Validation of genomic predictions and genomic reliability

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Presentation transcript:

Validation of genomic predictions and genomic reliability Mel Tooker and Paul VanRaden USDA, Agricultural Research Service, Animal Genomics and Improvement Laboratory, Beltsville, MD, USA melvin.tooker@ars.usda.gov

Topics Interbull validation of genomic predictions (GPTAs) Predict later deregressed GPTA from earlier GPTA, weighted by later genomic reliability (GREL) Simpler validation of GPTA Predict later GPTA from earlier GPTA Simple validation of GREL Estimate earlier GREL from later GREL, genetic standard deviation (SD), and SD of change (later GPTA – earlier GPTA) Gains in reliability (REL) from more frequent updates

Interbull International Bull Evaluation Service (Uppsala, Sweden) Permanent subcommittee of the International Committee for Animal Recording (ICAR) Responsible for: Documenting evaluation systems Forming technical workgroups Multiple-trait, across-country evaluation (MACE) Selling domestic semen in many other countries requires that methods used to calculate PTA and GPTA are validated by Interbull every 2 years ICAR - world-wide organization for the standardization of animal recording and productivity evaluations 19 million units of semen were exported in 2016 worth about 143 million dollars (NAAB)

Trend validation – ideal net merit (NM$)

Trend validation – NM$ for proven bulls

Trend validation – NM$ for all bulls

Holstein GPTA validation (preliminary) Trait Slope* Intercept* Milk 0.98 –113 Fat 0.92 –2.4 Protein 0.88 –1.7 Somatic cell score 1.06 0.3 Daughter pregnancy rate 1.04 0.1 Heifer conception rate –0.3 Final score 1.03 –0.2 Strength *After base change adjustment

Simple GPTA validation results 3,984 young Holstein bulls from Aug. 2014 with >100 daughters in Aug. 2017 Trait R2 (%) Intercept* Slope* Net merit 82 –12 1.00 Milk 81 –131 1.01 Fat –3 0.95 Protein 80 0.94 Productive life 83 –0.3 1.07 Somatic cell score 77 –0.04 Daughter pregnancy rate 79 0.1 1.06 Cow conception rate 0.4 1.08 Heifer conception rate 68 0.97 *After base change adjustment

Top proven Holstein bulls (August 2014) Now with >1,000 daughters NM$ Progeny (2017) Bull 2017 2014* PA 2014* Daughters AI sons Robust 744 615 339 2,319 102 Erdman 704 593 304 5,433 10 Twist 551 567 249 2,302 4 AltaGreatest 644 555 1,365 AltaFairway 571 550 376 2,460 Diesel 457 522 302 1,242 Yano 466 518 328 11,074 14 Facebook 440 504 320 5,766 Awesome 492 322 7,334 Manifold 558 501 260 58,030 3 Top 10 average 563 543 311 9,733 16 *Base-adjusted values

Top young Holstein bulls (August 2014) Now with >1,000 daughters NM$ Progeny (2017) Bull 2017 2014* PA 2014* Daughters AI sons Troy 635 747 494 1,209 58 Rogers 688 708 533 1,338 17 Cabriolet 866 680 507 6,354 10 Ponder 726 660 1,295 6 Emerald 512 656 500 1,198 Bombero 693 648 538 1,245 35 Halogen 323 642 475 1,922 83 Jayden 626 464 2,927 3 Supersire 855 510 16,627 260 Donatello 755 639 561 4,743 18 Top 10 average 668 666 511 3,886 50 *Base-adjusted values

Top proven Jersey bulls (August 2014) Now with >100 daughters NM$ Progeny (2017) Bull 2017 2014* PA 2014* Daughters AI sons Volcano 433 515 313 4,167 29 Magnum 501 497 218 5,985 10 Link 427 450 213 1,760 3 Daybreak 510 410 281 935 5 Hickey 429 405 292 266 4 Bindy 395 404 153 619 Arhil 346 402 149 106 Zimpel 408 396 190 100 Victory 351 385 295 1,522 2 Memo 158 383 –64 243 Top 10 average 425 204 1,570 *Base-adjusted values

Top young Jersey bulls (August 2014) Now with >100 daughters NM$ Progeny (2017) Bull 2017 2014* PA 2014* Daughters AI sons Harris 654 567 386 2,113 59 Mackenzie 525 536 313 383 8 Machete 421 535 379 2,283 Formidable 424 530 405 642 6 Hector 393 528 376 138 1 Walter 561 522 389 289 3 Revolution 457 516 347 215 2 Marlo 684 508 420 407 19 Pilgrim 559 486 298 600 4 Chili 514 335 984 17 Top 10 average 519 365 805 13 *Base-adjusted values

Data for validating GREL Published GPTAs April 2014 (GREL2014) April 2017 (GREL2017) SD of difference in GPTAs REML estimates of SD of true transmitting ability (TA) from Interbull MACE

Example GREL validation (Holstein protein) Average published GREL2014 was 0.76 GREL2017 was 0.95 SD of change was 8.4 REML TA SD was 17.5 Observed GREL2014 for protein calculated as: GREL2014= 0.95 – 8.42/17.52 = 0.72

Observed vs. published GREL (2014) Jersey Holstein Trait* Obs Pub Diff Milk 73 68 +5 72 76 –4 Fat +4 74 –2 Protein 71 +3 PL 47 55 -8 65 70 –5 SCS 64 62 +2 77 DPR 63 52 +11 69 +1 NM$ Average *PL = productive life; SCS = somatic cell score; DPR = daughter pregnancy rate

Average REL for NM$ by age

Phenotypic update frequency Suppose REL increases steadily from REL1 to REL2 across a year Gain in REL from n updates per year (RELn) instead of 1 annual update should average: Example: Suppose average bull REL increases from 75% (REL1) to 91% (REL2) when 4 years old (no daughters → many daughters) Minimum gain is 0% with an annual update because bulls would stay at 75% for the whole year Maximum gain is 8% with instant updating; bulls would average (75 + 91)/2 = 83% during that year

Phenotypic update frequency (continued)

REL gains by update frequency Updates Young Proven REL (%) Marginal gain Annual 1 73.10 0.00 75.00 6 months 2 73.70 0.60 79.00 4.00 4 months 3 73.90 0.20 80.30 1.30 3 months 4 74.00 0.10 81.00 0.70 2 months 6 74.10 81.60 Monthly 12 74.20 82.30 Weekly 52 74.28 0.08 82.80 0.50 Daily 365 74.29 0.01 82.97 0.17 Instant ∞ 74.30 83.00 0.03

Conclusions Simpler validation of GPTA is easier to compute and explain, but not quite as independent New procedure developed to validate GREL GPTA properties are very close to expected GRELs were slightly too high (2%) for Holsteins, slightly too low (3%) for Jerseys Recent GPTAs (young and old) may all be too low (genetic trend is underestimated) Genetic progress is fast!

Acknowledgements Interbull Working Group on Genomic Reliability (Zengting Liu, Paul VanRaden, Martin Lidauer, Mario Calus, Vincent Ducrocq, Haifa Benhajali, and Hossein Jorjani) Council on Dairy Cattle Breeding for DHI data from dairy farmers USDA-ARS project 1265-31000-101-00, “Improving Genetic Predictions in Dairy Animals Using Phenotypic and Genomic Information”