A genome scan in multigenerational families with dyslexia: Identification of a novel locus on chromosome 2q that contributes to phonological decoding efficiency

Abstract

Dyslexia is a common and complex developmental disorder manifested by unexpected difficulty in learning to read. Multiple different measures are used for diagnosis, and may reflect different biological pathways related to the disorder. Impaired phonological decoding (translation of written words without meaning cues into spoken words) is thought to be a core deficit. We present a genome scan of two continuous measures of phonological decoding ability: phonemic decoding efficiency (PDE) and word attack (WA). PDE measures both accuracy and speed of phonological decoding, whereas WA measures accuracy alone. Multipoint variance component linkage analyses (VC) and Markov chain Monte-Carlo (MCMC) multipoint joint linkage and segregation analyses were performed on 108 families. A strong signal was observed on chromosome 2 for PDE using both VC (LOD ¼ 2.65) and MCMC methods (intensity ratio (IR) ¼ 32.1). The IR is an estimate of the ratio of the posterior to prior probability of linkage in MCMC analysis. The chromosome 2 signal was not seen for WA. More detailed mapping with additional markers provided statistically significant evidence for linkage of PDE to chromosome 2, with VC-LOD ¼ 3.0 and IR ¼ 59.6 at D2S1399. Parametric analyses of PDE, using a model obtained by complex segregation analysis, provided a multipoint maximum LOD ¼ 2.89. The consistency of results from three analytic approaches provides strong evidence for a locus on chromosome 2 that influences speed but not accuracy of phonological decoding. Molecular Psychiatry (2005) 10, 699-711.

Genomewide scan for real‐word reading subphenotypes of dyslexia: Novel chromosome 13 locus and genetic complexity

“…These families were initially selected for genotyping because they appeared likely to be informative for a measure of memory for pseudowords (the nonword memory portion of the prepublication version of the Comprehensive Test of Phonological Processing; Wagner et al, 1999) . Subset 1 differs from that used previously (Chapman et al, 2004) because of removal of one family and eight individuals from a second family (Raskind et al, 2005). The remaining 57 families, Subset 2, were included in SA, but these families lacked genotype data.…”

“…In three instances (Chapman et al, 2004;Raskind et al, 2005) we changed the map slightly to reflect map distances from the deCODE map (Kong et al, 2002) and our dataset. Marker positions were converted from a Kosambi map to a Haldane map for use in linkage analyses.…”

“…In this model, the number of QTLs is a random variable rather than a fixed quantity. Covariate adjustment was performed during the MCMC analysis, and details of the prior distributions and run conditions used were the same as described previously (Raskind et al, 2005), with initial linkage scans based on 200,000 MCMC iterations/chromosome, and runs of 400,000 iterations used for estimating genetic models associated with particular linkage signals. SA was generally based on 50,000 iterations.…”

“…Using 102 of the 108 pedigrees analyzed here, we had earlier estimated a correlation of 0.61 between SWE and WID in relatives of probands (Raskind et al, 2000), which suggests that the two measures do not have identical underlying architectures. Because SWE measures both rate and accuracy, and WID measures accuracy alone, our goal was to use the adjusted measure to try to isolate the speed component of RWR and therefore to identify with greater precision trait loci that affect ability to read quickly (Raskind et al, 2005).…”

Genetics of developmental dyslexia

“…Eight of these were genome-wide linkage screens [39,46,47,54,93,94,117,127,129,146], whilst the remainder generally targeted loci highlighted by the genome-wide screens. Another two genome-wide linkage screens for general reading and spelling ability have also been performed with samples not specifically selected for DD [5,163].…”