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Genetic improvement programs for US dairy cattle

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Talk on the genetic and genomic evaluation system for US dairy cattle made to scientists at Embrapa Gado de Leite in Juiz de Fora, MG, Brasil, on September 10, 2014.

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Genetic improvement programs for US dairy cattle

  1. 1. 2014 Genetic improvement programs for US dairy cattle John B. Cole Animal Genomics and Improvement Laboratory Agricultural Research Service, USDA Beltsville, MD john.cole@ars.usda.gov
  2. 2. U.S. dairy population and milk yield 10,000 8,000 6,000 4,000 2,000 0 30 25 20 15 10 5 0 40 50 60 70 80 90 00 10 Milk yield (kg/cow) Cows (millions) Year Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (2) Cole
  3. 3. U.S. DHI dairy statistics (2011) l 9.1 million U.S. cows l ~75% bred AI l 47% milk recorded through Dairy Herd Information (DHI) w 4.4 million cows − 86% Holstein − 8% crossbred − 5% Jersey − <1% Ayrshire, Brown Swiss, Guernsey, Milking Shorthorn, Red & White w 20,000 herds w 220 cows/herd w 10,300 kg/cow Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (3) Cole
  4. 4. Collaboration with industry l Council on Dairy Cattle Breeding (CDCB) responsible for receiving data and for computing and delivering US genetic evaluations for dairy cattle l AIP responsible for research and development to improve the evaluation system l CDCB and AIP employees co-located in Beltsville l Dr. João Dürr is CDCB CEO Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (4) Cole
  5. 5. Council on Dairy Cattle Breeding CDCB PDCA NAAB DRPC DHIA Purebred Dairy Cattle Association National Association of Animal Breeders Dairy Records Processing Centers Information Association l 3 board members from each organization l Total of 12 voting members l 2 nonvoting industry members Dairy Herd Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (5) Cole
  6. 6. Genetic evaluation advances Year Advance Gain, % 1862 USDA established 1895 USDA begins collecting dairy records 1926 Daughter-dam comparison 100 1962 Herdmate comparison 50 1973 Records in progress 10 1974 Modified contemporary comparison 5 1977 Protein evaluated 4 1989 Animal model 4 1994 Net merit, productive life, and somatic cell score 50 2008 Genomic selection >50 Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (6) Cole
  7. 7. Animal model 1989 to present Introduced by Wiggans and VanRaden Advantages Information from all relatives Adjustment for genetic merit of mates Uniform procedures for males and females Best prediction (BLUP) Crossbreds included (2007) Genomic information added (2008) Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (7) Cole
  8. 8. Traits evaluated Year Trait Year Trait 1926 Milk & fat yields 2000 Calving ease1 1978 Conformation (type) 2003 Daughter pregnancy rate 1978 Protein yield 2006 Stillbirth rate 1994 Productive life 2006 Bull conception rate2 1994 Somatic cell score (mastitis) 2009 Cow and heifer conception rates 1Sire calving ease evaluated by Iowa State University (1978–99) 2Estimated relative conception rate evaluated by DRMS in Raleigh, NC (1986–2005) Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (8) Cole
  9. 9. Evaluation methods for traits Heritability Animal model (linear) Yield (milk, fat, protein) Type (AY, BS, GU, JE) Productive life Somatic cell score Daughter pregnancy rate Heifer conception rate Cow conception rate Sire–maternal grandsire model (threshold) Service sire calving ease Daughter calving ease Service sire stillbirth rate Daughter stillbirth rate 25 – 40% 7 – 54% 8.5% 12% 4% 1% 1.6% 8.6% 3.6% 3.0% 6.5% Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (9) Cole
  10. 10. Type traits Stature Strength Body depth Dairy form Rump angle Thurl width Rear legs (side) Rear legs (rear) Foot angle Feet and legs score Fore udder attachment Rear udder height Rear udder width Udder cleft Udder depth Front teat placement Rear teat placement Teat length Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (10) Cole
