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Autoregressive and random regression test‐day models for multiple lactations in genetic evaluation of Brazilian Holstein cattle
Author(s) -
Silva Delvan Alves,
Costa Claudio Nápolis,
Silva Alessandra Alves,
Silva Hugo Teixeira,
Lopes Paulo Sávio,
Silva Fabyano Fonseca,
Veroneze Renata,
Thompson Gertrude,
Aguilar Ignacio,
Carvalheira Júlio
Publication year - 2020
Publication title -
journal of animal breeding and genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.689
H-Index - 51
eISSN - 1439-0388
pISSN - 0931-2668
DOI - 10.1111/jbg.12459
Subject(s) - statistics , akaike information criterion , mathematics , heritability , herd , selection (genetic algorithm) , autoregressive model , random effects model , genetic correlation , rank correlation , regression , bayesian information criterion , biology , zoology , genetic variation , medicine , genetics , meta analysis , artificial intelligence , computer science , gene
Autoregressive (AR) and random regression (RR) models were fitted to test‐day records from the first three lactations of Brazilian Holstein cattle with the objective of comparing their efficiency for national genetic evaluations. The data comprised 4,142,740 records of milk yield (MY) and somatic cell score (SCS) from 274,335 cows belonging to 2,322 herds. Although heritabilities were similar between models and traits, additive genetic variance estimates using AR were 7.0 (MY) and 22.2% (SCS) higher than those obtained from RR model. On the other hand, residual variances were lower in both traits when estimated through AR model. The rank correlation between EBV obtained from AR and RR models was 0.96 and 0.94 (MY) and 0.97 and 0.95 (SCS), respectively, for bulls (with 10 or more daughters) and cows. Estimated annual genetic gains for bulls (cows) obtained using AR were 46.11 (49.50) kg for MY and −0.019 (−0.025) score for SCS; whereas using RR these values were 47.70 (55.56) kg and −0.022 (−0.028) score. Akaike information criterion was lower for AR in both traits. Although AR model is more parsimonious, RR model assumes genetic correlations different from the unity within and across lactations. Thus, when these correlations are relatively high, these models tend to yield to similar predictions; otherwise, they will differ more and RR model would be theoretically sounder.