@conference {41968,
	title = {Landcorp Farming Lecture : Genomic selection for accelerated genetic gain in livestock},
	booktitle = {Proceedings of the New Zealand Society of Animal Production},
	volume = {67},
	year = {2007},
	month = {Jan},
	pages = {143-146},
	publisher = {New Zealand Society of Animal Production},
	organization = {New Zealand Society of Animal Production},
	address = {Wanaka},
	abstract = {The sequencing of a number of the livestock genomes and developments in genotyping technology has made it possible to genotype animals for tens of thousands of markers relatively cheaply. Genomic selection exploits this information to calculate genomic breeding values (GEBVs), as the sum of the effects of small chromosome segments across the genome. Effects of chromosome segments are first estimated in a large population with phenotypic information. In subsequentgenerations, only marker information is required to calculate GEBVs. Accuracies of GEBVs calculated for animals with marker information only, can be as high as 0.85. Results from analysis of real data from dairy cattle suggest approximately 30,000 single nucleotide polymorphism markers will be required to calculate GEBVs with maximum accuracy. Chromosome segment effects should be estimated from at least 2000 phenotypic records. A trial of genomic selection in the Australian Holstein population, with 384 bulls typed for 9919 markers, achieved accuracies of GEBVs of up to 0.82, despite sub optimal marker density and limited number of phenotypic records. In dairy cattle, maximum genetic gain using genomic selection will be achieved by selection of young bulls for widespread use without progeny test, allowing a halving of the generation interval.},
	issn = {1176-5283},
	author = {Hayes BJ and Goddard ME}
}
@conference {40764,
	title = {A computationally feasible test day model with separate first and later lactation genetic effects},
	booktitle = {Proceedings of the New Zealand Society of Animal Production},
	volume = {56},
	year = {1996},
	month = {Jan},
	pages = {19-21},
	publisher = {New Zealand Society of Animal Production},
	organization = {New Zealand Society of Animal Production},
	abstract = {In a two step analysis test day effects were estimated within herd with  adjustment for across herd effects and then the adjusted data were analysed  across herd. Genetic effects were defined for each of 10 months in milk  within first and later lactation and for milk, fat and protein giving 60 traits.  The rank of the genetic (co)variance matrix (G) was reduced to 6 such that  G retained the information to evaluate the selection objective. A  repeatability model allowed for multiple lactations with each lactation,  conceptually, expressing all 60 traits, but missing observations for 30 or  more. A canonical transformation was applied to create uncorrelated traits.  Missing values were replaced by their expectations at each round. Because  of the rank reduction, only 6 canonical traits were solved for. This system  should make evaluations more stable by removing biases due to genetic  differences in persistency and rate of maturity. },
	issn = {1176-5283},
	author = {Wiggans GR and Goddard ME}
}
