurology (SyNergy), Munich, Germany

Alessandro Gialluisi, Till F. M. Andlauer & Bertram Müller-Myhsok

Department of Epidemiology and Prevention, IRCCS Istituto Neurologico Mediterraneo Neuromed, Pozzilli, Italy

Alessandro Gialluisi

Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany

Till F. M. Andlauer

Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany

Nazanin Mirza-Schreiber

Department of Child and Adolescent Psychiatry, Psychosomatic, and Psychotherapy, Ludwig-Maximilians University, Munich, Germany

Kristina Moll & Gerd Schulte-Körne

Department of Genomics, Life and Brain Center, Institute of Human Genetics, University of Bonn, Bonn, Germany

Jessica Becker, Per Hoffmann, Kerstin U. Ludwig, Johannes Schumacher & Markus M. Nöthen

Language and Genetics Department, Max Planck Institute for Psycholinguistics and Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands

Beate St Pourcain, Clyde Francks & Simon E. Fisher

MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK

Beate St Pourcain

Brain Imaging Centre, Research Centre of Natural Sciences of the Hungarian Academy of Sciences, Budapest, Hungary

Ferenc Honbolygó, Dénes Tóth & Valéria Csépe

Human Genetics and Cognitive Functions Unit, Institut Pasteur and University Paris Diderot, Sorbonne Paris Cité, Paris, France

Guillaume Huguet, Fabien Fauchereau & Thomas Bourgeron

ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, Toulouse, France

Children’s Hospital, Purpan University Hospital, Toulouse, France

Yves Chaix & Stephanie Iannuzzi

Leenaards Memory Centre, Department of Clinical Neurosciences Lausanne University Hospital (CHUV), University of Lausanne, Lausanne, Switzerland

Jean-Francois Demonet

Department of Biostatistics, University of Liverpool, Liverpool, UK

Andrew P. Morris

Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester, UK

Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK

Andrew P. Morris, Anthony P. Monaco & Thomas S. Scerri

Institute for Behavioral Genetics and Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA

Jacqueline Hulslander, Erik G. Willcutt, John C. DeFries & Richard K. Olson

Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA

Shelley D. Smith

Developmental Neuropsychology Lab and Clinic, Department of Psychology, University of Denver, Denver, CO, USA

Bruce F. Pennington

Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience and Maastricht Brain Imaging Center (M-BIC), Maastricht University, Maastricht, The Netherlands

Anniek Vaessen & Milene Bonte

Department of Psychology, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

Centre for Research on Learning and Teaching, Department of Psychology, University of Jyväskylä, Jyväskylä, Finland

Heikki Lyytinen & Paavo H. T. Leppänen

Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden

Myriam Peyrard-Janvid & Juha Kere

Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, Zurich, Switzerland

Daniel Brandeis

Zurich Center for Integrative Human Physiology (ZIHP), University of Zurich and ETH Zurich, Zurich, Switzerland

Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland

Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

Department of Physiology, University of Oxford, Oxford, UK

John F. Stein

School of Life and Health Sciences, Aston University, Birmingham, UK

Joel B. Talcott

Cognitive Genetics Unit, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany

Arndt Wilcke, Holger Kirsten & Bent Müller

Institute for Medical Informatics, Statistics and Epidemiology and LIFE—Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany

Holger Kirsten

Tufts University, Medford, MA, USA

Anthony P. Monaco

Laboratoire de Sciences Cognitives et Psycholinguistique, Ecole Normale Supérieure, CNRS, EHESS, PSL University, Paris, France

Franck Ramus

Institute of Psychology, University of Graz and BioTechMed, Graz, Austria

Karin Landerl

Stem Cells and Metabolism Research Program, Biomedicum, Folkhälsan Institute of Genetics, University of Helsinki, Helsinki, Finland

The Walter and Eliza Hall Institute of Medical Research, Melbourne University, Melbourne, VIC, Australia

Thomas S. Scerri

School of Medicine, University of St Andrews, St Andrews, UK

Silvia Paracchini

Institute of Translational Medicine, University of Liverpool, Liverpool, UK

Bertram Müller-Myhsok

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Contributions

AG, TFMA, NM-S, DC and KM contributed to genotype QC and imputation, and to phenotype QC. AG and TFMA carried out statistical analyses. AG, BMM and GSK wrote the paper. AG, TFMA, NM-S, KM, JB, PH, KUL, DC, BSP, FH, DT, VC, GH, YC, SI, JFD, APMor., JH, EGW, JCD, RKO, SDS, BP, AV, UM, HL, MPJ, PHTL, DB, MB, JFS, JBT, FF, AW, HK, BM, CF, TB, APMon., FR, KL, JK, TSS, SP, SEF, JS, MMN, BMM, GSK contributed to collection, phenotypic assessment and/or genotyping of the datasets included in the present study, and critically reviewed the manuscript. BMM and GSK supervised the present work.

