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Genetic Evaluations: Past, Present, and Future

Genetic Evaluations: Past, Present, and Future. Curt Van Tassell USDA Agricultural Research Service AIPL – Animal Improvement Programs Laboratory BFGL – Bovine Functional Genomics Laboratory. curtvt@anri.barc.usda.gov 301-504-6501. Background. Bovine Functional Genomics Laboratory (BFGL)

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Genetic Evaluations: Past, Present, and Future

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  1. Genetic Evaluations: Past, Present, and Future Curt Van Tassell USDA Agricultural Research Service AIPL – Animal Improvement Programs Laboratory BFGL – Bovine Functional Genomics Laboratory curtvt@anri.barc.usda.gov 301-504-6501

  2. Background • Bovine Functional Genomics Laboratory (BFGL) • Structural and functional genomics of cattle • Emphasis on health and productivity • Bioinformatics (storage and use of genomic data) • Animal Improvement Programs Laboratory (AIPL) • “Traditional” genetic improvement of dairy cattle • Increasing emphasis on animal health and reproduction

  3. http://aipl.arsusda.gov

  4. Definitions • Phenotype – actual measurements from the cow, affected by genotype, environment, and errors P = G + E +  • Predicted breeding value (PBV) – the expected (average) deviation of offspring • Predicted transmitting ability (PTA) – half the PBV – is our best guess of what deviation from an average would result in offspring from a parent • Pedigree index or parent average – estimated genetics of an animal, based solely on its sire and dam

  5. Definitions – More… • Heritability – how much of the phenotype is controlled by genetics – do children resemble their parents? • Reliability – a measure of accuracy of prediction of genetic merit (specifically, the fraction of genetic variation captured in the predicted merit) • Repeatability – how much of the phenotype is controlled by the cow across records (as opposed to random noise and environment)

  6. Traditional Selection Programs • Estimate genetic merit for animals in a population • Select superior animals as parents of future generations

  7. Genetic Evaluation System • Traditional selection has been very effective for many economically important traits • Example: Milk yield • Moderately heritable • ~30 million animals evaluated 4x/yr • Uses ~70 million lactation records • Includes ~300 million test-day records • Genetic improvement is near theoretical expectation

  8. 2000 Cows Bulls 0 -2000 BV Milk -4000 -6000 1960 1970 1980 1990 2000 Year of Birth Dairy Cattle Genetics Success

  9. A Little History…

  10. A Little History…

  11. A Little History…

  12. Upcoming Developments

  13. Revised productive life • DIM > 305 credited • Differential weighting by DIM gives some advantage to cows that calve more often • Heritability reduced from 8.5 to 8% • Implementation planned for August 2006 along with revised net merit index

  14. Productive life credits - current 140 120 100 80 Credit (%) 60 40 1 2 3 20 0 0 1 2 3 4 5 Years

  15. Productive life credits – revised 140 120 100 80 Credit (%) 60 40 1 2 3 20 0 0 1 2 3 4 5 Years

  16. Evaluation for stillbirth • Stillbirth – born dead or died within 48 hours • Reported for about half of calving-ease records • Evaluation and reporting similar to calving ease • To be included in Net Merit index • Implementation planned for August 2006

  17. Stillbirth Data Stillbirth Score

  18. A Brief Tutorial on Genetic Evaluations

  19. Animal Model Genetic Evaluation • Includes an animal (genetic) effect for all animals with data or in the pedigree • That genetic prediction is a complex combination of information from • Animal’s own performance • Sire and dam predicted genetic merit • Progeny predicted merit • All these genetic effects are inter-related • e.g., granddaughter performance trickles up to a sire through the dam

  20. Animal model describes a cow's lactation record as the sum of the effects • management group, m • genetic merit (animal effect), a • permanent environment, p • interaction of her herd and sire, c • unexplained residual, e For: • herd i • year-season, parity, and registry group j • sire k • daughter l

  21. Calculation of PTA w values depend on relative amounts of information: w1 + w2 + w3 = 1

  22. What Can a Producer or Consultant Do?

  23. Predicting Genetic Merit of a Cow • Just use pedigree prediction • Sire • Dam? • Just use phenotype –milk • Use a mixture of both!!

  24. Pedigree-Based Prediction PTA of a cow is average of the sire and dam PTA. So… Cow PTA = ½ [Sire PTA + Dam PTA] Cow PBV = [Sire PTA + Dam PTA] Dam PTA = ½ [MGS PTA + MGD PTA] This prediction has a maximum reliability of 50%

  25. Genetic Variation in O Man Offspring

  26. Genetic Variation in GROUPS of O Man Offspring

  27. Phenotypic Variation in O Man Offspring

  28. Phenotypic Variation in GROUPS of O Man Offspring

  29. Pedigree-Based Prediction Cow PTA = ½ [Sire PTA + ½ MGS PTA] This prediction has a maximum reliability of 37%

  30. Phenotype-Based Prediction Cow PTA = ½ Cow PBV = ½ heritability * deviation from herd Deviation accounts for environmental effects (herd, year, season), age and parity, stage of lactation, registry status, and other effects

  31. Milk – 30% Heritabilty 25,000 Cow production 20,000 Herd Average 5,000 Deviation 1,500 PBV: predicted breeding value 30% * (+5000) 750 PTA: predicted transmitting ability 50% of breeding value

  32. SCC – 12%Heritabilty 3.00 Cow phenotype 3.70 Herd Average -.70 Deviation -0.084 PBV: predicted breeding value 12% * (-0.70) -0.042 PTA: predicted transmitting ability 50% of breeding value

  33. Reliability (Accuracy) • A measure of how confident we are about the PTA. • It ranges from 0 to .99, and increases with the added information. • Reliability depends on: • Number of information (reported as daughters equivalents) • Heritability

  34. Relationship of Reliability, Number of Progeny, and Heritability

  35. Daughter Equivalents (DE) • A measure to standardize value of information • Parents with accurate PTA are valuable • A phenotypic record is valuable • Daughters in many herds are most valuable.

