Behrouz Mohammad Nazari; Ardeshir Nejati Javaremi; Mohammad Moradi Shahre Babak; Rostam AbdolahiArpanahi
Volume 22, Issue 4 , December 2020, , Pages 515-527
Abstract
In order to evaluate the effect of genotype by environment interaction on production traits of Holstein cattle of Iran, first lactation test day records of 344170, 135000 and 156840 of milk, fat and protein yield on 34417, 13500 and 15684 cows and SNP markers of 1935 genotyped bulls were used. The ...
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In order to evaluate the effect of genotype by environment interaction on production traits of Holstein cattle of Iran, first lactation test day records of 344170, 135000 and 156840 of milk, fat and protein yield on 34417, 13500 and 15684 cows and SNP markers of 1935 genotyped bulls were used. The production data were retrieved from the Animal Breeding Center and Productions Improvement of Iran’s database which were collected from 2013 to 2018. To consider the interaction of genotype and environment, mean of temperature-humidity index (THI) in three days before each test day records as continuous environmental effect were retrieved from the 35 closest meteorological stations in the vicinity of 139 Holstein herds from 13 provinces. Variance and covariance components were estimated through a single-trait random regression model with orthogonal Legendre polynomials of second order for days in milk and THI using AIREMLF90 software. The results showed that changes in THI across lactation led tofluctuations in additive genetic variance over time. The change in heritability of milk production traits over lactation followed the same trend as additive genetic variance. The results from cross-validation analysis showed that including genomic information into the predictive model, increased prediction accuracy and including THI information increased unbiasedness. Due to the changes in milk production of daughters of bulls across days and THI , genotype by environment interaction should be considered when selecting bulls under different conditions.
Rostam Abdollahi-Arpanahi; Abas Pakdel; Ardeshir Nejati-Javaremi; Mohammad Moradi Shahrbabak
Volume 15, Issue 1 , July 2014, , Pages 65-77
Abstract
The objective of this study was to compare six statistical methods for prediction of genomic breedingvalues for traits with different genetic architecture in term of gene effects distributions and number ofQuantitative Traits Loci (QTLs). A genome consisted of 500 bi-allelic single nucleotide polymorphism(SNP) ...
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The objective of this study was to compare six statistical methods for prediction of genomic breedingvalues for traits with different genetic architecture in term of gene effects distributions and number ofQuantitative Traits Loci (QTLs). A genome consisted of 500 bi-allelic single nucleotide polymorphism(SNP) markers distributed over a chromosomes with 100 cm length was simulated. Three different geneeffects distributions (uniform, normal and gamma) were considered. Number of QTLs varied from 50 to200. Finally, nine quantitative traits with different genetic architecture were generated. The performanceof six statistical methods of genomic prediction that differ with respect to assumptions regardingdistribution of marker effects, including i) Genomic Best Linear Unbiased Prediction (GBLUP), ii) RidgeRegression Best Linear Unbiased Prediction (RRBLUP), iii) Bayes A, iv) Bayes B, v) Bayes C, and vi)Bayesian least absolute shrinkage and selection operator (Bayes L) are presented. The accuracy ofprediction declined significantly over generations (P< 0.05) but Bayesian methods outperformed GBLUPand RRBLUP in persistence of accuracy of genomic estimated breeding values over generations.Bayesian methods were superior to GBLUP and RRBLUP when the gene effects distribution generatedfrom gamma distribution. The highest accuracy of genomic breeding values was observed when the geneeffects come from normal distribution. In all statistical evaluation methods with increasing the number ofQTLs towards 200, the accuracy of predicted genomic values has been decreased. In general, Bayesianand GBLUP methods performed better in prediction than RRBLUP method. These results gave someevidences that when the genetic architecture of quantitative traits deviated from infinitesimal modelassumptions, Bayesian methods usually perform better than GBLUP and RR-BLUP.