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Published in final edited form as: Autism Res. 2014 Dec 1;8(2):229–234. doi: 10.1002/aur.1436

Language impairment and dyslexia genes influence language skills in children with autism spectrum disorders

John D Eicher 1, Jeffrey R Gruen 1,2
PMCID: PMC4412753  NIHMSID: NIHMS633767  PMID: 25448322

Abstract

Language and communication development is a complex process influenced by numerous environmental and genetic factors. Many neurodevelopment disorders include deficits in language and communication skills in their diagnostic criteria, including autism spectrum disorders (ASD), language impairment (LI), and dyslexia. These disorders are polygenic and complex with a significant genetic component contributing to each. The similarity of language phenotypes and comorbidity of these disorders suggest that they may share genetic contributors. To test this, we examined the association of genes previously implicated in dyslexia, LI, and/or language-related traits with language skills in children with ASD. We used genetic and language data collected in the Autism Genome Research Exchange (AGRE) and Simons Simplex Collection (SSC) cohorts to perform a meta-analysis on performance on a receptive vocabulary task. There were associations with LI risk gene ATP2C2 and dyslexia risk gene MRPL19. Additionally, we found suggestive evidence of association with CMIP, GCFC2, KIAA0319L, the DYX2 locus (ACOT13, GPLD1, and FAM65B), and DRD2. Our results show that LI and dyslexia genes also contribute to language traits in children with ASD. These associations add to the growing literature of generalist genes that contribute to multiple related neurobehavioral traits. Future studies should examine whether other genetic contributors may be shared among these disorders and how risk variants interact with each other and the environment to modify clinical presentations.

Keywords: language, autism spectrum disorders, ATP2C2, MRPL19, dyslexia, language impairment

Introduction

The development of adequate communication and language skills is an important milestone in child development. There are numerous neurodevelopmental disorders, including autism spectrum disorders (ASD), language impairment (LI), and dyslexia, that include specific language deficits in their diagnosis and symptomology. Typically, children with ASD lack coherence in conversation and verbal utterances, have difficulties in manipulating language with the proper syntax, show delays or regressions in speech and language skills, and are unable to comprehend and follow the language of others (Bishop 2010, Leyfer et al. 2008, Pennington & Bishop 2009). The language deficits observed in children with ASD have similarities to the ones seen in children with LI (Bishop 2010). In fact, there are a subset of children with ASD that are categorized as having comorbid LI (Bishop 2010, Kjelgaard & Tager-Flusberg 2001). Children with LI have difficulties in the development of comprehension and utilization of verbal language (Pennington & Bishop 2009). A primary deficit observed in children with LI is difficulty in phonological processing and awareness (Gathercole & Baddeley 1990, Pennington & Bishop 2009). However, these deficits in phonology are not unique to LI, as children with dyslexia typically have difficulties in these same phonological domains (Catts 2005, Pennington & Bishop 2009). In fact, children with LI are more likely to develop dyslexia later in childhood than their peers (Pennington 2006). The degree of similarity between language traits in ASD and LI, as well as LI with dyslexia, suggests that these disorders may share core communication deficits and determinants. Nonetheless, clinical or phenotypic similarities or comorbidities between dyslexia and ASD have not been noted in the literature.

In addition to similarities and comorbidity in clinical presentation, there are also similarities in the genes implicated in ASD, LI, and dyslexia. Heritability studies have demonstrated that LI and ASD share substantial genetic components (Bartlett et al. 2004, Bartlett et al. 2012, Bartlett et al. 2014). Unfortunately, the relative low number of LI candidate genes has made the interrogation of specific shared genetic associations somewhat difficult. However, both FOXP2 and CNTNAP2 are associated with LI and ASD, with the functional link that FOXP2 regulates the transcriptional activity of CNTNAP2 (Vernes et al. 2008, Vernes et al. 2011). There is also evidence that FOXP2 contributes to dyslexia as well (Wilcke et al. 2012). There is less evidence suggesting that dyslexia and ASD may share genetic components. In fact, there are no known reports examining the heritability of shared genetic components between the two. However, there is limited evidence that dyslexia and ASD share genetic contributors. FOXP2 has further been linked to dyslexia and reading-related traits (Kaminen et al. 2003, Peter et al. 2011, Wilcke et al. 2012). The axon guidance gene ROBO1 has been implicated in both dyslexia and ASD, although with limited evidence supporting the association of ROBO1 to each (Anitha et al. 2008, Bates et al. 2011, Tran et al. 2014). Nonetheless, the shared genetic associations of dyslexia and LI as well as LI and ASD suggest all three disorders may share genetic contributors.