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Correspondence to Bertram Müller-Myhsok or Gerd Schulte-Körne .

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Gialluisi, A., Andlauer, T.F.M., Mirza-Schreiber, N. et al. Genome-wide association study reveals new insights into the heritability and genetic correlates of developmental dyslexia. Mol Psychiatry 26 , 3004–3017 (2021). https://doi.org/10.1038/s41380-020-00898-x

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DOI : https://doi.org/10.1038/s41380-020-00898-x

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Dyslexia as a Learning Disability: Teachers' Perceptions and Practices at School Level.

  • Tahira Kalsoom , Abdul Haseeb Mujahid , A. Zulfqar
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Research shows risks and opportunities of GenAI for dyslexic, neurodivergent and disabled students

by Nottingham Trent University

 ChatGPT

In November 2022, ChatGPT made significant headlines around its release due to its advanced language processing capabilities. There has since been a significant increase in both academic and research conversations across the globe about its potential use and implications in the higher education.

The ability of ChatGPT to process natural language and generate human-like conversational explanations is often claimed to make learning more accessible and engaging for students.

This recent rise in the use of Generative AI (GenAI) tools has sparked the interest of support teams across schools and universities in considering recommending them to aid the learning of dyslexic, neurodivergent and disabled students. For example, for use with tasks such as explaining concepts and ideas and summarizing articles.

We worked with an undergraduate student to get the views of these students on GenAI in higher education. Specifically, we were interested in understanding how the students use this tool in their daily lives and the benefits and risks to their learning.

We received 54 complete responses from students who are known to the university's disability and inclusion team. Our preliminary analysis revealed several interesting trends that could benefit learning and teaching practice, student support, and university policies on using AI.

Training on specialist software and assistive technology

The existing provision of specialist software and assistive technology training through DSA's (Disabled Students' Allowance) may provide various useful lessons regarding the initial take-up and sustained usage of assistive technology.

However, ensuring that the training provided is accessed, effective, adapted to individual needs and is available at the right time during students' studies is challenging.

Support for student learning and daily lives

Of the respondents, 46% felt that GenAI tools were well designed to accommodate their specific needs; 59% of the respondents reported that they have used GenAI to explain concepts.

The potential for using GenAI tool such as ChatGPT as a "private tutor" is an area of growing interest, however when asked directly in our survey, only 9% of students claim to have used GenAI in this way.

While some students report being willing to experiment with virtual advice, less than 30% are currently likely to use GenAI for non-study related advice such as social, relationship, and mental health.

Learning and teaching

For the learning and teaching aspect, students feel that giving personal or pastoral advice, delivering teaching and marking summative assessments are the least appropriate uses of GenAI by a university.

We found that 50% of the respondents were more worried about being accused of plagiarism since the emergence of GenAI, while many feel "about the same" (48%). Most students are not sure if any GenAI detection technology can work as claimed (59%).

Future aspirations and concerns

Most respondents (91%) felt that "keeping up with current technologies" was somewhat or very important to their course and career. Most were unsure about the impact of GenAI on their current career aspirations, although this view tended to get more negative in the longer term (>5 years' time).

Students have a clear level of concern around the wider effects and abuses of GenAI technology: from misinformation, scams, impact on artists and copyright, to uses in government, surveillance, harassment and more.

Conclusion and next steps

The preliminary results of our study provide insights into the experiences of dyslexic, neurodivergent and disabled students with GenAI technology . When comparing the results with another survey which did not specifically target disabled students, our respondents are more likely to use GenAI for study tasks such as explaining concepts, suggesting ideas and summarizing articles.

However, both groups of students have a similar view on the use of GenAI in assessment, that is GenAI should be used sparingly or not at all in assessments. From the teaching practice perspective, assessments may be designed to be "AI-proof," however there is clearly a concern that any alternatives may result in assessments that are potentially less inclusive or less amenable to individualized reasonable adjustments where needed.

Social and ethical concerns will likely continue to require ongoing debate and discussion, particularly from courses that seek to make greater use of GenAI to enhance student learning experience.

Provided by Nottingham Trent University

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Specific Learning Disabilities: Issues that Remain Unanswered

Adarsh kohli.

