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G.R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD G.R. WiggansSelect Sire.

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Presentation on theme: "G.R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD G.R. WiggansSelect Sire."— Presentation transcript:

1 G.R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD george.wiggans@ars.usda.gov G.R. WiggansSelect Sire committee meeting, March 2010 (1) Genomics: what we have and what is coming

2 G.R. Wiggans Select Sire committee meeting March 2009 (2) How the system works l Studs and Breeds nominate animals through AIPL web site l Hair, blood, Semen, or extracted DNA sent to 1 of 4 Labs l Genotypes sent to AIPL monthly l Starting in April monthly updates will be released on the first Tuesday of most months l All official evaluations updated at tri-annual traditional runs

3 G.R. Wiggans Select Sire committee meeting March 2009 (3) Recent improvements l Studs may submit pedigree and nominate in batch files l Pedigree from CAN, AUS, GBR automatically collected from web sites l Polygenic effect set at 10% to include genetic variation not captured by SNP l Net Merit calculated from component traits, not analyzed as a separate trait

4 G.R. Wiggans Select Sire committee meeting March 2009 (4) Changes planned for April l Deviations of predictor cows adjusted to be like bulls with similar reliability to improve their contribution to accuracy l Genotypes of dams of genotyped animals imputed to add predictor animals l Sum of genomic relationships of each animal with the predictor animals used to improve estimation of Reliability

5 G.R. Wiggans Select Sire committee meeting March 2009 (5) Imputation l Determine an animal’s genotype from genotypes of its parents and progeny l Genotype separated into sire and dam contributions. Identifies the allele on each member of a chromosome pair l Inheritance of haplotypes tracked l Accuracy of imputation improves with number of progeny l Crossovers during meiosis contribute to uncertainty

6 G.R. Wiggans Select Sire committee meeting March 2009 (6) Genotyped Holstein by run Run Date Old*Young** Total MaleFemaleMaleFemale 0904760027119690194321944 09067883304911459297425365 09088512372812137367028047 09108568396513288479730618 10018974434814061603133414 10029378508615328762037412 10049770730016002873241804 * Animals with traditional evaluation ** Animals with no traditional evaluation

7 G.R. Wiggans Select Sire committee meeting March 2009 (7) Cow Problem l Evaluations of elite cows appear biased upward l Cutoff studies show only a small benefit from including cows as predictors l Reducing heritability would reduce the problem but appears unacceptable l Adjustment of cow evaluations investigated

8 G.R. Wiggans Select Sire committee meeting March 2009 (8) SD of Cow Deviation from PA 0 500 1000 1500 2000 2500 0.40.61.02.5 Daughter Equivalent (progeny) Std. Dev of Deregressed Value (Milk) Cow Bull

9 G.R. Wiggans Select Sire committee meeting March 2009 (9) Mean of Cow Deviation from PA -400 -200 0 200 400 600 800 1000 20002001200220032004200520062007 Birth year Milk (lbs.) Cow Bull Cow SD Adj

10 G.R. Wiggans Select Sire committee meeting March 2009 (10) Cow Adjustment Parameters l PTA calculated from adjusted deregressed values and used in PA l High reliability bulls (99%) not adjusted l Adjusted values used to calculate % traits Trait Std. DeviationMean HolsteinJerseyHolsteinJersey Milk.84.72784643 Fat.72.6727.531.4 Protein.77.6723.024.2

11 G.R. Wiggans Select Sire committee meeting March 2009 (11) Effect of Adjustment on Holstein BiasRegressionGain REL NoYesDiffNoYesDiffNoYesDiff Milk (lb)-75.3-27.947.4.93.90-.0329.532.53.0 Fat (lb)-5.7-2.92.8.98.97-.0134.037.13.1 Protein (lb)-0.20.81.0.90.97.0725.027.12.1 Fat (%)0.0.97.99.0249.852.42.6 Protein (%)0.0.87.88.0138.841.52.7

12 G.R. Wiggans Select Sire committee meeting March 2009 (12) Effect of Adjustment on Jersey BiasRegressionGain REL NoYesDiffNoYesDiffNoYesDiff Milk (lb)-44.081.5125.5.99.0010.819.68.8 Fat (lb)-7.37.915.2.78.84.069.418.28.8 Protein (lb)1.74.32.6.86.90.044.112.78.6 Fat (%)0.0.90.95.0529.937.67.7 Protein (%)0.0.87.93.0624.834.29.4

13 G.R. Wiggans Select Sire committee meeting March 2009 (13) l Increased reliability of genomic predictions l Genomic evaluations of the top cows, top young bulls, and top heifers decreased l Among bulls, foreign bulls with a high proportion of genotyped daughters had largest changes l Adjusted PTA will be reported in XML traditional fields Cow Adjustment Summary

14 G.R. Wiggans Select Sire committee meeting March 2009 (14) Reliability for young HO Bulls 0 500 1000 1500 2000 2500 3000 3500 4000 52535455565758596061626364656667686970717273747576777879 Milk REL Number of Bulls N = 15,226

15 G.R. Wiggans Select Sire committee meeting March 2009 (15) Reliabilities for HO born ≥ 2005 No Traditional EvaluationWith Traditional Evaluation TraitMaleFemaleMaleFemale N1522675367523191 Milk (lb)73.973.785.877.9 Protein (lb)73.973.785.877.8 PL (months)64.063.670.167.0 SCS69.769.578.173.0 DPR (%)61.661.266.564.6 PTAT70.470.178.374.5 Sire CE64.961.780.863.5 Daughter CE60.259.069.561.8 Sire SB59.858.766.259.6 Daughter SB58.357.664.959.6 Net Merit ($)68.668.377.872.0

