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. 2016 Jun 17;6:27901. doi: 10.1038/srep27901

Association, characterisation and meta-analysis of SNPs linked to general reading ability in a German dyslexia case-control cohort

Bent Müller 1,a, Arndt Wilcke 1,2, Ivonne Czepezauer 1, Peter Ahnert 3,4, Johannes Boltze 1,5,6,*, Holger Kirsten 1,2,3,4,*; The LEGASCREEN consortium
PMCID: PMC4911550  PMID: 27312598

Abstract

Dyslexia is a severe disorder in the acquisition of reading and writing. Several studies investigated the role of genetics for reading, writing and spelling ability in the general population. However, many of the identified SNPs were not analysed in case-control cohorts. Here, we investigated SNPs previously linked to reading or spelling ability in the general population in a German case-control cohort. Furthermore, we characterised these SNPs for functional relevance with in silico methods and meta-analysed them with previous studies. A total of 16 SNPs within five genes were included. The total number of risk alleles was higher in cases than in controls. Three SNPs were nominally associated with dyslexia: rs7765678 within DCDC2, and rs2038137 and rs6935076 within KIAA0319. The relevance of rs2038137 and rs6935076 was further supported by the meta-analysis. Functional profiling included analysis of tissue-specific expression, annotations for regulatory elements and effects on gene expression levels (eQTLs). Thereby, we found molecular mechanistical implications for 13 of all 16 included SNPs. SNPs associated in our cohort showed stronger gene-specific eQTL effects than non-associated SNPs. In summary, our results validate SNPs previously linked to reading and spelling in the general population in dyslexics and provide insights into their putative molecular pathomechanisms.

Dyslexia is a neurodevelopmental disorder which affects the ability of accurate and/or fluent word recognition and spelling1. These problems are thought to result from deficits in phonological decoding and are independent of general intelligence. With a prevalence of ~5% in Germany, it is one of the most common learning impairments for young school children2. Twin studies estimated the heritability of dyslexia at 50–70%3. The underlying genetics is complex and appears to consist of a large number of factors with rather small effect sizes. In recent years, several candidate genes and single nucleotide polymorphisms (SNPs) associated with dyslexia were found4. From those genes, DYX1C1, KIAA0319 and DCDC2 are the most prominent candidate genes for dyslexia. KIAA0319 is strongly expressed in the human cortex5 and knockdown in rats revealed disturbed neuronal migration in the neocrotex6. Similar findings were reported for DCDC2 and DYX1C1 where embryonic knockdown resulted in neuronal overmigration past the desired layer6. All three genes were reported to be linked with reduced white matter volume in the left hemisphere of dyslexics7, a well-known dyslexia brain phenotype. In addition to findings from dyslexia-control studies, several candidate genes were also found to be associated with reading or spelling ability in the general population8,9,10,11,12. These SNPs were mainly located in the DYX2 locus (DCDC28, KIAA031913 and TDP12 (also known as TTRAP)) but also variants in ROBO110, DYX1C19 and NKAIN211 were identified (Table 1).

Table 1. Reference studies for all 16 analyzed SNPs with the reported risk allele.

