Familial aggregation of dyslexia phenotypes. II: paired correlated measures.

Dyslexia is a common and complex behavioral disorder characterized by unexpected difficulty in learning to read. Psychometric measures used to assess dyslexia often evaluate overlapping processes or abilities. To identify subphenotypes amenable to model-based linkage analyses, we have used careful language phenotyping, familial aggregation analyses of single phenotype measures, and segregation analyses. In the current study, to identify covariates to use in future segregation analyses we examined six pairs of related measures selected from among the most promising candidates in the initial aggregation analyses whose aggregation patterns were most consistent with a genetic basis. For these reciprocal aggregation analyses each measure is evaluated with the paired measure as the covariate to obtain information about the interdependence of the paired measures on shared genetic factors. Six pairs of measures were evaluated: 1) accuracy and efficiency of phonological decoding; 2) phonological nonword memory and written spelling; 3) phonological decoding accuracy and written spelling; 4) inattention ratings and rapid automatized naming for switching letters and numerals (RAS); 5) inattention ratings and oral reading rate; and 6) RAS and oral reading rate. Results of these analyses provide evidence that there may be a genetic contribution to efficiency of phonological decoding in addition to the genetic contribution it shares with accuracy of phonological decoding, a genetic contribution to phonological nonword memory in addition to the genetic contribution it shares with written spelling, a genetic contribution to written spelling in addition to the genetic contribution it shares with accuracy of phonological decoding, and a genetic contribution to inattention ratings in addition to the genetic contribution it shares with either RAS or oral reading rate.

Reproducibility of proton MR spectroscopic imaging (PEPSI): comparison of dyslexic and normal-reading children and effects of treatment on brain lactate levels during language tasks.

BACKGROUND AND PURPOSE: We repeated a proton echo-planar spectroscopic imaging (PEPSI) study to test the hypothesis that children with dyslexia and good readers differ in brain lactate activation during a phonologic judgment task before but not after instructional treatment. METHODS: We measured PEPSI brain lactate activation (TR/TE, 4000/144; 1.5 T) at two points 1-2 months apart during two language tasks (phonologic and lexical) and a control task (passive listening). Dyslexic participants (n = 10) and control participants (n = 8) (boys and girls aged 9-12 years) were matched in age, verbal intelligence quotients, and valid PEPSI voxels. In contrast to patients in past studies who received combined treatment, our patients were randomly assigned to either phonologic or morphologic (meaning-based) intervention between the scanning sessions. RESULTS: Before treatment, the patients showed significantly greater lactate elevation in the left frontal regions (including the inferior frontal gyrus) during the phonologic task. Both patients and control subjects differed significantly in the right parietal and occipital regions during both tasks. After treatment, the two groups did not significantly differ in any brain region during either task, but individuals given morphologic treatment were significantly more likely to have reduced left frontal lactate activation during the phonologic task. CONCLUSION: The previous finding of greater left frontal lactate elevation in children with dyslexia during a phonologic judgment task was replicated, and brain activation changed as a result of treatment. However, the treatment effect was due to the morphologic component rather than the phonologic component.

Segregation analysis of phenotypic components of learning disabilities. II. Phonological decoding.

Dyslexia is a common, complex disorder, which is thought to have a genetic component. The study of the genetics of dyslexia is complicated by a lack of consensus on diagnostic criteria, and the probability of genetic heterogeneity-it is possible that deficits in different language processes are caused by different underlying genes. In order to address these difficulties, we study continuous phenotypes that are part of the psychometric test batteries often used to diagnose dyslexia. Prior to embarking on a linkage study, it is helpful to employ segregation analysis, both to identify phenotypes that may be amenable to mapping by linkage analysis, and to determine the best models to use for model based analyses. We study 409 people in 102 nuclear families, and employ (1) oligogenic segregation analysis to estimate the number of quantitative trait loci (QTLs) contributing to each phenotype, and (2) complex segregation analysis in order to identify the most parsimonious inheritance model. In this paper, we consider two measures of phonological decoding ability-word attack and phonemic decoding efficiency. We find evidence for one or two genes of at least modest effect contributing to phonemic decoding efficiency, and the best fitting model is a dominant major gene model with residual familial correlations. For word attack, we find evidence for one or two genes of at least modest effect, and the variation in the trait is best explained by a polygenic model.

Linkage analyses of four regions previously implicated in dyslexia: confirmation of a locus on chromosome 15q.

Dyslexia is a common, complex disorder, which is thought to have a genetic component. There have been numerous reports of linkage to several regions of the genome for dyslexia and continuous dyslexia-related phenotypes. We attempted to confirm linkage of continuous measures of (1) accuracy and efficiency of phonological decoding; and (2) accuracy of single word reading (WID) to regions on chromosomes 2p, 6p, 15q, and 18p, using 111 families with a total of 898 members. We used both single-marker and multipoint variance components linkage analysis and Markov Chain Monte Carlo (MCMC) joint segregation and linkage analysis for initial inspection of these regions. Positive results were followed with traditional parametric lod score analysis using a model estimated by MCMC segregation analysis. No positive linkage signals were found on chromosomes 2p, 6p, or 18p. Evidence of linkage of WID to chromosome 15q was found with both methods of analysis. The maximum single-marker parametric lod score of 2.34 was obtained at a distance of 3 cM from D15S143. Multipoint analyses localized the putative susceptibility gene to the interval between markers GATA50C03 and D15S143, which falls between a region implicated in a recent genome screen for attention-deficit/hyperactivity disorder, and DYX1C1, a candidate gene for dyslexia. This apparent multiplicity of linkage signals in the region for developmental disorders may be the result of errors in map and/or model specification obscuring the pleiotropic effect of a single gene on different phenotypes, or it may reflect the presence of multiple genes.