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![Page 1: Use of DNA information in Genetic Programs.. Next Four Seminars John Pollak – DNA Tests and genetic Evaluations and sorting on genotypes. John Pollak.](https://reader036.fdocuments.in/reader036/viewer/2022062308/56649efa5503460f94c0cc93/html5/thumbnails/1.jpg)
Use of DNA information in Genetic
Programs.
![Page 2: Use of DNA information in Genetic Programs.. Next Four Seminars John Pollak – DNA Tests and genetic Evaluations and sorting on genotypes. John Pollak.](https://reader036.fdocuments.in/reader036/viewer/2022062308/56649efa5503460f94c0cc93/html5/thumbnails/2.jpg)
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
![Page 3: Use of DNA information in Genetic Programs.. Next Four Seminars John Pollak – DNA Tests and genetic Evaluations and sorting on genotypes. John Pollak.](https://reader036.fdocuments.in/reader036/viewer/2022062308/56649efa5503460f94c0cc93/html5/thumbnails/3.jpg)
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
![Page 4: Use of DNA information in Genetic Programs.. Next Four Seminars John Pollak – DNA Tests and genetic Evaluations and sorting on genotypes. John Pollak.](https://reader036.fdocuments.in/reader036/viewer/2022062308/56649efa5503460f94c0cc93/html5/thumbnails/4.jpg)
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.
![Page 5: Use of DNA information in Genetic Programs.. Next Four Seminars John Pollak – DNA Tests and genetic Evaluations and sorting on genotypes. John Pollak.](https://reader036.fdocuments.in/reader036/viewer/2022062308/56649efa5503460f94c0cc93/html5/thumbnails/5.jpg)
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
![Page 6: Use of DNA information in Genetic Programs.. Next Four Seminars John Pollak – DNA Tests and genetic Evaluations and sorting on genotypes. John Pollak.](https://reader036.fdocuments.in/reader036/viewer/2022062308/56649efa5503460f94c0cc93/html5/thumbnails/6.jpg)
DNA Tests for Carcass Merit Traits
•Thyroglobulin
•Calpain (MARC Discovery)
•Calpistatin
•Leptin
•Three QTL from NCBA Carcass Merit Project (genes unknown)
•DGAT1
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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)
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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
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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
![Page 10: Use of DNA information in Genetic Programs.. Next Four Seminars John Pollak – DNA Tests and genetic Evaluations and sorting on genotypes. John Pollak.](https://reader036.fdocuments.in/reader036/viewer/2022062308/56649efa5503460f94c0cc93/html5/thumbnails/10.jpg)
Initial MARC Results
MarkerFavorable
AlleleUnfavorable
Allele
316 C G
530 G A
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Calpain Marker Genotype Counts
SNP 316
CC CG GG
SNP530
AA 0 4 26
AG 3 40 81
GG 6 37 74
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Frequency at SNP 316
Genotype CC CG GG
Count 9 81 181
Frequency .033 .299 .669
f(C allele) = .18 f(G allele) = .82
Equilibrium Genotype Frequencies:
CC = .032
CG = .296
GG = .672
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Frequency at SNP 530
Genotype AA AG GG
Count 30 124 117
Frequency .110 .458 .432
f(A allele) = .23 f(G allele) = .77
Equilibrium Genotype Frequencies:
AA = .053
AG = .354
GG = .593
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Calpain: 2 Additive Genotypes
SNP GenotypeWBSF(lbs)
SE(lbs)
316
CC -1.11 0.64
CG -0.39 0.22
GG 0 -
530
AA 0.68 0.34
AG 0.03 0.22
GG 0 -
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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.
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Indicus-influenced CattleContrast (vs GG) SE
316 genotype
Santa Gertrudis Simbrah
CC-.840.60
N = 18--
N = 0
CG-.71 0.29
N = 113-1.47.39
N = 41
GG0
N =1660
N = 185
![Page 17: Use of DNA information in Genetic Programs.. Next Four Seminars John Pollak – DNA Tests and genetic Evaluations and sorting on genotypes. John Pollak.](https://reader036.fdocuments.in/reader036/viewer/2022062308/56649efa5503460f94c0cc93/html5/thumbnails/17.jpg)
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
![Page 18: Use of DNA information in Genetic Programs.. Next Four Seminars John Pollak – DNA Tests and genetic Evaluations and sorting on genotypes. John Pollak.](https://reader036.fdocuments.in/reader036/viewer/2022062308/56649efa5503460f94c0cc93/html5/thumbnails/18.jpg)
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.
