Methods

Subjects

Subjects, 6–16 years of age, with evidence of reading problems were recruited for this study from local schools. All siblings in the same age range as the probands, regardless of reading ability, were recruited into the study.

Information about the symptoms of neurological, medical, and psychiatric disorders was obtained from a structured interview for parents, the Children's Interview for Psychiatric Syndromes (ChIPS), that screens for 20 psychiatric disorders as well as psychosocial stressors,³⁹ and from a semi-structured interview for teachers.⁴⁰ Subjects were excluded if they scored below 80 on both the Performance and Verbal Scales of the Wechsler Intelligence Scale for Children III,⁴¹ or showed evidence of neurological or chronic medical illness, bipolar affective disorder, psychotic symptoms, Tourette Syndrome, or chronic multiple tics.

Reading, spelling, phonological awareness, and rapid naming skills were assessed with the Wide Range Achievement Test-III (WRAT-III),⁴² the Word Attack, and Word Identification Subtests of the Woodcock Reading Mastery Test-Revised (WRMT-R),⁴³ and the Comprehensive Test of Phonological Processing (CTOPP).⁴⁴ The Spelling subtest of the WRAT-3 measures the ability to copy marks resembling letters, spell one's name, and write (ie spell) single words from dictation. The Reading subtest of the WRAT-3 is a standardized measure of single-word reading. The Word Attack subtest of the WRMT-R is a standardized measure comprising a total of 45 printed nonsense words (34 monosyllabic, 11 polysyllabic) to be read aloud, which assess the ability to decode 67 letter-sound categories (vowel patterns, consonant sounds) and syllabification. The Word Identification subtest is a standardized measure comprising 106 real monosyllabic and polysyllabic words of increasing difficulty.

Language ability was assessed with the Clinical Evaluation of Language Fundamentals-3rd Edition.⁴⁵ The Clinical Evaluation of Language Fundamentals, 3rd Edition (CELF-3), is a widely used standardized measure of receptive and expressive oral language skills, comprising several subtests that assess semantics and syntax. Three receptive subtests (concepts and directions; word classes; and sentence structure (for 7–8-year olds) and semantic relationships (for 9 years and older)) and three Expressive subtests (formulated sentences; recalling sentences, and word structure (7–8-year olds) and sentence assembly (9 and older)) were used.

The CTOPP is a comprehensive assessment of phonological awareness, phonological memory, and rapid naming, and has extensive norms. The Rapid Digit Naming subtest measures the speed at which an individual can name the 72 digits displayed on two pages. The Phonological Awareness composite score for 5–6-year olds is composed of the Elision (segmenting spoken words into smaller parts), Blending Words (blending individual presented segments into whole words), and Sound Matching (matching words on the basis of initial and final sounds) subtests. For 7 years and older, the Phonological Awareness composite score is composed of the Elision and Blending Words subtests. Verbal short-term memory was assessed with the CTOPP Nonword Repetition subtest. This test is an 18-item subtest that measures an individual's ability to repeat a series of nonwords that range in length from 3 to 15 sounds. All these measures have acceptable reliability and validity and good psychometric properties.

For the categorical analysis, we designated individuals as having dyslexia if they scored 1.5 standard deviations below the mean (standard score 78 or lower) on two of the three core reading tests (Word Attack, Word Identification, and Wide Range Achievement Test—III Reading subtests) or 1 standard deviation (standard score of 85) on the average of the three.

In total, 148 nuclear families were genotyped for this study; these families include 54 siblings, 120 families with both parents participating, and 28 families where one parent participated. Of the 202 probands and siblings that were recruited for this study, 83 of the probands and 18 of the siblings were classified as having dyslexia, as defined by our diagnostic criteria, and, therefore, were included in the categorical analysis. All the probands and siblings were used for the quantitative analysis. The characteristics of all of the probands and siblings in this sample are shown in Table 1. The correlations between the reading and reading-related phenotypes are shown in Table 2. The correlation coefficients were significant for all measures at the P<0.001 level, with the exception of CTOPP rapid digit naming and CTOPP non-word repetition that was significant at the P<0.015 level. The mean performance IQ of the subjects was 93.48, SD 13.78, and verbal IQ was 94.62, SD 13.15.