Therefore, the overall goal of this study is to examine whether genes previously implicated in dyslexia, LI, and/or language-related traits are also associated with language skills in individuals with ASD. To accomplish this, we specifically assess associations with performance on a receptive vocabulary task in two family-based ASD cohorts: the Autism Genome Research Exchanges (AGRE) and Simons Simplex Collection (SSC). We hypothesize that both dyslexia and LI risk genes also contribute to language phenotypes observed in children with ASD.

Methods

AGRE (www.agre.org) and SSC (www.sfari.org) are family cohorts recruited based on a clinic diagnosis of ASD in a family member. The AGRE is a collection of well-characterized multiplex and simplex families with a large amount of phenotypic and genotypic data (Geschwind et al. 2001, Weiss et al. 2009). Following enrollment in AGRE, diagnosis of ASD was confirmed using the Autism Diagnostic Interview-Revised (ADI-R) and the Autism Diagnostic Observation Schedule (ADOS) (Lord et al. 1994, Lord et al. 1989). The SSC is a more recent study, where, similar to AGRE, probands were recruited along with unaffected parents and siblings across 12 participating sites in the United States (Abrahams et al. 2013, Fischbach & Lord 2010). Each affected proband completed a wide range of neurobehavioral measures including the ADI-R and ADOS (Lord et al. 1994, Lord et al. 1989). Informed consent was completed under the guidance of each site’s institutional review board. De-identified data were obtained by the authors for analysis.

Subjects completed a language measure, the Peabody Picture Vocabulary Test (PPVT), in both the AGRE and SSC cohorts. The PPVT is a measure of receptive vocabulary (Dunn & Dunn 1997). Subjects are verbally prompted with a word and four pictures. The subject chooses the picture that most closely matches the meaning of the prompted word. Raw scores are converted to a quantitative standard score corrected for age norms. This test quantitatively measures the ability of the subject to process verbally presented language and to connect it to a concrete meaning. In total, 941 and 1048 individuals with genetic information completed the PPVT in the AGRE and SSC cohorts, respectively.

The AGRE cohort was genotyped with the Illumina 500K SNP chip, while the SSC cohort was genotyped with the Illumina 1M and 1M Duo chips. The difference in genotyping chips resulted in increased marker coverage on average for the SSC sample when compared to the AGRE cohort. However, the use of the same manufacturer ensured that there was substantial overlap among the genotyped markers in the two samples. Markers within the risk LI and dyslexia genes and/or loci listed in Table 1 were selected for association analyses. Genes were selected based on prior evidence of association or linkage to dyslexia and/or LI. Notably, we excluded markers in CNTNAP2, as the relationship of this gene with language has already been characterized in the AGRE sample (Alarcon et al. 2008), and markers within ZNF385D and ROBO1, as the sheer size of these genes would have overwhelmed the marker composition in our study design. Markers with a minor allele frequency <0.05, call rate <0.85, Mendelian errors, were not in Hardy-Weinberg equilibrium (p<0.0001) or had more than 2 alleles were excluded from the analysis. In total, there were 2022 and 904 markers within these risk genes and loci in the SSC and AGRE cohorts, respectively. Of these, 805 genotyped markers were in common between the two samples.

Table 1.

