Genetic parameters and association of national evaluations with breeding values for health traits in US organic Holstein cows

Abstract

Among other regulations, organic cows in the United States cannot receive antibiotics and preserve their organic status, emphasizing the importance of prevention of illness and benefit of high genetic merit for disease resistance. At the same time, data underlying national genetic evaluations primarily come from conventional cows, drawing concern to the possibility of a genotype by environment interaction whereby the value of a genotype varies depending on the environment, and potentially limits the relevance of these evaluations to organic cows. The objectives of this study were to characterize the genetics of and determine the presence of genotype by environment interaction for health traits in US organic dairy cows. Individual cow health data were obtained from 16 US Department of Agriculture certified organic dairy farms from across the United States that used artificial insemination and maintained detailed records. Data were obtained for the following traits: died, lameness, mastitis, metabolic diseases (displaced abomasum, ketosis, and milk fever), reproductive diseases (abortion, metritis, and retained placenta), transition health events (any health event occurring 21 d before or after parturition), and all health events. Binary phenotypes (1 = diseased, 0 = otherwise) for 38,949 lactations on 19,139 Holstein cows were used. Genotypes from 2,347 cows with 87.5% or greater Holstein breed-based representation were incorporated into single-step multitrait threshold animal models that included stayability (1 = completed lactation, 0 = otherwise). Gibbs sampling was used. Genomic predicted transmitting abilities (gPTA) from national genetic evaluations were obtained for sires for production, fitness, health, and conformation traits. We approximated genetic correlations for sires using these gPTA and our estimated breeding values. We also regressed health phenotypes on cow estimated breeding values and sire gPTA. Heritabilities (± standard error) ranged from 0.03 ± 0.01 (reproductive diseases) to 0.11 ± 0.03 (metabolic diseases). Most genetic correlations among health traits were positive, though the genetic correlation between metabolic disease and mastitis was −0.42 ± 0.17. Approximate genetic correlations between disease resistance for our health trait categories and disease resistance for the nationally-evaluated health traits generally carried the expected sign with the strongest correlation for mastitis (0.72 ± 0.084). Regression coefficients carried the expected sign and were mostly different from zero, indicating that evaluations from primarily conventional herd data predicted health on organic farms. In conclusion, use of national evaluations for health traits should afford genetic improvement for health in US organic herds.

An overview on mastitis-associated Escherichia coli: Pathogenicity, host immunity and the use of alternative therapies

“…Se ha observado que el holstein mejora cuando se cruza con jersey en calidad de leche y desempeño reproductivo. Pero ningún cruce disminuye la aparición de mastitis (38,39).…”

Impact of different search strategies on the results of a meta-analysis for mastitis in dairy cattle

“…Search strategies must be objective and specific while covering as many works as possible so that estimates and associated precision are not compromised. (25) 0.05 0.0261 0.0064 -0.1088 Abdelsayed et al, 2017 (10) 0.065 0.0153 0.0365 -0.0965 Pritchard et al, 2013 (11) 0.06 0.0261 0.0127 -0.1152 Zwald et al, 2004 (26) 0.04 0.0155 0.0104 -0.0820 Lund et al, 1994 (9) 0.04 0.0168 0.0163 -0.0820 Strategy 2 Wolf et al, 2009 (12) 0.06 0.0066 0.0497 -0.0754 Hass et al, 2003 (13) 0.06 0.0068 0.0534 -0.0802 Kirsanova et al, 2018 (27) 0.06 0.0070 0.0521 -0.0793 Oliveira et al, 2015 (28) 0.06 0.0067 0.0501 -0.0764 Heringstad et al, 2003 (14) 0.06 0.071 0.0516 -0.0793 Zavadilova et al, 2020 (15) 0.06 0.0069 0.0528 -0.0800 Carlen et al, 2004 (16) 0.06 0.0067 0.0542 -0.0802 Mrod et al, 2012 (17) 0.06 0.0071 0.0516 -0.0793 Zwald et al, 2006 (29) 0.06 0.0068 0.0504 -0.0770 Heringstad et al, 2006 (18) 0.06 0.0070 0.0507 -0.0781 Irano et al, 2014 (30) 0.06 0.0066 0.0503 -0.0762 Perez-Cabal et al, 2009 (19) 0.06 0.0071 0.0511 -0.0787 Hardie et al, 2021 (31) 0.06 0.0070 0.0517 -0.0790 Rupp e Boichard, 1999 (20) 0.06 0.0062 0.0542 -0.0802 Sorensen et al, 2009 0.06 0.0066 0.0497 -0.0754 Urioest et al, 2012 (22) 0.06 0.0070 0.0507 -0.0781 Zadeh et al, 2011 (23) 0.06 0.0071 0.0516 -0.0793 Koeck et al, 2012 (24) 0.06 0.0071 0.0511 -0.0787 Chegini et al, 2009 (25) 0.06 0.0071 0.0516 -0.0793 Abdelsayed et al, 2017 (10) 0.06 0.0064 0.0550 -0.0802 Pritchard et al, 2013 (11) 0.06 0.0069 0.0528 -0.0800 Zwald et al, 2004 (26) 0.06 0.0069 0.0503 -0.0773 Lund et al, 1994 (9) 0.06 0.0067 0.0512 -0.0773…”