SNP Chromosome Position[bp] General population studies
 Case-control studies
Study Reportedrisk allele Effect size Reportingassociation Not Reportingassociation
rs1091047-DCDC2 6 24295256 Lind et al.8 G expl. variance = 0.64% – –
rs1419228-DCDC2 6 24178306 Lind et al.8 G expl. variance = 0.87% – –
rs9467075-DCDC2# 6 24205236 Lind et al.8 A expl. variance = 0.35% – –
rs9467076-DCDC2# 6 24209255 Lind et al.8 C expl. variance = 0.34% – –
rs6922023-DCDC2 6 24348117 Lind et al.8 G expl. variance = 0.40% – –
rs7765678-DCDC2 6 24330544 Lind et al.8 T expl. variance = 0.45% – –
rs8037376-DYX1C1 15 55768321 Paracchini et al.9 C −0.147 SD (spelling) per risk allele – –
rs8043049-DYX1C1# 15 55777788 Paracchini et al.9 C −0.163 SD (spelling) per risk allele – –
rs7174102-DYX1C1# 15 55719687 Paracchini et al.9 T −0.135 SD (spelling) per risk allele – –
rs8040756-DYX1C1 15 55798599 Paracchini et al.9 G −0.196 SD (reading) per risk allele – –
rs2038137-KIAA0319 6 24645943 Luciano et al.12; Paracchini et al.13 G 0.06 SD (reading) per copy of haplotype (rs4504469-rs2038137-rs2143340)12; Beta = −0.067 (adjusted reading values) per copy of haplotype (rs4504469-rs2038137-rs2143340)13 Harold et al.19; Cope et al.20 Couto et al.21; Scerri et al.27
rs4504469-KIAA0319# 6 24588884 Luciano et al.12; Paracchini et al.13 C 0.06 SD (reading) per copy of haplotype (rs4504469-rs2038137-rs2143340)12; Beta = −0.003 (adjusted reading values) per copy of haplotype (rs4504469-rs6935076)13; Beta = −0.067 (adjusted reading values) per copy of haplotype (rs4504469-rs2038137-rs2143340)13 Cope et al.20; Newbury et al.24; Becker et al.23 Couto et al.21; Brkanac et al.22, Harold et al.19
rs6935076-KIAA0319 6 24644322 Luciano et al.12; Paracchini et al.13 T 0.06 SD (reading) per copy of haplotype (rs4504469-rs2038137-rs2143340)12; Beta = −0.003 (adjusted reading values) per copy of haplotype (rs4504469-rs6935076)13 Cope et al.20; Couto et al.21 Harold et al.19
rs1842129-NKAIN2 6 124838090 Luciano et al.11 G Not reported – –
rs1995402-ROBO1 3 79790407 Bates et al.10 A Not reported – –
rs2143340-TDP2 6 24659071 Luciano et al.12 ; Paracchini et al.13 C Expl. variance:1% (reading)12 ;Beta = −0.074 (adjusted reading values) per risk allele13; Beta = −0.067 (adjusted reading values) per copy of haplotype (rs4504469-rs2038137-rs2143340)13 Newbury et al.24 Cope et al.20; Couto et al.21, Scerri et al.27, Becker et al.23
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Note that rs2038137 and rs4504469 were reported to associate as 3-marker-haplotype rs4504469-rs2038137-rs2143340 and rs4504469 as 2-marker-haplotype rs4504469-rs6935076 in a case-control study. SNP positions are based on HG19. Explained variances for SNPs identified by Lind et al.8 were calculated for a principal factor score computed from six reading and spelling related measures. For the other SNPs, phenotypes are given in brackets. Shown is the absolute value of the effect size as presented in the paper. SNPs with independent effects identified by pruning (R2 = 0.5) are bold, and SNPs without independent effects are marked with # and were tagged by the respective independent SNP shown above.

Genetic variants associating with a quantitative trait are highly relevant genetic candidate variants in a corresponding binary trait as seen in other diseases14. However, we identified that many of these SNPs (12 of 16 in four genes shown in Table 1) were not yet analysed in a dyslexia case-control setting, in these four genes only different SNPs were previously investigated (Supplemental Tables 1 and 2). As correlation between SNPs within the same gene can vary largely and SNPs may have independent effects due to locus heterogeneity, association results among different SNPs of the same gene may differ considerably. Furthermore, for the four of 16 SNPs case-control analyses already exist, information of additional association studies will provide valuable validation information helping to resolve partly contracting findings (Table 1).

Therefore, the primary aim of our study was to systematically investigate all these 16 SNPs in a German dyslexia case-control cohort. Previously, other reported dyslexia candidate SNPs were analysed in this cohort (or subsets of this cohort). Thereby, association was found for three of eight candidates from chromosome 1815, for a variant in FOXP216, and for a large 2445 bp deletion and certain SNPs in DCDC217 but not for a functional variant in KIAA031918 language gene.

A secondary aim of our study addresses the lack of functional hypotheses about potential molecular risk-mechanisms of these 16 SNPs. This information is valuable for an improved understanding of a potential pathomechanisms of an observed association. Moreover, it is also important as additional evidence for the validity of an observed association. Therefore, in order to address this secondary aim, we characterise all these 16 variants using in silico methods on large datasets from recent available high-throughput studies.

Results

We analysed 16 SNPs in five genes from six studies8,9,10,11,12,13 affecting reading and spelling in the general population that were genotyped in our cohort (see Table 1 for details). All SNPs were in Hardy-Weinberg-equilibrium (HWE) in controls (p-value > 0.05), the call rate for each SNP exceeded 98%, and the call rate per individual was in average 99% across all 16 SNPs.

Associations on single marker level

On single marker level, nominal significant allelic association was observed for the minor allele of rs7765678-DCDC2 (p-value = 0.023, odds ratio (OR) = 0.65 (0.5–0.9)) and rs6935076-KIAA0319 (p-value = 0.023, OR = 1.25 (1.0–1.5)) (Table 2). Nominal significant genotypic association was also observed for the major homozygous state of rs2038137-KIAA0319 (p-value = 0.036, genetic relative risk (GRR) = 1.35 (1.0–1.8)) and rs7765678-DCDC2 (p-value = 0.035, GRR = 1.52 (1.0–2.2)). Originally reported effect size of these associations was not markedly higher than for those without association. No associations reached significance when considering Bonferroni’s correction method. For all nominally associating SNPs, the observed risk alleles were in accordance to reported associations from the general population studies. Two of the three nominally associating SNPs were previously reported in case-control settings: For rs2038137-KIAA0319 the risk alleles were in accordance with the reported associations19,20 and for rs6935076-KIAA0319 one study reported same allele20 and one study reported the opposite allele as risk allele21.

Table 2. Association statistics.

