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Discovering Genes for Beef Production. Mike Goddard University of Melbourne and Department of Primary Indusries, Victoria. Traditional Genetic Improvement. Genes Breeding Value. Introduction. Genomics Identify genes for economic traits. Background on Genomics. Genomics revolution - PowerPoint PPT Presentation
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DEPARTMENT OFPRIMARY INDUSTRIES
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Discovering Genes for Beef Production
Mike Goddard
University of MelbourneandDepartment of Primary Indusries, Victoria
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Traditional Genetic Improvement
Genes
Breeding Value
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Introduction
Genomics
Identify genes for economic traits
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Background on Genomics
• Genomics revolution
• Human genome project• Based on
– high throughput techniques– computer analysis of databases
• Spin-off to agriculture– knowledge– techniques
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Genomics - International investment
MetaMorphix invests $10m with Cargill to find genes for meat quality
Ovita in NZ in sheepVialactia in NZ in dairy cattle
Dairy CRCNRE and AgResearchBeef CRC $5MAWI - MLA sheep genomics $30M
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Applications to Beef Industry
• Selection of bulls and cows carrying the favourable genes
• Non-genetic manipulation of physiology
• Transgenic cattle
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Introduction
This talk
Discovering genes for economic traits
Progress in Beef CRC research
Using these genes in beef cattle breeding
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Discovering Gene Function
High Throughput Techniques
DNA sequence
Naturally occurring variants– gene mapping
Gene expression pattern– microarrays
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Naturally occurring gene variants
Genes are a sequence of DNA eg AGTCTAG
Genetic differences are due to differences in DNA sequence
eg AGTCTAG AGTGTAG
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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
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Distribution of effects of genes on quantitative traits
0
1
2
3
4
5
6
0.05 0.25 0.45 0.65 0.85 1.05 1.25 1.45
Effect (phenotypic standard deviations)
De
nsity
Pigs
Dairy
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Naturally occurring gene variants Problem
Finding the differences in DNA sequence (ie genes) that cause differences in performance
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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
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Gene mapping
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Bull chromosomes
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Gene mapping
M1 +sire
M2 -
offspring
M1 + M2 -
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Linkage equilibrium
M1 +sire1
M2 -
M1 -sire2
M2 +
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Fine scale mapping
Linkage map gene to about 30 cMDepends on size of effectFine scale map by linkage disequilibrium
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Linkage disequilibrium...Linkage disequilibrium...
A chunk of an ancestral animal’s chromosome is conserved in the current population
1 Q 1 2
Marker Haplotype
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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
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Candidate genes
Problem
– Thousands of possible candidates
– Only 5-10 with moderate effect
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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
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*
*
Human Cattle
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CRC for Cattle and Beef QualityProject 2.1 Genetic Markers
Overall AimGenetic markers for
MarblingTendernessMeat yieldTropical adaptationFood conversion efficiency
That can be used regardless of family
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Organizations
CSIROAGBUVIASUni of AdelaideTrangie
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Overall Strategy
Linkage analysis chromosomal region
Fine scale map small chromosomal region
haplotype of markers test positional candidate
direct markers
commercial test
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Linkage mapping results
Trait
Tenderness and retail beef yield
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Linkage mapping of LD Peak Force
CBX experiment Maximum Likelihood Summed over sires November 1998 CAST (calpastatin) Strong evidence
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CAST effects on LD Peak force (kg)
Breed C11 C12 C22
Angus 0.17 0 -0.21Brahman 0.08 0 -0.18Belmont Red 0.10 0 -0.22Hereford -0.36 0 -0.11Murray G 0.88 0 -0.15Santa G 0.02 0 -0.12Shorthorn -0.14 0 -0.10
Allbreeds 0.06 0 -0.16
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Marbling
Gene star
New gene patented February
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Other traits
Meat yieldfine scale mapping gene
Tick resistancelinkage mapping
NFIgenes mapped to chromosomes in Jersey x Limousinstarting project to map and identify in Angus
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Using DNA information
• Independent of EBVs
• Combine into EBVs
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Combining DNA and other information
phenotypephenotype
pedigreepedigree
DNADNA
EBVsEBVs
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Introduction
Assay DNA sequence change+
phenotypes and pedigrees
--> more accurate EBVsat a younger age
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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
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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
Collect RNA
Make cDNA libraries
PCRpurification
Microarrayslide
0.1nl print
Prepare mRNA target
hybridise
Overview of Microarray Technology
Detection of signal
analysis
excitation
laser 1laser 2
emission
scanning
overlay images
Close up at column 3, row 1
Channel 1Lactating
Channel 2Pregnant
Overlay
Microarray Technology at VIAS
-5
-4
-3
-2
-1
01
2
3
4
5
-20000 0 20000 40000 60000
Series1
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
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Conclusion
Genomics new knowledge
applications
selection of bulls and cowstransgenic cowsnon-genetic manipulation
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Conclusions
5-10 genes explain 50% variation in a typical economic trait
Genomics is helping us to find these genes
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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
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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
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Conclusions
Transgenic cattle
Non-genetic manipulation of growth and composition