Dissection of genetic associations with language-related traits in population-based cohorts
© The Author(s) 2011
Received: 28 April 2011
Accepted: 24 July 2011
Published: 6 September 2011
Recent advances in the field of language-related disorders have led to the identification of candidate genes for specific language impairment (SLI) and dyslexia. Replication studies have been conducted in independent samples including population-based cohorts, which can be characterised for a large number of relevant cognitive measures. The availability of a wide range of phenotypes allows us to not only identify the most suitable traits for replication of genetic association but also to refine the associated cognitive trait. In addition, it is possible to test for pleiotropic effects across multiple phenotypes which could explain the extensive comorbidity observed across SLI, dyslexia and other neurodevelopmental disorders. The availability of genome-wide genotype data for such cohorts will facilitate this kind of analysis but important issues, such as multiple test corrections, have to be taken into account considering that small effect sizes are expected to underlie such associations.
The genetics of language and reading disorders
Genetic factors are expected to contribute significantly to neurodevelopmental disorders. Recent advances in the field have been marked by the identification of several genes as susceptibility factors for dyslexia (or reading disability) and specific language impairment (SLI). Dyslexia is characterised by unexpected difficulties in learning to read, whereas SLI refers to difficulties in the acquisition of oral language (Pennington and Bishop 2009). In both disorders, a diagnosis is met in the absence of other co-occurring medical conditions (e.g., hearing loss for SLI) or neurological disorders (e.g., generalised learning disability) (Skuse et al. 1997). The biology of dyslexia and SLI remains largely unexplained, and the causes are expected to be the results of multiple interacting factors of both genetic and environmental origin affecting the early stages of brain development. It is estimated for both disorders that, the prevalence in first-degree relatives of affected individuals is 30–50%, compared to the general population prevalence of approximately 5–10% (Barry et al. 2007; Fisher and DeFries 2002). Dyslexia and SLI show extensive comorbidity. Estimates show that 40–55% of children diagnosed with either dyslexia or SLI meet criteria also for the other disorder (Snowling et al. 2000; McArthur et al. 2000). It remains an open question whether common genetic factors may contribute to clinically distinct disorders, partially explaining the consistently observed comorbidity (Pennington and Bishop 2009; Smith 2007).
Most of the candidate genes for dyslexia and SLI have been identified in family-based samples through genetic association studies targeting chromosomal regions previously mapped by linkage studies (positional cloning approach). These associations are characterised by p values that would not stand the magnitude of significance currently expected for genome-wide association studies (GWAS; P < 10−7). However, consistent replications in independent samples at the same locus and often with the same genetic markers provide robust support to these associations.
Genetic linkage studies have identified several loci which may contribute to dyslexia including DYX1 on chromosome 15q21, DYX2 on chromosome 6p21, DYX3 on chromosome 2p, DYX5 on chromosome 3p, DYX6 on chromosome 18p11, DYX8 on chromosome 1p, and DYX9 on chromosome Xq27 (Williams and O’Donovan 2006; Scerri and Schulte-Korne 2010). SLI linkage loci include SLI1 on chromosome 16q, SLI2 on chromosome 19q and SLI3 on chromosome 13q (Newbury et al. 2005). Positional cloning, candidate gene analysis and translocation breakpoint mapping have pinpointed candidate genes within some of these linkage regions for both dyslexia (Scerri and Schulte-Korne 2010; Paracchini et al. 2007) and SLI (Newbury and Monaco 2010). Some of these genes have been analysed in population-based cohorts.
The first candidate gene proposed for dyslexia was DYX1C1 (dyslexia susceptibility 1 candidate 1) (Taipale et al. 2003). The breakpoint of a chromosome translocation (t(2;15)(q11;q21)) co-segregating with dyslexia in a single family was located between exons 8 and 9 of DYX1C1 (Taipale et al. 2003). In addition, two variants in DYX1C1, with a putative functional effect, were identified through association analysis in a sample of individuals with dyslexia (Taipale et al. 2003). Replication studies in independent samples selected for dyslexia have consistently reported modest size associations (Dahdouh et al. 2009; Cope et al. 2005a; Meng et al. 2005a; Marino et al. 2007; Brkanac et al. 2007; Scerri et al. 2004; Wigg et al. 2004). Many of these studies reported associations with an opposite allelic trend which could be due to differences in linkage disequilibrium patterns between populations (Scerri and Schulte-Korne 2010) suggesting that these two markers may detect a signal for a different genuine functional variant.
