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AIPL 5-Year Genomics Research Plan

This plan focuses on genomics research, crossbreeding, fertility, and other aspects in dairy traits. The objectives include collecting genotypes and new phenotypes, improving prediction accuracy using SNP data, and estimating economic values of traits for maximum profitability.

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AIPL 5-Year Genomics Research Plan

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  1. Research Plans for Genomics, Crossbreeding, Fertility, etc.

  2. AIPL 5-Year Plan 2007-2012 • Objectives • Collect genotypes, new phenotypes • Document current status and effects of management on dairy traits • Improve accuracy of predictions by including SNP data, refining models • Estimate economic values of traits to maximize lifetime profit

  3. Genomic Goals • Predict young bulls and cows more accurately • Compare actual DNA inherited • Use exact relationship matrix G instead of expected values in A • Trace chromosome segments • Locate genes with large effects

  4. How Related are Relatives? • Example: Full sibs • are expected to share 50% of their DNA on average • may actually share 45% or 55% of their DNA because each inherits a different mixture of chromosome segments from the two parents. • Combine genotype and pedigree data to determine exact fractions

  5. Genomic Relationships • Measures of genetic similarity • A = Expected % genes identical by descent from pedigree (Wright, 1922) • G = Actual % of DNA shared (using genotype data) • T = % genes shared that affect a given trait (using genotype and phenotype) • Best measure depends on use

  6. QTL Relationship Matrix (T) • Three bulls each +50 PTA protein. • Are their QTL alleles the same? • Possibly, but probably not. • Bull A could have 10 positive genes. • Bull B could have 10 positive genes, not on same chromosomes as bull A. • Bull C could have 20 positive and 10 negative genes.

  7. Genes in Common at One Locus w = gene from sire of sire x = gene from dam of sire y = gene from sire of dam z = gene from dam of dam

  8. Alleles Shared by Sibs

  9. Unrelated Individuals? • No known common ancestors • Many unknown common ancestors born before the known pedigree • G = Z Z’ / number of loci • Elements of Z are –p and (1 – p), where p is allele frequency • Relationships in base = 0 +/- LD

  10. Traditional Pedigree

  11. Genomic Pedigree

  12. Example of a SNP haplotype SNP SNP SNP Chr1 caacgtat … atccgcat … tctaggat … Chr2 caacggat … atccgaat … tctcggat … Haplotype 1 tca Haplotype 2 gac Haplotype is a set of single nucleotide polymorphisms (SNPs) associated on a single chromosome. Identification of a few alleles of a haplotype block can identify other polymorphic sites in the region.

  13. SNP Pedigree atagatcgatcg ctgtagcgatcg ctgtagcttagg agatctagatcg agggcgcgcagt cgatctagatcg ctgtctagatcg atgtcgcgcagt cggtagatcagt agagatcgcagt agagatcgatct atgtcgctcacg atggcgcgaacg ctatcgctcagg

  14. Haplotype Pedigree atagatcgatcg ctgtagcgatcg ctgtagcttagg agatctagatcg agggcgcgcagt cgatctagatcg ctgtctagatcg atgtcgcgcagt cggtagatcagt agagatcgcagt agagatcgatct atgtcgctcacg atggcgcgaacg ctatcgctcagg

  15. Genotype PedigreeCount number of copies of second allele 0 = homozygous for first allele 1 = heterozygous 2 = homozygous for second allele

  16. Reliability from Full SibsMarker and QTL positions identical, sib REL = 99% A = traditional additive relationships, G = genomic relationships

  17. Bulls to Genotype58,533 SNP Project • Choose HO bulls with semen at BFGL • Genotype 1777 proven bulls • Born 1994-1996 with >75% REL NM • Plus 172 ancestor bulls born 1952-1993 • Predict 500 bulls sampled later • Born 2001 with >75% REL NM • Include other bulls in gap years? • Born 1997-2000 (proven) or >2002 (waiting)

  18. Birth Years of Bulls to Genotype Data cutoff

  19. Contributors of DNA500 CDDR bulls to predict, born in 2001

  20. Potential ResultsSimulation of 50,000 SNPs • QTLs normally distributed, n = 100 • Reliability vs parent average REL • 58% vs 36% if QTLs are between SNPs • 71% vs 36% if QTLs are located at SNPs (not likely) • Higher REL if major loci and Bayesian methods used, lower if many loci (>100) affect trait

  21. Reliability from Genotyping • Daughter equivalents • DETotal = DEPA + DEProg + DEY + DEG • DEG is additional DE from genotype • REL = DEtotal / (DETotal + k) • Gains in reliability • DEG could be about 15 for Net Merit • More for traits with low heritability • Less for traits with high heritability

  22. Genomic Computer Programs • Simulate SNPs and QTLs • Compare SNP numbers, size of QTLs • Calculate genomic EBVs • Use selection index, G instead of A • Use iteration on data for SNP effects • Form haplotypes from genotypes • Not programmed yet