List of genes previously associated with LI, dyslexia, and/or language-related traits examined in AGRE and SSC

Gene Location # in
AGRE		 # in
SSC		 Gene Location # in
AGRE		 # in
SSC
RCAN3 1p35.3-
1p33		 8 20 ANKK1/
DRD2** 		11q23 26 59
KIAA0319L 1p34.2 6 66 DYX1C1 15q21.3 11 37
GCFC2 2p12 9 20 CYP19A1 15q21.1 36 258
MRPL19 2p11.1-
q11.2		 17 30 CMIP 16q23 111 183
DYX2* 6p22 260 657 ATP2C2 16q24.1 88 128
FOXP2 7q31 69 151 ABCC13 21q11.2 21 36
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Abbreviations: # in AGRE, number of markers examined in AGRE; # in SSC, number of markers examined in SSC

*

DYX2 refers to the dyslexia risk locus DYX2 on chromosome 6, which contains 12 genes including risk genes DCDC2 and KIAA0319

**

ANKK1 and DRD2 are located adjacent to one another on chromosome 11 and therefore grouped together

Genetic associations with quantitative performance, as measured by standard score, on the PPVT were performed with PLINK using the QFAM function with 100,000 permutations to correct for family structure (Purcell et al. 2007). The association results of the 805 shared markers in each individual cohort were then used in a meta-analysis using METAL (Willer et al. 2010). To conservatively correct for multiple testing, we used a statistical Bonferonni threshold of 6.21 × 10−5 (p = 0.05 / 805 shared markers tested). Associations from the meta-analysis with p<0.001 are presented in the main text to present suggestive associations (Table 2). Full association results are available upon request.

Table 2.

Associations (p<0.001) with performance on the Peabody Picture Vocabulary Test from meta-analysis of AGRE and SSC.

Marker Ch BP Gene Allele 1 Allele 2 Dir. AGRE P-value SSC P-value Combined P-value
rs11149652* 16 84414399 ATP2C2 A G − − 1.47×10−2 7.00×10−5 5.00×10−6
rs7588016* 2 75649214 MRPL19 T C ++ 2.64×10−2 3.70×10−4 3.90×10−5
rs12446219 16 84415015 ATP2C2 T C − − 2.70×10−3 7.76×10−3 6.44×10−5
rs17671350 6 24898628 FAM65B T C − − 9.10×10−3 2.43×10−3 6.45×10−5
rs6928074 6 24680062 ACOT13 A G − − 1.10×10−2 3.41×10−3 1.06×10−4
rs17011662 2 75691522 GCFC2 T C ++ 1.51×10−2 2.46×10−3 1.09×10−4
rs12039012 1 35472284 KIAA0319L A G − − 2.23×10−2 2.26×10−3 1.52×10−4
rs2966099 16 81516843 CMIP T C ++ 2.85×10−2 2.03×10−3 1.79×10−4
rs12562622 1 35439579 KIAA0319L T C − − 2.23×10−2 3.60×10−3 2.29×10−4
rs4782624 16 84431560 ATP2C2 A G ++ 6.90×10−2 8.20×10−4 2.34×10−4
rs2242592 11 113408708 DRD2 T C ++ 1.92×10−2 4.50×10−3 2.40×10−4
rs793671 6 24428434 GPLD1 A G ++ 4.10×10−3 2.16×10−2 2.71×10−4
rs2927332 16 81475688 CMIP T G ++ 9.26×10−3 1.41×10−2 3.53×10−4
rs12596138 16 81457144 CMIP T C − − 3.40×10−2 3.67×10−3 3.61×10−4
rs2734849 11 113399438 ANKK1 T C − − 1.46×10−2 1.02×10−2 3.94×10−4
rs4581480 11 113453752 DRD2 T C ++ 3.72×10−3 5.20×10−2 6.59×10−4
rs9393532 6 24164229 A G ++ 1.85×10−2 1.47×10−2 6.97×10−4
rs8059665 16 84429583 ATP2C2 T G − − 1.39×10−1 1.42×10−3 8.56×10−4
rs2927321 16 81481164 CMIP T C ++ 2.48×10−2 1.47×10−2 9.17×10−4
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Abbreviations: Ch, chromosome; BP, base pair; Dir, direction of effect