Two genes have been proposed for the DYX2 locus following association analysis by fine mapping in this region in many independent samples selected for dyslexia (Scerri and Schulte-Korne 2010; Paracchini et al. 2007); DCDC2 (Doublecortin domain containing) (Brkanac et al. 2007; Schumacher et al. 2006; Meng et al. 2005b; Harold et al. 2006; Newbury et al. 2011; Deffenbacher et al. 2004; Ludwig et al. 2008) and KIAA0319 (Deffenbacher et al. 2004; Cope et al. 2005b; Francks et al. 2004). The associations with the KIAA0319 gene are clustered around the first exon and a single variant has been shown to regulate the expression of this gene (Paracchini et al. 2006; Dennis et al. 2009).
The region spanning the MRPL19 and C2ORF3 genes was identified following fine mapping association analysis at the DYX3 locus (Anthoni et al. 2007). Overlapping haplotypes across this region showed association with dyslexia in two independent samples of Finnish and German origin. These associations have not been replicated yet in independent studies.
The ROBO1 (Roundabout 1) gene was identified through the breakpoint mapping of a chromosome translocation involving the DYX5 locus in an individual with dyslexia (Nopola-Hemmi et al. 2001). A rare single nucleotide polymorphism (SNP) haplotype spanning ROBO1 also segregated with dyslexia in a large multi-generational pedigree, where the DYX3 linkage was originally identified (Hannula-Jouppi et al. 2005). However no evidence of association with common variants in larger cohorts have been reported so far, suggesting that ROBO1 may be relevant only in isolated cases (Hannula-Jouppi et al. 2005).
The associations with SLI have all been identified in one and the same UK cohort collected by the SLI Consortium (SLIC) (SLI Consortium 2002). Two associations were identified by a high-density SNP screen in the SLI1 region of linkage on chromosome 16 (Newbury et al. 2009). Two clusters of variants showed significant associations with the non-word repetition measure, which is a verbal short-term memory score and is regarded as a good marker for heritable SLI (Bishop et al. 1996). The first cluster fell within the CMIP (C-MAF Inducing Protein) gene and the second within the ATP2C2 (ATPase, Ca2+ transporting, type 2C, member 2) gene. CNTNAP2 (contactin-associated protein-like 2), on chromosome 7q, was proposed as a candidate for SLI after being identified as a downstream target of FOXP2, which has been implicated in severe and rare forms of language impairment (Lai et al. 2001). The SLIC cohort was used to evaluate the effects on language performance of the CNTNAP2 gene, and found variants significantly associated with the non-word repetition task (Vernes et al. 2008).
Candidate genes for dyslexia and SLI analysed in population-based cohorts
Taipale et al. 2003
Wigg et al. 2005
Bates et al. 2009
Paracchini et al. 2010
Luciano et al. 2007
Couto et al. 2009
Scerri et al. 2011a
Lind et al. 2010
Hannula-Jouppi et al. 2005
Anitha et al. 2008
Bates et al. 2011
Newbury et al. 2009
Newbury et al. 2011
Scerri et al. 2011a
Vernes et al. 2008
Elia et al. 2010
Whitehouse et al. 2011
Comorbidity explained by genes?
Common genetic variants associated with reading and language disorders can be used directly to investigate whether genetic risk factors can explain the observed comorbidity between dyslexia and SLI. One recent study directly addressed this question by testing for association the same panel of SLI or dyslexia associated SNPs in cohorts selected for either SLI or dyslexia (Newbury et al. 2011). CNTNAP2 and CMIP were found to be associated with reading measures but only in the SLI cohort, suggesting that a pleiotropic effect on multiple traits would be dependent on a background of language impairment. KIAA0319 was the only gene that showed association with reading measures in both the dyslexia and SLI cohorts; in addition it was found to be associated with language measures in the SLI cohort. These findings both support the role of KIAA0319 in contributing to reading skills and suggest that this gene may also contribute to language abilities. This last observation is in agreement with an independent study that reported KIAA0319 to be associated with language-related measures in a different cohort of language impaired individuals (Rice et al. 2009). Interestingly, rare variants at the DYX2 locus in between KIAA0319 and DCDC2 have been found to be associated with speech perception in children with dyslexia (Czamara et al. 2011).