SNP Reported risk allele and accordance Major homozygous genotype Minor homozygous genotype Allelic association
p GRR p GRR p OR
rs1091047-DCDC2 Major (acc.) 0.062 1.34 (1.0–1.8) 0.720 0.88 (0.5–1.7) 0.085 0.78 (0.6–1.0)
rs1419228-DCDC2 Minor (acc.) 0.491 0.90 (0.7–1.2) 0.051 1.77 (1.0–3.1) 0.292 1.15 (0.9–1.5)
rs9467075-DCDC2# Major 0.892 1.02 (0.7–1.4) 0.481 1.29 (0.6–2.6) 0.990 1.00 (0.7–1.3)
rs9467076-DCDC2# Minor (acc.) 0.985 0.99 (0.7–1.4) 0.130 1.79 (0.8–3.8) 0.767 1.05 (0.8–1.4)
rs6922023-DCDC2 Major (acc.) 0.268 1.19 (0.9–1.6) 0.186 0.61 (0.3–1.3) 0.186 0.83 (0.6–1.1)
rs7765678-DCDC2 Major (acc.) 0.035* 1.52 (1.0–2.2) 0.178 0.37 (0.1–1.6) 0.023* 0.65 (0.5–0.9)
rs8037376-DYX1C1 Minor (acc.) 0.951 1.01 (0.8–1.3) 0.314 1.21 (0.8–1.7) 0.741 1.04 (0.8–1.3)
rs8043049-DYX1C1# Major 0.576 1.08 (0.8–1.4) 0.494 1.13 (0.8–1.6) 0.970 0.99 (0.8–1.2)
rs7174102-DYX1C1# Minor 0.594 1.08 (0.8–1.4) 0.439 1.15 (0.8–1.6) 0.989 1.00 (0.8–1.2)
rs8040756-DYX1C1 Major (acc.) 0.305 1.19 (0.9–1.7) 0.701 1.17 (0.5–2.6) 0.38 0.87 (0.6–1.2)
rs2038137-KIAA0319 Major (acc.) 0.036* 1.35 (1.0–1.8) 0.887 0.97 (0.7–1.4) 0.132 0.85 (0.7–1.0)
rs4504469-KIAA0319# Major (acc.) 0.987 0.99 (0.8–1.3) 0.562 0.90 (0.6–1.3) 0.802 0.97 (0.8–1.2)
rs6935076-KIAA0319 Minor (acc.) 0.088 0.78 (0.6–1.0) 0.036* 1.46 (1.0–2.1) 0.039* 1.25 (1.0–1.5)
rs1842129-NKAIN2 Major 0.100 0.78 (0.6–1.0) 0.845 1.04 (0.7–1.5) 0.261 1.13 (0.9–1.4)
rs1995402-ROBO1 Minor (acc.) 0.680 0.94 (0.7–1.3) 0.708 0.94 (0.7–1.3) 0.977 1.01 (0.8–1.2)
rs2143340-TDP2 Minor 0.442 1.14 (0.8–1.6) 0.665 0.84 (0.4–1.8) 0.406 0.88 (0.7–1.2)
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Shown are the respective p-values and genetic relative risks (GRR) for the homozygous major allele genotype and the homozygous minor allele genotype. Allelic associations relate to the effect of the minor allele reported in Supplemental Table 7. Shown is also the accordance (acc.) of the reported risk allele from literature with the observed risk allele of our study. SNPs with independent effects identified by priority pruning (R2 = 0.5) are bold, and SNPs without independent effects are marked with # and were tagged by the respective independent SNP shown above.

In literature, the haplotype rs4504469-rs2038137-rs2143340 was reported to associate with measures for reading12,13. We could not identify an association in our sample (Supplemental Table 3). No association was also observed of gene-wide haplotypes in DCDC2, KIAA0319, and DYX1C1 (Supplemental Table 3).

Meta-analysis

For four of the 16 investigated SNPs, case-control data of six different studies from a total of eight countries was available for meta-analysis19,20,21,22,23,24 (Table 1). These four SNPs included nominally associated variants rs2038137-KIAA0319 and rs693507-KIAA0319 but not rs7765678-DCDC2.

In line with our findings from our cohort alone, we found a significant meta-effect for SNPs rs2038137 and for rs6935076 (OR = 0.79 (0.64–0.96); p-value = 0.019 and OR = 1.24 (1.07–1.44); p-value = 0.004, respectively) across all studies (Fig. 1). For rs4504469-KIAA0319, only a trend-level significant meta-effect was found (p = 0.059). However, when stratifying the meta-analysis for languages, a significant meta-effect was found for this variant within English-speaking cohorts (OR = 0.84 (0.73–0.96); p-value = 0.009, Supplemental Fig. 1). No meta-effect was found for rs2143340-TDP2.

Figure 1. Forest plot representing the individual results of seven studies and the meta-effects for each SNP.

