Transcript Combining FBO’s GENE System with DNA = FBO Predicted

Use of DNA information in Genetic
Programs.
Next Four Seminars
• John Pollak – DNA Tests and genetic
Evaluations and sorting on genotypes.
• John Pollak – Parent Identification With
DNA
• Rob Templeman – Parent Uncertainty
Models
• Bob Weaber – Application to Commercial
Bull Evaluations
Outline
1. DNA Information in Genetic Evaluation:
• DNA Tests
• Inclusion in Genetic Evaluations
2. Commercial Ranch Genetic Evaluations
• Sorting Bulls on DNA Genotyping
• DNA Parent identification
DNA Tests
One use of DNA test information is to incorporate that
information into genetic evaluation systems.
We view ourselves as the gate keepers to what information should go
into evaluations.
The process of validation is a means to insure DNA test results going
into our genetic evaluations are reproducible.
Terminology
Discovery, Validation, Assessment and Application
Discovery: Process of identifying QTL
Validation: Process of replicating results in independent data
through blind testing
Assessment: Process of evaluating the effect of the QTL in a broader
context (other traits and environments)
Application: Process of using the DNA information in genetic
decisions
DNA Tests for Carcass Merit
Traits
•Thyroglobulin
•Calpain (MARC Discovery)
•Calpistatin
•Leptin
•Three QTL from NCBA Carcass Merit Project
(genes unknown)
•DGAT1
SNPs in Calpain1 Gene
• CAPN1 gene
– -Calpain enzyme  post-mortem tenderness
• MARC: 2 SNP that alter amino acid at positions
(codons) 316 and 530 of μ-calpain
• Public domain marker
• Genotyping performed as a service by GeneSeek
Incorporated (Lincoln, NE)
Calpain Commercial Tests
• Frontier Beef Systems  Merial
– Igenity TenderGENE
• Calpain codons/SNPs/markers 316 & 530
• Bovigen Solutions (Genetic Solutions products)
– GeneStar Tenderness II
• Calpain1 (exon 9=codon316) + Calpastatin
• MMI Genomics
– Calpain codons 316 & 530
NBCEC Taurus Data
• 14d post-mortem WBSF measurements
on 362 AI-sired cattle
• 23 Simmental sires
• Predominately commercial Angus dams
• 19 CG = same source, sex, days on feed
and harvest date
Initial MARC Results
Marker
Favorable
Allele
Unfavorable
Allele
316
C
G
530
G
A
Calpain Marker Genotype Counts
SNP 316
SNP
530
CC
CG
GG
AA
0
4
26
AG
3
40
81
GG
6
37
74
Frequency at SNP 316
Genotype
CC
CG
GG
Count
9
81
181
Frequency
.033
.299
.669
f(C allele) = .18
Equilibrium Genotype Frequencies:
CC = .032
CG = .296
GG = .672
f(G allele) = .82
Frequency at SNP 530
Genotype
AA
AG
GG
Count
30
124
117
Frequency
.110
.458
.432
f(A allele) = .23
Equilibrium Genotype Frequencies:
AA = .053
AG = .354
GG = .593
f(G allele) = .77
Calpain: 2 Additive Genotypes
SNP
316
530
Genotype
WBSF
(lbs)
SE
(lbs)
CC
-1.11
0.64
CG
-0.39
0.22
GG
0
-
AA
0.68
0.34
AG
0.03
0.22
GG
0
-
Indicus-influenced Cattle
• 297 King Ranch Santa Gertrudis carcasses
• 226 Simbrah carcasses from CMP (10 sires)
• Separate analyses by breed; similar results
– Highly significant genotype effect, either
individually or jointly
– No interaction between SNP316 & SNP530
– SNP530 NOT significant after fitting SNP316, i.e.,
SNP 530 provides no additional information if you
know the SNP316 genotype.
Indicus-influenced Cattle
Contrast (vs GG) SE
316
genotype
CC
CG
GG
Santa Gertrudis
Simbrah
-.840.60
N = 18
-.71 0.29
N = 113
-N=0
0
N =166
-1.47.39
N = 41
0
N = 185
Outline
1. DNA Information in Genetic Evaluation:
• DNA Tests
• Inclusion in Genetic Evaluations
2. Commercial Ranch Genetic Evaluations
• Sorting Bulls on DNA Genotyping
• DNA Parent identification
Marker Assisted EPD’s
The evolution of the use of marker data for traits
where EPD’s are available will be to include that
DNA data in genetic evaluation.
Test Case: Marker Assisted EPD
•
•
•
•
WBSF measurements
Calpain genotypes
Small data set
Relatively large fraction of WBSF measurements
on progeny of genotyped sires
Progeny Genotype vs. Sire Genotype
Sire Genotype
Dam Haplotype
Sire Haplotype
Progeny Genotype
Progeny Genotype
Progeny Phenotype
Progeny Phenotype
Haplotype
• Marker allele make-up of a sperm or egg
• Examples:
(316 alleles = C & G, 530 alleles = A & G)
– CCGG  only CG gametes
– CCGA  CG & CA gametes
– CGGA  CG, CA, GA & GG gametes (without
knowing phase)
Marker Assisted EPD’s
• EPD
– Expected Haplotype Effect given sire
genotype
– Polygenic effect
EPD data
• SF data in current WBSF sire evaluation
– 1833 WBSF records
– 120 Simmental sires
– 93 Contemporary Groups
• Genotypes (only sires’ used in EPD analysis)
– ~1/2 of sires were genotyped
– ~ 2/3 of animals had genotyped sire
ASA Simmental Sire Genotype
316
Frequency
530
CC
CG
GG
AA
0
2
12
0.2
AG
0
8
31
0.6
GG
0
3
7
0.2
0.0
0.2
0.8
Geno
Freq.