  36. Heritability (h2) and Repeatability (r) h2 r k Trait .30 .55 11.3 Milk .25 .40 14 Udder .15 .25 24.7 Feet .12 .25 31.3 SCS .08 .135 48 Productive life .04 .13 98 DPR k = (4 - 2*herit) / herit

  37. Examples of DE and Reliability REL = DE / (DE + k)

  38. Female Selection • DHIA, animal ID, pedigree • Get optimal PTA from AIPL • Non-DHIA • Pedigree index derived PTA • Simple production derived PTA • Blended PTA

  39. Fundamental Principal • Zero is defined arbitrarily in evaluation system! • Absolute values of PTA are meaningless • Example: • O Man PTA NM$ is 706 • This does NOT mean that a daughter of O Man will earn you $706 more than her dam • Sharky has PTA NM$ of 599 • What you can say is that ON AVERAGE an O Man daughter will earn 706-599 = $107 MORE than a Sharky daughter from the same cow

  40. Bovine Genome Sequence

  41. Genome Project Objectives • PHASE I - 8X Genome Sequence • Line 1 Hereford cow and her sire • 6 genome equivalents in fragments from Dominette (the cow) • 2 genome equivalents in fragments from Domino (sire) based on physical location L1 Dominette 01449

  42. Shotgun Genome Sequence AGCTTTAAGCCATACCTTAG . . . GACATTACCTAGGAGCTTTAAGCCATAC AATGTACACACACACACAC . . . ACGTGCGTCGT AACTGGTCTACAG . . . GTTCAACGTCCTTGAC ATCGTTCAAGTATGCGTAAATCGTTGT . . . ACGTAATAGTACGT GTCGTAACCTGA . . . TCAACTGGTACA GTCGTACATGT . . . TGACGTAACTGA TCAACTGGTACGT . . . ACTTCCAGGAGACCTGTATC GCCACATGTAGCGT . . . TATGCGTATGTGTAAACGTGGGTACTA GTGCAACCACTGTATGCGA . . . AGTTGTGCCACGT AAACTACGTTGTTTACCAG . . . GTGGGACACTAGTGATCG TTAGACGATATCG . . . TATGACACGTACGT

  43. Shotgun Assembly ...AGCTTTAAGCCATACCTTAGGACATTACCTAGGAGCTTTAAGCCATAC... ...AGCTTTAAGCCATACCTTAGGACATTACCTAGG ...AGCTTTAAGCCATACCTTAGGACATTACCTAGGAGCTTTAAGCCATAC... GCCATACCTTAGGACATTACCTAGGAGCTTTAAGCCATAC... ...AGCTTTAAGCCATACCTTAGGACATTACCTAGGAGCTTTAAGCCATAC... ...AGCTTTAAGCCATACCTTAGGACATTACCTAGGAGCTTTAAGCCATAC... Consensus ...AGCTTTAAGCCATACCTTAGGACATTACCTAGGAGCTTTAAGCCATAC...

  44. Polymorphism “poly”= many“morph”= form General population 94% Single nucleotide polymorphism (SNP) 6% Genetic Markers • Allow inheritance to be followed in a region across generations • Single nucleotide polymorphisms (SNiP) are the markers of the future! • Need lots! • 3 million in the genome • 10,000 initial goal

  45. SNP Discovery Brahman Holstein Jersey Limousin Hereford Angus Norwegian Red

  46. SNP Discovery • Dominette data: • Consensus: • Martha: ...AGCTTTAAGCCATACCTTAGGACATTACCTAGG GCCATACCTTAGGACATTACCTAGGAGCTTTAAGCCATAC... ...AGCTTTAAGCCATACCTTAGGACATTACCTAGGAGCTTTAAGCCATAC... ...AGCTTTAAGCCATACCTTAGGACATTACCTAGGAGCTTTAAGCCATAC... ...AGCTTTAAGCCATACCTTAGGACATTACCTAGGAGCTTTAAGCCATAC... ...AGCTTTAAGCCATACCTTAGGACATTACCTAGGAGCTTTAAGCCATAC... ...AGCTTTAAGCCATACCTTAGGACATTACCTAGGAGCTTTAAGCCATAC... ...AGCTTTAAGCCATACCTTAGGATATTACCTAGGAGCTTTAAGCCATAC... SNP

  47. Validate and Genotype SNP • Characterize potential SNP across ~25 breeds • Preliminary goal was to characterize at least 20,000 SNP • Industry assisting in the funding of the validation through genotyping cost

  48. SNP Project Outcomes • Parentage verification & traceability panels • Enhanced quantitative trait locus mapping • Genome-wide selection

  49. Haplotype • From “haploid genotype.” A set of closely linked alleles (genes or DNA polymorphisms) inherited as a unit. Different combinations of polymorphisms are known as haplotypes.

  50. Linkage disequilibrium (LD) • The non-random association of alleles at two or more loci, not necessarily on the same chromosome. • It is not the same as linkage, which describes the association of two or more loci on a chromosome with limited recombination between them. • LD describes a situation in which some combinations of alleles or genetic markers occur more or less frequently in a population than would be expected from a random formation of haplotypes from alleles based on their frequencies.

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