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Title: Genetic analyses of quantitative traits in human twins
Author: Benyamin, Beben
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2006
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The aim of this thesis is to understand the genetic basis of the variation of human quantitative traits using data from twins and (to some extent) their families. Traits investigated include behavioural traits (intelligence), clinical traits (the metabolic syndrome) and anthropometric measures (height). The importance of human twins for understanding genetic variation in human quantitative traits is reviewed. The use of a novel finite mixture distribution model to partition phenotypic co(variance) of a trait into underlying genetic and environmental factors from twins of unknown zygosity is presented. The Scottish Mental Surveys of 1st June 1932 and 4th June 1947, respectively, administered the same validated verbal reasoning test (the Moray House Test) to almost everyone born in 1921 or 1936 and attending school in Scotland. Information on zygosity was unavailable. A novel application of a finite mixture distribution model estimated a large and consistent heritability of cognitive ability of about ~0/7. This study is the first to estimate genetic and environmental components of cognitive ability in entire school-attending populations and implies that large (national) data collections can provide sufficient information on twin pairs to estimate genetic parameters, even without known zygosity. The precision and bias of the finite mixture distribution model were assessed using computer simulations and application to IQ measures from a large sample of known zygosity twins (i.e. twins from the UK Twins’ Early Development Studies). It is shown that the mixture distribution is unbiased provided that the twins’ trait values are (bivariate) normally distributed and the sample size is large. However, it the bivariate normality assumption is violated, then the mixture distribution provides biased estimates.  The extension of the model to multivariate analysis is discussed. The simulations show that multivariate analysis decreases the standard error of the variance component estimates. Another statistical model, a mixed linear model is used to partition the phenotypic (co)variances of traits into genetic and environmental factors from twins of known zygosity (twins from the Danish Twin Registry). Its application to understand underlying genetic and environmental aetiology showed that endophenotypes associated with the metabolic syndrome do not appear to share a substantial common genetic or familial environmental background. Finally, a genome-wide linkage analysis to identify gene/chromosomal regions associated with adult height reveals several chromosomal regions that showed a modest linkage to adult height. This analysis provides further evidence for the polygenic nature of body height.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available