Table 1 - Characteristics of sample.

Full table

Table 2 - Pearson correlations between phenotypic measures.

Full table

This protocol was approved by The Hospital for Sick Children (Toronto, Ontario) Research Ethics Board, and written informed consent was obtained for all participants.

Isolation of DNA and marker genotyping

DNA was extracted directly from blood lymphocytes using a high-salt extraction method.⁴⁶ For the analysis, we used the two markers previously reported to be associated with dyslexia, -3G/A (rs3743205) and 1249G/T.³⁷ Of the other six markers reported in the Taipale et al paper, only one was reasonably polymorphic (G191E) and that marker showed no evidence for association. We genotyped two other markers (-2G/A and +4C/T), but these were not polymorphic in our sample. We therefore searched for all confirmed polymorphisms in public databases and for predesigned and tested assays from Applied Biosystems (ABI, Foster City, CA, USA, Assay-on-Demand by Applied Biosystems^®). We identified two markers in the ABI database and two in the NCBI SNP database (http://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?locusId=161582). The markers rs2007494, rs3743204, rs11629841, 1249G/T, and rs692691 were genotyped with the ABI 7900-HT Sequence Detection System^® (Applied Biosystems) using the TaqMan 5' nuclease assay for allelic discrimination. Primer and probe sequences and annealing temperatures are listed in Table 3a. Reference sequences for Assay-on-Demand by Applied Biosystems^® are also shown in Table 3b. The PCR reactions (10 l volume) contained 30 ng of genomic DNA, 10 mol of TaqMan^® Universal PCR Master Mix (Applied Biosystems), and 0.25 l of allelic discrimination mix (Applied Biosystems) containing 36 M of each primer and 8 M of each probe. The thermal cycling conditions were 50°C for 2 min, 95°C for 10 min, followed by 40 cycles of 94°C for 15 s, and an annealing temperature for 1 min. Each 96-well plate contained two negative controls. Plates were then read on the ABI 7900HT Sequence Detection System (SDS) using the allelic discrimination end-point analysis mode of SDS software package version 2.0 (Applied Biosystems).

Table 3a - Markers genotyped by TaqMan^® MGB probes.

Full table

Table 3b - Markers genotyped using Assay-on-Demand^® from Applied Biosystems.

Full table

The -3G/A, -2G/A, and +4C/T DNA variants were genotyped using restriction enzyme analysis. Taipale et al classified these nucleotide changes according to their position 5' to the proposed translation initiation site in exon 2. We continued with this nomenclature for these three DNA variants to avoid confusion for this paper; however, it should be noted that these three DNA changes are all located within exon 2. The PCR reaction was performed in a total volume of 20 l containing 100 ng of each primer (EKN1-3F: AGG GCT GGC GCA TGG T and EKN1-3R: GAG ACC GGC AGG CAA GAC), 0.2 mM dNTP, 1.5 mM magnesium chloride, and 0.5 U Taq polymerase. The PCR reaction consisted of an initial denaturing step at 94°C for 3 min, followed by 35 cycles at 94°C for 30 s, 60°C for 30 s, and 72°C for 30 s, followed by a 10 min extension step at 72°C. PCR products (5 l) were digested with 5 U of restriction enzyme (New England Biolabs, Beverly, MA, USA) at 37°C for 1.5–2 h. Alleles were identified on 2% agarose gels. Individuals with the GG haplotype for the -3 and -2 DNA changes were identified by the creation of an MspI site. Individuals with the AG haplotype could be distinguished from the AA and GA haplotype by digestion with the restriction enzyme BsmI, which cuts both the GG and the GA haplotypes. The presence of the T at the +4C/T polymorphism would also result in the loss of a BsmI site; therefore, the PCR product was digested with BsmAI to identify individuals with +4T. Neither the -2A nor the +4T alleles were observed in any of the individuals in this sample.

