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Increasing Genotype-Phenotype Model Determinism: Application to Bivariate Reading/Language Traits and Epistatic Interactions in Language-Impaired FamiliesSimmons T.R.a · Flax J.F.b, c · Azaro M.A.c · Hayter J.E.c · Justice L.M.d · Petrill S.A.e · Bassett A.S.f · Tallal P.b · Brzustowicz L.M.c · Bartlett C.W.a
aBattelle Center for Mathematical Medicine, Research Institute at Nationwide Children’s Hospital and Department of Pediatrics, Ohio State University, Columbus, Ohio, bCenter for Molecular and Behavioral Neuroscience, Rutgers University, Newark, N.J., cDepartment of Genetics, Rutgers University, Piscataway, N.J., dSchool of Teaching and Learning and eDepartment of Human Development and Family Science, Ohio State University, Columbus, Ohio, USA; fDepartment of Psychiatry, University of Toronto, and Schizophrenia Research Program, Queen Street Division, Centre for Addiction and Mental Health, Toronto, Ont., Canada Corresponding Author
Assistant Prof. Christopher W. Bartlett, PhD, Battelle Center for
Mathematical Medicine, Research Institute at Nationwide Children’s Hospital and Ohio State University, JW3926, 700 Children’s Drive, Columbus, OH 43205 (USA)
Tel. +1 614 722 2688, Fax +1 614 355 2728
While advances in network and pathway analysis have flourished in the era of genome-wide association analysis, understanding the genetic mechanism of individual loci on phenotypes is still readily accomplished using genetic modeling approaches. Here, we demonstrate two novel genotype-phenotype models implemented in a flexible genetic modeling platform. The examples come from analysis of families with specific language impairment (SLI), a failure to develop normal language without explanatory factors such as low IQ or inadequate environment. In previous genome-wide studies, we observed strong evidence for linkage to 13q21 with a reading phenotype in language-impaired families. First, we elucidate the genetic architecture of reading impairment and quantitative language variation in our samples using a bivariate analysis of reading impairment in affected individuals jointly with language quantitative phenotypes in unaffected individuals. This analysis largely recapitulates the baseline analysis using the categorical trait data (posterior probability of linkage (PPL) = 80%), indicating that our reading impairment phenotype captured poor readers who also have low language ability. Second, we performed epistasis analysis using a functional coding variant in the brain-derived neurotrophic factor (BDNF) gene previously associated with reduced performance on working memory tasks. Modeling epistasis doubled the evidence on 13q21 and raised the PPL to 99.9%, indicating that BDNF and 13q21 susceptibility alleles are jointly part of the genetic architecture of SLI. These analyses provide possible mechanistic insights for further cognitive neuroscience studies based on the models developed herein.
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