Figure 1

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Note, that Brkanac et al.22 and Couto et al.21 performed TDT-studies and ORs were computed from transmitted allele counts.

Polygenic analyses

Next, we aimed to investigate effects that may not have been detectable in our single marker analysis. Therefore, we jointly analysed association of all 16 general-reading SNPs by creating a score summing individual-wise all risk alleles. In order to avoid bias due to linkage disequilibrium among these SNPs, we thereby considered the subset of 11 unlinked SNPs (see Material and Methods). We found that for nine of these 11 SNPs effect direction was concordant with the literature. Thereby, the reported risk alleles were increased in our cases. This was more than expected by chance (p-value = 0.033). Accordingly, an increased number of risk variants was observable within cases compared to controls (Fig. 2a). An increased number of reported risk variants in cases was also observable when including only the SNPs not associating on single marker level (Fig. 2b). However, a formal quantitative association analysis of the corresponding risk score did not meet significance (p-value > 0.2). Findings were similar when jointly considering these eleven SNPs and 14 additional genetic dyslexia candidate variants analysed in previous association studies of our cohort (Supplemental Fig. 2).

Figure 2. Distribution of risk alleles among dyslexia-cases and controls.

Figure 2

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(a) Cases and controls with the total sum of all reported risk alleles from all included 11 independent SNPs. (b) Cases and controls with the total sum of all reported risk alleles but excluding all three SNPs associating at single marker level in our cohort.

Functional analyses

SNPs were evaluated for expression quantitative trait loci (eQTL)-effects by screening published eQTL-analyses. In total, twelve of all 16 SNPs showed a direct effect on expression levels of neighbouring genes (cis-eQTLs, Table 3). For 14 SNPs, proxy SNPs (R2 ≥ 0.5) were observed to be associated with the expression of a total of 16 genes in cis as well as in trans (see Table 3 and Supplemental Table 4). For DCDC2, KIAA0319, and DYX1C1, SNPs located within these genes also affected the expression of DCDC2, KIAA0319, and DYX1C1, respectively. This included nominally associated variants rs2038137 and rs693507 in KIAA0319 that actually affected gene expression of KIAA0319. However, nominally associated SNP rs7765678-DCDC2 was most strongly linked with the expression of MRS2. An overview of the eQTL effects of the SNPs from DYX2 (DCDC2, KIAA0319 and TDP2) is provided in Fig. 3. Consistently, this figure demonstrates that SNPs associated in our cohort showed stronger gene-specific eQTL effects than non-associated SNPs.

Table 3. EQTL and regulatory annotations for all analysed SNPs.

SNP eQTLs Regulome DB
eQTN (R2) Affected gene Predicted elements
rs1091047-DCDC2 rs17302582 (R2 = 0.79) ALDH5A1 (p = 0.0013)67 no data
rs1419228-DCDC2 SNP = eQTN MRS2 (p = 9.9E-06)67 Motifs-PWM (FOXC1)
rs9467075-DCDC2 SNP = eQTN MRS2 (p = 0.0027)67 Chromatin structure (DNase-seq, FAIRE) Protein binding-ChIPseq (RAD21, CTCF)
rs9467076-DCDC2 rs9460977 (R2 = 1.00) MRS2 (p = 0.0003)67 no data
rs6922023-DCDC2 SNP = eQTN DCDC2 (p = 3.8E-06)69 no data
rs7765678-DCDC2 SNP = eQTN MRS2 (p = 1.8E-07)67 Chromatin structure (DNase-seq, FAIRE) Protein binding-ChIPseq (HNF4A, EP300, FOXA1, TCF4, FOXA2)
rs8037376-DYX1C1 SNP = eQTN PIGB (p = 1.9E-44)67CCPG1 (p = 5.2E-20)65DYX1C1 (p = 1.1E-05)69DYX1C1-CCPG1 (p = 1.4E-05)69RP11-139H15.1 (p = 1.7E-05)69RSL24D1 (p = 0.0005)67 Chromatin structure (DNase-seq, FAIRE) Protein binding-ChIPseq (MEF2A)
rs8043049-DYX1C1 SNP = eQTN PIGB (p = 2.7E-47)67CCPG1 (p = 3.8E-19)67DYX1C1-CCPG1 (p = 2E-05)69RSL24D1 (p = 0.0025)67 Chromatin structure (DNase-seq, FAIRE)
rs7174102-DYX1C1 SNP = eQTN PIGB (p = 5E-23)67CCPG1 (p = 1.2E-17)65DYX1C1 (p = 3.1E-06)69 Motifs-PWM (Plagl1) Chromatin structure (DNase-seq, FAIRE)
rs8040756-DYX1C1 SNP = eQTN CCPG1 (p = 0.00018)55PIGB (p = 1.6E-05)67C15orf65 (p = 7.4E-08) no data
rs2038137-KIAA0319 SNP = eQTN KIAA0319 (p = 1.3E-14)69RP1-30M3.5 (p = 3.6E-10)69ACOT13 (p = 9.9E-09)69KRT8P43 (p = 3.2E-06)69TDP2 (p = 8.5E-06)69 Motifs-Footprinting(Staf) Motifs-PWM (Staf) Chromatin structure (DNase-seq, FAIRE) Protein binding-ChIPseq (RFX3, POLR2A, E2F6)
rs4504469-KIAA0319 SNP = eQTN KIAA0319 (p = 1.3E-13)65ACOT13 (p = 0.0003)67 no data
rs6935076-KIAA0319 SNP = eQTN KIAA0319 (p = 6.4E-14)69ACOT13 (p = 7.6E-07)69 Chromatin structure (DNase-seq, FAIRE)
rs1842129-NKAIN2 no data no data Motifs-PWM (Lhx5, Lhx3, Lhx9)
rs1995402-ROBO1 no data no data Motifs-PWM (Bbx)
rs2143340-TDP2 SNP = eQTN ACOT13 (p = 2.6E-25)67C6ORF62 (p = 2.5E-16)67RP1-30M3.5 (p = 2E-09)69 Motifs-PWM (CSBP, ISGF-3, IRF-1)
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eQTL annotations include the gene whose expression levels were linked to the SNP and the study from which the evidence originates. The analyses were done for eQTLs identified in all tissues and for eQTLs identified in brain related tissue. Reported are the best-linked eQTNs (R2 ≥ 0.5), the linkage to the original SNP (R2), the affected gene and the strength of association (if an eQTL-association was reported in different studies, the strongest p-value is reported). EQTL-effects were identified in cis unless otherwise stated. Functional annotations classified as “predicted” and “known” from ‘RegulomeDB’ are also shown.