Statistical analysis

For the categorical analysis, the TDT statistic was calculated using the extended TDT (ETDT) program.⁴⁷ The transmission of the haplotypes was analyzed with the TRANSMIT program using the robust estimator option.⁴⁸ P values are reported only for the haplotypes with frequency greater than 0.10. For the analysis of the quantitative measures, the FBAT program was used.^{49, 50} The statistic implemented in the FBAT program eliminates the need for assumptions about the phenotype distribution⁵⁰ and is not sensitive to the ascertainment strategy.⁴⁹ The analysis was run with the empirical variance option because of the previous evidence for linkage to this region, and data from siblings were used in the analysis. The additive model was used for FBAT as several studies have shown that the additive model performs well even when the true genetic model is not additive. Linkage disequilibrium (LD) between the markers was estimated using the ldmax program (http://www.sph.umich.edu/csg/abecasis/GOLD/download/index.html). Descriptive statistics and Pearson correlations were analyzed with SPSS (http://www.spss.com/). Two-sided P-values were reported for all results. The phenotypic measures used for the quantitative analyses are highly correlated and the markers are in LD to each other; therefore, the correction for multiple tests using Bonferroni's correction is too conservative as these are not independent tests. Moreover, there is not a generally agreed approach about how to correct for this situation. As our objective was to replicate the findings of Taipale et al, and our primary analysis was a categorical approach using single markers, we decided to correct for the number of markers tested, adjusting the significance levels by the false discovery rate (FDR) method, the rate that the significance levels are truly null.⁵¹ Then, as a consequence of our analysis of categorical results, we analyzed the markers using a quantitative approach as a secondary hypothesis: in this case, our analyses represent a hypothesis-generating approach, and were not corrected for multiple tests.

Results

We genotyped six markers spanning the coding region of EKN1 for this study; rs2007494 in intron 1, -3G/A in exon 2, rs3743204 in intron 2, rs11629841 and rs692691 in intron 4, and 1249G/T in exon 10 (Figure 1). Of these markers, we found significant evidence for biased transmission of the alleles of one marker (rs11629841) to dyslexia as a categorical trait (Table 4) when analyzed individually (²=5.586, 1 d.f., P=0.018), which remained significant (q=0.036) when corrected for multiple testing with the FDR method. We did not observe significant evidence for biased transmission of the alleles of the two markers (-3G/A, 1249G/T) previously reported to be associated with dyslexia; however, the number of informative transmissions for these markers was low (23 and 25, respectively).

Figure 1.

Position of polymorphic markers.

Full figure and legend (7K)

Table 4 - Categorical analysis.

Full table

To analyze the transmission of the haplotypes of the markers, we used a sliding window of two consecutive markers across the gene (Table 5). Evidence for biased transmission of haplotypes was seen in the dyslexia sample for the markers rs11629841 with rs3743204 (haplotype C/G) and rs11629841 with rs692691 (T/T biased nontransmission and G/T biased transmission). We also analyzed the haplotypes composed of the -3G/A polymorphism and the G/T polymorphism at 1249 because of the previous association reported with a haplotype of these markers (Table 6). We observed significant evidence for biased transmission of the most common haplotype of -3G/1249G (²=4.947, 1 d.f., P=0.026); however, the previous report of biased transmission was for the rare haplotype of -3A/1249T.

Table 5 - Two-by-two haplotype analysis.

Full table

Table 6 - Haplotype analysis for the –3G/A and 1249G/T.

Full table

We analyzed the markers using quantitative analysis for key reading measures and reading-related processes in the entire sample (Table 7). We found significant evidence for a relationship of the biased transmission of the alleles of the -3G/A polymorphism to the reading skills of phonological awareness (CTOPP phonological awareness composite score), phonological decoding (WRMT-R word attack), spelling (WRAT-III spelling) and word identification (WRMT-R word identification and WRAT-III reading). We also found significant evidence for a relationship of this marker to expressive and receptive language skills (CELF-3 expressive and receptive language composite scores), verbal short-term memory (CTOPP Nonword repetition), and rapid automatized naming (CTOPP rapid digit naming).

Table 7 - Quantitative analysis for key reading measures.

Full table

The degree of LD between the polymorphisms is shown in Table 8. We observed significant LD across the gene as measured with D' with the exception of the rs692691 marker located in intron 4, where there was no evidence of LD between this marker and the rs2007494 marker in intron 1 or rs3743204 located in intron 2.