16 G.R. Wiggans Select Sire committee meeting March 2009 (16) Bulls First Traditional Eval. Jan., 2010 Genomic AugustJanuary TraitNPTARELPTARELDiff PTA Milk70353376.347583.4-58 Protein70317.676.317.083.6-0.6 PTAT4711.070.41.077.50.0 Traditional AugustJanuary TraitNPARELPTARELDiff PTA Milk70365742.047874.9-179 Protein70322.142.017.074.9-5.1 PTAT4711.040.90.965.1-0.1

17 G.R. Wiggans Select Sire committee meeting March 2009 (17) Cows First Traditional Eval. Jan., 2010 Genomic AugustJanuary TraitNPTARELPTARELDiff PTA Milk59477274.484575.473 Protein57025.574.429.475.13.9 PTAT2941.769.41.871.60.1 Traditional AugustJanuary TraitNPARELPTARELDiff PTA Milk59477435.694351.1167 Protein57026.235.232.949.86.7 PTAT2941.636.81.748.90.1

18 G.R. Wiggans Select Sire committee meeting March 2009 (18) Accommodating chip diversity l Impute to highest density l Calculate SNP effects for all HD SNP l Mechanism for accounting for loss in accuracy due to imputation error needed w Percent missing may be enough l Only observed genotypes stored in database l Evaluations labeled as to source of genotype

19 G.R. Wiggans Select Sire committee meeting March 2009 (19) Illumina 3K chip l SNP chosen to w Be evenly spaced w Include some Y specific SNP w Include 90 SNP for breed determination l Expect to impute genotypes for 43,385 SNP with high accuracy l Expect breeds to use 3K chip to replace microsatellites for parentage verification l Breeds allowed to genotypes bulls for parentage only.

20 G.R. Wiggans Select Sire committee meeting March 2009 (20) Proposed Stud use of 3K l Genomic evaluation accuracy adequate for first stage screening l HD genotyping reserved for bulls acquired. w Confirm ID w Second stage selection l Lower cost enables genotyping more candidates l Savings could be applied to genotype more predictor bulls to meet EuroGenomics challenge

21 G.R. Wiggans Select Sire committee meeting March 2009 (21) HD chip l Includes current 43,385 SNP so can replace 50K chip in current evaluations l 5,000+ genotypes at HD required to support imputation of HD from current 50K SNP l Expected gain in Rel < 2 l May allow HO genotypes to contribute to accuracy of JE & BS genomic evaluations

22 G.R. Wiggans Select Sire committee meeting March 2009 (22) HD chip (Cont.) l Could share cost of HD genotyping with Europe to ensure enough animals to enable accurate imputation l Trend is toward higher densities. l Continued genotyping at 50K may be shortsighted l May allow reduction in polygenic effect giving increased accuracy

23 G.R. Wiggans Select Sire committee meeting March 2009 (23) Will data recording survive l Progeny test no longer required to market bulls l In 2013, new entrants may have no data collection expense l Loss in accuracy of SNP effect estimates occurs over time l How much data is needed?

24 G.R. Wiggans Select Sire committee meeting March 2009 (24) Assumptions About Future Data Trait and heritability Dtrs /YieldSCSDPR BullStat.30.10.04 100RELtrad.90.76.60 100RELpa.40.35.32 50RELtrad.83.65.48 50RELpa.38.32.29 RELtrad of foreign bulls multiplied by square of genetic correlation (.9) 2

25 G.R. Wiggans Select Sire committee meeting March 2009 (25) Reliability from Additional Data Young bull REL for: Options to add data:YieldSCSDPR 9,000 current bulls726860 +16,000 from Europe878579 +7,500 with 50 dtrs848173 +7,500 with 100 dtrs858276 7,500 N. American bulls = 1500 / year over next 5 years

26 G.R. Wiggans Select Sire committee meeting March 2009 (26) What replaces the PT program l G bulls will have 1,000s of daughters in their early Trad evaluations l Milk recording is justified for management information l Type data may come from breeder herds because they use G bulls l Data on new traits will require investment

27 G.R. Wiggans Select Sire committee meeting March 2009 (27) Data into National Evaluations l Progeny Test herds could become Data Supply herds l Data acquisition could be supported by a fee based on bulls genotyped l Plan must be perceived as fair by all industry players l Quality Certification model could apply

28 G.R. Wiggans Select Sire committee meeting March 2009 (28) Questions l How to match accuracy of evaluations from EuroGenomics l Should young bull purchases be based on 3K genotypes l How will continued flow of data into genetic evaluations be assured

29 G.R. Wiggans Select Sire committee meeting March 2009 (29) Financial support l National Research Initiative grants, USDA l NAAB (Columbia, MO) w ABS Global (DeForest, WI) w Accelerated Genetics (Baraboo, WI) w Alta (Balzac, AB) w Genex (Shawano, WI) w New Generation Genetics (Fort Atkinson, WI) w Select Sires (Plain City, OH) w Semex Alliance (Guelph, ON) w Taurus-Service (Mehoopany, PA) l Holstein Association USA (Brattleboro, VT) l American Jersey Cattle Association (Reynoldsburg, OH) l American Brown Swiss Association (Beloit, WI) l Agricultural Research Service, USDA

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