Figure 3. Local association plot of analysed DYX2-SNPs with reported eQTL-effect.

Figure 3

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Depicted are the analysed SNPs of DYX2 whereby SNPs with nominal significant associations with dyslexia in our sample are coloured in green. Next to each SNP following the arrow we report the gene whose expression level was strongest associated with the SNP. P-values represent the association of the SNP with gene expression levels. Details can be found in Supplemental Table 4. The data points are coloured according to the linkage to the SNP with the strongest effect in the association and the meta-analysis (rs2038137).

To obtain complementary functional information for eQTLs, we analysed information regarding gene expression available in database ‘RegulomeDB’25 (Table 3). For SNP rs2038137-KIAA0319, we found the highest evidence of altered transcription factor binding. Ten of 16 SNPs revealed minor evidence to affect binding of transcription related factors, for five SNPs no data was available. All investigated genes, except TDP2, were expressed in brain according to the ‘Human Protein Atlas’26 (Supplemental Table 5).

Discussion

In this study, we investigated 16 candidate SNPs representing 11 independent genetic effects reported to alter reading or spelling ability in the general population. We analysed the relevance of these variants in a German-speaking dyslexia specific case-control cohort and meta-analysed our findings. Additionally, we characterised these SNPs for molecular evidence in order to get insights into potential molecular risk-mechanisms underlying these genetic variations.

Associations in case-control studies can provide evidence for additional, epidemiological features of risk variants described for the general population. Compared to studies of the general population, case-control studies are enriched for severe cases. Therefore, the relevance of such risk variants for rather extreme, but clinically highly relevant phenotypes is of interest. This investigation may prioritise variants that not only modify the normal range of reading/spelling but are also critical for the extreme ends.

To our knowledge, from all 16 investigated SNPs only four were yet analysed in case-control settings (rs4504469-KIAA0319, rs2038137-KIAA01319, rs6935076-KIAA0319, rs2143340-TDP2, Table 1). From those four SNPs, we could nominally replicate rs2038137-KIAA0319 and rs6935076-KIAA0319 in a genuine German dyslexia population and confirm this association in our meta-analysis (Fig. 1). Both SNPs were previously described for English speaking dyslexics and the latter also for a mixed central European population19,20,21,23,24,27. We did not find evidence that these associations are explained by rs9451046, a previously reported functional variant in KIAA0319 as linkage disequilibrium of our associated SNPs and this variant is rather weak (R2 = 0.22). In line with this, a direct association analysis of rs9451046 investigated previously in our cohort did not find a significant associations17,18. Contrarily, associating SNP rs2038137-KIAA0319 is in perfect LD (R2 = 1) with rs2179515 reported in the NeuroDys study23, therefore both associations refer to the same genetic phenomenon (Supplemental Table 1).

The third investigated variant in KIAA0319, rs4504469, was not associated in our cohort and reached only trend-level significance in the meta-analysis across all studies. Interestingly, when stratifying the meta-analysis for language, we found significant association of this variant among English speaking cohorts (Supplemental Fig. 1a). Therefore, we speculate that this SNP is more relevant in the development of dyslexia in English speaking humans. Indeed, regarding the language, the German language is a relatively transparent language with a clear grapheme-phoneme correspondence compared to English28. These differences may lead to the involvement of different brain regions, different neuronal networks and, consequently, different contributing genetic factors for speech processing29. For SNP rs4504469-KIAA0319, results from our meta-analysis helped to identify, whether non-replication in our cohort is more likely due to language differences or due to the described differences between case-control studies and studies in the general population. For the other SNPs not associated in our cohort, we do not have external data to perform a similar meta-analysis and therefore cannot resolve this ambiguity.

