[en] Very few studies have been conducted to infer genotype × environment interaction (G×E) based in genomic prediction models using SNP markers. Therefore, our main objective was to compare a conventional genomic-based single-step model (HBLUP) with its reaction norm model extension (genomic 1-step linear reaction norm model [HLRNM]) to provide EBV for tick resistance as well as to compare predictive performance of these models with counterpart models that ignore SNP marker information, that is, a linear animal model (ABLUP) and its reaction norm extension (1-step linear reaction norm model [ALRNM]). Phenotypes included 10,673 tick counts on 4,363 Hereford and Braford animals, of which 3,591 were genotyped. Using the deviance information criterion for model choice, ABLUP and HBLUP seemed to be poorer fitting in comparison with their respective genomic model extensions. The HLRNM estimated lower average and reaction norm genetic variability compared with the ALRNM, whereas ABLUP and HBLUP seemed to be poorer fitting in comparison with their respective genomic reaction norm model extensions. Heritability and repeatability estimates varied along the environmental gradient (EG) and the genetic correlations were remarkably low between high and low EG, indicating the presence of G×E for tick resistance in these populations. Based on 5-fold K-means partitioning, mean cross-validation estimates with their respective SE of predictive accuracy were 0.66 (SE 0.02), 0.67 (SE 0.02), 0.67 (SE 0.02), and 0.66 (SE 0.02) for ABLUP, HBLUP, HLRNM, and ALRNM, respectively. For 5-fold random partitioning, HLRNM (0.71 ± 0.01) was statistically different from ABLUP (0.67 ± 0.01). However, no statistical significance was reported when considering HBLUP (0.70 ± 0.01) and ALRNM (0.70 ± 0.01). Our results suggest that SNP marker information does not lead to higher prediction accuracies in reaction norm models. Furthermore, these accuracies decreased as the tick infestation level increased and as the relationship between animals in training and validation data sets decreased.
REIS MOTA, Rodrigo ; Université de Liège - ULiège > Agronomie, Bio-ingénierie et Chimie (AgroBioChem) > Ingénierie des productions animales et nutrition
Lopes, Paulo Sávio; Universidade Federal de Viçosa > Animal Science
Tempelman, Robert John; Michigan State University > Animal Science
Fonseca e Silva, Fabyano; Universidade Federal de Viçosa > Animal Science
Aguilar, Ignacio; National Agricultural Research Institute, 90200 Las Brujas, Uruguay
Gomes, Cláudia; Embrapa South Livestock, Bagé, Brazil
Flores Cardoso, Fernando; Embrapa South Livestock, Bagé, Brazil
Language :
English
Title :
Genome-enabled prediction for tick resistance in Hereford and Braford beef cattle via reaction norm models
Publication date :
06 May 2016
Journal title :
Journal of Animal Science
ISSN :
0021-8812
eISSN :
1525-3163
Publisher :
American Society of Animal Science, Savoy, United States - Illinois
Volume :
94
Pages :
1834–1843
Peer reviewed :
Peer Reviewed verified by ORBi
Funders :
EMBRAPA - Empresa Brasileira de Pesquisa Agropecuária [BR] USDA NIFA - United States. Department of Agriculture. National Institute of Food and Agriculture [US-DC] [US-DC]
Funding number :
Embrapa – Brazilian Agricultural Research Corporation grants 02.09.07.004 and 01.11.07.002.07; Embrapa – Brazilian Agricultural Research Corporation grants 02.09.07.004 and 01.11.07.002.07; Agriculture and Food Research Initiative Competitive Grant number 2011-67015-30338 from the USDA National Institute of Food and Agriculture
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