Familial aggregation of dyslexia phenotypes.
There is evidence for genetic contributions to reading disability, but the phenotypic heterogeneity associated with the clinical diagnosis may make identification of the underlying genetic basis difficult. In order to elucidate distinct phenotypic features that may be contributing to the genotypic heterogeneity, we assessed the familial aggregation patterns of Verbal IQ and 24 phenotypic measures associated with dyslexia in 102 nuclear families ascertained through probands in grades 1 through 6 who met the criteria for this disorder. Correlations between relatives were computed for all diagnostic phenotypes, using a generalized estimating equation (GEE) approach. GEE is a recently developed semiparametric method for handling correlated data. The method is robust to model misspecification and flexible in adjusting for the subjects' characteristics and pedigree sizes as well as for the ascertainment process, while estimating the correlations between related subjects. The Nonword Memory (NWM) subtest of a prepublication version of the Comprehensive Test of Phonological Processing (CTOPP) and Phonemic Decoding Efficiency (PDE) subtest of a prepublication version of the Test of Word Reading Efficiency (TOWRE) showed correlation patterns in relatives that are strongly supportive of a genetic basis. The Wechsler Scale Digit Span, the Word Attack subtest of the Woodcock Reading Mastery Test--Revised, and the Spelling subtest of the Wide Range Achievement Test--Third Edition had slightly weaker evidence of a genetic basis. Five additional phenotypes (the Spelling subtest of the Wechsler Individual Achievement Test, the Accuracy, Rate, and Comprehension subtests of the Gray Oral Reading Test--Third Edition, and Rapid Automatized Naming of Letters and Numbers) gave suggestive evidence of such a pattern. The results cross-validate in that evidence for a pattern consistent with a genetic basis was obtained for two measures of phonological short-term memory (CTOPP Nonword Memory and WISCIII or WAIS-R Digit Span), for two measures of phonological decoding (WRMT-R Word Attack and TOWRE Phonemic Decoding Efficiency), and for two measures of spelling from dictation (WRAT-3 Spelling and, to a lesser extent, WIAT Spelling). These measures are thus good candidates for more sophisticated segregation analyses that can formulate models for incorporation into linkage analyses.
Raskind
W H
WH
Department of Medicine, University of Washington, Seattle 98195-7720, USA. wendyrun@u.washington.edu
Hsu
L
L
Berninger
V W
VW
Thomson
J B
JB
Wijsman
E M
EM
D000328:Adult; D002648:Child; D004410:Dyslexia / Q000175:diagnosis / Q000235:genetics*; D005260:Female; D005820:Genetic Testing; D006801:Humans; D007360:Intelligence / Q000235:genetics; D008297:Male; D008875:Middle Aged; D009483:Neuropsychological Tests; D010641:Phenotype