Department of Psychiatry, PGIMER, Chandigarh, India

Samita Sharma

Susanta k. padhy, introduction.

Specific learning disabilities (SLDs) are defined as “heterogeneous group of conditions wherein there is a deficit in processing language, spoken or written, that may manifest itself as a difficulty to comprehend, speak, read, write, spell, or to do mathematical calculations and includes such conditions as perceptual disabilities, dyslexia, dysgraphia, dyscalculia, dyspraxia and developmental aphasia.”[ 1 ]

DISTINCTION BETWEEN TERMINOLOGIES – DISORDER, DISABILITY, DIFFICULTY, AND SLOW LEARNER

The terms learning disorders, learning disability (LD), and learning difficulty are often used interchangeably but differ in many ways. Disorder refers to significant problems faced by children in academic areas, but this is not sufficient to warrant an official diagnosis. The word “disorder” is a medical term as mentioned in the Diagnostic and Statistical Manual of Mental Disorders,[ 2 ] and International Statistical Classification of Diseases and Related Health Problems,[ 3 ] both of which are considered authoritative guides for mental health professionals.

The word “disability” in SLDs is a legal term that is mentioned in the Right of Persons with Disabilities Act (RPWD Act, 2016),[ 4 ] notification issued by the Ministry of Social Justice and Empowerment (Department of Empowerment of Persons with Disabilities) and Individuals with Disabilities Education Act (United States federal law).[ 5 ] These federal laws protect the rights of students with disabilities. To receive special disability certificates and services under these acts, a student must be a “child with a disability.”[ 6 ] SLD is an official clinical diagnosis where the individual meets certain criteria as assessed by a professional (psychologist, pediatrician, etc.).

Children with “learning difficulties” underachieve academically for a wide range of reasons, including factors such as behavioral, psychological, and emotional issues; English being their second language and not their mother tongue; ineffective instruction; high absenteeism; or inadequate curricula. These children have the potential to achieve age-appropriate levels once they are provided support and evidence-based instruction.[ 7 ]

Students with below average cognitive abilities whom we cannot term as disabled are called “slow learners.” The slow learning child is not considered mentally retarded because he is capable of achieving a moderate degree of academic success even though at a slower rate than the average child.[ 8 ]

TYPES OF LEARNING DISABILITIES

Reading disability (also known as dyslexia) is the most common LD, accounting for at least 80% of all LDs.[ 9 ] Reading should be taught; it is not an innate entity. Reading requires the ability to understand the relationship between letters and the associated sound, which is known as phonetics. Dyslexia reflects a specific problem in processing individual speech sounds (e.g., the ssss sound, the mmm sound) in words (phonemes). There can also be problems with holding sounds in sequence in short-term memory (e.g., holding the sequence of sounds in a new word in mind long enough to recognize it). Children with a reading disability may also have difficulties with reading fluency, resulting in reading skills that are accurate but effortful and slow.[ 9 ]

Dyscalculia is generally characterized by difficulty in learning or understanding mathematical operations. A student with arithmetic disorder might have difficulty organizing problems on the page; following through on multiple step calculations such as long division; transposing numbers accurately on paper or on to a calculator, such as turning 89 into 98; distinguishing right from left; and using mathematical calculation signs. They may also be confused about basic operations and facts.[ 9 ]

Dysgraphia is generally characterized by distorted writing despite thorough instruction. A student with dysgraphia exhibits inconsistent and illegible writing, mixing upper and lowercase letters, and writing on a line and inside margins. He or she might have fine motor difficulties such as trouble holding the pencil correctly, inability to use scissors well, or coloring inside the lines.[ 9 ] Overall writing does not communicate at the same level as his or her other language skills.[ 9 ]

LDs are associated with psychological comorbidities.[ 10 , 11 ] Approximately 30% of children have behavioral and emotional problems.[ 12 ] Children with SLD are at an increased risk of hyperactivity. There is a strong relationship between inattentiveness and reading disabilities. The comorbidity of attention deficit hyperactivity disorder (ADHD) in children with LD varies from about 10% to as high as 60% depending on the sample taken.[ 13 , 14 ] Co-occurrence of major depressive disorder (MDD) and LDs was studied in 100 children age 9–12 years. It was seen that 62% of children with MDD had LD, whereas only 22% of children without depression had LD.[ 15 ] The comorbidity of LD with both internalizing and externalizing disorders implicates the need for cognitive and behavioral approaches in the remediation programs offered to dyslexic children. Diagnosis at an early age results in boosting self-confidence and social competency.[ 16 ]

LDs do not become evident till the child starts going to school. Many children do not exhibit any signs until they engage in tasks which require certain kind of cognitive processing which becomes apparent then.