Table 8 - Linkage disequilibrium.

Full table

Discussion

In this paper, we investigated the EKN1 gene recently reported as the candidate gene for susceptibility to dyslexia located in the 15q21 region. The evidence that this gene is related to dyslexia was based on the finding of a translocation breakpoint in a family segregating dyslexia, and by positive results from association studies.³⁷ Specifically, the results were significant with two polymorphisms, both of which could potentially affect the function of the gene. The DNA change located at the -3 position could influence translation efficiency and the other change in the last exon results in a premature stop codon. Biased transmission of a haplotype (-3A/1249T) of these two markers was also reported in a sample of nine informative trios.

In our sample of families, we did not find significant evidence for biased transmission of the specific alleles of these two polymorphisms using a categorical TDT approach; however, the number of informative transmissions was low. Quantitative analysis of reading and reading-related phenotypes was significant with biased transmission of the -3G allele. Our finding of biased transmission of the -3G allele contrasts with the previous finding of an association with the alternative allele (-3A) of this polymorphism in the study by Taipale et al.

Significant evidence was found for biased transmission of the alleles of the rs11629841 polymorphism and haplotypes of this marker with two additional markers (rs3743204 and rs692691). We also observed significant evidence for biased transmission of the haplotypes of the two markers previously reported to be associated with dyslexia (-3G/A and 1249G/T); however, the evidence was for biased transmission of the most common haplotype, -3G/1249G, not the more rare -3A/1249T haplotype as previously reported. The -3A/1249T haplotype was found in only 6.5% of the parental chromosomes in our study. Consequently, the number of informative transmissions was very low and there may not be sufficient power to detect an association.

The use of a quantitative approach allowed the use of the entire sample that essentially doubled the sample size (n=202 subjects) compared to the categorical analysis (n=101), thereby potentially increasing the power; however, the inclusion of individuals with less extreme phenotypic scores may also reduce the power. Power for tests dependent on LD such as the TDT are affected by multiple parameters including the contribution of the particular locus to the phenotype, distance between the disease and the marker locus, the age of the disease mutation, marker and disease locus mutation rates, population growth rates, and effective population sizes. On the other hand, empirical experience shows that LD between disease alleles and proximal markers is typically quite strong.⁵² Our use of multiple markers for this gene and haplotype analysis also increases the power. The association study of the -3G/A marker by Taipale et al first identified an association in a sample size of 54 cases and 113 controls and then in a replication sample of 52 cases and 81 controls. While this would argue that our sample of 101 affected children would have ample power to detect the same association, the more ethnically heterogeneous Toronto population compared to the Finnish population may also be a factor influencing the power. Further, the different ethnic composition of this sample may result in association to different alleles.

While initial linkage findings using reading processes as phenotypes suggested that different loci contributed to reading components,²² these distinctions have become less clear, with subsequent studies showing support for different phenotypes to the same chromosomal regions.^{28, 31} A number of factors can influence the linkage findings using quantitative measures including the ascertainment method, the measures of the phenotype chosen, and frequency and variance of the phenotype in the sample.¹ Our results for the quantitative analysis of this locus were significant for multiple components of the reading process: phonological awareness, phonological decoding, and word identification. The results were also significant for reading-related processes, verbal short-term memory, rapid automatized naming, and receptive and expressive language ability, thus suggesting that this locus contributes to multiple reading and reading-related processes. This is perhaps not particularly surprising, given that the inter-trait correlations are high between reading- and language-related measures and that twin studies support evidence for common as well as independent genetic factors for these phenotypes ^{2, 3, 4, 12}

As either of the -3G/A or the 1249G/T could potentially change the function of the EKN1 gene, Taipale et al suggested that either, or both, of these two DNA changes in combination could be contributing to dyslexia. Since our findings were not significant for these alleles and haplotype analyses supported the biased transmission of a different haplotype of these two markers, than previously reported, it is unlikely that these specific DNA changes are contributing to the phenotype. Therefore, continued screening of this gene is now required. The predicted coding regions of this gene have been previously screened for DNA changes in 20 Finnish probands.³⁷ Of the eight DNA changes identified in that study, only the -3G/A and 1249G/T polymorphisms were significantly associated with dyslexia. Therefore, the relevant DNA changes may be located in regulatory sequences, and the regions that must be screened may be quite large given that these regions have not yet been delineated. While the translocation breakpoint is located between exons 8 and 9 in the EKN1 gene, a positional effect influencing neighboring genes cannot be ruled out at this time and further fine-mapping studies of the region are called for to determine if nearby genes are potentially involved.

