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

Abstract

Background Egg production traits are economically important in poultry breeding programs. Previous studies have shown that incorporating genomic data can increase the accuracy of genetic prediction of egg production. Our objective was to estimate the genetic and phenotypic parameters of such traits and compare the prediction accuracy of pedigree-based random regression best linear unbiased prediction (RR-PBLUP) and genomic single-step random regression BLUP (RR-ssGBLUP). Egg production was recorded on 7422 birds during 24 consecutive weeks from first egg laid. Hatch-week of birth by week of lay and week of lay by age at first egg were fitted as fixed effects and body weight as a covariate, while additive genetic and permanent environment effects were fitted as random effects, along with heterogeneous residual variances over 24 weeks of egg production. Predictions accuracies were compared based on two statistics: (1) the correlation between estimated breeding values and phenotypes divided by the square root of the trait heritability, and (2) the ratio of the variance of BLUP predictions of individual Mendelian sampling effects divided by one half of the estimate of the additive genetic variance. Results Heritability estimates along the production trajectory obtained with RR-PBLUP ranged from 0.09 to 0.22, with higher estimates for intermediate weeks. Estimates of phenotypic correlations between weekly egg production were lower than the corresponding genetic correlation estimates. Our results indicate that genetic correlations decreased over the laying period, with the highest estimate being between traits in later weeks and the lowest between early weeks and later ages. Prediction accuracies based on the correlation-based statistic ranged from 0.11 to 0.44 for RR-PBLUP and from 0.22 to 0.57 for RR-ssGBLUP using the correlation-based statistic. The ratios of the variances of BLUP predictions of Mendelian sampling effects and one half of the additive genetic variance ranged from 0.17 to 0.26 for RR-PBLUP and from 0.17 to 0.34 for RR-ssGBLUP. Although the improvement in accuracies from RR-ssGBLUP over those from RR-PBLUP was not uniform over time for either statistic, accuracies obtained with RR-ssGBLUP were generally equal to or higher than those with RR-PBLUP. Conclusions Our findings show the potential advantage of incorporating genomic data in genetic evaluation of egg production traits using random regression models, which can contribute to the genetic improvement of egg production in turkey populations.

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

“…Based on the ssGBLUP approach, Abdalla et al (2019) observed a 16% increased accuracy for walking score in turkeys over traditional pedigree BLUP. In addition, Emamgholi Begli et al (2021) observed that accuracies obtained with RR ssGBLUP were generally equal to or higher than those obtained with RR-PBLUP for egg production traits. Similarly, Oliveira et al (2019) reported higher validation reliabilities for genomic estimated breeding values (GEBV) in comparison to parent averages for milk production traits in dairy cattle when using a multi-trait RR test-day model.…”

Effect of Plumage Colour on The Productive and Physiological Performance of Two Lines of Turkey During Egg Production Period

“…Brady et al (2020) reported that the primary cause of variations in the egg production is the gonadal axis hormones, as high-producing hens tend to show higher basal levels of the LH messenger ribonucleic acid (mRNA) than low-producing hens. Additionally, Begli et al (2021) used a pedigree-based random regression best linear unbiased predictor (BLUP) model to estimate the variables of egg production over 24 weeks. They found that environmental differences had a significant impact on the egg production, with estimated heritability values for this trait during the first 18 -20 weeks ranging from moderate to high (0.22).…”

Role of SNPs located in the exon 9 of ATAPA1 gene on goose egg production

“…The mechanisms of these regulatory factors are different and interact with each other. Due to the diverse influencing factors and low heritability ( Emamgholi Begli et al, 2021 ), the genetic progress of egg production in goose obtained by the traditional phenotypic selection method is slow.…”