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 316CG 530GA

Calpain Marker Genotype Counts SNP 316 CCCGGG SNP 530 AA0426 AG34081 GG63774

Frequency at SNP 316 GenotypeCCCGGG Count Frequency f(C allele) =.18 f(G allele) =.82 Equilibrium Genotype Frequencies: CC =.032 CG =.296 GG =.672

Frequency at SNP 530 GenotypeAAAGGG Count Frequency f(A allele) =.23 f(G allele) =.77 Equilibrium Genotype Frequencies: AA =.053 AG =.354 GG =.593

Calpain: 2 Additive Genotypes SNPGenotype WBSF (lbs) SE (lbs) 316 CC CG GG AA AG GG0-

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 Santa GertrudisSimbrah CC -.84  0.60 N = N = 0 CG -.71  0.29 N = .39 N = 41 GG 0 N =166 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 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 Genotype 316Frequency 530CCCGGGGenoAllele AA AG GG Geno Freq Allele Freq

Four Gametes

WBSF: EPD vs MA-EPD Genotype

WBSF: EPD vs MA-EPD

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

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/ / /116

Pasture 1 Sire Sorting 112/ / /116 Damsf(110)f(112)f(114)f(116) Sire /112112/112112/114112/ / /114114/116

Pasture 1 Sire Sorting 112/ /116 Damsf(110)f(112)f(114)f(116) Sire /112112/112112/114112/ /114112/114114/114114/116 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/ / /116 Damsf(110)f(112)f(114)f(116) Sire /112112/112112/114112/ /114112/114114/114114/116 P(not excluded)=0.5 Produces calf

Pasture 1 Sire Sorting 112/ / /116 Damsf(110)f(112)f(114)f(116) Sire /110110/112110/114110/116 P(not excluded)=0.4 Produces calf