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S A Knott

Publications and source records attributed to S A Knott.

20 records · Page 2Linked to original sources

Methods of segregation analysis for animal breeding data: parameter estimates.

Three approximations to the combined model likelihood (including variation due to both polygenes and a major gene) used in segregation analysis were compared in their ability to estimate the parameters describing the model. Paternal half-sib data simulated under four different combined models were used. In general, when a combined model provides the best explanation of the data the parameter estimates from all three approximations investigated are, on average, in good agreement with the values simulated. The use of Hermite integration to replace the integration in the combined model likelihood provided the parameter estimates closest to those stimulated. The other two approximations, based on extensions of linear mixed-model theory, gave estimates further from the expected values, however, these latter two methods can estimate transmitting abilities for the sires directly from the analyses. The three approximations are similar in their ability to genotype sires at the major locus. On average, with the models studied here, about 65 per cent of sires can be successfully genotyped.

Alleles↗

Maximum likelihood mapping of quantitative trait loci using full-sib families.

A maximum likelihood method is presented for the detection of quantitative trait loci (QTL) using flanking markers in full-sib families. This method incorporates a random component for common family effects due to additional QTL or the environment. Simulated data have been used to investigate this method. With a fixed total number of full sibs power of detection decreased substantially with decreasing family size. Increasing the number of alleles at the marker loci (i.e., polymorphism information content) and decreasing the interval size about the QTL increased power. Flanking markers were more powerful than single markers. In testing for a linked QTL the test must be made against a model which allows for between family variation (i.e., including an unlinked QTL or a between family variance component) or the test statistic may be grossly inflated. Mean parameter estimates were close to the simulated values in all situations when fitting the full model (including a linked QTL and common family effect). If the common family component was omitted the QTL effect was overestimated in data in which additional genetic variance was simulated and when compared with an unlinked QTL model there was reduced power. The test statistic curves, reflecting the likelihood of the QTL at each position along the chromosome, have discontinuities at the markers caused by adjacent pairs of markers providing different amounts of information. This must be accounted for when using flanking markers to search for a QTL in an outbred population.

Animals↗