Methods of segregation analysis for animal breeding data: a comparison of power

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Abstract
Maximum likelihood segregation analysis provides potentially the most powerful method for the detection of segregating major genes. Segregation analysis requires the comparison of the likelihood of the data under the combined model (allowing both polygenic and major gene genetic variation) with the likelihood of the data under the polygenic model (allowing only polygenic genetic variation). In this study three approximations to the combined model likelihood were compared using simulated data, both with and without a segregating major gene, containing observations on paternal half-sibs. The use of Hermite integration to replace the integration in the combined model likelihood provided the most powerful test for a major gene. Two approximations, based on extensions of linear-mixed-model theory and estimating transmitting abilities for sires, were also considered. These approximations were less powerful than the use of Hermite integration, although the approximation estimating a transmitting ability for each major genotype for the sires was an improvement over the approximation estimating a single transmitting ability. For each approximation the frequency of detection of a major gene depended on the proportion of the genetic variance explained by the simulated major gene and whether the major gene caused the distribution to be skewed.