*

Survived correction for multiple testing

Results

Results for the meta-analysis of performance on the PPVT in the AGRE and SSC cohorts are presented in Table 2, with the top results in individual cohorts presented in Supplemental Tables 1-2. There were two associations that survived correction for multiple testing: rs11149652 in ATP2C2 (p=5.00×10−6) and rs7588016 in MRPL19 (p=3.90×10−5) (Table 2). Three other markers within ATP2C2, rs12446219 (p=6.44×10−5), rs4782624 (p=2.34×10−4), and rs8059665 (p=8.56×10−4), showed suggestive associations. Another LI risk gene located within the SLI1 Locus on chromosome 16, CMIP, showed suggestive evidence of association (p=1.79×10−4). No other markers in MRPL19 showed suggestive associations in these cohorts; however, there were suggestive associations with a marker in GCFC2, rs17011662 (p=1.09×10−4), which is located in the same DYX3 locus on chromosome 2. In the SSC sample, there were multiple associations with markers in KIAA0319L (p=1.00×10−5) (Supplemental Table 2), with suggestive evidence in the meta-analysis (p=1.52×10−4) (Table 2). There were other notable suggestive associations with markers in DRD2 and several genes in the DYX2 locus including ACOT13, GPLD1, and FAM65B. However, in the meta-analysis, we found no evidence of association for other language genes such as DCDC2, KIAA0319, FOXP2, DYX1C1 and CYP19A1 in the DYX1 locus, RCAN3,and ABCC13. However, there was suggestive evidence in the association results in the individual cohorts (Supplementary Tables 1-2).

Discussion

In this study, we examine whether genes previously implicated in dyslexia, LI, and/or other language related traits are also associated with language skills in children with ASD. We aimed to determine whether genes identified with related communication traits may also contribute to language deficits observed in children with ASD. There were associations of ATP2C2 and MRPL19 with performance on a measure of receptive vocabulary. In addition, we found suggestive evidence for other language-related genes, including CMIP, GCFC2, DRD2, and KIAA0319L, as well as several DYX2 genes (ACOT13, GPLD1, and FAM65B). These results demonstrate that genes that contribute to other communication disorders also influence language traits in children in ASD.

ATP2C2 is one of two risk genes, the other being CMIP, in the SLI1 locus on chromosome 16. The SLI1 locus was first identified in genome-wide linkage studies (SLI Consortium 2002 and 2004) of LI, with subsequent association studies implicating ATP2C2 and CMIP in LI and phonological short-term memory deficits in children with LI (Newbury et al. 2009, Newbury et al. 2011). Phonological short-term memory is a common endophenotype for LI, while receptive vocabulary typically is a marker of general verbal cognition (Bishop 2010, Pennington & Bishop 2009). The association of ATP2C2 with a more general language measure expands its role for one solely in LI to now also include language deficits in ASD. Further supporting a more general neurocognition role of ATP2C2 is the previous association of ATP2C2 with attention deficit hyperactivity disorder (ADHD), a condition commonly comorbid with dyslexia and LI (Lesch et al. 2008). The evidence here implicating ATP2C2 in language skills in children with ASD further suggests that the SLI1 risk genes contribute to general language and neurobehavioral traits and not specifically to LI.

The other gene for which we found substantial evidence for association, MRPL19, is located within the DYX3 locus on chromosome 2. MRPL19 was initially implicated in dyslexia through family-based studies (Anthoni et al. 2007, Fagerheim et al. 1999, Kaminen et al. 2003, Paracchini et al. 2011, Peyrard-Janvid et al. 2004). Markers in this locus have been associated with Verbal IQ (Scerri et al. 2012). Receptive vocabulary measures, including the PPVT, are sometimes used to estimate Verbal IQ, indicating that here we expand the association of MRPL19 from Verbal IQ performance in the general population and dyslexia cases to include ASD cases. Additionally, Roberts et al. recently reported a deletion including this locus in a case with ASD (Roberts et al. 2014). The DYX3 locus has shown repeated associations with overall language skills, as measured by Verbal IQ and receptive vocabulary tasks, in multiple sample types (general population, dyslexia, and ASD), suggesting that DYX3 genes (e.g., MRPL19) influence general language and communication traits.