  11. 11. Holstein milk (kg) 1,000 0 -1,000 -2,000 -3,000 -4,000 Phenotypic base = 11,828 kg Cows Sires 1960 1970 1980 1990 2000 2010 Breeding value (kg) Birth year Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (11) Cole
  12. 12. Holstein productive life (mo) 2 0 -2 -4 -6 -8 -10 Phenotypic base = 27.2 mo Sires Cows 1960 1970 1980 1990 2000 2010 Breeding value (mo) Birth year Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (12) Cole
  13. 13. Holstein somatic cell score (log2) 3.10 3.00 2.90 2.80 2.70 Sires Cows Phenotypic base = 3.0 1984 1988 1992 1996 2000 2004 2008 Breeding value (log2) Birth year Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (13) Cole
  14. 14. Holstein daughter pregnancy rate (%) 8.0 6.0 4.0 2.0 0.0 -2.0 Sires Cows Phenotypic base = 22.6% 1960 1970 1980 1990 2000 2010 Breeding value (%) Birth year Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (14) Cole
  15. 15. Holstein calving ease (%) 11.0 10.0 9.0 8.0 7.0 6.0 Daughte r Service-sire phenotypic base = 7.9% Daughter phenotypic base = 7.5% Service sire 0.01%/yr 1980 1985 1990 1995 2000 2005 2010 PTA (% difficult births in heifers) Birth year Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (15) Cole
  16. 16. Genetic-economic indices (2010) Trait Relative value (%) Net Cheese merit merit Fluid merit Milk (lb) 0 –15 19 Fat (lb) 19 13 20 Protein (lb) 16 25 0 Productive life (PL, mo) 22 15 22 Somatic cell score (SCS, log2) –10 –9 –5 Udder composite (UC) 7 5 7 Feet/legs composite (FLC) 4 3 4 Body size composite (BSC) –6 –4 –6 Daughter pregnancy rate (DPR, %) 11 8 12 Calving ability (CA$, $) 5 3 5 Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (16) Cole
  17. 17. Index changes Trait Relative emphasis on traits in index (%) PD$ 1971 MFP$ 1976 CY$ 1984 NM$ 1994 NM$ 2000 NM$ 2003 NM$ 2006 NM$ 2010 Milk 52 27 –2 6 5 0 0 0 Fat 48 46 45 25 21 22 23 19 Protein … 27 53 43 36 33 23 16 PL … … … 20 14 11 17 22 SCS … … … –6 –9 –9 –9 –10 UDC … … … … 7 7 6 7 FLC … … … … 4 4 3 4 BDC … … … … –4 –3 –4 –6 DPR … … … … … 7 9 11 SCE … … … … … –2 … … DCE … … … … … –2 … … CA$ … … … … … … 6 5 Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (17) Cole
  18. 18. Traditional evaluation summary Evaluation procedures have improved Fitness traits have been added Effective selection has produced substantial annual genetic improvement Indices enable selection for overall economic merit Fertility evaluations prevent continued decline Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (18) Cole
  19. 19. Genomic evaluation system Provides timely evaluations of young bulls for purchasing decisions Increases accuracy of evaluations of bull dams Assists in selection of service sires, particularly for low-reliability traits High demand for semen from genomically evaluated 2-year-old bulls Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (19) Cole
  20. 20. Genomic data flow Dairy Herd Improvement (DHI) producer DNA samples genotypes Council on Dairy Cattle Breeding (CDCB) DNA laboratory AI organization, breed association Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (20) Cole
  21. 21. Progression of chips Bovine3K BeadChip (3K) Jul BovineHD BeadChip (777K) Jan 2008 2009 2010 Dec Official 3K evaluations Sep Unofficial 3K evaluations Aug Official 50K Brown Swiss evaluations Jan Official 50K Holstein & Jersey evaluations BovineSNP50 BeadChip Apr (50K) Jan Unofficial 50K evaluations Zoetis LD BeadChip (12K) Sep GGP HD BeadChip (77K) GGP v2 BeadChip (19K) May Dec GeneSeek Genomic Profiler (GGP) BeadChip (8K) Feb BovineLD BeadChip (7K) Sep 2011 2012 2013 Oct Official 12K evaluations May Official 19K evaluations Jan Official 77K evaluations Mar Official 8K evaluations Dec Official 7K & 648K evaluations Aug Affymetrix BOS 1 Official 777K evaluations Plate Array (648K) Jan Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (21) Cole