While our studies support this locus as contributing to dyslexia in our sample based on the significant evidence for biased transmission of the alleles of the rs11629841 polymorphism and haplotypes of this polymorphism, our results are not a direct replication of the previous Taipale et al study, because we did not observe association of the same alleles. Clearly, these are preliminary findings and further replication studies are necessary before definitive conclusions can be made.

References

1. Pennington BF. Using genetics to dissect cognition (editorial; comment). Am J Hum Genet 1997; 60: 13–16. | PubMed | ISI | ChemPort |
2. Olson R, Forsberg H, Wise B, Rack J. Measurement of word recognition, orthographic, and phonological skills. In: Lyon GR (ed). Frames of Reference for the Assessment of Learning Disabilities. Paul H Brookes: Baltimore, 1994 pp 243–277.
3. Wadsworth SJ, DeFries JC, Fulker DW, Olson RK, Pennington BF. Reading performance and verbal short-term memory: a twin study of reciprocal causation. Intelligence 1995; 20: 145–167. | Article | ISI |
4. Davis CJ, Gayan J, Knopik VS, Smith SD, Cardon LR, Pennington BF et al. Etiology of reading difficulties and rapid naming: the Colorado Twin Study of Reading Disability. Behav Genet 2001; 31: 625–635. | Article | PubMed | ISI | ChemPort |
5. Lefly DL, Pennington BF. Reliability and validity of the adult reading history questionnaire. J Reading Disabil 2000; 33: 286–296.
6. Bruck M. Persistence of dyslexics' phonological awareness deficits. Dev Psychol 1992; 28: 874–886. | Article | ISI |
7. Bishop DV, Adams C. A prospective study of the relationship between specific language impairment, phonological disorders and reading retardation. J Child Psychol Psychiatry 1990; 31: 1027–1050. | PubMed |
8. Gathercole SE, Baddeley AD. The processes underlying segmental analysis. Eur Bull Cogn Psychol 1987; 7: 462–464.
9. Scarborough HS. Very early language deficits in dyslexic children. Child Dev 1990; 61: 1728–1743. | PubMed | ISI | ChemPort |
10. Scarborough HS. Early identification of children at risk for reading disabilities. In: Shaprio BK, Accardo PJ, Capute AJ (eds). Specific Reading Disability: A View of the Spectrum. York Press: Timonium, MD, 1998 pp 75–119.
11. Snowling M, Bishop DV, Stothard SE. Is preschool language impairment a risk factor for dyslexia in adolescence? J Child Psychol Psychiatry 2000; 41: 587–600. | Article | PubMed |
12. Hohnen B, Stevenson J. The structure of genetic influences on general cognitive, language, phonological, and reading abilities. Dev Psychol 1999; 35: 590–603. | Article | PubMed | ISI | ChemPort |
13. Wolf M, Bowers PG. The double-deficit hypothesis for the developmental dyslexias. J Educ Psychol 1999; 91: 415–438. | Article | ISI |
14. Wolf M, Goldberg A, Gidney C, Lovett MW, Cirino P, Morris RD. The second deficit: an investigation of the independence of phonological and naming-speed deficits in developmental dyslexia. Reading Writing Interdiscipl J 2002; 15: 43–72. | Article |
15. Lovett MW, Steinbach KA, Frijters JC. Remediating the core deficits of developmental reading disability: a double deficit perspective. J Learn Disabil 2000; 33: 334–358. | PubMed |
16. Jorm AF. The cognitive and neurological basis of developmental dyslexia: a theoretical framework and review. Cognition 1979; 7: 19–33. | Article | PubMed |
17. Rugel RP. WISC subtest scores of disabled readers: a review with respect to Bannatyne's recategorization. J Learn Disabil 1974; 7: 57–64.