Candidate genes for dyslexia and language impairment have also been reported to be associated with other disorders (Table 1). CNTNAP2 has been consistently implicated in autism with both common (Alarcon et al. 2008; Arking et al. 2008) and rare rearrangements (Rossi et al. 2008; Bakkaloglu et al. 2008; Poot et al. 2010; Jackman et al. 2009). One of these studies reported a specific association with the “age at the first spoken word” measure (Alarcon et al. 2008), which provides an indication for late language development. Language impairment is a distinctive symptom of autism. CNTNAP2 has also been associated with attention deficit/hyperactivity disorder (ADHD), Gilles de la Tourette syndrome (Verkerk et al. 2003), mental retardation (Ballarati et al. 2009; Zweier et al. 2009), schizophrenia and epilepsy (Strauss et al. 2006; Friedman et al. 2008), and selective mutism (Stein et al. 2011).
Taken together, these studies suggest that candidate genes underlying SLI and dyslexia may contribute to other conditions in particular those with a neurodevelopmental origin, such as autism and ADHD. Most of these genes have been shown to be specifically expressed during the development of the human foetal brain and, in particular, a role in neuronal migration has been proposed for many of the dyslexia candidates (Paracchini et al. 2007; Galaburda et al. 2006). Neuronal migration is an important process at the basis of cortex development which will determine the correct location of neurons across the different cortical layers. Therefore it would be expected that genes controlling such a general mechanism would have broad phenotypic effects rather leading to specific conditions. However, the evidence for pleiotropic effects, with the exception of CNTNAP2 which is robustly supported, are mainly based on weak associations reported in isolated studies which still require replication. One of the main limitations of these studies is the small sample size. The effect of a particular gene may be enriched in a particular sample on a particular phenotype and, without adequate power, effects on other phenotypes might be hindered. An additional complication is the high variability of psychometric measures used across multiple studies which makes replication more difficult.
Replication studies in population-based cohorts
Population-based cohorts characterised with both genetic data and quantitative cognitive phenotypes offer a very valid alternative to investigating the role of dyslexia and SLI candidate genes (1) (Table 1). These are usually relatively large longitudinal cohorts with the main advantage of presenting a wide range of cognitive phenotypes in most individuals. Independent studies have shown that the effect of some dyslexia and SLI candidates can be detected on the normal range of variation observed at population level using quantitative approaches. Therefore, providing that this observation is valid for most of the susceptibility genes, population-based cohorts can help to elucidate whether these genes affect specific traits or have wider effects.
Three main cohorts have been employed so far for such studies, one in the UK and two in Australia. The Avon Longitudinal Study of Parents and Children (ALSPAC) cohort consists of over 15,000 children from the southwest of England that had expected dates of delivery between 1 April 1991 and 31 December 1992 (Golding et al. 2001). From age 7, all children were invited annually for assessments on a wide range of physical, behavioral, and neuropsychological traits, including reading and language-related measures. DNA is available for approximately 11,000 ALSPAC children while cognitive phenotypes are available in 3,000–7,000 of those.
One Australian cohort is twin-based, but also includes non-twin siblings, and was recruited in the greater Brisbane area (McGregor et al. 1999). Genome-wide SNPs data and phenotypes are available in >1,100 individuals.
Lastly, the Raine Study, is a pregnancy cohort that was recruited in Western Australia around Perth (Newnham et al. 1993). From the original cohort of women, 2,868 of their children have been followed over the last two decades with detailed assessments performed every 2 to 3 years. Both genome-wide SNPs data and cognitive measures are available in >500 individuals.