  22. 22. Evaluation flow Animal nominated for genomic evaluation by breed association or AI organization Hair or other DNA source sent to genotyping lab DNA extracted and placed on chip for 3-day genotyping process Genotypes sent from genotyping lab to AIPL for accuracy review Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (22) Cole
  23. 23. Laboratory quality control Each SNP evaluated for Call rate Portion heterozygous Parent-progeny conflicts Clustering investigated if SNP exceeds limits Number of failing SNPs indicates genotype quality Target of <10 SNPs in each category Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (23) Cole
  24. 24. Evaluation flow (continued) Genotype calls modified as necessary Genotypes loaded into database Nominators receive reports of parentage and other conflicts Pedigree or animal assignments corrected Genotypes extracted and imputed to 45K SNP effects estimated Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (24) Cole
  25. 25. Imputation Based on splitting genotype into individual chromosomes (maternal and paternal contributions) Missing SNPs assigned by tracking inheritance from ancestors and descendants Imputed dams increase predictor population Genotypes from all chips merged by imputing SNPs not present Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (25) Cole
  26. 26. findhap Developed by Dr. Paul VanRaden, ARS, USDA Divides chromosomes into segments Allows for successively shorter segments (usually 3 runs) Long segments lock in identical by descent Shorter segments fill in missing SNPs Separates genotype into maternal and paternal contribution, haplotypes (phasing) Builds haplotype library sequenced by frequency Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (26) Cole
  27. 27. Evaluation flow (continued) Final evaluations calculated Evaluations released to dairy industry Download from CDCB FTP site with separate files for each nominator Monthly release for new animals All genomic evaluations updated 3 times each year with traditional evaluations Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (27) Cole
  28. 28. Genomic evaluation results Source: https://www.cdcb.us/Report_Data/Marker_Effects/marker_effects.cfm?Breed=HO&Trait=Net_Merit Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (28) Cole
  29. 29. Information sources for evaluations Traditional evaluations of genotyped bulls and cows used to estimate SNP effects Combined final evaluation Sum of SNP effects for an animal’s alleles Polygenetic effect Traditional evaluation Pedigree data used and validated by genotypes Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (29) Cole
  30. 30. Genotypes received since July 2013 Breed Female Male All animals % female Ayrshire 1,359 229 1,588 86 Brown Swiss* 892 6,253 7,145 12 Holstein 172,956 31,657 204,613 85 Jersey** 26,434 4,804 31,238 85 All 201,641 42,943 244,584 82 *Includes >5,000 bulls added from Interbull in June 2014 **Includes 1,068 Danish bulls added in November 2013 Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (30) Cole
  31. 31. Genotypes evaluated 400,000 350,000 300,000 250,000 200,000 150,000 100,000 50,000 0 Jun A O Jan Young imputed Old imputed Female Young <50K Male Young <50K Female Old <50K Male Old <50K Female Young >=50K Male Young >=50K Female Old >=50K Male Old >=50K F A M J J A S O N D Jan F M A M J J A S O N D Jan F M A M J J A S O N D Jan F M A M J J A S Animals genotyped (no.) 2009 2010 2011 2012 2013 Evaluation date Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (31) Cole
  32. 32. Growth in bull predictor population Breed May 2014 12-mo gain Ayrshire 678 30 Brown Swiss 5,862 366 Holstein 25,276 2,361 Jersey 4,262 1,391 Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (32) Cole