A lot of research and efforts are being done in the field of LDs in the western world. However, in India, the experience and research are limited. The government and educational authorities are also progressing toward the betterment of education system. There are many gray areas in this field which need more efforts, clarity, understanding, and discussion.

TEACHERS' ATTITUDE, TEACHING METHODS, AND PRACTICES

Despite the fact that millions of people around the world suffer silently from SLD, there remains widespread confusion and misinformation with regard to identification of and interventions for SLDs. Due to this, children do not enjoy their school life and resist going to school. Some efforts have been made, like the one by National Council of Educational Research and Training in 2015, when a handbook on the inclusion of children with special needs was prepared. It was a very sincere effort in which a series of workshops were held in different parts of the country, involving regular school teachers, teacher educators, special educators, and experts from universities and nongovernmental and governmental organizations.[ 17 ] The handbook emphasized access and participation of children in the learning process, more than just placing them in schools. The question arises how far these efforts are being implemented successfully.

A study conducted in Haridwar, India (2015),[ 18 ] showed that 67% of teachers had no knowledge of LDs. Overall, teacher educators who participated in that study had a low level of knowledge about SLDs, irrespective of their gender or teaching experience. Another study conducted in Chandigarh on the perception of teachers about LDs showed some positive results. Approximately 67.5% teachers perceived that they do encounter children with LD in the school, 43.8% supported special schools for such children, and 36.3% were in favor of integrated education. About one-fifth of the teachers were ready to undergo special skill training for teaching students with learning disorders.[ 19 ] The level of awareness among teachers was explored in Puducherry (UT).[ 20 ] The study found that the teachers in the inclusive classroom require skill training to impart education to students with SLD. The data showed that in an inclusive education setup, the information regarding SLD is average. The authors recommended the Government of India to implement intensive and methodical training to fulfill the educational needs.[ 20 ] This is certainly important because when we talk about inclusive education, we must have means to support the idea to the fullest. Teachers should be sensitized and trained to screen for this problem at the primary school level itself so that remediation can be started at an early stage.

There is a huge difference in private and government school setups. In private schools, there is a counselor and special educator with a specialization in intervention for SLDs, but this facility is lacking in government schools. The students of private schools usually belong to middle and high socioeconomic status, paying hefty fees for education. These schools are better equipped to provide all the necessary services to the students. On the contrary, in government schools, the majority of the students are from lower socioeconomic status, with parents who are completely unaware of the concept of SLD. They are not able to avail these services and remain underprivileged. This is very disheartening because the teachers are also not keen to put in extra efforts to help these students. Many policies and rules are made only on paper, but implementation is missing.

In today's society, there are schools which are result-oriented and focus on producing “toppers.” They are not interested in keeping the so-called “slow child” in their classrooms. This attitude hinders the child's learning progress and results in worsening of prognosis of the problem. SLDs result in unexpected academic underachievement. Teaching authorities are demanding and lack patience for slow learners. The teacher certification programs in India are short of sufficient courses in special education to prepare general teachers for inclusive classrooms. Owing to the lack of proper training in the area and lack of familiarity with reading process and areas of reading skills which require assessment, creativity and “trial and error” are what guide the course of remediation.[ 21 ]

Teaching methods and styles adopted by the teachers differ from school to school and also have regional differences.[ 15 ] Some schools focus on phonetics and teach accordingly; some adopt the traditional rote learning pattern in which the child crams the alphabets without understanding their formation and sound. Rote learning methods focus on grades and good marks, ignoring the overall development of the child. Multisensory teaching aids, visual and auditory cues, computer software providing text-to-text and speech-to-text capabilities, and so on are restricted to only a few schools which can afford to provide such quality teaching practices.[ 22 ] It is very difficult to achieve this technological sophistication in all the classrooms in a developing country like India which suffers from wide economic disparity and fluctuating literacy rate. Resultantly, again the question arises whether the learning disorder is confounded by faulty teaching practices or due to the natural course of the problem. Sight word teaching, phonemic awareness difficulties, or specialized vision problems can also cause reading difficulty and are often mistaken for true organic dyslexia. Teaching methods such as sight words result in reading difficulty that mimic dyslexia. This method inhibits the development of left–right reading and eye jumps all around the word. According to the American Child Development Institute, “Children who have an average or above average IQ and are reading one and a half grades or more below grade level may be dyslexic.” “True dyslexia affects about 3%–6% of the population. Yet in some parts of the country, up to 50% of the students are not reading at grade level. The reason for most children not being able to read at grade level could be ineffective reading instruction. The child with dyslexia is often a victim of having SLD and is being exposed to ineffective instruction as well.”[ 23 ] In France, it was proved that schools that taught with whole word method produced more students with dyslexia than schools teaching with phonics. The brains of dyslexic students can be retrained with phonics.[ 24 ] In India, teachers are not trained enough to understand this and help the students in need. Traditional teaching methods such as spelling games and cursive writing exercises during vacations have almost disappeared. The expectations of parents and early induction of children into school have resulted in more damage than gains.

