Discovering Genes for Beef Production

1. Discovering Genes for Beef Production Mike Goddard University of Melbourne and Department of Primary Indusries, Victoria

2. Traditional Genetic Improvement Genes Breeding Value

3. Introduction Genomics Identify genes for economic traits

4. Background on Genomics • Genomics revolution • Human genome project • Based on • high throughput techniques • computer analysis of databases • Spin-off to agriculture • knowledge • techniques

5. Genomics - International investment MetaMorphix invests $10m with Cargill to find genes for meat quality Ovita in NZ in sheep Vialactia in NZ in dairy cattle Dairy CRC NRE and AgResearch Beef CRC $5M AWI - MLA sheep genomics $30M

6. Applications to Beef Industry • Selection of bulls and cows carrying the favourable genes • Non-genetic manipulation of physiology • Transgenic cattle

7. Introduction This talk Discovering genes for economic traits Progress in Beef CRC research Using these genes in beef cattle breeding

8. Discovering Gene Function High Throughput Techniques DNA sequence Naturally occurring variants • gene mapping Gene expression pattern • microarrays

9. Naturally occurring gene variants Genes are a sequence of DNA eg AGTCTAG Genetic differences are due to differences in DNA sequence eg AGTCTAG AGTGTAG

10. Naturally occurring gene variants Number of genes causing variation in a trait At least 20 experimentally Hayes and Goddard (2001) 50-100 segregating Effect varies from small to medium

11. Distribution of effects of genes on quantitative traits

12. Naturally occurring gene variants Problem Finding the differences in DNA sequence (ie genes) that cause differences in performance

13. Naturally occurring gene variants Research strategy Map genes for traits to chromosomal region Find candidate genes in correct region of chromosome Test natural variants in candidate genes for affect on the trait

14. Gene mapping

15. Bull chromosomes

16. Gene mapping M1 + sire M2 - offspring M1 + M2 -

17. Linkage equilibrium M1 + sire1 M2 - M1 - sire2 M2 +

18. Fine scale mapping Linkage map gene to about 30 cM Depends on size of effect Fine scale map by linkage disequilibrium

19. Linkage disequilibrium... A chunk of an ancestral animal’s chromosome is conserved in the current population Marker Haplotype 1 Q 1 2

20. Candidate gene approach Select genes with a physiological role in trait (eg muscle growth) Find variations in DNA sequence Test gene variants for effect on trait

21. Candidate genes Problem • Thousands of possible candidates • Only 5-10 with moderate effect

22. Position candidate genes Among the genes that map to the right chromosome region Find list of all genes in a region of bovine chromosome from homologous human chromosome

23. * * Human Cattle

24. CRC for Cattle and Beef QualityProject 2.1 Genetic Markers Overall Aim Genetic markers for Marbling Tenderness Meat yield Tropical adaptation Food conversion efficiency That can be used regardless of family

25. Organizations CSIRO AGBU VIAS Uni of Adelaide Trangie

26. Overall Strategy Linkage analysis  chromosomal region Fine scale map  small chromosomal region haplotype of markers test positional candidate direct markers commercial test

27. Linkage mapping results Trait Tenderness and retail beef yield

28. Linkage mapping of LD Peak Force • CBX experiment • Maximum Likelihood • Summed over sires • November 1998 • CAST (calpastatin) • Strong evidence

29. CAST effects on LD Peak force (kg) Breed C11 C12 C22 Angus 0.17 0 -0.21 Brahman 0.08 0 -0.18 Belmont Red 0.10 0 -0.22 Hereford -0.36 0 -0.11 Murray G 0.88 0 -0.15 Santa G 0.02 0 -0.12 Shorthorn -0.14 0 -0.10 All breeds 0.06 0 -0.16

30. Marbling Gene star New gene patented February

31. Other traits Meat yield fine scale mapping gene Tick resistance linkage mapping NFI genes mapped to chromosomes in Jersey x Limousin starting project to map and identify in Angus

32. Using DNA information • Independent of EBVs • Combine into EBVs

33. Combining DNA and other information phenotype pedigree EBVs DNA

34. Introduction Assay DNA sequence change + phenotypes and pedigrees --> more accurate EBVs at a younger age

35. Factors affecting the gain in accuracy from DNA data • Accuracy of existing EBV • Proportion of genetic variance explained by DNA data • Accuracy of estimating QTL allele effects • Generation length

36. Gene Expression Where and when a gene is expressed tells you a lot about its function Now measure mRNA in 20,000 genes at once with microarrays

37. Collect RNA Prepare mRNA target Make cDNA libraries PCR purification Overview of Microarray Technology 0.1nl print hybridise Microarray slide

38. overlay images Detection of signal analysis

39. Close up at column 3, row 1 Channel 1 Lactating Channel 2 Pregnant Overlay

40. Microarray Technology at VIAS y-axis: log 2 ratio of fluorescence intensity Cy3/Cy5 + more highly expressed in lactating MG - more highly expressed in pregnant MG x-axis: total fluoresence intensity

41. Conclusion Genomics new knowledge applications selection of bulls and cows transgenic cows non-genetic manipulation

42. Conclusions 5-10 genes explain 50% variation in a typical economic trait Genomics is helping us to find these genes

43. ConclusionsIdentifying genes with natural variants • Two genes patented for marbling • One commercialised • One gene commercialised for tenderness • Others genes mapped for beef yield and NFI • Experiments under way for tick count

44. Conclusions In 20 years we will know 200 genes that affect beef production We will use these genes and existing technology to breed the right cattle for each task

45. Conclusions Transgenic cattle Non-genetic manipulation of growth and composition