Transcript Module_molecular_Genetics_VET_10-28

Current and Future Role of DNA
Technology in the Livestock Industry
Mark Allan, PhD
Beef Cattle Geneticist
ARS, U.S. Meat Animal Research Center
Clay Center, NE
Young and Changing Technology
Seed Stock
Commercial Cow/calf
Finishing Phase
Information Recorded on Individual
Animal
Family pedigree
Birth date
Birth weight
Individual calving difficulty score
Weaning weight
Yearling weight
Ultrasound - ~yearling
Hip height
Mature wt body condition scores
Udder and teat - Ames
Breeding records
Carcass data
Scrotal
Docility
Traditional Selection Works Well
Selection Practices
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Visual
Performance Data ***
EPDs****
Pedigree
DNA Marker Information
Modeling
Economic Indexes
Selection Index
$VALUE INDEXES
•Weaned Calf Value ($W)
•Grid Value ($G)
•Quality Grade ($QG)
•Yield Grade ($YG)
•Beef Value ($B)
Different indexes for different phases of production!
How Do We Collect DNA?
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Blood
Hair Roots
Saliva
Skin
Semen
Fecal Samples
Other Tissues
Bases
Gene A
Allele 1, Allele 2
Chromosomes
G
C
A
T
Marker-Assisted Selection (MAS)
-Inherited Diseases
-Coat Color
-Embryo Sexing
-Horned/Polled
-Quantitative Traits
-Feed Efficiency, Growth,
Reproduction, Carcass Traits
Marker-Assisted Management (MAM)
Populations QTL Scans
• Nellore (Indicus)/Hereford sire
n=547
x
• Brahman/Angus sire
n=620
Sires were mated to
Angus, Hereford and
MARCIII females
x
• Belgian Blue/MARCIII sire
n=246
x
• Piedmontese/Angus sire
MARCIII
n=209
x
Carcass Traits – Minor Success
Traitsa
QTLb
Chromosomesc
TEND
8
2
MAR
24
2 3 4 5 6 7 8 9 10
PCHOICE
7
12
5
FATD
24
123
5678
14
BFAT EBV
12
56
14
AFAT
2
EEFAT
1
FATTYD
3
RIBFAT
5
KPH
7
FAa
5
LMA
7
2
REA
6
2
RIBB
1
SWT
10
123
RPYD
13
123
CW
27
12
DP
8
1
5
YG
10
12
5
45
7 8 9 10 11
15
12 13 14
11
18 19 20
16 17 18
14
29
20 21
23
26 27
19
16
1
29
26
19
21
23
19
21
23
19
19
1
5
13
23
11
18
26
18
26
15 16 17
19
4
6
12
14
12
14
12
14
26
19
21
5
5
4567
9
16 17
12 13
18 19
10
12 13 14
16
10
13
16
11 12
24
14
16
26
18
19
21
29
22 23 24
29
24
29
26
Discovery of QTL
Phenotypes
Carcass Traits
hot carcass wt
fat depth
marbling score
est. k & p fat, heart fat
rib bone
ribfat
ribmus
USDA yield grade
shear force
Predicted Carcass Traits
retail product yield
fat yield
whole sale rib-fat yield
Growth Traits
birth wt
weaning wt
yearling wt
average daily gain
Reproductive Traits
FSH -males
testicular weight
testicular volume
twinning rate
ovulation rate
Strategy to Identify Genes/Markers
Quantitative
Trait Locus
(QTL)
Candidate
Position
Fine
Mapping
Positional
Candidate
Gene/Marker
Limiting
Need additional laboratory tools
Validation
Industry
Application
Development of DNA Markers
Fine mapping
Progeny
testing
Genetically
modified
animals
Mutant
models
Comparative
maps
Genome
sequence
DNA Marker
Gene
candidates
Differential gene
expression
Biology
Metanomics
Proteome
analysis
Bioinformatics
CAPN1 Story
• QTL found on BTA29
for shear force in the
Piedmontese/Angus
sired population
x
CAPN1
• CAPN1 mapped to
region
Q
T
L
Parentage Verification
m2P
Sire 1
Sire 2
?
