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- W2616021608 abstract "A variance component method (VCM) considering quantitative trait loci (QTL) effects as random in a linear model may be computational expensive, especially with multiple QTL and interactions, because of a large number of genetic effects. This thesis deals with possibilities to reduce this computational burden by modifying elements of the underlying random model for inbred line-derived F2 populations, considering additive genetic, dominance and epistatic interaction effects.A reduced random model (RRM), as suggested in Chapter One, considers average genetic effects for all possible marker genotypes instead of genetic effects for each individual as the traditional individual random model (IRM) does. It could be shown that the genetic covariance structure of the RRM is asymptotically equivalent to the genetic covariance in the IRM. Because the number of parameters to be estimated is essentially decreased in the RRM it clearly outperforms the IRM with respect to computational speed.Comprehensive comparisons as done in Chapter Two show that the RRM is competitive to the IRM in terms of the precision of the estimated QTL positions and the observed power. Both VCM were also compared to fixed models like regression interval mapping (RIM) and multiple interval mapping (MIM). No major differences between RRM compared to IRM, RIM and MIM in terms of the QTL detection power and the accuracy of the estimated QTL positions and their effects occurred. Hence, the RRM is a computationally tractable method and is recommended for QTL analyses instead of the IRM, especially in experiments with multiple families.Chapter Three revisits additive and additive-by-additive genetic relationship matrices. Alternative covariance matrices are proposed, capitalizing on the prior knowledge of only two different QTL alleles in the considered type of experiments. The resulting covariance matrices and their inverses have a considerable amount of zero elements, leading to a remarkable gain in computational speed if this sparse structure is exploited in the estimation of genetic variance components. Thereby the restricted log-likelihood function remains unaltered. Moreover, more realistic standard errors for genetic effects are obtained.In conclusion QTL analyses in inbred line-derived F2 families can be speeded up by applying the RRM and, in case of only additive genetic effects and their interactions, by applying a sparse type of genetic covariance matrices." @default.
- W2616021608 created "2017-05-26" @default.
- W2616021608 creator A5027719771 @default.
- W2616021608 date "2011-08-01" @default.
- W2616021608 modified "2023-09-24" @default.
- W2616021608 title "Modified random models for QTL discovery in F2 populations" @default.
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