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Test Case: Marker Assisted EPD
• WBSF measurements
• Calpain genotypes
• Small data set
• Relatively large fraction of WBSF measurements on progeny of genotyped sires
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Progeny Genotype vs. Sire Genotype
Progeny Genotype
Progeny Phenotype
Progeny Genotype
Progeny Phenotype
Sire Haplotype
Sire Genotype
Dam Haplotype
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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)
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• EPD
– Expected Haplotype Effect given sire
genotype
– Polygenic effect
Marker Assisted EPD’s
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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
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ASA Simmental Sire Genotype316 Frequency
530 CC CG GG Geno Allele
AA 0 2 12 0.2 0.5
AG 0 8 31 0.6
GG 0 3 7 0.2 0.5
Geno Freq.
0.0 0.2 0.8
Allele Freq.
0.1 0.9
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-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
blank CG AA CG AG CG GG GG AA GG AG GG GG
Observed Sire Genotype Effects (Constructed from Haplotype Effects)
Four Gametes
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-1.2
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8
EPD (without marker)
Ma
rke
r A
ssis
ted
EP
D
blank CG AA CG AG CG GG GG AA GG AG GG GG
WBSF: EPD vs MA-EPDGenotype
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-1.2
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8
EPD (without marker)
Ma
rke
r A
ssis
ted
EP
D
unit slope CG AA CG AG CG GG GG AA GG AG GG GG
WBSF: EPD vs MA-EPD
-0.4
-0.2
0.0
0.2
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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
![Page 29: Use of DNA information in Genetic Programs.. Next Four Seminars John Pollak – DNA Tests and genetic Evaluations and sorting on genotypes. John Pollak.](https://reader036.fdocuments.in/reader036/viewer/2022062308/56649efa5503460f94c0cc93/html5/thumbnails/29.jpg)
Progeny Testing Commercial Bulls
The commercial ranch project centers on the progeny test of yearling bulls brought into a
commercial ranch each year.
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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.
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Progeny Test Costs
Individual identification
Data recording
Multiple sire pastures (calf sire identification)
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Progeny Test Revenues
Increased revenue that results from increase “product” generated by bull selection.
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Progeny Test Costs
Multiple sire pastures
(Tool = DNA)
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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
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Exclusions
A mismatch between the genotype of the putative sire and the calf in question.
Sire = 110/110
Calf = 112/114
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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.
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The probability of uniquely identifying the sire in a group of “N” bulls is:
( Exclusion rate ) N
Sire Identification
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Bulls 0.90 0.95 0.98
2 0.81 0.90 0.96
3 0.73 0.86 0.94
4 0.66 0.81 0.92
5 0.59 0.77 0.90
6 0.53 0.74 0.89
7 0.48 0.70 0.87
8 0.43 0.66 0.85
9 0.39 0.63 0.83
10 0.35 0.60 0.82
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Bull Sorting
We use the DNA genotypes to create the breeding groups of bulls.
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Create genetically diverse groups.Objective: is to maximize the probability of uniquely identifying one
sire to a calf.
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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
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Pasture 1
Pasture 2
Sire Sorting
Randomly assign one bull to each pasture.
N*(N-1)2
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Pasture 1
Sire Sorting
112/114 110/110
112/116
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Pasture 1
Sire Sorting
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
114 110/114 110/114 114/114 114/116
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Pasture 1
Sire Sorting
112/114 112/114
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
114 110/114 112/114 114/114 114/116
P(not excluded)=0.65
Not this one
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P (Excluded)
P(excluded) = 1 - { P(not excluded)i }
Across all marker loci
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Pasture 1
Sire Sorting
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
Produces calf
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Pasture 1
Sire Sorting
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
Produces calf