  23. Computing Times • Inversion including G matrix • Animals2 x markers to form G matrix • Animals3 to invert selection index • 10 hours for 3000 bulls, 50,000 SNPs • Iteration on genotype data • Markers x animals x iterations • 16 hours for 1000 iterations

  24. Distribution of Marker Effects

  25. Linear vs Non-linear Models

  26. All-Breed Model: Goals • Evaluate crossbred animals without biasing purebred evaluations • Accurately estimate breed differences • Compare crossbreeding strategies • Compute national evaluations and examine changes • Display results without confusion

  27. Methods • All-breed animal model • Purebreds and crossbreds together • Relationship matrix among all • Unknown parents grouped by breed • Variance adjustments by breed • Age adjust to 36 months, not mature • Within-breed-of-sire model examined but not used

  28. Data • Numbers of cows of all breeds • 22.6 million for milk and fat • 16.1 million for protein • 22.5 million for productive life • 19.9 million for daughter pregnancy rate • 10.5 million for somatic cell score • Type traits are still collected and evaluated in separate breed files

  29. Purebred and Crossbred DataUSA milk yield records

  30. Crossbred Cowswith 1st parity records

  31. Number of Cows with Records(with > 50% heterosis; March 2007)

  32. Number of Cows with Records(with > 50% heterosis; March 2007)

  33. Crossbred Daughters Addedfor sires in top 10 NM$ within breed

  34. Heterosis for Yield TraitsPercent of Parent Breed Average

  35. All-Breed Analyses • Crossbred animals • Now have PTAs, only 3% did before if in breed association grading-up programs • Reliable PTAs from both parents • Purebred animals • Information from crossbred relatives • More herdmates (other breeds, crossbreds) • Routinely used in other populations • New Zealand (1994), Netherlands (1997) • USA goats (1989), calving ease (2005)

  36. Unknown Parent Groups • Look up PTAs of known parents • Estimate averages for unknowns • Group unknown parents by • Birth year • Breed • Path (dams of cows, sires of cows, parents of bulls) • Origin (domestic vs other countries)

  37. All- vs Within-Breed EvaluationsCorrelations of PTA Milk

  38. Display of PTAs • Genetic base • Convert all-breed base to within-breed bases (or vice versa) • PTAbrd = (PTAall – meanbrd) SDbrd/SDHO • PTAall = PTAbrd (SDHO/SDbrd) + meanbrd • Heterosis and inbreeding • Both effects removed in the animal model • Heterosis added to crossbred animal PTA • Expected Future Inbreeding (EFI) and merit differ with mate breed

  39. All-Breed PTAs – March Test Run • Genetic correlations mostly same • JE increase .02 for PL and .01 for SCS • BS decrease .01 for fat and SCS • AY increase .01 for PL • USA bulls in top 100 differ little • Numbers are averages across all scales • JE improve for SCS, fat (26 vs 25) • JE decline for milk, protein (59 vs 62) • BS decline for yield (10 vs 15) • HO improve for yield (17 vs 16)

  40. Jersey and Swiss PTAs • Base cow means changed little • Base cow SD changed little • Top bulls for protein dropped by ~9 lbs, bottom bulls dropped by ~4 lbs in both breeds • Unknown parent grouping, heterosis may be responsible

  41. All-breed Trend Validation • 85 tests, 6 were significant (.05) • None significant for milk or SCS • 1 of 15 for fat and for protein • 2 of 15 for PL and for DPR • Increase in DPR repeatability made trend more negative, helped tests

  42. Daughter Pregnancy RateGenetic trend on all-breed base

  43. Assumed Effects – Other TraitsTransmitting ability differences from Holstein

  44. Merit of F1 Holstein Crossbreds2006 Merit Indexes Compared to 2005 genetic base for Holstein

  45. Later Generation Crosses Compared to 2005 genetic base for Holstein

  46. Three-Breed Crosses USDA Yearbook of Agriculture 1947 Butterfat yield of three breed crosses was greater than from their F1 crossbred dams. Three breed crosses averaged 14,927 pounds of milk and 641 pounds of butterfat as 2-year-olds in 1947.

  47. Crossbreeding Conclusions • All-breed model accounts for: • Breed effects and general heterosis • Unequal variances within breed • Implemented in May 2007 • PTA converted back to within-breed bases, crossbreds to breed of sire • PTA changes larger in breeds with fewer animals

  48. Cow Fertility Research • Daughter Pregnancy Rate works well, except that • Other traits are evaluated by Interbull • Other countries don’t use DPR in their indexes, and their calving interval data comes too late • Synchronization changes traits

  49. Emphasis on Fertility, Longevity(% of total merit)

  50. Days Open Genetic Correlations Jorjani, 2005 Interbull Bulletin DFS = Denmark-Finland-Sweden

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