18. Stanovich KE. Individual differences in the cognitive processes of reading: II. Text-level processes. J Learn Disabil 1982; 15: 549–554. | PubMed |
19. Rabin M, Wen XL, Hepburn M, Lubs HA, Feldman E, Duara R. Suggestive linkage of developmental dyslexia to chromosome 1p34–p36. Lancet 1993; 342: 178. | Article | PubMed | ISI | ChemPort |
20. Grigorenko EL, Wood FB, Meyer MS, Pauls JE, Hart LA, Pauls DL. Linkage studies suggest a possible locus for developmental dyslexia on chromosome 1p. Am J Med Genet 2001; 105: 120–129. | Article | PubMed | ISI | ChemPort |
21. Smith SD, Kimberling WJ, Pennington BF, Lubs HA. Specific reading disability: identification of an inherited form through linkage analysis. Science 1983; 219: 1345–1347. | Article | PubMed | ISI | ChemPort |
22. Grigorenko EL, Wood FB, Meyer MS, Hart LA, Speed WC, Shuster A et al. Susceptibility loci for distinct components of developmental dyslexia on chromosomes 6 and 15. Am J Hum Genet 1997; 60: 27–39. | PubMed | ISI | ChemPort |
23. Morris DW, Robinson L, Turic D, Duke M, Webb V, Milham C et al. Family-based association mapping provides evidence for a gene for reading disability on chromosome 15q. Hum Mol Genet 2000; 9: 843–848. | Article | PubMed | ISI | ChemPort |
24. Smith SD, Kimberling WJ. Reading disabilities. In: Pennington B (ed). Genetic and Neurological Influences. Kluwer Academic: Boston, 1991.
25. Cardon LR, Smith SD, Fulker DW, Kimberling WJ, Pennington BF, DeFries JC. Quantitative trait locus for reading disability on chromosome 6. Science 1994; 266: 276–279. | Article | PubMed | ISI | ChemPort |
26. Turic D, Robinson L, Duke M, Morris DW, Webb V, Hamshere M et al. Linkage disequilibrium mapping provides further evidence of a gene for reading disability on chromosome 6p21.3–22. Mol Psychiatry 2003; 8: 176–185. | Article | PubMed | ISI | ChemPort |
27. Fisher SE, Marlow AJ, Lamb J, Maestrini E, Williams DF, Richardson AJ et al. A quantitative-trait locus on chromosome 6p influences different aspects of developmental dyslexia. Am J Hum Genet 1999; 64: 146–156. | Article | PubMed | ISI | ChemPort |
28. Grigorenko EL, Wood FB, Meyer MS, Pauls DL. Chromosome 6p influences on different dyslexia-related cognitive processes: further confirmation. Am J Hum Genet 2000; 66: 715–723. | Article | PubMed | ISI | ChemPort |
29. Fagerheim T, Raeymaekers P, Tonnessen FE, Pedersen M, Tranebjaerg L, Lubs HA. A new gene (DYX3) for dyslexia is located on chromosome 2. J Med Genet 1999; 36: 664–669. | PubMed | ISI | ChemPort |
30. Petryshen TL, Kaplan BJ, Hughes ML, Tzenova J, Field LL. Supportive evidence for the DYX3 dyslexia susceptibility gene in Canadian families. J Med Genet 2002; 39: 125–126. | Article | PubMed | ISI | ChemPort |
31. Fisher SE, Francks C, Marlow AJ, MacPhie IL, Newbury DF, Cardon LR et al. Independent genome-wide scans identify a chromosome 18 quantitative-trait locus influencing dyslexia. Nat Genet 2002; 30: 86–91. | Article | PubMed | ISI | ChemPort |
32. Petryshen TL, Kaplan BJ, Liu MF, Schmill de French N, Tobias R, Hughes ML et al. Evidence for a susceptibility locus on chromosome 6q influencing phonological coding dyslexia. Am J Med Genet (Neuropsychiatr Genet) 2001; 105: 507–517. | Article |
33. Nopola-Hemmi J, Myllyluoma B, Haltia T, Taipale M, Ollikainen V, Ahonen T et al. A dominant gene for developmental dyslexia on chromosome 3. J Med Genet 2001; 38: 658–664. | Article | PubMed | ISI | ChemPort |
