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

MarkerFavorable

AlleleUnfavorable

Allele

316 C G

530 G A

Calpain Marker Genotype Counts

SNP 316

CC CG GG

SNP530

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 f(G allele) = .82

Equilibrium Genotype Frequencies:

CC = .032

CG = .296

GG = .672

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

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 -

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 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

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

Progeny Genotype

Progeny Phenotype

Progeny Genotype

Progeny Phenotype

Sire Haplotype

Sire Genotype

Dam Haplotype

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)

• EPD

– Expected Haplotype Effect given sire

genotype

– Polygenic effect

Marker Assisted EPD’s

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 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

-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

-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

-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

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.

The probability of uniquely identifying the sire in a group of “N” bulls is:

( Exclusion rate ) N

Sire Identification

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

Bull Sorting

We use the DNA genotypes to create the breeding groups of bulls.

Create genetically diverse groups.Objective: is to maximize the probability of uniquely identifying one

sire to a calf.

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

Pasture 2

Sire Sorting

Randomly assign one bull to each pasture.

N*(N-1)2

Pasture 1

Sire Sorting

112/114 110/110

112/116

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

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

P (Excluded)

P(excluded) = 1 - { P(not excluded)i }

Across all marker loci

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

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