Elsevier

Cortex

Volume 71, October 2015, Pages 291-305
Cortex

Research report
Working-memory endophenotype and dyslexia-associated genetic variant predict dyslexia phenotype

https://doi.org/10.1016/j.cortex.2015.06.029Get rights and content

Abstract

Developmental dyslexia, a severe impairment of literacy acquisition, is known to have a neurological basis and a strong genetic background. However, effects of individual genetic variations on dyslexia-associated deficits are only moderate and call for the assessment of the genotype’s impact on mediating neuro-endophenotypes by the imaging genetics approach. Using voxel-based morphometry (VBM) in German participants with and without dyslexia, we investigated gray matter changes and their association with impaired phonological processing, such as reduced verbal working memory. These endophenotypical alterations were, together with dyslexia-associated genetic variations, examined on their suitability as potential predictors of dyslexia. We identified two gray matter clusters in the left posterior temporal cortex related to verbal working memory capacity. Regional cluster differences correlated with genetic risk variants in TNFRSF1B. High-genetic-risk participants exhibit a structural predominance of auditory-association areas relative to auditory-sensory areas, which may partly compensate for deficient early auditory-sensory processing stages of verbal working memory. The reverse regional predominance observed in low-genetic-risk participants may in turn reflect reliance on these early auditory-sensory processing stages. Logistic regression analysis further supported that regional gray matter differences and genetic risk interact in the prediction of individuals' diagnostic status: With increasing genetic risk, the working-memory related structural predominance of auditory-association areas relative to auditory-sensory areas classifies participants with dyslexia versus control participants. Focusing on phonological deficits in dyslexia, our findings suggest endophenotypical changes in the left posterior temporal cortex could comprise novel pathomechanisms for verbal working memory-related processes translating TNFRSF1B genotype into the dyslexia phenotype.

Introduction

Developmental dyslexia is defined by severe difficulties in reading acquisition, often accompanied by spelling difficulties (American Psychiatric Association, 2000, World Health Organization, 2009), and affects 5–10% of children across literate societies (Katusic et al., 2001, Shaywitz et al., 1990). Dyslexia often persists into adulthood, and can affect individuals' everyday reading and writing activities as well as academic success (Raskind et al., 1999, Shaywitz et al., 1999). In cognitive science, classical dual-route (Coltheart et al., 2001, Ziegler et al., 2000) and connectionist models (Harm and Seidenberg, 1999, Seidenberg, 2005) have established the constituent components of the reading process from behavioral double dissociations. In contrast, contemporary approaches in cognitive neuroscience also focus on the neurological basis and potential genetic causes of dyslexia (see Carrion-Castillo et al., 2013, Richlan et al., 2009, Richlan et al., 2013), aiming to provide a more complimentary picture. Given the long-lasting and wide-ranging consequences of dyslexia, the long-term objective of these approaches is a diagnosis and intervention prior to reading onset. The current study follows this aim and considers behavioral, neurological, as well as genetic aspects in a multistage assessment of developmental dyslexia.

Evidence from cognitive science and neuroscience suggests that a phonological deficit in the processing and/or representation of speech sounds forms the basis for developmental dyslexia (see Goswami, 2015, Peterson and Pennington, 2015). Rules of grapheme-to-phoneme conversion and phoneme assembly need to be acquired in order to develop reading capabilities during early education. Once this is successfully completed, experienced readers can rely on their orthographic and lexical knowledge, mapping visual word forms directly to their associated lexicon and phonology (see Coltheart et al., 2001, Ziegler et al., 2000). In contrast, infants at risk for dyslexia have been found to display aberrant speech sound processing, suggesting a predictive relation of phonological skills to literacy acquisition prior to reading onset (Guttorm et al., 2010, van Zuijen et al., 2013). Individual differences in phonological processing have been consistently found to predict reading abilities across languages (Seymour et al., 2003, Ziegler et al., 2010, Ziegler and Goswami, 2005). Phonological processing deficits in dyslexia (Snowling, 2000, Vellutino et al., 2004) are often accompanied by deficits in auditory (Tallal, 1980) and visual processing (Stein & Walsh, 1997), attention (Facoetti et al., 2001, Valdois et al., 2003), and automaticity (Nicolson, Fawcett, & Dean, 2001). These cognitive manifestations have been suggested to dissociate different dyslexia subtypes (e.g., van Ermingen-Marbach et al., 2013, Heim et al., 2008). However, it has been argued that the non-phonological manifestations may primarily result from reduced reading experience in individuals with dyslexia (for discussion, see Goswami, 2015, Peterson and Pennington, 2015). On account of this evidence, our study focuses on phonological processing deficits of dyslexia. Thus, in a symptom-oriented approach we assessed clinically defining literacy impairments (i.e., reading and spelling) and accompanying phonological deficits that have been repeatedly reported for individuals with developmental dyslexia. Phonological deficits involve reduced verbal working memory (WM; Jorm, 1983, Stone and Brady, 1995), impaired phonemic awareness (Bradley & Bryant, 1983), and slowed rapid automatized naming (Catts, 1986). Note that impaired rapid naming has been controversially discussed as reflecting a phonological deficit (i.e., deficient access to phonological representations; see Ramus and Szenkovits, 2008, Wagner and Torgesen, 1987) or a non-phonological naming speed deficit (i.e., reduced automaticity in literacy-related processes; see Wolf and Bowers, 1999, Wolf et al., 2002).