KIAA0319 was the first gene to be analysed in population-based samples and was found to be associated with measures of single word reading or single word spelling in both the ALSPAC (Paracchini et al. 2008) and the Australian twin cohort (Luciano et al. 2007). Both studies reported association with the same SNPs and haplotype previously reported to be associated with dyslexia (Francks et al. 2004), but it has to be noted that the associations in the Australian sample was with an opposite allelic trend. Both studies concluded that the KIAA0319 candidate gene for dyslexia contribute to the general reading abilities, supporting the idea that dyslexia can be considered as the lower tail of the phenotypic distribution across the entire population. The Australian twins have also been analysed for other dyslexia candidate genes. DCDC2 was associated with measures of single word reading and single word spelling but with SNPs that differed from the original reports in clinical samples (Lind et al. 2010). Associations with reading and spelling measures were reported also for a SNP located in one DYX1C1 exon, implying a putative functional effect (Bates et al. 2010). However, this SNP (rs17819126) failed to show association in previous studies (where it was named 271 G > A) (Taipale et al. 2003; Scerri et al. 2004). Common variants spanning ROBO1 were also analysed in this cohort for both reading and language measures (Bates et al. 2011). Given the rarity of the dyslexia-associated haplotype, ROBO1 has not been selected for replication and this is the most comprehensive follow-up study for this gene so far. The strongest signals were for non-word reading and the related digit-span forward task assessing verbal short-term memory while only weaker signals were reported for the reading measures.
Single gene analysis for SLI candidates was conducted for CNTNAP2 in the Raine sample (Whitehouse et al. 2011). The same pattern of SNP associations previously reported for SLI (Vernes et al. 2008) was associated with the early stages of language development in children from the general population.
Two other studies analysed specific common variants across different candidate genes. The first study was conducted in the Australian Raine cohort and analysed SNPs in candidate genes for dyslexia for association with reading and spelling measures (Paracchini et al. 2010). SNPs previously associated with dyslexia were selected for DYX1C1, KIAA0319, DCDC2 and MRPL19/C2ORF3. An initial signal in DYX1C1 was supported by associations with other SNPs across this gene.
In the second study the same four dyslexia loci and the two SLI candidates CMIP and ATP2C2 were tested for association with different reading and language related measures in the ALSPAC cohort (Scerri et al. 2011a). KIAA0319, DCDC2 and CMIP showed association specifically with single word reading and single word spelling. These observations suggest effects on specific cognitive functions opposed to a contribution on multiple traits. Therefore, this study does not support the hypothesis that KIAA0319 contributes to language skills at population level, but does not exclude that such effects may be restricted to individuals with a background of language impairment (Newbury et al. 2011; Rice et al. 2009). Similarly, CMIP and ATP2C2 did not show association with language abilities in the general population ALSPAC sample, while an association with non-word repetition was detected in an ALSPAC subgroup selected for being language impaired (Newbury et al. 2009). Instead, the association of CMIP with single word reading is supported by similar findings in the SLIC cohort (Newbury et al. 2011).
In summary quantitative analyses on cognitive measures available for population-based cohorts have been useful for replicating genetic associations with candidate genes for dyslexia and SLI discovered in clinical samples. In addition, the availability of different phenotypes has helped to interpret the associations and formulate novel hypothesis about the cognitive functions controlled by these genes.
Beyond replicating associations
Two recent studies illustrate further how population-based cohorts can be used to dissect genetic associations with cognitive traits (Scerri et al. 2011a, b). Both studies were based on the ALSPAC sample that, given the large sample size, has the power to apply explorative approaches. The study that analysed both dyslexia and SLI candidate genes addressed several questions in addition to testing for association with different reading and language-related traits (Scerri et al. 2011a). One of these questions was whether the associations observed for KIAA0319, DCDC2 and CMIP were truly with general reading abilities in the normal range or might have been driven by the individuals with the most severe phenotypes. To test this hypothesis a subgroup of children meeting criteria for a dyslexia diagnosis was identified and then excluded from quantitative association analysis. While KIAA0319 and CMIP showed the same degree of association with reading abilities as in the initial sample, the associations with DCDC2 tended to disappear suggesting that the signal observed originally in the entire sample was actually due to a small proportion of individuals meeting a clinical diagnosis for dyslexia. This hypothesis was supported by a case-control analysis conducted on the same subgroups of children classified with dyslexia in the ALSPAC population. The same subgroup of individuals meeting the dyslexia criteria were compared with controls showing high scores on the reading test. While KIAA0319 and CMIP show weak or no association, DCDC2 yielded the strongest signal supporting a specific role in contributing to dyslexia. The same case-control setting allowed testing the effects of comorbodity on the associations. The subgroup of children with dyslexia was enlarged to include the children with dyslexia together with SLI and/or ADHD. Case control analysis with this larger sample led to stronger associations with the DCDC2 gene. These results suggest it is important not to exclude individuals with comorbidity for SLI or ADHD when designing association studies of dyslexia. Generally, comorbid individuals are excluded from ascertainment to obtain samples as homogeneous as possible. Conversely, these data would suggest that the effect of genes on reading skills is the same even in the background of clinically distinct disorders. If similar findings could be generalised they may help to better understand the biology of comorbidity, and in this case they would suggest that the same cognitive deficit leads to reading impairment regardless of clinical classifications.