Allele
Freq.
0.1
0.9
Geno Allele
0.5
0.5
Observed Sire Genotype Effects
(Constructed from Haplotype Effects)
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
-0.4
blank
CG AA CG AG CG GG GG AA GG AG GG GG
Four Gametes
WBSF: EPD vs MA-EPD
Genotype
blank
CG AA
CG AG
CG GG
GG AA
GG AG
GG GG
0.8
0.6
Marker Assisted EPD
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
EPD (without marker)
0.2
0.4
0.6
0.8
WBSF: EPD vs MA-EPD
unit slope
CG AA
CG AG
CG GG
GG AA
GG AG
GG GG
0.8
0.6
Marker Assisted EPD
0.4
0.2
0.0
-0.2
0.2
-0.4
0.0
-0.6
-0.8
-0.2
-1.0
-1.2
-1.0
-0.4
-0.8
-0.6
-0.4
-0.2
0.0
EPD (without marker)
0.2
0.4
0.6
0.8
Outline
1. DNA Information in Genetic Evaluation:
• DNA Tests
• Inclusion in Genetic Evaluations
2. Commercial Ranch Genetic Evaluations
• Sorting Bulls on DNA Genotyping
• DNA Parent identification
Progeny Testing Commercial Bulls
The commercial ranch project centers on the
progeny test of yearling bulls brought into a
commercial ranch each year.
Economic Genetic Programs
We can treat genetic programs as economic
enterprises with costs and returns.
Process: Define current genetic program then
assess changes to that program relative to
costs and returns.
Progeny Test Costs
Individual identification
Data recording
Multiple sire pastures (calf sire identification)
Progeny Test Revenues
Increased revenue that results from increase
“product” generated by bull selection.
Progeny Test Costs
Multiple sire pastures
(Tool = DNA)
DNA Panels
Typically use microsatellites: Anomalies in the
genome where DNA sequences of two (or more)
base pairs are repeated.
Alleles at the microsatellite loci are the number of repeats.
Example of a genotype at one microsatellite locus = 110/116
Exclusions
A mismatch between the genotype of the putative
sire and the calf in question.
Sire = 110/110
Calf = 112/114
Panel Exclusion Rate
Measure of the effectiveness of a DNA panel
to exclude an animal as a parent.
Probability of excluding as the parent any
animal drawn at random from the
population.
Sire Identification
The probability of uniquely identifying the
sire in a group of “N” bulls is:
( Exclusion rate ) N
Bulls
0.90
0.95
0.98
2
3
4
0.81
0.73
0.66
0.90
0.86
0.81
0.96
0.94
0.92
5
6
7
8
0.59
0.53
0.48
0.43
0.77
0.74
0.70
0.66
0.90
0.89
0.87
0.85
9
10
0.39
0.35
0.63
0.60
0.83
0.82
We use the DNA genotypes to create the
breeding groups of bulls.
Bull Sorting
Create genetically diverse groups.
Objective: is to maximize the probability of uniquely identifying one
sire to a calf.
Sire Sorting
Pasture 1
Pasture 2
Criteria: Minimize the probability
that both bulls would qualify as the
sire of a calf produced by either bull.
Sire Sorting
Pasture 1
N*(N-1)
2
Pasture 2
Randomly assign one bull to each
pasture.
Sire Sorting
Pasture 1
112/114
110/110
112/116
Sire Sorting
Pasture 1
110/110
112/114
112/116
Dams
f(110)
f(112)
f(114)
f(116)
Sire
0.5
0.2
0.2
0.1
112
110/112
112/112
112/114
112/116
114
110/114
110/114
114/114
114/116
Sire Sorting
Pasture 1
112/114
112/114
112/116
Not this one
Dams
f(110)
f(112)
f(114)
f(116)
Sire
0.5
0.2
0.2
0.1
112
110/112
112/112
112/114
112/116
114
110/114
112/114
114/114
114/116
P(not excluded)=0.65
P (Excluded)
P(excluded) = 1 -  { P(not excluded)i }
Across all marker loci
Sire Sorting
Produces calf
Pasture 1
112/114
110/110
112/116
Dams
f(110)
f(112)
f(114)
f(116)
Sire
0.5
0.2
0.2
0.1
112
110/112
112/112
112/114
112/116
114
110/114
112/114
114/114
114/116
P(not excluded)=0.5
Sire Sorting
Pasture 1
Produces calf
112/114
110/110
112/116
Dams
f(110)
f(112)
f(114)
f(116)
Sire
0.5
0.2
0.2
0.1
110
110/110
110/112
110/114
110/116
P(not excluded)=0.4