Literature DB >> 12195797

Monthly model for genetic evaluation of laying hens. II. Random regression.

A Anang1, N Mielenz, L Schüler.   

Abstract

1. We investigated the use of monthly production records for genetic evaluation of laying hens, derived from a test day model with random regression in dairy cattle and compared it with other models. 2. Records of 6450 hens, daughters of 180 sires and 1335 dams, were analysed using a model with restricted maximum likelihood (REML): traits considered were monthly and cumulative egg production. Five models were studied: (1) random regression with covariates derived from the regression of Ali and Schaeffer (Canadian Journal of Animal Science, 67: 637-644, 1987) (RRMAS), (2) random regression with covariates derived from quartic polynomial (RRMP4), (3) fixed regression with covariates derived from Ali and Schaeffer (FRM), (4) multiple trait (MTM) and (5) cumulative (CM). 3. The models were compared on the basis of Spearman rank correlations of individual breeding values and sire breeding values estimated from subsets of full-sib split data. The hens (about 10% per generation) which ranked highest on their estimated breeding values from different models were compared phenotypically with their full records. 4. The estimates of heritability resulting from RRMP4 were biased upward from the estimates obtained from MTM, so this model was discarded. The heritabilities for monthly productions from RRMAS and MTM showed a similar pattern. They were high for the 1st month of production, decreased to their lowest value at about month 5 of production and increased again to the end of lay. 5. Spearman rank correlations between animal breeding values estimated by monthly models (RRMAS, FRM and MTM) were high, between 0.91 and 0.98, whereas those between estimates of monthly models and CM were lower, from 0.85 to 0.87. The correlations estimated either from intermittent months of measurements (odd vs even months) or full records were generally high, from 0.93 to 0.99. Information from odd months of production could be sufficient for cost-efficient recording schemes. The RRMAS generally had the highest correlation of sire breeding values between subsets of full-sib records, followed by MTM, RM and CM. Monthly models selected hens with higher productivity than the cumulative model. 6. In conclusion, genetic evaluation based on monthly production may be better than using cumulative production and RRMAS appeared to be the best among the models tested here.

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Year:  2002        PMID: 12195797     DOI: 10.1080/00071660120103657

Source DB:  PubMed          Journal:  Br Poult Sci        ISSN: 0007-1668            Impact factor:   2.095


  6 in total

1.  Estimation of genetic parameters for monthly egg production in laying hens based on random regression models.

Authors:  A Wolc; T Szwaczkowski
Journal:  J Appl Genet       Date:  2009       Impact factor: 3.240

2.  Genetic and phenotypic parameter estimates for body weights and egg production in Horro chicken of Ethiopia.

Authors:  Nigussie Dana; E H Vander Waaij; Johan A M van Arendonk
Journal:  Trop Anim Health Prod       Date:  2010-07-14       Impact factor: 1.559

3.  Genetic parameters of weekly egg production using random regression models in two strains of Japanese quails.

Authors:  Neda Farzin; Abolghasem Seraj
Journal:  J Appl Genet       Date:  2022-08-29       Impact factor: 2.653

4.  Changes in the Control of the Hypothalamic-Pituitary Gonadal Axis Across Three Differentially Selected Strains of Laying Hens (Gallus gallus domesticus).

Authors:  Charlene Hanlon; Kayo Takeshima; Grégoy Y Bédécarrats
Journal:  Front Physiol       Date:  2021-03-25       Impact factor: 4.566

5.  Applicability of single-step genomic evaluation with a random regression model for reproductive traits in turkeys (Meleagris gallopavo).

Authors:  Bayode O Makanjuola; Emhimad A Abdalla; Benjamin J Wood; Christine F Baes
Journal:  Front Genet       Date:  2022-08-24       Impact factor: 4.772

6.  Genetic analysis of egg production traits in turkeys (Meleagris gallopavo) using a single-step genomic random regression model.

Authors:  Hakimeh Emamgholi Begli; Lawrence R Schaeffer; Emhimad Abdalla; Emmanuel A Lozada-Soto; Alexandra Harlander-Matauschek; Benjamin J Wood; Christine F Baes
Journal:  Genet Sel Evol       Date:  2021-07-20       Impact factor: 4.297

  6 in total

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