SHORTAGE OF SPECIAL EDUCATORS

Central Board of Secondary Education (CBSE) had made it mandatory for all the affiliated schools to appoint a special educator so that children with LDs could be assimilated with other students. It was ultimately seen that it was a big challenge for the schools to find qualified professionals in this area. According to school authorities, special educators are experienced in teaching physically challenged students; they lack theoretical and practical skills required for teaching learning disabled students. The teaching methods have to be tailor-made for these students since they have behavioral problems as well.[ 25 ]

In 2016, Special Educators' Forum of India had submitted a charter of demands to various education departments of every state. This was done because the government had not created a post of special educators or made it mandatory for the schools to appoint them.[ 26 ] In a recent report, it came to light that in Delhi, out of 927 posts of special educators, 432 are still vacant. This fault came into light when a mother of two sons with disability studying in a government school filed a complaint that her sons have not learned anything, instead they have become a source of entertainment for the students. Students and teachers bully them and authorities turn a deaf ear to them.[ 27 ] Special educators equipped with individualized educational program are the need of the hour to tackle the situation.

PROBLEMS WITH LANGUAGES

In the Oriental world, the LDs were considered a problem of English-speaking countries.[ 22 ] Due to lack of awareness and reportedly lower incidence rates in Asian countries like India and China, not many efforts were made in this field. Researchers in the Western world attributed this problem to the overcrowded classrooms and backward teaching strategies.[ 28 ] On the other hand, eastern researchers attribute it to the phonetic complexity of English language which resulted in problems in language adaptability.

Spelling–sound correspondence is direct in Hindi language, which means that we write what we speak. But in case of English, there are certain notorious traits of the language which makes it complex and it becomes necessary to remember the arbitrary spellings and words. For example, there are a lot of words in English language with silent letters which makes the language much more difficult, because here the person needs to remember formations such as psychology, pseudo, pneumonia, and walk. Those children having difficulty in process of learning find it difficult to comprehend. People reading and writing Hindi and other regional languages also do suffer from learning difficulties. It is seen equally in other languages as well. The child is unable to learn orthography, syntax, and phonetics of language because of which it becomes imperative for the teachers to adopt such teaching practices and for the school authorities to facilitate the learning process of these children.

LACK OF STANDARDIZED ASSESSMENT TOOLS

India is a multilingual country, so it is important to assess the problem of SLD in a child's mother tongue. There are numerous batteries used for the assessment of LDs, with their own merits and demerits. Some of the batteries are widely used for assessment, but there is a lack of well-established norms for all subtests, and these norms are based on a very small sample which makes generalization difficult like the AIIMS SLD: Comprehensive Diagnostic Battery and NIMHANS Index for Specific Learning Disabilities. Many batteries are prepared in regional languages (e.g., Marathi, Gujarati and Kannada) which lack nationwide applicability. Some batteries can only be administered on students of English medium schools like NIMHANS Index for Specific Learning Disabilities, whereas in India about 100.4 million students study in Hindi medium schools.[ 29 ] The content used in the batteries is not standardized. Existing batteries have not included all the age groups for assessment, which makes assessment difficult, especially when the student is to be assessed in tenth or twelfth board classes for the issuance of a certificate for availing benefits.

COMPLEXITY OF GRADATION IN LEARNING DIFFICULTY AND DISABILITY

According to standard assessment procedure for learning disorders, one class is taken as one standard deviation. So if a child is performing two classes below his actual standard/class, then he or she is diagnosed as LD, and if the performance is one class below, then it is diagnosed as learning difficulty not amounting to disability. Now, various education boards, including CBSE[ 30 ] The Indian Certificate of Secondary Education ICSE, Kerala Board, and Maharashtra Board, provide various concessions for students with LD; but there are no facilities for students with learning difficulty. The awareness among policy makers regarding this point of differentiation is limited. There is no provision for students with difficulties. Lack of support from school authorities and parents worsen the situation. Students are not able to avail relaxations and suffer silently. Pediatricians and psychiatrists rely on clinical psychologists to distinguish students with learning difficulty and disability. This confusion creates problems for the process of certification and intervention. The problems of students with learning difficulty not amounting to disability needs to be dealt with specialized techniques of intervention at early stages by a special educator and a parent together.