As a neurobiological correlate of dyslexia, neuronal misplacements in left perisylvian regions have been proposed, potentially resulting from disturbed neuronal migration in language-related areas early during development (Galaburda et al., 1985, Humphreys et al., 1990). Indeed, morphometric and functional brain analyses in individuals with dyslexia revealed structural gray matter alterations (Brambati et al., 2004, Kronbichler et al., 2008, Pernet et al., 2009, Raschle et al., 2011, Richlan et al., 2013, Silani et al., 2005) and divergent activation patterns (Richlan et al., 2009) predominantly in left temporo-parietal and occipito-temporal regions. Converging on the gray matter findings, children and adults with dyslexia were also found to exhibit white matter alterations in temporo-parietal brain regions (Ben-Shachar et al., 2007, Carter et al., 2009, Eckert et al., 2005, Klingberg et al., 2000, Steinbrink et al., 2008), which were associated with reduced reading and phonological abilities (Beaulieu et al., 2005, Deutsch et al., 2005, Vandermosten et al., 2012).

On the genetic level, several dyslexia-associated genes have been identified (Burkhardt et al., 2012, Carrion-Castillo et al., 2013, Kere, 2014, Wilcke et al., 2012). Particularly well-replicated genes are located within so-called chromosomal susceptibility regions DYX1 to DYX9 (see Gibson and Gruen, 2008, Scerri and Schulte-Körne, 2010). A prominent example is the first gene found to be associated with dyslexia, that is, DYX1C1 on chromosomal region DYX1 (Paracchini, Scerri, & Monaco, 2007). Variant DYX1C1-rs3743204 has been, at least nominally, replicated as genetic risk factor in German- and English-speaking individuals (Dahdouh et al., 2009, Wigg et al., 2004). This genetic variant was also reported in a meta-analysis of dyslexic individuals from Austria, France, Germany, Switzerland, and the Netherlands (Becker et al., 2014), and was found in association with reading in non-affected individuals (Bates et al., 2010). On chromosomal region DYX2, the dyslexia-associated genes DCDC2 and KIAA0319 have been consistently replicated in different languages: A large deletion in DCDC2-rs71745442 was reported as genetic risk factor in German-, English-, and Italian-speaking participants with dyslexia (Cope et al., 2012, Marino et al., 2012, Meng et al., 2005, Wilcke et al., 2009), while variant KIAA0319-rs6935076 was confirmed as a risk factor in numerous studies on reading abilities in unaffected and dyslexic populations (Cope et al., 2005, Couto et al., 2010, Luciano et al., 2007, Newbury et al., 2011, Paracchini et al., 2008, Scerri et al., 2011). In addition to these well-known hypothesis-driven dyslexia-associated genes, a recent hypothesis-free microarray-based screening approach of validated dyslexia regions DYX1 to DYX9 identified further potential dyslexia candidate genes in a large German population, where variant TNFRSF1B-rs496888 on DYX8 showed the best nominal association with dyslexia in an independent replication (Kirsten, Wilcke, Ligges, Ahnert, & Boltze, 2011).

