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

10b57aa