1. Genetic interactions for heat stress and herd yield level: predicting foreign genetic merit from domestic data J. R. Wright*, P. M. VanRaden Animal Genomics & Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350 INTRODUCTION  Genetic by environmental effects such as temperature-humidity index or production level can be modeled with random regression to define differences within and across country  Selection for heat tolerance could have major benefits in warm or low production environments CONCLUSIONS  Addition of heat stress interaction term to the model improved predictions by a small amount (R 2 difference < 0.0003)  Most warmer, southern hemisphere countries (ARG, URY) had positive heat stress coefficients while cooler, more northern countries were negative  Addition of herd yield level interaction term improved prediction very little (R 2 difference < 0.0002)  Overall, as evidenced by the small correlation gains when adding HS and HL interactions, the current models predict well in a variety of environments  Individual bull differences resulting from addition of interaction term could enhance bull selection when planned usage is solely in one environment OBJECTIVE  Improve prediction of genetic rankings in other climates and production situations DATA & METHODS  Data used in August 2014 US national evaluations Yield: 79 million lactations, 40 million cows Somatic cell score: 44 million records Productive life: 30 million records Daughter pregnancy rate: 70 million records  Each G X E added separately as a random regression term using current national evaluation model Heat stress (HS): State mean annual temperature-humidity index calculated: (1.8*T + 32) – (0.55 – 0.0055*RH) * (1.8*T – 26) (where T=temperature, RH=relative humidity) Herd yield level (HL): Ratio of management level year-mean energy corrected milk (ECM) divided by breed-year mean ECM; standardized to a mean of 0 and variance of 1  Time truncation test: Predictions of August 2014 from August 2011 with model including herd management group, sire and dam EBV, and interaction term. Records weighted by lactation length and herd heritability, similar to the national evaluation  Multitrait across-country EBV (MACE) prediction test Predict MACE evaluation on foreign scale from US EBV and interaction term for bulls with 100 or more daughters in the US and one of 14 other countries RESULTS  Time truncation test for heat stress: Predict yield for young cows from sire and dam EBV with and without heat stress in the model  MACE prediction test for heat stress: Model: MACE = US EBV + HS Predict MACE evaluation from EBV with adjustment for heat stress for bulls with ≥100 daughters in both US and 14 other countries RESULTS – cont.  Time truncation test for herd yield level: Predict yield for young cows from sire and dam EBV with and without herd yield level in the model  MACE prediction test for herd yield level: Model: MACE = US EBV + HL Predict MACE evaluation from EBV with adjustment for herd yield level for bulls with ≥100 daughters in both US and 14 other countries Poster T103 Abstract #63788 ADSA-ASAS Joint Meeting July 14, 2015, Orlando, FL http://aipl.arsusda.gov APPLICATION / FUTURE WORK Application: Possible alternate rankings of bulls depending on location of use: Ranking of US prefix bulls born ≥2004 with ≥ 50 daughters for EBV protein: original and after applying heat stress factor for different climates 1 Defined as: EBV + HS factor * Mean annual Florida THI 2 Defined as: EBV + HS factor * Mean annual Wisconsin THI  Correlation between alternative rankings of bulls based on heat stress solutions: 0.912 between original model and warm climate (FL) 0.986 between original model and cool climate (WI ) Regression coefficientsR2R2 Variable/model EBVsireEBVdam Heat stress Milk No interaction 0.4750.5630.4585 Interaction 0.4740.5540.9270.4588 Fat No interaction 0.4800.5750.5042 Interaction 0.4780.5670.7980.5044 Protein No interaction 0.4490.5110.5163 Interaction 0.4480.5040.7970.5165 Somatic cell score No interaction 0.4310.453 0.2083 Interaction 0.4300.4480.6200.2083 Productive life No interaction 0.5140.497 0.1499 Interaction 0.5140.4920.8760.1501 Dau. preg. rate No interaction 0.4520.4320.1189 Interaction 0.4520.4280.5610.1190 Expected value 0.500 1.000 Regression coefficientsR2R2 Variable/model EBVsireEBVdam Herd yield level Milk No interaction 0.4540.5370.4749 Interaction 0.4540.5330.7200.4751 Fat No interaction 0.4570.5490.5217 Interaction 0.4560.5440.6110.5218 Protein No interaction 0.4300.4870.5335 Interaction 0.4290.4840.6090.5336 Expected value 0.500 1.000 Heat stress coefficient MilkFatProtein Number of bulls ARG 0.04-0.020.07 c 416 AUS -0.06-0.11 452 CAN -0.18 a -0.22 a -0.20 a 1184 DEU -0.15 a -0.18 a -0.10862 DFS -0.01 a -0.15 a -0.21 a 531 ESP -0.13 b -0.18 a -0.15 b 609 FRA -0.28 a -0.19 b -0.29 a 605 GBR -0.12 a -0.03-0.08 a 969 HUN -0.14 c -0.08-0.07641 IRL -0.01-0.11 a -0.09 b 317 ITA -0.08 c -0.13 b -0.16 a 868 NLD -0.27 a -0.19 a -0.20 a 766 POL -0.03-0.16 b -0.09562 URY 0.02-0.07 b 0.00303 a P<.001 b P<.01 c P<.05 Herd yield level coefficient MilkFatProtein Number of bulls ARG -0.00 -0.03416 AUS 0.020.080.14452 CAN -0.14 c -0.01-0.101184 DEU -0.34 a -0.15 b -0.32 a 862 DFS 0.00-0.07 -0.05531 ESP -0.12-0.06-0.02609 FRA -0.22 b -0.080.05605 GBR 0.07 c 0.040.03969 HUN -0.22 b -0.08-0.21 c 641 IRL -0.06-0.020.01317 ITA -0.23 a -0.03-0.29 a 868 NLD -0.17 b -0.04-0.16 c 762 POL -0.27 b -0.03-0.20 c 559 URY -0.06-0.040.00303 a P<.001 b P<.01 c P<.05 Bull name Original EBV protein rank EBV protein rank 1 in warm climate EBV protein rank 2 in cold climate Coyne121 Nobleland214 Listen372 Tyron446 Altagreatest537 Ruble6123 Altastone7615 Lonzo81133 Syrup92011 Picardus10958 Mercedes11559 Fathom123445 Dahlia133417 Altafairway1411512 Altasuperjet1512010 Robust27849 Eureka6615126 10b57aa