Browsing Animal & Grassland Research & Innovation Programme by Funder "RERAD"
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Merging and characterising phenotypic data on conventional and rare traits from dairy cattle experimental resources in three countriesThis study set out to demonstrate the feasibility of merging data from different experimental resource dairy populations for joint genetic analyses. Data from four experimental herds located in three different countries (Scotland, Ireland and the Netherlands) were used for this purpose. Animals were first lactation Holstein cows that participated in ongoing or previously completed selection and feeding experiments. Data included a total of 60 058 weekly records from 1630 cows across the four herds; number of cows per herd ranged from 90 to 563. Weekly records were extracted from the individual herd databases and included seven traits: milk, fat and protein yield, milk somatic cell count, liveweight, dry matter intake and energy intake. Missing records were predicted with the use of random regression models, so that at the end there were 44 weekly records, corresponding to the typical 305-day lactation, for each cow. A total of 23 different lactation traits were derived from these records: total milk, fat and protein yield, average fat and protein percentage, average fat-to-protein ratio, total dry matter and energy intake and average dry matter intake-to-milk yield ratio in lactation weeks 1 to 44 and 1 to 15; average milk somatic cell count in lactation weeks 1 to 15 and 16 to 44; average liveweight in lactation weeks 1 to 44; and average energy balance in lactation weeks 1 to 44 and 1 to 15. Data were subsequently merged across the four herds into a single dataset, which was analysed with mixed linear models. Genetic variance and heritability estimates were greater (P,0.05) than zero for all traits except for average milk somatic cell count in weeks 16 to 44. Proportion of total phenotypic variance due to genotype-by-environment (sire-by-herd) interaction was not different (P.0.05) from zero. When estimable, the genetic correlation between herds ranged from 0.85 to 0.99. Results suggested that merging experimental herd data into a single dataset is both feasible and sensible, despite potential differences in management and recording of the animals in the four herds. Merging experimental data will increase power of detection in a genetic analysis and augment the potential reference population in genome-wide association studies, especially of difficult-to-record traits.