Our associations of dyslexia and LI associated genes with language skills in ASD add to the growing literature showing that genes appear to contribute to multiple related neurobehavioral, neurocognitive, and language traits, as opposed to specific disorders (e.g. a specific dyslexia gene, a specific ASD gene, or a specific LI gene). Risk variants of associated genes whether common or rare alter a cellular process, such as calcium transport with ATP2C2 or neuronal migration with DCDC2/KIAA0319 (Meng et al. 2005, Paracchini et al. 2006, Xiang et al. 2005). These alterations either individually, as seen in syndromic and rare variants, or interactively with other genetic variants or environment, including both common and rare variants, yield the nuanced clinical presentations of these disorders. Discerning how these variants and/or environmental factors interact to cause specific types of clinical presentations will be vital to determining the etiologies of these disorders and to improving remediation of language skills.

Our study is subject to several limitations including (1) selection bias of genetic markers included in the analyses based on the current literature of dyslexia and LI, (2) differences in genotyping chips used in the AGRE and SSC samples limiting our coverage of the genes examined, and (3) the use of only a receptive vocabulary measure in assessing language skills in both cohorts. However, we hope the results of our study prompt the expansion of language batteries used in studies of individuals with ASD as well as highlight the importance of resources with large amounts of genetic and behavioral data such as AGRE and SSC.

In conclusion, our study shows that the LI associated gene ATP2C2 and dyslexia associated gene MRPL19 also contribute to language skills in children with ASD, linking language and communication process in these three disorders. For example, genetic associations shared between dyslexia, LI, and ASD likely influence general language skills, as measured by tasks similar to receptive vocabulary measures used in this study. On the other hand, genes associated with dyslexia and LI, but not ASD, may influence phonological processes, known to be important in dyslexia and LI, but not necessarily in ASD. Genes associated with language and behavior appear to be associated with general communication and behavior phenotypes as opposed to specific disorders or traits. Future studies should further interrogate the degree to which these and other related disorders share genetic factors and begin to examine how these factors may interact to bring about individual conditions, marking a beginning of personalized medicine in polygenic, complex disorders.

Supplementary Material

Supp TableS1
Supp TableS2

Acknowledgements

We thank all the families and participants who took part in these studies. This research was funded by the NIH (T32 GM007223 [JDE], F31 DC012270 [JDE], R01 NS043530 [JDE, JRG], P50 HD027802 [JRG]) and the Manton Family Foundation [JDE, JRG]. We gratefully acknowledge the resources provided by the Autism Genetic Resource Exchange (AGRE) Consortium and the participating AGRE families. The Autism Genetic Resource Exchange is a program of Autism Speaks and is supported, in part, by grant 1U24MH081810 from the National Institute of Mental Health to Clara M. Lajonchere (PI). We are grateful to all of the families at the participating Simons Simplex Collection (SSC) sites, as well as the principal investigators (A. Beaudet, R. Bernier, J. Constantino, E. Cook, E. Fombonne, D. Geschwind, R. Goin-Kochel, E. Hanson, D. Grice, A. Klin, D. Ledbetter, C. Lord, C. Martin, D. Martin, R. Maxim, J. Miles, O. Ousley, K. Pelphrey, B. Peterson, J. Piggot, C. Saulnier, M. State, W. Stone, J. Sutcliffe, C. Walsh, Z. Warren, E. Wijsman). We appreciate obtaining access to phenotypic data on SFARI Base. Approved researchers can obtain the SSC population dataset described in this study by applying at https://base.sfari.org. The content is solely the responsibility of the authors and does not represent the official views of the NIH.

Abbreviations

ASD

autism spectrum disorders

LI

language impairment

AGRE

Autism Genome Research Exchange

SSC

Simons Simplex Collection

ADI-R

Autism Diagnostic Interview-Revised

ADOS

Autism Diagnostic Observation Scale

PPVT

Peabody Picture Vocabulary Test

GWAS

genome-wide association study

PING

Pediatric Imaging Neurocognition Genetics

ADHD

attention deficit-hyperactivity disorder

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Supplementary Materials

Supp TableS1
NIHMS633767-supplement-Supp_TableS1.docx (18KB, docx)
Supp TableS2
NIHMS633767-supplement-Supp_TableS2.docx (19KB, docx)

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