  33. 33. Reliabilities for young Holsteins* 50K genotypes 3K genotypes 40 45 50 55 60 65 70 75 80 Reliability for PTA protein (%) *Animals with no traditional PTA in April 2011 9000 8000 7000 6000 5000 4000 3000 2000 1000 0 Number of animals Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (33) Cole
  34. 34. Holstein prediction accuracy Trait Bias* Final score 0.1 58.8 22.7 Stature −0.2 68.5 30.6 Dairy form −0.2 71.8 34.5 Rump angle 0.0 70.2 34.7 Rump width −0.2 65.0 28.1 Feed and legs 0.2 44.0 12.8 Fore udder attachment −0.2 70.4 33.1 Rear udder height −0.1 59.4 22.2 Udder depth −0.3 75.3 37.7 Udder cleft −0.2 62.1 25.1 Front teat placement −0.2 69.9 32.6 Teat length −0.1 66.7 29.4 *2013 deregressed value – 2009 genomic evaluation Reliability (%) Reliability gain (% points) Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (34) Cole
  35. 35. Parent ages of marketed Holstein bulls 140 120 100 80 60 40 20 0 Sire Dam 2007 2008 2009 2010 2011 2012 2013 Parent age (mo) Bull birth year Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (35) Cole
  36. 36. Marketed Holstein bulls Year entered AI Traditional progeny-tested Young genotyped All bulls 2008 1,798 0 1,798 2009 1,909 337 2,246 2010 1,827 376 2,203 2011 1,441 467 1,908 2012 1,376 555 1,931 Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (36) Cole
  37. 37. Genetic merit of marketed Holstein bulls 800 700 600 500 400 300 200 100 0 -100 Average gain: $19.77/year Average gain: $52.00/year Average gain: $85.60/year 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 Average net merit ($) Year entered AI Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (37) Cole
  38. 38. Genomic prediction of progeny test 0 1 2 3 4 5 Select parents, transfer embryos to recipients Calves born and DNA tested Calves born from DNA-selected parents Bull receives progeny test Reduce generation interval from 5 to 2 years Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (38) Cole
  39. 39. Genetic choices Before genomics: Proven bulls with daughter records (PTA) Young bulls with parent average (PA) After genomics: Young animals with DNA test (GPTA) Reliability of GPTA ~70% compared to PA ~35% and PTA ~85% for Holstein NM$ Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (39) Cole
  40. 40. Young bulls: 2013 NM$ vs. 2010 PA 900 700 500 300 100 -100 -300 -500 -500 -300 -100 100 300 500 700 900 Net Merit, Dec. 2013 PA Net Merit, April 2010 Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (40) Cole
  41. 41. Proven bulls: 2013 vs. 2010 NM$ 900 700 500 300 100 -100 -300 -500 -500 -300 -100 100 300 500 700 900 Net Merit, Dec. 2013 Net Merit, April 2010 Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (41) Cole
  42. 42. Young bulls: 2013 vs. 2010 NM$ 900 700 500 300 100 -100 -300 -500 -500 -300 -100 100 300 500 700 900 Net Merit, Dec. 2013 Net Merit, April 2010 Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (42) Cole
  43. 43. % genotyped mates of top young bulls 100 90 80 70 60 50 40 30 20 10 0 Numero Uno Mogul Maurice Elvis ISY Altatrust Garrold Fernand Supersire S S I Robust Topaz 700 725 750 775 800 825 850 875 900 925 Net Merit (Aug 2013) Percentage of mates genotyped Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (43) Cole
  44. 44. Why genomics works for dairy cattle Extensive historical data available Well-developed genetic evaluation program Widespread use of AI sires Progeny-test programs High-value animals worth the cost of genotyping Long generation interval that can be reduced substantially by genomics Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (44) Cole
  45. 45. Key issues for the dairy industry Inbreeding and genetic diversity (including across breeds) Sequencing, new genes, and mutations Novel traits, resource populations (feed efficiency, health, milk properties) Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (45) Cole
  46. 46. Application to more traits Animal’s genotype good for all traits Traditional evaluations required for accurate estimates of SNP effects Traditional evaluations not currently available for heat tolerance or feed efficiency Research populations could provide data for traits that are expensive to measure Will resulting evaluations work in target population? Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (46) Cole