34. Schulte-Korne G, Grimm T, Nothen MM, Muller-Myhsok B, Cichon S, Vogt IR et al. Evidence for linkage of spelling disability to chromosome 15. Am J Hum Genet 1998; 63: 279–282. | Article | PubMed | ISI | ChemPort |
35. Bisgaard ML, Eiberg H, Moller N, Niebuhr E, Mohr J. Dyslexia and chromosome 15 heteromorphism: negative lod score in a Danish material. Clin Genet 1987; 32: 118–119. | PubMed | ISI | ChemPort |
36. Nopola-Hemmi J, Taipale M, Haltia T, Lehesjoki AE, Voutilainen A, Kere J. Two translocations of chromosome 15q associated with dyslexia. J Med Genet 2000; 37: 771–775. | Article | PubMed | ISI | ChemPort |
37. Taipale M, Kaminen N, Nopola-Hemmi J, Haltia T, Myllyluoma B, Lyytinen H et al. A candidate gene for developmental dyslexia encodes a nuclear tetratricopeptide repeat domain protein dynamically regulated in brain. Proc Natl Acad Sci USA 2003; 100: 11553–11558. | Article | PubMed | ChemPort |
38. Blatch GL, Lassle M. The tetratricopeptide repeat: a structural motif mediating protein–protein interactions. BioEssays 1999; 21: 932–939. | Article | PubMed | ISI | ChemPort |
39. Weller EB, Weller RA, Fristad MA, Rooney MT, Schecter J. Children's Interview for Psychiatric Syndromes (ChIPS). J Am Acad Child Adolesc Psychiatry 2000; 39: 76–84. | Article | PubMed | ISI | ChemPort |
40. Tannock R, Hum M, Masellis M, Humphries T, Schachar R. Teacher Telephone Interview for Children's Academic Performance, Attention, Behavior and Learning: DSM-IV Version (TTI-IV). The Hospital for Sick Children: Toronto, Canada, 2002 (unpublished document).
41. Wechsler DI. Examiner's Manual: Wechsler Intelligence Scale for Children-Third Edition. Psychological Corporation: New York, NY, 1991.
42. Wilkinson GS. Wide Range Achievement Test 3—Revision 3. Jastak Associates: Wilmington, DE, 1993.
43. Woodcock RW. Woodcock Reading Mastery Tests—Revised. American Guidance Service Inc, 1987.
44. Wagner R, Torgesen J, Rashotte C. Comprehensive Test of Phonological Processing (CTOPP). Pro-Ed: Austin, TX, 1999.
45. Semel E, Wing E, Secord W. Clinical Evaluation of Language Fundamentals—3rd edn (CELF-3). The Psychological Corporation: San Antonio, TX, 1995.
46. Miller SA, Dykes DD, Polesky HF. A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res 1988; 16: 1215. | Article | PubMed | ISI | ChemPort |
47. Sham PC, Curtis D. An extended transmission/disequilibrium test (TDT) for multi-allele marker loci. Ann Hum Genet 1995; 59: 323–336. | Article | PubMed | ISI | ChemPort |
48. Clayton D. A generalization of the transmission/disequilibrium test for uncertain-haplotype transmission. Am J Hum Genet 1999; 65: 1170–1177. | Article | PubMed | ISI | ChemPort |
49. Horvath S, Xu X, Laird NM. The family based association test method: strategies for studying general genotype–phenotype associations. Eur J Hum Genet 2001; 9: 301–306. | Article | PubMed | ISI | ChemPort |
50. Laird NM, Horvath S, Xu X. Implementing a unified approach to family-based tests of association. Genet Epidemiol 2000; 19(Suppl 1): S36–S42. | Article | PubMed | ISI |
51. Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci USA 2003; 100: 9440–9445, (Epub 2003 July 25). | Article | PubMed | ChemPort |
52. Devlin B, Risch N. A comparison of linkage disequilibrium measures for fine-scale mapping. Genomics 1995; 29: 311–322. | Article | PubMed | ISI | ChemPort |

Acknowledgements

This work was supported by grants from the Canadian Institutes of Health Research number MOP-36358.