department of primary industries 1 discovering genes for beef production mike goddard university of...

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