  47. 47. Parentage validation and discovery Parent-progeny conflicts detected Animal checked against all other genotypes Reported to breeds and requesters Correct sire usually detected Maternal grandsire (MGS) checking SNP at a time checking Haplotype checking more accurate Breeds moving to accept SNPs in place of microsatellites Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (47) Cole
  48. 48. Haplotypes affecting fertility Rapid discovery of new recessive defects Large numbers of genotyped animals Affordable DNA sequencing Determination of haplotype location Significant number of homozygous animals expected, but none observed Narrow suspect region with fine mapping Use sequence data to find causative mutation Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (48) Cole
  49. 49. Haplotypes affecting fertility frequency (%) Earliest known ancestor HH1 5 63.2* 4.5 Pawnee Farm Arlinda Chief HH2 1 94.9–96.6 4.6 Willowholme Mark Anthony HH3 8 95.4* 4.7 Glendell Arlinda Chief, Gray View Skyliner HH4 1 1.3* 0.7 Besne Buck HH5 9 92.4–93.9 4.4 Thornlea Texal Supreme JH1 15 15.7* 23.4 Observer Chocolate Soldier BH1 7 42.8–47.0 14.0 West Lawn Stretch BH2 19 10.6–11.7 15.4 Rancho Rustic My Design AH1 17 65.9–66.2 23.6 Selwood Betty’s *Causative mutation known Name Chromo-some Location (Mbp) Carrier Improver Commander Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (49) Cole
  50. 50. Haplotypes to track known recessives Concord-ance (%) New carriers (no.) BLAD HHB 1* 11,782 99.9 314 CVM HHC 3* 13,226 — 2,716 DUMPS HHD 1* 3,242 100.0 3 Mule foot HHM 15* 87 97.7 120 Horned HHP 1 345 — 2,050 Red coat HHR 18* 4,137 — 5,927 color SDM BHD 11* 108 94.4 108 SMA BHM 24* 568 98.1 111 Weaver BHW 4 163 96.3 32 *Causative mutation known Recessive Haplotype Chromo-some Tested animals (no.) Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (50) Cole
  51. 51. International dairy breeding Genotype alliances North America (US, Canada, UK, Italy) Ireland, New Zealand Netherlands, Australia Eurogenomics (Denmark/Sweden/Finland, France, Germany, Netherlands/Belgium, Spain, Poland) Interbull genomic multitrait across-country evaluation (GMACE) Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (51) Cole
  52. 52. Impact on breeders Haplotype and gene tests in selection and mating programs Trend towards a small number of elite breeders that are investing heavily in genomics About 30% of young males genotyped directly by breeders since April 2013 Prices for top genomic heifers can be very high (e.g., $265,000 ) Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (52) Cole
  53. 53. Impact on dairy producers General Reduced generation interval Increased rate of genetic gain More inbreeding/homozygosity? Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (53) Cole
  54. 54. Impact on dairy producers (continued) Sires Higher average genetic merit of available bulls More rapid increase in genetic merit for all traits Larger choice of bulls in terms of traits and semen price Greater use of young bulls Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (54) Cole
  55. 55. Conclusions Genomic evaluation has dramatically changed dairy cattle breeding Rate of gain is increasing primarily because of a large reduction in generation interval Genomic research is ongoing Detect causative genetic variants Find more haplotypes affecting fertility Improve accuracy through more SNPs, more predictor animals, and more traits Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (55) Cole
  56. 56. U.S. genomic evaluation team Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (56) Cole
  57. 57. Questions? Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (57) Cole

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