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Combined breed analysis ~ technical issues Kirsty Moore.

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1 Combined breed analysis ~ technical issues Kirsty Moore

2 22 Introduction Improved efficiency –Increased number of evaluations –Increased accuracies Ability to evaluate cross bred animals Commercial producer Be on par with international methods and systems No impact if not interested in benefits

3 33 Issues along the way Heterosis Genetic parameters Breed effects Genetic groups Rebasing Different indices Running time Publishing results

4 44 The road is partially paved... Combined breed analysis is not a new concept UK dairy evaluations have been combined breed since ~2010 2013 EasyRams SPARK award Have started a test terminal combined breeds evaluation

5 55 Some of the easier questions Heterosis Genetic parameters Breed effects Genetic groups Rebasing Different indices Running time Publishing results Benchmark EBVs across runs over time. 1990 born animals have an average EBV of 0. Modernise the ‘base animals’ Yes breeds can have different indices depending on their different breeding goals Yes it will take longer to run but we have the computing power and software to handle this. The dairy evaluation ~ 20 million milk records The changes proposed concern the calculation of EBVS we can still publish onto BASCO, web search, extract specific groups etc

6 66 Heterosis / Hybrid Vigour When cross bred progeny perform better than expected given the 2 parental breeds –Reproduction –Survival –fitness In this example we expected 55kg but observed 65kg. The extra 10kg is due to hybrid vigour 50kg60kg 55kg 65kg

7 77 Heterosis / Hybrid Vigour But heterosis is not genetic! If we ignore heterosis –estimate that an animal (and its relatives) is better/worse genetically than it really is Relatively easy to account for –Different crosses express different amounts of heterosis –knowledge of the breed makeup –Proportion of Each Breed (PEB) Based on information from BASCO/breed 16ths many breeds to be present records breed make up as a % Good agreement with existing breed 16ths Better able to record breed of complex breed crosses –Breed or breed type?

8 88 Genetic parameters Can only use 1 set Review of parameters –show very similar heritabilities/correlations –In some cases differences in variances Not sure if this a true reflection of the data today –Need to extract some data and look at the phenotypic variances to see if there are breed differences Not a problem if variances are different as we can model this Dairy pre scale data so variances are comparable

9 99 Breed effects / genetic groups Genetic group definitions will need to revised to include breed –Provide the opportunity to improve the definitions We will need to account for breed effects

10 10 EasyRams SPARK award Opportunity in a small dataset to start to work with some of the issues Robyn Hulme –Suffolk, Texel and SufTex –NZ importations 2 parts to the SPARK –Across country –Combined breed

11 11 The data Data from 2007-2012 –Robyn’s flocks or flocks with a number of progeny from a All Black ram produced by Robyn –~4,500 with data –~18,000 5 generation pedigree PEB procedures / breed makeup Data extraction –Some modifications required Dam breed Genetic groups

12 12 The EBVS EBVs produced for All Black animals –8wk weight –scan weight –muscle depth –fat depth Correlation between the combined breed EBV and within breed EBV –0.69 – 0.94 –Indicates that heterosis does change the ranking but not radically –Some of the re-ranking is due to changes in models and data sets

13 13 How did EBVs compare with phenotype SWTMDFD R(phen &within breed) combined breed) within breed) combined breed) within breed) combined breed) Suffolk0.410.440.540.550.62 SufTex0.480.630.600.630.510.59 Mostly Tex0.220.390.810.760.630.70 n=~1300 animals

14 14 EBVs for 17 sires

15 15 Summary We have started an combined terminal breed analysis test run –Still working out which methods and approaches are appropriate for our data A massive step forward for the UK sheep industry –Vast improvements in efficiency –Better service the users –Improvement in EBVs and accuracy of the EBVs –Utilising cross bred data

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