LIC | Research


Research & Development

 Genetics, Genomics and Analytics

The science team is led by Bevin Harris.

Within the team, Anne Winkelman and Richard Spelman co-lead Gemonics, while Steve Davis leads the Analytics group. The group is responsible for developing and enhancing the across-breed genetic evaluation system that produces LIC's breeding values and selection index. 

Since the commercial availability of SNP chips for dairy cattle, the group has been developing methods of incorporating genomic information into the genetic evaluation system.

The addition of genomic information has the potential to increase the reliability of prediction of breeding values of young bulls, thus allowing them to be selected for widespread use prior to receiving a daughter proof.

Enhancing the statistical methods of combining genomic with traditional phenotypic and pedigree information for the purpose of genetic prediction is an active area of research.

The initial research was done using a 10K SNP chip. Later the 50K chip, on which all the past and current LIC bulls were genotyped, became available.

The group has also had access to high-density (HD) (700K) genotypes on selected animals, thereby providing a reference panel to impute the 50K genotypes to HD.

Initial work is being done using sequence data on 600 animals, with a view to obtaining a greater understanding of the bovine genome and ultimately to enhancing selection decisions. 

The team also supported LIC's diagnostics business, GeneMark, through the development of a parentage verification tool. 

The team has branched out into the area of analytics.

The current focus is the development of advanced analytical and learning models for the interrogation and interpretation of data from the on-farm automated data capture on animals (eg. in line milk meters, walk over weigh scales and pedometers) and the environment (soil moisture probes, weather stations, grass growth recorders). 

The objective of this work is to deliver a suite of predictive tools that improve the ability of farmers to make sound management decisions.





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