Progeny
(P)
Animal Identification Youth Livestock
Shows
Tracing Products
First Case BSE
Announced by the USDA on
December 23, 2003
First recorded case in the U.S.
APHIS requested our assistance
on the DNA-based traceback
Washington
BSE
Sequencing the Bovine Genome
PHASE1 - $53 million
• NHGRI - $25 million
• New Zealand - $1 million
Baylor College of MedicineHuman Genome Sequencing
Center
• Texas - $5 to10 million
• National Cattlemen’s Beef Association,
Texas and South Dakota cattle producers - $820,000
Hereford Cow from Miles City
SNPs- Single Nucleotide Polymorphisms
-Occur much more frequently throughout the genome
-2 alleles possible
ATGCAATTGCCACGTTGCAAT
ATGCAATTGCTACGTTGCAAT
ATGCAATTGCC/TACGTTGCAAT
SNP Genotyping
Allele 1
Allele 2
Primer
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Linkage Disequilibrium- LD
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Illumina Infinium Bovine BeadChip
~ 54,008 SNP markers
across the bovine genome
- On average SNP every
<67,000 base pair
- Discovery SNP includes
many breeds
BARC
USMARC
University of Missouri
University of Alberta
(Van Tassell et al., 2008 Nature Methods)
Genotyping of Animals GPEVII
• 2,020 F12 animals with feed intake record for ~52,156 SNP/animal
• 152 GPEVII AI sires
• 73 GPEVII F1 sires
• 580 GPEVII F1 steers
• 150 GPEVII F1 dams
~155,164,000 genotypes
BFGL-NGS-119315
1.80
52,156 call rate >= 0.99
1.60
1.40
41,264 minor allele >= 0.1
Norm R
1.20
1
0.80
31,466 minor allele >= 0.2
0.60
0.40
0.20
0
79
346
472
-0.20
0
0.20
0.40
0.60
Norm Theta
0.80
1
WGA vs. WGS
Bonferroni correction 1.03 e-6 = LOD 6.0
WGS?
• Using a large panel of markers to estimate genetic merit
(MBV) marker breeding value.
• Using the marker information from across the wholegenome to estimate the sum of effects.
• How - Uses foundation information for the estimation
process derived from training data sets (Equations).
• Animals are genotyped that may or may not have
phenotypic information and genetic merit is estimated.
WGS – Whole-Genome Selection
• “There is no doubt that whole genomeenabled selection has the potential for being
the most revolutionary technology since
artificial insemination and performancebased index selection to change the nature
of livestock improvement in the foreseeable
future”
Dorrian Garrick, Iowa State University
Future Genetic Improvement
of Beef Cattle?
Grandsires
U.S. Beef Cattle
Gene Flow
Information Flow
USMARC
Granddams
F1 Parents
Accurate
Multi-trait
Selection
SNP
Genotypes
Phenotypes
3rd Generation Progeny
Release the Data- Breed Association
• The results of the DNA tests will be critical in the
National Sire Evaluations in the future.
• To estimate genetic effects for a trait all the data needs
to be used (“good and bad alleles”).
• Selective reporting is a long-term disadvantage.
Players Changing
Genetic Visions (WI)
Infigen (WI)
Celera AgGen (California, Maryland)
Frontier Beef Systems
Genaissance
Genmark
Pfizer (Bovigen- Catapult)
Igenity
MMI
Maxxam
SCR
Genmark
Genetic Solutions
Pyxis
ImmGen
Geneseek
Viagen
Identigen
Using DNA in Selection Programs
•
Just because animal is not carrying the favorable allele for a
specific test does not mean the animal is not genetically superior
for the trait.
•
Increase the accuracy of
EPD
•
Hard (expensive) traits to
measure
•
Sex-limited traits
•
Lowly heritable traits
•
Speed selection decisions
•
Merchandising genetics
Tools for Selection
• - Growth
• - Feed efficiency
• - Carcass composition - quality
• - Reproduction
• - Disease resistance
Will markers replace “traditional” selection?