Our in silico characterisation on the molecular, functional level using ‘RegulomeDB’ further supported especially associated SNP rs2038137-KIAA0319. For this SNP strongest evidence for functionality was found in this database (Table 3): This SNP affected binding of the transcription factor RFX330, required for corpus callosum development31. In humans, corpus callosum deficits could lead to impaired motor coordination and balance problems, attributes known for dyslexics32. RFX3 was also associated with relative hand skill in individuals with dyslexia33, which is in line with the reported correlation of left-handedness and dyslexia34. Furthermore, RFX3 is involved in ciliogenesis which is suggested to play an important role in the aetiology of dyslexia4,35. When regarding eQTL findings for both associated SNPs rs2038137-KIAA0319 and rs6935076-KIAA0319, the strongest effect on gene expression was found on KIAA0319 itself. Effect sizes on KIAA0319 were consistent with association level observed in our case-control analysis (Fig. 3) as the two SNPs associated in our case-control and in our meta-analysis revealed the strongest effect on KIAA0319. This point to a mechanism of action of these SNPs including the regulation of expression levels of KIAA0319. Note that the strongest SNP affecting the expression levels of KIAA0319 is rs761100 (Supplemental Table 4). This SNP was previously associated with expressive language scores24 and, thus seems to be an additional reasonable candidate for future analyses.

To the best of our knowledge, SNP rs7765678-DCDC2 was identified for the first time in a case-control setting with our study. The effect size direction was also in accordance with the association in the general population. This variant is the second variant in DCDC2 with association in our sample as we previously found association with the intron 2 deletion of DCDC217,18. Note that we did not find linkage disequilibrium with other SNPs in DCDC2 for which association with dyslexia in a case-control cohort was previously reported, supporting novelty (Supplemental Table 1). Originally, SNP rs7765678-DCDC2 was associated with quantitative measures of regular word reading8. Interestingly, for SNP rs7765678-DCDC2 we did not found an association altered expression of DCDC2 itself, but with levels of MRS2 (Table 3 and Fig. 3). Consistently, the SNPs associated in our case-control cohort showed the strongest effect on expression levels of MRS2. MRS2 is associated with ‘hyperphosphatasia with mental retardation syndrome’ (HPMRS)36 and, besides other brain regions, highly expressed in the cerebellum26. Future studies have to show whether this relation might be part of a pathomechamism of this SNP. Not that in general, in early periods of neuronal development expression levels need to be tightly controlled, which is relevant for dyslexia37.

We are aware that due to the moderate sample size our results are limited and therefore require comparison with findings from additional studies as done for the SNPs with available external data in our meta-analysis. Nevertheless, advantages of our study also exist, e.g. the homogeneity of our cohort regarding language and local recruitment. It’s also worth mentioning, that we cannot completely exclude that population stratification and cryptic relationship might have a certain effect on the analyses. However, due to our recruitment strategy focussing on the local well defined population, this bias might be considerably low.

Still, given our limitations, we suggest that for an overall conclusion about general differences in the genetic architecture of reading in the general population and clinical dyslexia, additional studies have to be done.

Since dyslexia has a polygenic background and contributions of individual genetic markers might be low, it seems promising to not only analyse the SNPs on single marker level but jointly. Encouragingly, we found that the percentage of risk alleles was higher for more SNPs than expected by chance (p-value = 0.033). Supporting, we observed a general increase of the sums of all originally reported risk alleles in cases, Fig. 2a). Importantly, this increase was not only driven by the three SNPs that associated nominally at single level as this effect persisted when analysing only non-associated SNPs (Fig. 2b). When adding SNPs analysed in previous studies to the risk score, the characteristic risk allele pattern remains (Supplemental Fig. 2). Note that given the different measures used in the original studies, we could not apply a weighted score. Our observed shift between the risk score of cases and controls appears to be very similar to shifts found when summing up highly validated risk alleles from large genome-wide studies in other complex diseases38,39. It is in line with the finding that our study had 80% power to replicate the direction of a reported risk allele with very small effect sizes down to an OR of 1.093–1.152. Hence, our power to detect a reported trend is considerably more sensitive than the power to detect a reported association (i.e., here we have 80% power to detect an OR of 1.36–1.62). To the best of our knowledge our results provide first evidence in a relevant dataset that well-known strategies to sum risk alleles across multiple genes may be also promising in the field of dyslexia. Therefore, our findings may motivate future larger collaborative studies in dyslexia targeting variants with smaller genetic effect sizes.