PROVISIONS FOR SPECIFIC LEARNING-DISABLED STUDENTS

After a series of consultation meetings and drafting process, the Rights of People with Disabilities Act, 2016 was passed by both the houses of the Parliament. It was notified on December 28, 2016 after receiving presidential assent. The list was expanded and it included SLDs in it. A bill was introduced in Rajya Sabha on March 24, 2017, entitled “The Children with Specific Learning Disabilities (Identification and Support in Education).” It highlighted the need for special facilities in educational institutions, setting up detection and remediation centers, guidelines for certification of children with SLDs, and so on.[ 31 ] On January 15, 2018, the Ministry of Social Justice and Empowerment (Department of Empowerment of Persons with Disabilities) issued a notification regarding the procedure to be followed while certifying people with disabilities. The Gazette laid emphasis on screening, diagnosis, and certification of SLD. Figure 1 gives the summary of standard operating procedure of certification for SLDs.[ 1 ]

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The screening, diagnosis, and certification procedure for Specific learning disabilities

This effort by the Government of India deserves appreciation as it has highlighted the importance of certification and has tried to standardize it. Despite this step, there are certain issues which are a matter of concern. Psychiatrists have been excluded from the procedure of certification. Students with academic difficulties or scholastic decline are usually referred to Child and Adolescent Psychiatry clinics from the school. A team of psychologists and psychiatrists carry out the complete assessment of the students referred from schools. It is highly recommended that psychiatrists should also be included into the procedure along with pediatricians and psychologists because they have specialized training in mental health and developmental disorders of children and adolescents. Second, the Gazette mentions which tests shall be used for the assessment of IQ for uniformity, but in case of SLD assessment, it should be left to the discretion and experience of the psychologists. Instruments that are to be used should be latest and should have norms that can be generalized to the population concerned. The same tests cannot be used in the entire country because of a wide range of sociodemographic and regional differences which can influence test results.

Initiative steps have been taken by CBSE to provide a concession for LD students. These concessions are in the form of a scribe and complementary time, exemption from a third language, flexibility in choosing subjects, permission to use calculators in mathematics, and provision to read out question paper to a student with dyslexia. These students are also exempted from spelling errors and from writing answers in detail, and so on. According to the recent circular issued by CBSE, no school can deny admission to students with disabilities in mainstream education. It has also recommended regular in-service training of teachers in inclusive education at elementary and secondary level, as per CBSE guidelines.[ 30 ] Many other boards and state boards are also offering concessions, but there is no uniformity in rules for demanding certificates. Some boards demand only a certificate of SLD and some require a detailed report along with the certificate; some need renewal while some accept one-time certification.[ 32 ]

There are pros and cons of these provisions. Some parents have a mindset of demanding certificates even when their children do not have a, LD. They do not focus on remedial intervention. This leads to misuse of these provisions and certification. This is a sensitive issue which needs to be handled carefully. There are others who are not aware of these concessions, and the child keeps struggling with disability.

STIGMA AND LABELING

In India, acceptance of children suffering from LDs in schools largely depends on the capability of the schools to provide necessary services to the children and the attitude of the teachers to put some sincere efforts to help these children. Inclusion, therefore, has rather become selective inclusion of children with disabilities in the mainstream, especially in private schools.[ 33 ] These children suffer from many behavioral problems and certain comorbid conditions such as ADHD which is again not known to many. They are labeled as dull, lazy, mischievous, troublesome, and so on without knowing the actual reason behind this. Social attributes play a very important role in the overall course of illness. Acceptance from society, peers, teachers, and so on affect their successful inclusion.[ 34 ] The label of LD carries its own burden, baggage, and complications.

OVERLAP WITH SCHOOL DROPOUT

It is difficult to treat various students who drop out from the school as a homogeneous group. Dropping out from the school can be attributed to factors such as low socioeconomic status, behavioral issues, LDs, or intellectual disability. There is a lot of overlap between these categories. There are students who are first-generation learners. According to the National Policy on Education, 1986, these students should be allowed to set their own pace of learning and should be given remedial supplementary instructions.[ 35 ] Their slow pace of acquiring information may be due to their background which is not stimulating enough to induce learning, but these children can often be diagnosed with SLDs. This again creates confusion.