Marker-Assisted Selection (MAS)
- At the seed stock/multiplier level
Marker-Assisted Management (MAM)
- At the commercial level
Seed Stock
MAS
Commercial Cow/calf
MAM & MAS
Finishing Phase
MAM
Implementation to the Industry
2000 – Industry sires
Marker data- added to the
databases to contribute to our
national genetic evaluation
system already in place
Feed efficiency EBV through WGS
Additional tool to be used in making genetic progress
Where has animal genetic
improvement lagged the most?
Animal Health - all species
BRD – Bovine Respiratory Disease
• Most costly disease to the cattle
industry
– 97.6% of feedlots treat
– 14.4% of cattle are treated for symptoms
– Accounts for over 50% of feedlot deaths
NAHMS, 1999
– Cattle treated for BRD are expected to
return at least $40 less than untreated
Fulton et al., 2002
calves
Where has animal genetic improvement
lagged the most?
Reproduction - Beef, Dairy cattle
Low Heritability
Multi-component Trait
Ovulate one/two eggs
Fertilization
None
Open Cow
Pregnancy
Twins/singles
Dystocia
Live Calves
Death
Survival
Embryonic/fetal death
Weaning
Survives to Endpoint
Open Cow
Where has animal genetic improvement
lagged the most?
• Lifetime productivity – all species
– Longevity of female production makes the system more
profitable and is more environmentally friendly
– Female production efficiency
Dairy cows – 2.8-3.2 parities
Sows – 3.6 parities
Where has animal genetic improvement
lagged the most?
• Feed Efficiency – ?
Hard to measure trait
Expensive!
Cattle Fax Issue 25, Vol. 40 June 20, 2008
• Little effort has been focused on the amount or causes of individual
variation in efficiency of energy utilization by cattle, even though
differences among individuals have long been recognized. (Johnson
et al., 2002). USMARC & CSU
Dry matter intake x RFI
Day DMI
kg
n=1032
RFI
kg/day
Where has animal genetic improvement
lagged the most?
Stage of production - Diet x Genetic interaction
Growing
Cow production
Avoid single-trait selection
Finishing
Heat Production of Mature Hereford and Simmental Cows Pooled
Over Physiological States Fed at Varying Dry Matter Intakes
0.16
0.14
0.12
0.1
0.08
Hereford
Simmental
0.06
0.04
0.02
0
0
0.005 0.01 0.015 0.02 0.025
Daily Dry Matter Intake/ unit cow weight
Where has animal genetic improvement
lagged the most?
Matching genetic potential to
the Climatic Environment
Ability of animals to adapt to
various environments
“Adaptability”
Where has animal genetic improvement
lagged the most?
“On the radar” may become important
Treatment x Genetic Interaction
Implants & feed additives (muscle enhancement)
Health interventions (antibiotics)
Healthfulness of Product
Omega3 FA content of protein products
Managing breed composition
When breed composition is unknown
Using DNA in Selection Programs
•
Just because the animal is not carrying the favorable allele for a
specific test does not mean the animal is not genetically superior
for the trait.
•
Increase the accuracy of
EPDs
•
Hard (expensive) traits to
measure
•
Sex-limited traits
•
Lowly heritable traits
•
Speed selection decisions
•
Merchandising genetics
Present/Future
•
•
Will DNA testing play a role in the future of beef
cattle- yes
Parental ID, Quantitative tests (panels),
Simple genetic inheritance, WGS
Will implementation be tough- Maybe; Yes
(implementation of EPDs 80s, acceptance of
crossbreeding programs, ultrasound)
Marbling
Known Disease
Mature Wt
Feed Intake
•
Collect tissues for DNA analysis
Populations with phenotypes
•
Breed Association responsibilities
database, education
•
Build database structure and become proactive in the implementation of the new
technology
Udder/teat
Feet/legs
B Wt
W Wt
Y Wt
R
e
p
r
o
Tenderness
But... another valuable tool for the breeder’s tool box
Vision
• Larger panels of markers that explain greater portions of the
genetic variation for traits. MAS MAM
• WGS - ?
Example - BW EPD 1.2 acc .75 on yearling bull
– Validation, implementation
• Change in costs?
– Technology driven
Questions