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      Uncovering shared common genetic risk factors for various aspects of complex disorders captured in multiple traits

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          Abstract

          Identifying shared genetic risk factors for multiple measured traits has been of great interest in studying complex disorders. Marlow's (2003) method for detecting shared gene effects on complex traits has been highly influential in the literature of neurodevelopmental disorders as well as other disorders including obesity and asthma. Although its method has been widely applied and has been recommended as potentially powerful, the validity and power of this method have not been examined either theoretically or by simulation. This paper establishes the validity and quantifies and explains the power of the method. We show the method has correct type 1 error rates regardless of the number of traits in the model, and confirm power increases compared to standard univariate methods across different genetic models. We discover the main source of these power gains is correlations among traits induced by a common major gene effect component. We compare the use of the complete pleiotropy model, as assumed by Marlow, to the use of a more general model allowing additional correlation parameters, and find that even when the true model includes those parameters, the complete pleiotropy model is more powerful as long as traits are moderately correlated by a major gene component. We implement this method and a power calculator in software that can assist in designing studies by using pilot data to calculate required sample sizes and choose traits for further linkage studies. We apply the software to data on reading disability in the Russian language.

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          Most cited references 28

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          A developmental model for similarities and dissimilarities between schizophrenia and bipolar disorder.

          Schizophrenia and mania have a number of symptoms and epidemiological characteristics in common, and both respond to dopamine blockade. Family, twin and molecular genetic studies suggest that the reason for these similarities may be that the two conditions share certain susceptibility genes. On the other hand, individuals with schizophrenia have more obvious brain structural and neuropsychological abnormalities than those with bipolar disorder; and pre-schizophrenic children are characterised by cognitive and neuromotor impairments, which are not shared by children who later develop bipolar disorder. Furthermore, the risk-increasing effect of obstetric complications has been demonstrated for schizophrenia but not for bipolar disorder. Perinatal complications such as hypoxia are known to result in smaller volume of the amygdala and hippocampus, which have been frequently reported to be reduced in schizophrenia; familial predisposition to schizophrenia is also associated with decreased volume of these structures. We suggest a model to explain the similarities and differences between the disorders and propose that, on a background of shared genetic predisposition to psychosis, schizophrenia, but not bipolar disorder, is subject to additional genes or early insults, which impair neurodevelopment, especially of the medial temporal lobe.
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            Bivariate quantitative trait linkage analysis: pleiotropy versus co-incident linkages.

            Power to detect linkage and localization of a major gene were compared in univariate and bivariate variance components linkage analysis of three related quantitative traits in general pedigrees. Although both methods demonstrated adequate power to detect loci of moderate effect, bivariate analysis improved both power and localization for correlated quantitative traits mapping to the same chromosomal region, regardless of whether co-localization was the result of pleiotropy. Additionally, a test of pleiotropy versus co-incident linkage was shown to have adequate power and a low error rate.
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              Highly significant linkage to the SLI1 locus in an expanded sample of individuals affected by specific language impairment.

                (2004)
              Specific language impairment (SLI) is defined as an unexplained failure to acquire normal language skills despite adequate intelligence and opportunity. We have reported elsewhere a full-genome scan in 98 nuclear families affected by this disorder, with the use of three quantitative traits of language ability (the expressive and receptive tests of the Clinical Evaluation of Language Fundamentals and a test of nonsense word repetition). This screen implicated two quantitative trait loci, one on chromosome 16q (SLI1) and a second on chromosome 19q (SLI2). However, a second independent genome screen performed by another group, with the use of parametric linkage analyses in extended pedigrees, found little evidence for the involvement of either of these regions in SLI. To investigate these loci further, we have collected a second sample, consisting of 86 families (367 individuals, 174 independent sib pairs), all with probands whose language skills are >/=1.5 SD below the mean for their age. Haseman-Elston linkage analysis resulted in a maximum LOD score (MLS) of 2.84 on chromosome 16 and an MLS of 2.31 on chromosome 19, both of which represent significant linkage at the 2% level. Amalgamation of the wave 2 sample with the cohort used for the genome screen generated a total of 184 families (840 individuals, 393 independent sib pairs). Analysis of linkage within this pooled group strengthened the evidence for linkage at SLI1 and yielded a highly significant LOD score (MLS = 7.46, interval empirical P<.0004). Furthermore, linkage at the same locus was also demonstrated to three reading-related measures (basic reading [MLS = 1.49], spelling [MLS = 2.67], and reading comprehension [MLS = 1.99] subtests of the Wechsler Objectives Reading Dimensions).
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                Author and article information

                Journal
                14 April 2009
                Article
                0904.2229

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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