title: Segregation analysis of phenotypic components of learning disabilities. II. Phonological decoding.
abstract: 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.
authors.affiliation: Department of Medicine, University of Washington, Seattle, Washington 98195-7720, USA.
authors.forename: Nicola H
authors.initials: NH
authors.lastname: Chapman
authors.forename: Wendy H
authors.initials: WH
authors.lastname: Raskind
authors.forename: Jennifer B
authors.initials: JB
authors.lastname: Thomson
authors.forename: Virginia W
authors.initials: VW
authors.lastname: Berninger
authors.forename: Ellen M
authors.initials: EM
authors.lastname: Wijsman
mesh_terms: D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D007859:Learning Disabilities / Q000235:genetics* / Q000523:psychology; D008297:Male