Generating genotype data for large samples is no longer an issue from an experimental nor a financial point of view. The real bottleneck in genetic studies is now becoming the availability of samples properly phenotyped samples. GWASs have been successful in mapping many disease- associated genes but the success is largely dependent on adequate sample sizes (Donnelly 2008). Genome-wide SNP data are now available for the three cohorts described here and it is important to consider how to handle these data. While it would be tempting to use a genome-wide analytical approach for cognitive traits, several issues need to be considered. First of all, such cohorts were not designed to conduct genome-wide scans for discovery experiments and do not have adequate power to lead to significant findings. Power calculations show that a sample of 5,874 individuals is required to detect a genetic effect contributing to at least 1% of the total phenotypic variation, assuming a MAF of 10% (Purcell et al. 2003). ALSPAC in principle would have the adequate power for detecting such an effect but larger samples are required if multiple phenotypes are entered in the analysis. Therefore, until larger samples will be available, current population-based cohorts should be used to follow-up established associations and test-specific hypothesises rather than conducting discovery studies.
The identification of candidate genes associated with reading and language disorders is an important milestone in our understanding of the underlying biology (Smith et al. 2010). However, this is only an initial step and extensive molecular characterisation is required to properly interpret these associations. The use of population-based cohorts has proved to be a useful tool to further dissect genetic association. First of all, such cohorts represent valid tools to replicate original association. In addition, the discovery that some genetic variants associated with dyslexia do influence reading abilities in the normal range of variation provides significant support to the idea that dyslexia can be viewed as the lower tail of a phenotypic distribution across the population rather than a categorically defined condition. Analysis with multiple cognitive traits that are available in population cohorts can shift the association with a disorder (i.e., dyslexia) to more subtle phenotypes (i.e., single world reading) leading to a more precise mapping of the affected cognitive function. These approaches can also help to elucidate whether genes have a very restricted effect on specific traits or impact more generally on cognition according to the hypothesis of the generalist genes (Plomin and Kovas 2005).
Language-related disorders are complex and caused by the interplay of multiple factors of both genetic and environmental origin. The genes described here are likely to represent only a minor component of the causative elements. It is essential to consider these elements together with findings from other disciplines in order to start visualizing the bigger picture. Functional imaging has started to factor in genetic variants to evaluate directly their impact on cognition. For example, CNTNAP2 variants associated with autism (Alarcon et al. 2008; Arking et al. 2008) and language impairment (Vernes et al. 2008) have recently been associated with frontal lobe connectivity (Scott-Van Zeeland et al. 2010). Functional characterisation of genes involved in rare and severe form of language impairment, such as FOXP2, may help to elucidate biological pathways relevant to milder and more common forms of related disorders. Epigenetic regulation of gene expression could play a role in neurodevelopmental disorders. Changes in DNA methylation have been implicated in processes like learning and memory through rapid and dynamic changes (Tsankova et al. 2007). Disruption of the epigenetic machinery in the brain leads to Rett Syndrome, and increasing evidence is supporting the role of epigenetic changes in neurodevelopmental conditions including mental retardation and schizophrenia, making a strong case for an involvement in language-related disorders. Therefore a multidisciplinary approach and the integration of findings from different research area are clearly a high priority in the study of language disorders.
The author was supported by the Wellcome Trust [076566/Z/05/Z]; [075491/Z/04] and the Medical Research Council [G0800523/86473]. The author thanks Dianne Newbury and Tom Scerri for useful comments to the manuscript. The Merrill Advanced Studies Center provided the opportunity to present and discuss this work at the Annual Merrill conference.
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