GOVERNMENT POLICIES AND APPLICABILITY

Sarva Shiksha Abhiyaan aimed at universalization of elementary education “in a time bound manner,” as mandated by the 86 th Amendment to the Constitution of India, making free and compulsory education to children between the ages of 6 and 14 years a fundamental right. It was decided under this scheme that no student shall be failed and will be promoted to the next class.[ 36 ] This is very important and necessary initiative; but because of this, LD remains undiagnosed and untreated for a longer period of time. Child's problems aggravate because parents do not bother until the child fails, and school authorities do not bother till the school result is affected. Since a child is promoted to next class without the need of minimum passing marks, parents and teachers become complacent and wake up only at secondary levels, and the child's problem remains unnoticed. Some parents try to get away with their child's problem by availing certificates of disability without any extra efforts which are actually required to be invested in.

Accommodations which are now being given to students with LDs in the classrooms are sometimes regarded as unfair by parents of students without SLD. It is important to make the parents aware of the fact that these concessions and accommodations are not unfair advantages to students. In fact, if appropriate and timely concessions are not used, students could be branded as having LDs, creating serious negative impact to their achievement and self-concept. The parents can be sensitized on the above issues through parent–teacher meetings and other awareness programs conducted in the school.

To understand LDs fully, it is necessary to examine the problem in black and white with all its shades of gray. These gray areas are the practical and experiential difficulties when dealing with these children in Child and Adolescent clinics. Constructing a standardized assessment battery, keeping in view of the diversity of Indian culture, is a mammoth task. Having a thorough insight into the overlapping areas can clear misconceptions and guide assessment, intervention, and welfare benefits to those children who genuinely deserve them.

IMAGES

  1. Dyslexia Research Paper Introduction

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  2. (PDF) Media Reports on Dyslexia among Celebrities: How does it help

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  3. Dyslexia Final

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  4. Learners With Learning Disability

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  5. Learning Disability: What Is Dyslexia?

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  6. Learning Disability A Case Study PDF

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VIDEO

  1. Specific Learning Disability Vs. Dyslexia Research Project

  2. DYSLEXIA AND THE DEVELOPING BRAIN / HARVARD MEDICAL SCHOOL

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  4. Do #LearningStrategies work?

  5. Literacy Strategies for Dyslexia and Related Reading Difficulties

  6. learning disabilities DYSLEXIA

COMMENTS

  1. Journal of Learning Disabilities: Sage Journals

    Journal of Learning Disabilities. Journal of Learning Disabilities (JLD) provides specials series (in-depth coverage of topics in the field, such as mathematics, sciences and the learning disabilities field as discursive practice), feature articles (extensive literature reviews, theoretical papers, … | View full journal description.

  2. Defining and understanding dyslexia: past, present and future

    Dyslexia is a difficulty in learning to decode (read aloud) and to spell. DSM5 classifies dyslexia as one form of neurodevelopmental disorder. Neurodevelopmental disorders are heritable, life-long conditions with early onset. For many years, research on dyslexia proceeded on the basis that it was a specific learning difficulty - specific ...

  3. The Prevalence of Dyslexia: A New Approach to its Estimation

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  4. An ecosystemic perspective of the factors affecting the learning

    Dyslexia is an educational psychology journal for research concerning the psychology, special education, therapy, neuroscience, & psychiatry associated with dyslexia.

  5. Learning Disabilities Research Studies: Findings from NICHD funded

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  6. An Examination of Dyslexia Research and Instruction With Policy

    Dyslexia is a specific learning disability that is neurobiological in origin. It is characterized by difficulties with accurate and/or fluent word recognition and by poor spelling and decoding abilities.

  7. Genome-wide association study reveals new insights into the ...

    Developmental dyslexia (DD) is a specific learning disorder affecting the ability to read that is not better accounted for by intellectual disabilities, uncorrected visual or auditory acuity ...

  8. The Inclusion of Students with Dyslexia in Higher Education: A

    Internationally, the number of students with disabilities enrolled in higher education institutions (HEIs) is on the rise, with the most commonly reported disability being specific learning difficulties (SpLDs), including dyslexia, dyscalculia and dyspraxia. In the UK, an estimated 4% of students enrolled at all higher educational levels (including undergraduate and postgraduate) had SpLDs in ...

  9. Full article: Dyslexic students' experiences in using assistive

    Dyslexia manifests in difficulties in learning to decode and spell words, and thus, students find it challenging to develop reading and writing fluency [ 8 ]. Language and speech disabilities are additional causes of fundamental phonological disability in dyslexia [ 2, 9-12 ].

