Genetic evaluation programs and future opportunities

Transcription

1 Genetic evaluation programs and future opportunities James Rowe Raul Ponzoni Daniel Brown Julius van der Werf (Sheep CRC, Australia) (Universidad de la República) (Sheep Genetics, Australia) (UNE, Australia) 10 th World Merino Conference 2018, Montevideo

2 Genetic evaluation estimating genetic merit (breeding values) NOT what sort of sheep to breed NOT what sort of sheep to produce

3 Accuracy Genetic gain determined by: accuracy of estimating genetic merit generation interval 1 Assumed heritability = 25%; Accuracy of genomic test = 50% 0.8 Progeny 0.6 Performance record 0.4 Parent EBVs Parent Performance No performance records Age (years)

4 Weighing the fleece (George Lambert 1921, Wanganella)

5 Average fleece weight (kg/head ) Average fleece weights Australia

6 Trait trends in Australian Merinos (Swan et al. 2017) Contribution to index gain (%) MPP (Mer) YWT AWT EMD WEC NLW CFW FD SS 0 Fining the clip Year of birth FD Increasing meat income YWT Focus on fleece weight CFW

7 Evolution of Sheep Genetics genetic evaluation

8 Estimating genetic merit (breeding values) Pedigree Performance Estimated Breeding Values (ASBV) Genotype (DNA) Indexes

9 Rate of gain Index trend (SD) Index trend (SD) Index trend (SD) Index trend (SD) Rate of Genetic Gain (index trends) 5 Maternal Merino Terminal Year of birth Year of birth Year of birth MATDOL (BL) MATDOL (CM) MPP (Mer) CPLUS (Term) Swan et al., 2017 AAABG

10 Cumulative Net Present Value ($1000 units) Faster genetic gain drives profit (Extra net income per 2,000 ewes) (Granleese 2018) Stud gains (Index points/yr) Years

11 Genetic evaluation is a key tool - helps achieve rapid genetic gain - contributes to well-balanced genetic gain - but... expensive Performance recording Reference flocks R&D of the genetic evaluation system Database management and computing Costly development of analytical tools Single step, MateSel, RamSelect, Flock profiling

12 Strong case for International collaboration Competing against other breeds & species not against Merino breeders in other countries Cloud computing makes data sharing easy Compelling economies of scale in genomics Standardised DNA testing in multiple countries Good examples in dairy and beef breeding G x E concerns increasingly well understood Shared access to tools (Single Step, MateSel, RamSelect, Flock Profiling )

13 MERINOSELECT evaluation for Australia and New Zealand (Brown & AGBU) G x E interactions? Studied a range of traits many environments Accounted for sire by flock & year (SxF) interaction 13

14 Conclusions All traits investigated had high genetic correlations when Sire x Flock interaction included Breeders can select on MERINOSELECT ASBVs regardless of the country of origin MERINOSELECT is open to concept of hosting single international evaluation for Merinos.????

15 Tools for improved genetic gain MateSel available to Sheep Genetics client s to help with mate selection. SingleStep evaluation analysis incorporating: pedigree, performance & genomics RamSelect.com.au a web-based app to help identify rams for specific breeding objectives Genomic Flock Profiling average flock breeding values from DNA testing 20 latest drop progeny. A benchmark to guide ram purchases.

16 Opportunities

17 Analysing genetic gain (From Swan et al 2017) How does actual gain for Merinos compare to potential gain? How do individual ram breeders compare?

18 % potential gain % potential gain % potential gain Actual gain as % of potential gain (Swan et al. 2017) Maternal Merino Terminal Maternal Merino Terminal Year of birth Year of birth Year of birth MATDOL (BL) MATDOL (CM) MPP (Mer) CPLUS (Term)

19 Comparing gains for individual breeders) (From Stephen et al 2018) Merino Top 20% Terminals Top 20% Bottom 20% Bottom 20% 0-20 MPP (Mer) CPLUS (Term)

20 % of animals with full pedigree Full pedigree data is one problem (From Stephen et al 2018) Merino Top 20% Terminals Top 20% Bottom 20% Bottom 20% 0 Merino Terminal

21 Genomic Selection: the big picture (Photo: Julius van der Werf 2018) 21

22 Information Nucleus innovation platform Information Nucleus DNA 1. Understanding complex phenotypes 2. Quantifying G x E 3. Genomic prediction of breeding values 4. Bio-bank (DNA and database) Phenotype data Sheep Wool Meat

23 Genomics Blending GBLUP EBVs with ASBVs (2012) Single Step 207 Carcase Analysis (2016) Full Single step in Main Analyses (2017)

24 Impact on industry through genomics (genetic gain - index points/year) Difference Merinos (MP+) % Terminals (C+) % Terminals (LEQ) % Some Confounding factors (Brown et al 2018) e.g. Index development & Reference population

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26 Prediction Accuracy Prediction accuracy: Meat Traits in Merino K 50K+Top Seq (2) ccfat cemd imf pemd sf5 pwt

27 Accuracy Value of genomics early information and difficult to measure traits 1 Assumed heritability = 25%; Accuracy of genomic test = 50% 0.8 Genomic Progeny 0.6 Performance record 0.4 Parent EBVs Parent Performance Conventional No performance records Age (years) DNA tests getting cheaper and predictions more accurate

28 Signs of rapid change

29 Number of studs registered in MERINOSELCT Increase in membership of MERINOSELECT y = 34.45x

30 Rapid increase in poll ram semen sales (Note: DNA test developed 2009) Doses sold (NSW AASMB)

31 Increase in poll ram sales ( Top 20 NSW studs) (Note: DNA test developed 2009) Rams sold (NSW AASMB top 20) Horn Poll

32 Increasing use of DNA (Genomic) testing (a) DNA parentage test numbers (per year) (b) Genomic test numbers (per year) 80,000 60,000 40,000 20, ,000 15,000 10,000 5,

33 Concluding comments Genetic evaluation programs are crucial for rapid and wellbalanced genetic gain Many Merino breeders can achieve much faster genetic gain for a range of traits required by their clients Genomics offers huge potential for Merinos New tools and services (Single Step, MateSel, RamSelect, Flock Profiling) assist in making best use of genetically superior sheep International collaboration a strong case a single evaluation program based on MERINOSELECT????