While these genetic-association studies provide evidence for a certain relationship between behavioral manifestations and underlying genetic predispositions, they neither allow for diagnostic inferences nor provide an immediate patho-mechanistical hypothesis. Instead, the moderate effect of individual genetic variations on behavior necessitates thorough assessment of such variations' impact on brain endophenotypes. The imaging genetics approach assumes that a particular brain structure provides a temporally stable endophenotype by which genetic variants and environmental factors affect behavior (Meyer-Lindenberg & Weinberger, 2006). This implicates an essential methodological advantage with respect to the dataset extent that delivers reliable results. In contrast to the high-input requirement in genetic association studies, imaging genetics frequently operates on participant numbers typically involved in imaging studies. The imaging genetics approach seems particularly promising in dyslexia: Imaging studies have revealed aforementioned dyslexia-associated gray matter alterations in language-related areas while, in turn, genetic variants can cause structural divergences being detectable by magnetic resonance imaging (MRI) (Blokland et al., 2012, Kremen et al., 2010, Peper et al., 2009), specifically in language-related areas (Thompson et al., 2001). In particular, variations in DCDC2 were found to associate with wide-spread gray matter changes and reading-related brain activation (Cope et al., 2012, Meda et al., 2008). Moreover, variations in DCDC2, DYX1C1, and KIAA0319 were shown to affect temporo-parietal white matter (Darki et al., 2012, Mahalanobis, 2014), even though these structural features may be only indirectly affected by genetically determined alterations in neuronal migration (Meng et al., 2005, Paracchini et al., 2006, Wang et al., 2006).

In sum, despite of strong evidence for dyslexia-associated phenotypes, candidate genotypes, and mediating neuronal endophenotypes, a tripartite link is still missing. We followed the imaging-genetics approach, conceptualizing brain structure as endophenotype through which genetic variants and environmental conditions are associated with behavior. Thus, we combined structural imaging data, dyslexia-associated behavioral data, and genetic data: First, we analyzed gray matter changes associated with dyslexia-relevant phonological disabilities (i.e., verbal WM, phonemic awareness, and rapid automatized naming), alongside measures clinically defining dyslexia (i.e., spelling and reading). Second, we tested whether the identified behaviorally-relevant gray matter changes are associated with candidate genetic risk variants reported for dyslexia. In a third step, we directly tested whether the gray matter changes and associated genetic variants are predictive of participants' diagnostic status (i.e., being diagnosed with dyslexia vs not being diagnosed).

Section snippets

Participants

Thirty-two male adults participated in the study. Based on participants' clinical history of having been diagnosed with dyslexia during literacy acquisition by a professional versus not having been diagnosed (i.e., participants' diagnostic status), there was a patient group (n = 16) and a control group (n = 16). To confirm affected individuals' diagnosis (and to ensure that control participants' were unimpaired) we obtained clinically relevant measures of reading and spelling. In acquiring the

Behavioral data

Participant groups differed in their performance in the assessed dyslexia-associated behavioral domains (Table 1). Participants with dyslexia generally showed significantly slower speed and decreased accuracy compared to control participants. Apart from significant group differences across all behavioral domains (although not present in all subtests), participants with dyslexia showed sub-standard performance in the diagnostic reading and spelling tests. As expected, reading was not as strongly

Discussion

Our study aimed at establishing a missing tripartite link between dyslexia phenotype, candidate genotypes, and potentially mediating neuro-endophenotypes (i.e., gray matter alterations related to a range of dyslexia-relevant behavioral domains). To this end, we assessed gray matter changes associated with dyslexia-specific impairments in phonological processing (i.e., verbal WM, phonemic awareness, and rapid automatized naming), reading, and spelling, and tested whether these brain-structural

Conclusion

Our study revealed verbal-WM-related gray matter alterations in HG/pSTG and the pSTS that interacted with genetic risk variants in TNFRSF1B in the prediction of dyslexia. Participants with a structural predominance of HG/pSTG and a low genetic risk tended to be classified as controls. Functionally, controls may thus employ early auditory-sensory stages of verbal WM as gateway for the conversion of sensory-based into phonological representations. Participants with a structural predominance of

Conflict of interest

A. W., H. K., and J. B. are co-inventors on a pending patent describing a method to identify risk for dyslexia by means of analyzing SNP rs496888. No further conflicts of interest, including financial ones, exist.

Acknowledgments

We thank Anke Kummer, Mandy Jochemko, and Domenica Wilfing for imaging data acquisition, and Elfi Quente for technical assistance. This work was supported by the Max Planck Society (C. M., L. M., and A. F.) and the German Federal Ministry of Education and Research (H. K. and A. W., grant number PtJ-Bio 0315883).

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    Claudia Mannel and Lars Meyer contributed equally.

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    Holger Kirsten and Angela D. Friederici contributed equally.

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