  10. Defining and understanding dyslexia: past, present and future

    Dyslexia is a difficulty in learning to decode (read aloud) and to spell. DSM5 classifies dyslexia as one form of neurodevelopmental disorder. Neurodevelopmental disorders are heritable, life-long conditions with early onset. For many years, research on dyslexia proceeded on the basis that it was a specific learning difficulty - specific ...

  11. PDF Dyslexia as a Learning Disability: Teachers' Perceptions and Practices

    Learning difficulties like dyslexia create a hindrance in students' success and achievement and need a rigorous research in the field. Present study explores teachers' perceptions regarding students learning difficulties with special reference to dyslexia.

  12. PDF Full Length Research Paper

    Full Length Research Paper. ucationGregory RichardsonDepartment of Special Education, Rehabilitation, and Counseling, California State University, San Be. y, Accepted 12 April, 2021The number of students with learning disabilities in post-secondary institutions has grown substantially, and those with dyslexia co.

  13. Learning Disability Quarterly: Sage Journals

    Learning Disability Quarterly. Learning Disability Quarterly (LDQ) publishes high-quality research and scholarship concerning children, youth, and adults with learning disabilities. Consistent with that purpose, … | View full journal description. This journal is a member of the Committee on Publication Ethics (COPE).

  14. Understanding and supporting learners with specific learning

    The review focused on three specific learning difficulties: dyslexia, dyspraxia and dyscalculia. Thematic analysis of papers included in the review led to the construction of three major themes, concluding that further neurodiverse research and scholarship is required.

  15. Learning Disabilities Research & Practice: Sage Journals

    Learning Disabilities Research & Practice (LDRP) publishes articles addressing the nature and characteristics of children and adults with, or with potential for, learning disabilities (specific learning disability; specific learning disorder) and/or attention … | View full journal description.

  16. The 100 Top-Cited Studies on Dyslexia Research: A Bibliometric Analysis

    Background: Citation analysis is a type of quantitative and bibliometric analytic method designed to rank papers based on their citation counts. Over the last few decades, the research on dyslexia has made some progress which helps us to assess this disease, but a citation analysis on dyslexia that reflects these advances is lacking.

  17. Understanding and supporting learners with specific learning

    SpLD conceptualisation for this paper includes dyslexia, dyspraxia, dyscalculia and dysgraphia, while ASC and ADHD are conceptualised separately as neurodevelopment conditions that can result in associated learning difficulties.

  18. [PDF] Dyslexia as a Learning Disability: Teachers' Perceptions and

    The study explored teachers' perceptions and practices at school level with special reference to dyslexia. Being deprived of proper learning environment learning difficulties are multiplied for dyslexic students. Poor reading skills are the reason of low vocabulary. The study was quantitative in nature.

  19. Early identification and interventions for dyslexia: a contemporary

    This paper reviews current proposals concerning the definition of dyslexia and contrasts it with reading comprehension impairment. We then discuss methods for early identification and review evidence that teacher assessments and ratings may be valid screening tools. Finally, we argue that interventions should be theoretically motivated and ...

  20. Full article: Educational technology for learners with disabilities in

    The use of EdTech for learners with disabilities in low- and middle-income countries requires further, robust and long-term research that considers the involvement of learners, pedagogy and curriculum design in order to understand its impact on improving educational experiences of children with disabilities.

  21. Identifying and Serving Students With Learning Disabilities, Including

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  22. Research shows risks and opportunities of GenAI for dyslexic

    Research shows risks and opportunities of GenAI for dyslexic, neurodivergent and disabled students. by Nottingham Trent University

  23. Specific Learning Disabilities: Issues that Remain Unanswered

    Specific learning disabilities (SLDs) are defined as "heterogeneous group of conditions wherein there is a deficit in processing language, spoken or written, that may manifest itself as a difficulty to comprehend, speak, read, write, spell, or to do mathematical calculations and includes such conditions as perceptual disabilities, dyslexia, dysgraphia, dyscalculia, dyspraxia and ...

  24. Prevalence of Specific Learning Disorders (SLD) Among Children in India

    Specific learning disorders (SLD), often referred to as learning disability, is a neurodevelopmental disorder (NDD) and refers to ongoing problems in one of the three basic skills-reading, writing, and arithmetic-which are the essential requisites for the learning process. 1 These difficulties, namely dyslexia, dysgraphia, dyscalculia ...