In summary, our work corroborates the importance of rs7765678-DCDC2, rs2038137-KIAA0319, and rs6935076-KIAA0319 in the aetiology of dyslexia. The relevance of rs2038137-KIAA0319, and rs6935076-KIAA0319 was further supported by our meta-analysis. The increased number of originally reported risk alleles in our dyslexia cases compared to controls and the replicated effect directions above chance may support a contribution to dyslexia risk for at least some of the other SNPs without associations on single marker level. Additionally, we provided functional attributes for many of these SNPs which can be included in future studies aiming to elucidate links between these genetic variants and dyslexia.

Materials and Methods

Ethical approval

The study was approved by the Ethics Committee of the University of Leipzig. Involvement of children in schools was approved by the Saxon Ministry of Culture and Sports. Informed and written consent was obtained from probands’ parents. All experiments were conducted in accordance with the informed consent and the approved guidelines of the Ethics Committee of the University of Leipzig.

Sample

Our sample comprised 383 dyslexia cases and 357 controls. Dyslexia-cases were selected according to a two-stage process: In a first step, schools with special dyslexia classes were contacted. Children of these classes were tested with different psychometric tests at the end of the 2nd grade. According to the admission criteria of these classes only children with a discrepancy between IQ and reading performance of at least 1.25 SD or higher get access. In a second step, students underwent additional testing to avoid the inclusion of ADHD affected probands (test d2)40, to ensure an IQ ≥ 85 (CFT20)41 and to get a quantitative measure for reading/spelling performance (KNUSPEL-L)42. For further cohort and test descriptions see Wilcke et al.17 and Mueller et al.15.

SNP selection and genotyping

The literature was thoroughly screened for SNPs associated with reading or spelling in the general population. In total, six studies with 16 SNPs within five genes were identified (Table 1). The majority of these SNPs lack of replication in case-control studies. DNA was extracted from saliva or blood using standard protocols including Qiagen DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany), Macherey-Nagel NucleoMag 96 Blood Kit, the MACHEREY-NAGEL NucleoSpin® 8 and 96 Blood Kit (Macherey-Nagel, Dueren, Germany), and the Oragene DNA Genotek Saliva Kit (Kanata, Ontario, Canada).

In total, 11 SNPs (rs1419228, rs9467075, rs9467076, rs1091047, rs7765678, rs6922023, rs8037376, rs8043049, rs8040756, rs1842129, rs1995402) were genotyped by mass spectrometry via iPlex (Sequenom, Hamburg, Germany). Five SNPs (rs6935076, rs2143340, rs4504469, rs2038137, rs7174102) were genotyped by mass spectrometry using the single-base extension (SBE) method GenoSNIP43. Primer sequences are provided in Supplemental Table 6. In detail, PCR was performed in 10 μl reaction volume according to the following conditions: Initial denaturation at 95 °C for 15 min, 45 cycles with denaturation at 95 °C for 45 s, primer hybridization at 58 °C for 45 s, elongation at 72 °C for 45 s and a final extension step at 72 °C for 5 min. The resulting product was digested with exonuclease and shrimp alkaline phosphatase to remove leftover primers and to dephosphorylate dNTPs. A SBE reaction was performed with specific primers with biotin residue and a photocleavage site44. The SBE reaction was performed with the purified PCR product according to the following conditions: Initial denaturation at 95 °C for 4 min, 44 cycles with denaturation at 94 °C for 10 s, primer hybridization at 60 °C for 30 s, and elongation at 72 °C for 10 s. The final SBE products were genotyped by MALDI-TOF mass spectrometry.

Associations on single marker and haplotype level

The study was designed to detect an OR of 1.36–1.62 with a minor allele frequency (MAF) of 0.42–0.10 with β = 0.8. This power is in accordance with previously reported effect sizes20,23,24. Allelic associations were analysed using Chi2 statistics in order to detect group differences between dyslexia cases and controls. Genotypic associations were assessed by GRR estimations according to Lathrop45. Moreover, haplotypic effects were investigated using Haploview 4.146. Associations of haplotypes were analysed using the implemented Chi2 approach.

To identify independent genetic effects, we pruned the selected SNPs (R2 = 0.5) while priority was given to SNPs with higher effect sizes (i.e. stronger allelic odds ratios) this is also known as clumping.

Meta-analysis

We conducted a meta-analysis by integrating SNP data from dyslexia case-control and transmission/disequilibrium (TDT) studies. Studies were identified by systematically screening PubMed and Google Scholar with the keywords ‘dyslexia’ and the SNP-names.

Allele counts from cases and controls and numbers of transmitted alleles were used to estimate the individual effect sizes. ORs for TDT-studies were calculated according to Kazeem and Farrall47. Since between-cohort heterogeneity was expected, meta effect sizes were computed using a random effects model and effects were visualised by forest plots implemented in the metafor package48. Analyses were performed for all identified cohorts and in a second step stratified according to language. Newbury et al.24 analysed two different cohorts of cases with the same cohort of controls. Both analyses were included indicated with a) and b) as individual studies in the meta-analysis.

