J.P. Gibson The Institute for Genetics and Bioinformatics University of New England Australia

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APPLICATION OF MOLECULAR GENETIC TECHNOLOGIES FOR GENETIC IMPROVEMENT OF RUMINANTS J.P. Gibson The Institute for Genetics and Bioinformatics University of New England Australia

Some applications of genomic technologies to ruminants Estimating genetic diversity Prioritising conservation Choosing among breeds globally Testing value of synthetic breeds Marker assisted selection Gene detection and biology (therapeutics/vaccines) Molecular EBV Strategy presented at AAAP 2002 Restructure of ruminant breeding programs Dealt with here

Functional genomics to identify genes and networks influencing survival following Trypanosome challenge. Shirakawa Institute of Animal Genetics Trinity College Dublin KARI-TRC

Trypanosomosis Is a fatal disease of livestock. The livestock equivalent of sleeping sickness in humans T. congolense, T. vivax T brucei rhodesiense T gambiense

Origins of N Dama and Boran cattle N Dama Boran Bovins Cattle Glossines Tsetse Bovins Cattle et and Glossines tsetse

Mouse models of trypanotolerance. 100 90 80 Survival of F6 and parental strains % Survival 70 60 50 40 30 20 10 0 1 21 41 61 81 101 121 Days Post Challenge F6 AJ C57BL

Mouse model of trypanotolerance C57 (resistant) vs A/J (susceptible) Three major QTL regions involved Experiment: Challenge strains with parasites Microarray assay of gene expression in liver at different time points Map differences in expression onto known pathways (e.g. Macrophage wiring) Wellcome Trust Trypanosome Consortium

A B Macrophage wiring: Day 3 vs 0, C57 Toll receptors and downstream signaling Activation of phagocytosis A B Wellcome Trust Trypanosome Consortium, 2006, unpublished

C Macrophage wiring: Day 7 vs 0, C57 CSF-1 colony stimulating factor C Wellcome Trust Trypanosome Consortium, 2006, unpublished

D E Macrophage wiring: Day 9 vs 0, C57 Stat 3 activation Lipid metabolism D E Wellcome Trust Trypanosome Consortium, 2006, unpublished

D E F Macrophage wiring: Day 9 vs 0, A/J Stat 3 activation + Stat 6.Th2 response Lipid metabolism G Cholesterol metabolism mitochondria disrupted D E G Wellcome Trust Trypanosome Consortium, 2006, unpublished F

QTL 1 congenic Differentially expressed genes resistant QTL vs susceptible QTL 4 Chromosome 17 3.5 3 2.5 2 1.5 QTL congenic region 1 0.5 0 0 20000000 40000000 60000000 80000000 Wellcome Trust Trypanosome Consortium, 2006, unpublished

QTL 1 congenic Differentially expressed genes resistant QTL vs susceptible QTL Chromosome 1 4 3 2 1 0 0 50000000 100000000 150000000 200000000 Wellcome Trust Trypanosome Consortium, 2006, unpublished

High density snp arrays > 500,000 snp assay for humans > 30,000 snp array for cattle (soon) 20,000 snp array for sheep (2007) Compared to 1990 s markers High speed (weeks vs years) > 100-fold lower cost per marker

Beef CRC 10,000 snp assay for net-feed intake Map significant markers to human homologues 20 Human chromosome A 18 Test statistic 16 14 12 10 8 6 4 2 0 0 20000000 40000000 60000000 80000000 100000000 120000000 140000000 Human chromosome position (M. Goddard, pers comm)

Accuracy of predicting haplotype effects (M. Goddard, pers comm) 0.9 0.8 0.7 Accuracy 0.6 0.5 0.4 0.3 0.2 0.1 0 Nearest Marker Two marker haplotype Four Marker Haplotype Six marker Haplotype 0 500 1000 1500 2000 Number of phenotypic records

Molecular EBV? 1. Undertake snp assay for variation across genome for all traits of economic importance 2. Find subset of markers that give high accuracy EBV for each trait 3. Produce a reduced snp assay for commercial use Result: a single assay giving EBV for all traits. Potential: could develop and validate assays in a few thousand animals and apply to the whole population. Sheep and cattle breeding structures would become same as pig and chicken breeding.

Information Nucleus of next Sheep CRC Information nucleus 5,000 sheep bred by industry sires every year Intensively recorded Breeders Annual sampling of germplasm Improved EBV Molecular EBV 2 to 4 years from discovery to application phenotypes SGA High density genotypes QTL for application QTL for biology QTL for validating SheepGenomics Genome tools; functional biology; gene discovery New therapeutics, vaccines, interventions Industry

Genetic research for a better future