• Asian-Australasian Journal of Animal Sciences
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• v.22 no.7
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• pp.931-936
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• 2009

Body weights of 862 Angora goats between birth and 36 months of age, recorded on a semiyearly basis from 1988 to 2000, were used to estimate genetic, permanent environmental and phenotypic covariance functions. These functions were estimated by fitting a random regression model with 6th order polynomial for direct additive genetic and animal permanent environmental effects and 4th and 5th order polynomial for maternal genetic and permanent environmental effects, respectively. A phenotypic covariance function was estimated by modelling overall animal and maternal effects. The results showed that the most variable coefficient was the intercept for both direct and maternal additive genetic effects. The direct additive genetic (co)variances increased with age and reached a maximum at about 30 months, whereas the maternal additive genetic (co)variances increased rapidly from birth and reached a maximum at weaning, and then decreased with age. Animal permanent environmental (co)variances increased with age from birth to 30 months with lower rate before 12 months and higher rate between 12 and 30 months. Maternal permanent environmental (co)variances changed little before 6 months but then increased slowly and reached a maximum at about 30 months. These results suggested that the contribution of maternal additive genetic and permanent environmental effects to growth variation differed from those of direct additive genetic and animal permanent environmental effects not only in expression time, but also in action magnitude. The phenotypic (co)variance estimates increased with age from birth to 36 months of age.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.11
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• pp.1535-1543
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• 2008

Microsatellites (MS) are useful for quantifying genetic variation within and between populations and for describing the evolutionary relationships of closely related populations. The main objectives of this work were to estimate genetic parameters, measure genetic distances and reconstruct phylogenetic relationships between Australian Angora/Angora_Aus/ and Cashmere/Cashmere_Aus/ populations and three Mongolian Cashmere goat (Bayandelger/BD/, Zavkhan Buural/ZB/, and Gobi Gurvan Saikhan/GGS/) populations based on variation at fourteen MS loci. The level and pattern of observed and expected heterozygosity and polymorphic information content of the fourteen loci studied across the populations were quite similar and high. Except for SRCRSP07, all studied microsatellites were in Hardy-Weinberg Equilibrium (p<0.001). Moderate genetic variation (7.5%) was found between the five goat populations with 92.5% of total genetic variation attributable to diversity existing between the individuals within each population. The greatest Nei's genetic distances were found between the Angora and four Cashmere populations (0.201-0.276) and the lowest distances were between the Mongolian Cashmere goat populations (0.026-0.031). Compared with other Cashmere goat populations, the GGS (crossbred with Russian Don Goats) population had the smallest pairwise genetic distance from the Australian Angora population (0.192). According to a three-factorial correspondence analysis (CA), the three different Mongolian Cashmere populations could hardly be distinguished from each other.