Impacto de diferentes estratégias de busca sob os resultados de uma metanálise para a característica mastite em gado de leite

“…Herdabilidade Estimada Erro-padrão IC -95% Estratégia 1 Chegini et al, 2009 (25) 0,05 0,0261 0,0064 -0,1088 Abdelsayed et al, 2017 (10) 0,065 0,0153 0,0365 -0,0965 Pritchard et al, 2013 (11) 0,06 0,0261 0,0127 -0,1152 Zwald et al, 2004 (26) 0,04 0,0155 0,0104 -0,0820 Lund et al, 1994 (9) 0,04 0,0168 0,0163 -0,0820 Estratégia 2 Wolf et al, 2009 (12) 0,06 0,0066 0,0497 -0,0754 Hass et al, 2003 (13) 0,06 0,0068 0,0534 -0,0802 Kirsanova et al, 2018 (27) 0,06 0,0070 0,0521 -0,0793 Oliveira et al, 2015 (28) 0,06 0,0067 0,0501 -0,0764 Heringstad et al, 2003 (14) 0,06 0,071 0,0516 -0,0793 Zavadilova et al, 2020 (15) 0,06 0,0069 0,0528 -0,0800 Carlen et al, 2004 (16) 0,06 0,0067 0,0542 -0,0802 Mrod et al, 2012 (17) 0,06 0,0071 0,0516 -0,0793 Zwald et al, 2006 (29) 0,06 0,0068 0,0504 -0,0770 Heringstad et al, 2006 (18) 0,06 0,0070 0,0507 -0,0781 Irano et al, 2014 (30) 0,06 0,0066 0,0503 -0,0762 Perez-Cabal et al, 2009 (19) 0,06 0,0071 0,0511 -0,0787 Hardie et al, 2021 (31) 0,06 0,0070 0,0517 -0,0790 Rupp e Boichard, 1999 (20) 0,06 0,0062 0,0542 -0,0802 Sorensen et al, 2009 0,06 0,0066 0,0497 -0,0754 Urioest et al, 2012 (22) 0,06 0,0070 0,0507 -0,0781 Zadeh et al, 2011 (23) 0,06 0,0071 0,0516 -0,0793 Koeck et al, 2012 (24) 0,06 0,0071 0,0511 -0,0787 Chegini et al, 2009 (25) 0,06 0,0071 0,0516 -0,0793 Abdelsayed et al, 2017 (10) 0,06 0,0064 0,0550 -0,0802 Pritchard et al, 2013 (11) 0,06 0,0069 0,0528 -0,0800 Zwald et al, 2004 (26) 0,06 0,0069 0,0503 -0,0773 Lund et al, 1994 (9) 0,06 0,0067 0,0512 -0,0773…”