Discovering Genes for Beef Production

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Discovering Genes for Beef Production

• Mike Goddard
• University of Melbourne
• and
• Department 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 sheep
• Vialactia in NZ in dairy cattle
• Dairy CRC
• NRE and AgResearch
• Beef CRC 5M
• AWI - 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
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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 cM
• Depends on size of effect
• Fine scale map by linkage disequilibrium

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Linkage disequilibrium...
A chunk of an ancestral animals chromosome is
conserved in the current population
Marker Haplotype
1
Q
1
2
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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 Aim
• Genetic markers for
• Marbling
• Tenderness
• Meat yield
• Tropical adaptation
• Food conversion efficiency
• That can be used regardless of family

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Organizations

• CSIRO
• AGBU
• VIAS
• Uni of Adelaide
• Trangie

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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 -0.21
• 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

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Marbling

• Gene star
• New gene patented February

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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

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Using DNA information

• Independent of EBVs
• Combine into EBVs

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Combining DNA and other information

phenotype
pedigree
EBVs
DNA
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Introduction

• Assay DNA sequence change
• phenotypes and pedigrees
• -- more accurate EBVs
• at 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

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Overview of Microarray Technology
Collect RNA
Prepare mRNA target
Make cDNA libraries
PCR purification
0.1nl
print
hybridise
Microarray slide
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Detection of signal
overlay images
analysis
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Close up at column 3, row 1
Channel 1 Lactating
Channel 2 Pregnant
Overlay
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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
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Conclusion

• Genomics new knowledge
• applications
• selection of bulls and cows
• transgenic cows
• non-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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Conclusions Identifying 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