Polygenic analyses

We computed an additive risk score composed of the sum among all individually observed risk variants, and its distribution between cases and controls was analysed. To only consider independent genetic effects identified by pruning. This avoids unintendedly increasing the weight of SNPs with other SNPs in LD. Note that this procedure is similar with the analysis of an unweighted genetic risk score using the PRSice approach49. This approach was performed for three different sets of SNPs: All SNPs analysed in this study (Fig. 2a), all SNPs except the ones nominally associated (Fig. 2b) and all SNPs of this study with addition of the SNPs analysed in previous studies on the same cohort (rs793862-DCDC217*, rs807701-DCDC217*, rs807724-DCDC217, the intron 2 deletion of DCDC217*, rs12533005-FOXP216*, rs10502812-AK13101115*, rs11873029-DYM15*, rs11874896-EPB41L315*, rs11661879-KIAA042715*, rs1299348-MC5R15*, rs555879-MYO5B15*, rs12606138-NEDD4L15, rs8094327-NEDD4L15*, rs9461045-KIAA031918 (SNPs marked with * show independent effects (R2 ≥ 0.5) and were included for analysis).

The probability of finding the observed effect directions under the NULL for the pruned SNPs was calculated via the binomial distribution. The power to detect a reported trend was calculated as previously described50.

In a supplementary analysis, we additionally considered for the polygenic score all dyslexia candidate SNPs previously reported in our cohort into the score, regardless whether we had found association or not. These SNPs were rs793862-DCDC217*, rs807701-DCDC217*, rs807724-DCDC217, the intron 2 deletion of DCDC217*, rs12533005-FOXP216*, rs10502812-AK13101115*, rs11873029-DYM15*, rs11874896-EPB41L315*, rs11661879-KIAA042715*, rs1299348-MC5R15*, rs555879-MYO5B15*, rs12606138-NEDD4L15, rs8094327-NEDD4L15*, rs9461045-KIAA031918. SNPs marked with * show independent effects (R2 ≥ 0.5) and were included for analysis. Data for all these SNPS was available for 269 cases and 357 controls.

All statistical analyses were done in R 3.2.151 or perl using in-house scripts available upon request.

Functional in silico characterisation

We analysed the SNPs for effects on local and distant gene expression levels (cis and trans-eQTLs) using data from published large-scale experiments52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70. These studies cover a broad range of tissues, like blood-derived cells, skin, liver and different brain regions. To identify proxy SNPs in our eQTL analysis, we used a minimum level of R2 ≥ 0.5, linkage data was used from 1000 Genomes reference phase 1, release V3 and from HapMap Data (Release #28, lifted over to GRCh37/hg19). We report results of the proxy SNPs that showed strongest correlation and we only considered eQTLs meeting study-wide significance level. Most analysed SNPs were located in the DYX2 locus on chromosome 6. To provide a spatial overview of these SNPs and their eQTL effects we generated a local association plot using LocusZoom71.

SNPs were also annotated for known and predicted local regulatory elements via ‘RegulomeDB’25, and tissue-specificity of gene products was analysed by ‘Human Protein Atlas’26.

Additional Information

How to cite this article: Müller, B. et al. Association, characterisation and meta-analysis of SNPs linked to general reading ability in a German dyslexia case-control cohort. Sci. Rep. 6, 27901; doi: 10.1038/srep27901 (2016).

Supplementary Material

Supplementary Information
srep27901-s1.pdf (615KB, pdf)
Supplementary Dataset
srep27901-s2.xls (70KB, xls)

Acknowledgments

We would like to thank all members of the Legascreen consortium for their support during this study. Legascreen is funded by the Fraunhofer Society and the Max-Planck-Society as a project within the framework of the “Pakt für Forschung und Innovation”. Dr. Arndt Wilcke and Dr. Holger Kirsten received funding from the Federal Ministry of Education and Research (grant PtJ-Bio 0315883). The authors thank Elfi Quente for expert technical assistance.

Footnotes

Author Contributions H.K. and J.B. contributed equally to the work. B.M., H.K., P.A., A.W. and J.B. designed and interpreted the experiments. B.M. I.C. and H.K. conducted the experiments. B.M. and H.K. analysed the data and performed in silico experiments. B.M. wrote the paper, with contributions of H.K., A.W. and J.B. A.W. carried out the psychometric testing, with expert advice and methodical consultations of the LEGASCREEN consortium. All authors read and approved the final manuscript.

Contributor Information

The LEGASCREEN consortium:

Angela D. Friederici, Frank Emmrich, Jens Brauer, Arndt Wilcke, Nicole Neef, Johannes Boltze, Michael Skeide, Holger Kirsten, Gesa Schaadt, Bent Müller, Indra Kraft, Ivonne Czepezauer, and Liane Dörr

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Information
srep27901-s1.pdf (615KB, pdf)
Supplementary Dataset
srep27901-s2.xls (70KB, xls)

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