Random regression analysis of test-day milk yields in the first and second lactations of Brazilian Gyr cows

Herrera, Luis Gabriel González; Faro, Lenira El; Bignardi, Annaíza Braga; Pereira, Rodrigo Junqueira; Machado, Carlos Henrique; Albuquerque, Lúcia Galvão de

doi:10.4238/2015.december.9.22

Cited by 6 publications

(2 citation statements)

References 23 publications

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“…Moreover, high-order LP can have some difficulties in modeling records in early and late lactation (López-Romero and Carabaño, 2003;Sawalha et al, 2005). Furthermore, Aspilcueta-Borquis et al ( 2013) described low genetic correlation estimates between TD at the curve extremes unlike cattle (Gonzalez-Herrera et al, 2015;Fazel et al, 2018;Trivunović et al, 2019). These results can be explained by the fact that few buffalo have the potential to produce milk until the last day considered in the study (DIM 305;Baldi et al, 2011).…”

Section: Genetic Correlation Estimatesmentioning

confidence: 99%

Genomic studies of milk-related traits in water buffalo (Bubalus bubalis) based on single-step genomic best linear unbiased prediction and random regression models

Lázaro

Tonhati

Oliveira

et al. 2021

Journal of Dairy Science

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Genomic selection has been widely implemented in many livestock breeding programs, but it remains incipient in buffalo. Therefore, this study aimed to (1) estimate variance components incorporating genomic information in Murrah buffalo; (2) evaluate the performance of genomic prediction for milk-related traits using single-and multitrait random regression models (RRM) and the single-step genomic best linear unbiased prediction approach; and (3) estimate longitudinal SNP effects and candidate genes potentially associated with time-dependent variation in milk, fat, and protein yields, as well as somatic cell score (SCS) in multiple parities. The data used to estimate the genetic parameters consisted of a total of 323,140 test-day records. The average daily heritability estimates were moderate (0.35 ± 0.02 for milk yield, 0.22 ± 0.03 for fat yield, 0.42 ± 0.03 for protein yield, and 0.16 ± 0.03 for SCS). The highest heritability estimates, considering all traits studied, were observed between 20 and 280 d in milk (DIM). The genetic correlation estimates at different DIM among the evaluated traits ranged from −0.10 (156 to 185 DIM for SCS) to 0.61 (36 to 65 DIM for fat yield). In general, direct selection for any of the traits evaluated is expected to result in indirect genetic gains for milk yield, fat yield, and protein yield but also increase SCS at certain lactation stages, which is undesirable. The predicted RRM coefficients were used to derive the genomic estimated breeding values (GEBV) for each time point (from 5 to 305 DIM). In general, the tuning parameters evaluated when constructing the hybrid genomic relationship matrices had a small effect on the GEBV accuracy and a greater effect on the bias estimates. The SNP solutions were back-solved from the GEBV predicted from the Legendre random regression coefficients, which were then used to estimate the longitudinal SNP effects (from 5 to 305 DIM). The daily SNP effect for 3 different lactation stages were performed considering 3 different lactation stages for each trait and parity: from 5 to 70, from 71 to 150, and from 151 to 305 DIM. Important genomic regions related to the analyzed traits and parities that explain more than 0.50% of the total additive genetic variance were selected for further analyses of candidate genes. In general, similar potential candidate genes were found between traits, but our results suggest evidence of differential sets of candidate genes underlying the phenotypic expression of the traits across parities. These results contribute to a better understanding of the genetic architecture of milk production traits in dairy buffalo and reinforce the relevance of incorporating genomic information to genetically evaluate longitudinal traits in dairy buffalo. Furthermore, the candidate genes identified can be used as target genes in future functional genomics studies.

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Section: Genetic Correlation Estimatesmentioning

confidence: 99%

Genomic studies of milk-related traits in water buffalo (Bubalus bubalis) based on single-step genomic best linear unbiased prediction and random regression models

Lázaro

Tonhati

Oliveira

et al. 2021

Journal of Dairy Science

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“…Genetic evaluations of productive traits in dairy Gir cattle in Brazil have been conducted using an animal model for the estimates of Y305 from multiple lactations (Panetto et al, 2018). In this respect, studies have tried to develop an adequate random regression model that uses test-day milk yield records for the evaluation of the breed (Herrera et al, 2008;Pereira et al, 2010Pereira et al, , 2012Pereira et al, , 2013aPereira et al, , 2013bGonzalez-Herrera et al, 2015;Santana Jr. et al, 2015;Oliveira et al, 2017). However, none of these researches showed a potential increase in the accuracy of estimated breeding values (EBVs) when applying random regression models to test-day milk yield records.…”

Section: Introductionmentioning

confidence: 99%

Test-day or 305-day milk yield for genetic evaluation of Gir cattle

Pereira

Ayres

Júnior

et al. 2019

Pesq. agropec. bras.

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The objective of this work was to compare genetic evaluations of milk yield in the Gir breed, in terms of breeding values and their accuracy, using a random regression model applied to test-day records or the traditional model (TM) applied to estimates of 305-day milk yield, as well as to predict genetic trends for parameters of interest. A total of 10,576 first lactations, corresponding to 81,135 test-day (TD) records, were used. Rank correlations between the breeding values (EBVs) predicted with the two models were 0.96. The percentage of animals selected in common was 67 or 82%, respectively, when 1 or 5% of bulls were chosen, according to EBVs from random regression model (RRM) or TM genetic evaluations. Average gains in accuracy of 2.7, 3.0, and 2.6% were observed for all animals, cows with yield record, and bulls (sires of cows with yield record), respectively, when the RRM was used. The mean annual genetic gain for 305-day milk yield was 56 kg after 1993. However, lower increases in the average EBVs were observed for the second regression coefficient, related to persistency. The RRM applied to TD records is efficient for the genetic evaluation of milk yield in the Gir dairy breed.

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Bayesian Models combining Legendre and B-spline polynomials for genetic analysis of multiple lactations in Gyr cattle

Oliveira

Silva

et al. 2017

Livestock Science

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Random regression analysis of test-day milk yields in the first and second lactations of Brazilian Gyr cows

Cited by 6 publications

References 23 publications

Genomic studies of milk-related traits in water buffalo (Bubalus bubalis) based on single-step genomic best linear unbiased prediction and random regression models

Genomic studies of milk-related traits in water buffalo (Bubalus bubalis) based on single-step genomic best linear unbiased prediction and random regression models

Test-day or 305-day milk yield for genetic evaluation of Gir cattle

Bayesian Models combining Legendre and B-spline polynomials for genetic analysis of multiple lactations in Gyr cattle

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