genetic interactions for heat stress and herd yield level: predicting foreign genetic merit from...
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Genetic interactions for heat stress and herd yield level: predicting foreign genetic merit from domestic data
J. R. Wright*, P. M. VanRadenAnimal 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 (R2 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 (R2 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 & METHODSData 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 coefficients R2
Variable/modelEBVsire EBVdam
Heat stress
Milk No interaction 0.475 0.563 0.4585 Interaction 0.474 0.554 0.927 0.4588
Fat No interaction 0.480 0.575 0.5042 Interaction 0.478 0.567 0.798 0.5044
Protein No interaction 0.449 0.511 0.5163 Interaction 0.448 0.504 0.797 0.5165
Somatic cell score No interaction 0.431 0.453 0.2083 Interaction 0.430 0.448 0.620 0.2083
Productive life No interaction 0.514 0.497 0.1499 Interaction 0.514 0.492 0.876 0.1501
Dau. preg. rate No interaction 0.452 0.432 0.1189 Interaction 0.452 0.428 0.561 0.1190
Expected value 0.500 0.500 1.000
Regression coefficients R2
Variable/model
EBVsire EBVdam
Herd yield level
Milk No interaction 0.454 0.537 0.4749 Interaction 0.454 0.533 0.720 0.4751
Fat No interaction 0.457 0.549 0.5217 Interaction 0.456 0.544 0.611 0.5218
Protein No interaction 0.430 0.487 0.5335 Interaction 0.429 0.484 0.609 0.5336
Expected value 0.500 0.500 1.000
Heat stress coefficient
Milk Fat ProteinNumber of bulls
ARG 0.04 -0.02 0.07c 416AUS -0.06 -0.11 -0.11 452CAN -0.18a -0.22a -0.20a 1184DEU -0.15a -0.18a -0.10 862DFS -0.01a -0.15a -0.21a 531ESP -0.13b -0.18a -0.15b 609FRA -0.28a -0.19b -0.29a 605GBR -0.12a -0.03 -0.08a 969HUN -0.14c -0.08 -0.07 641IRL -0.01 -0.11a -0.09b 317ITA -0.08c -0.13b -0.16a 868NLD -0.27a -0.19a -0.20a 766POL -0.03 -0.16b -0.09 562URY 0.02 -0.07b 0.00 303aP<.001 bP<.01 cP<.05
Herd yield level coefficient
Milk Fat ProteinNumber of bulls
ARG -0.00 -0.00 -0.03 416AUS 0.02 0.08 0.14 452CAN -0.14c -0.01 -0.10 1184DEU -0.34a -0.15b -0.32a 862DFS 0.00 -0.07 -0.05 531ESP -0.12 -0.06 -0.02 609FRA -0.22b -0.08 0.05 605GBR 0.07c 0.04 0.03 969HUN -0.22b -0.08 -0.21c 641IRL -0.06 -0.02 0.01 317ITA -0.23a -0.03 -0.29a 868NLD -0.17b -0.04 -0.16c 762POL -0.27b -0.03 -0.20c 559URY -0.06 -0.04 0.00 303aP<.001 bP<.01 cP<.05
Bull name
Original EBV protein rank
EBV protein rank 1 in warm climate
EBV protein rank2in cold climate
Coyne 1 2 1Nobleland 2 1 4Listen 3 7 2Tyron 4 4 6Altagreatest 5 3 7Ruble 6 12 3Altastone 7 6 15Lonzo 8 11 33Syrup 9 20 11Picardus 10 95 8Mercedes 11 55 9Fathom 12 344 5Dahlia 13 34 17Altafairway 14 115 12Altasuperjet 15 120 10Robust 27 8 49Eureka 66 15 126
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