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Advances in Cattle Genetic Evaluations: Insights and Future Directions

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abbott
59 diapositives · 753 Views · Aug 21, 2014

This presentation explores the evolution of genetic evaluations in cattle from traditional methods to modern bioinformatics approaches. It highlights the role of the Bovine Functional Genomics Laboratory and the Animal Improvement Programs Laboratory in enhancing cattle health and productivity. Key concepts such as phenotype, predicted breeding value (PBV), heritability, and reliability are defined and discussed. The effectiveness of traditional selection programs, integration of genomic data, and upcoming developments, including stillbirth evaluation, are examined. Best practices for predicting the genetic merit of cattle are also provided.

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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.