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Genetic improvement of body weight (BW) traits has received major consideration in the poultry industry due to their economic and environmental implications. With the rapid implementation of genomic selection (GS) in the poultry industry and a decrease in the cost of genotyping, genomic prediction (GP) is a feasible way to increase productivity. Moreover, a pre-selection of SNPs could represent a reasonable option to speed up GP. We used 312 F2 broiler chicken genotyped with 60K Illumina Beadchip to investigate the effect of reduced SNP densities on accuracy and bias of prediction using single-step genomic BLUP (ssGBLUP) for BW at 2-4 weeks of age (488 chickens). To investigate the effect of reduced SNP densities by varying minor allele frequency (MAF), SNPs were grouped into five subgroups with MAF of 0.05-0.1, 0.1-0.2, 0.2-0.3, 0.3-0.4 and 0.4-0.5. The accuracy and bias of genomic predictions from different MAF bins were compared to that using a standard array of 60k SNP genotypes and the traditional BLUP method. Our study showed that using a subset of common SNPs genotypes may increase accuracy of genomic predictions compared to using all SNPs, specifically in the studied F2 population with a limited number of genotyped/phenotyped individuals. [ABSTRACT FROM AUTHOR]

This study was performed to evaluate the population structure by genome-wide analysis using the Illumina 60K chicken Beadchip. One F2 population derived from the reciprocal cross between Arian fast-growing chickens and Urmia slow-growing indigenous fowls and consisting of 312 F2 birds was investigated. Quality control procedures, population clustering using the multidimensional scaling (MDS) method and allele frequency assessment in half-sib groups were applied. Then the population structure was considered, as provided by the outputs of MDS, Heatmap and neighbor-joining tree, which were consistent with structure analyses by clustering the birds into eight sub-populations of K1 to K8. The average expected heterozygosity in all half-sib families was K1= 0.4677, K2= 0.4635, K3= 0.4949, K4= 0.5235, K5= 0.4613, K6= 0.4273, K7= 0.4618, and K8= 0.4459, respectively. The heterozygosity values were found to be higher than 42% for all the eight clusters in this study, indicating a relatively high genetic variability within the groups. The fixation index (Fst) among F2 chicken clusters ranged from 0.01 to 0.29. One possible explanation for the high estimated Fst (>0.1) can be a signal of selection in the present population. Our findings can be applied in future GWASs, conservation plans and genetic improvement programs of chickens. [ABSTRACT FROM AUTHOR]

In recent years, metabolomics has been used to clarify the biology underlying biological samples. In the field of animal breeding, investigating the magnitude of genetic control on the metabolomic profiles of animals and their relationships with quantitative traits adds valuable information to animal improvement schemes. In this study, we analyzed metabolomic features (MFs) extracted from the metabolomic profiles of 843 male Holstein calves. The metabolomic profiles were obtained using nuclear magnetic resonance (NMR) spectroscopy. We investigated 2 alternative methods to control for peak shifts in the NMR spectra, binning and aligning, to determine which approach was the most efficient for assessing genetic variance. Series of univariate analyses were implemented to elucidate the heritability of each MF. Furthermore, records on BW and ADG from 154 to 294 d of age (ADG154–294), 294 to 336 d of age (ADG294–336), and 154 to 336 d of age (ADG154–336) were used in a series of bivariate analyses to establish the genetic and phenotypic correlations with MFs. Bivariate analyses were only performed for MFs that had a heritability significantly different from zero. The heritabilities obtained in the univariate analyses for the MFs in the binned data set were low (<0.2). In contrast, in the aligned data set, we obtained moderate heritability (0.2 to 0.5) for 3.5% of MFs and high heritability (more than 0.5) for 1% of MFs. The bivariate analyses showed that ~12%, ~3%, ~9%, and ~9% of MFs had significant additive genetic correlations with BW, ADG154–294, ADG294–336, and ADG154–336, respectively. In all of the bivariate analyses, the percentage of significant additive genetic correlations was higher than the percentage of significant phenotypic correlations of the corresponding trait. Our results provided insights into the influence of the underlying genetic mechanisms on MFs. Further investigations in this field are needed for better understanding of the genetic relationship among the MFs and quantitative traits. [ABSTRACT FROM AUTHOR]

In recent years, metabolomics has been used to clarify the biology underlying biological samples. In the field of animal breeding, investigating the magnitude of genetic control on the metabolomic profiles of animals and their relationships with quantitative traits adds valuable information to animal improvement schemes. In this study, we analyzed metabolomic features (MFs) extracted from the metabolomic profiles of 843 male Holstein calves. The metabolomic profiles were obtained using nuclear magnetic resonance (NMR) spectroscopy. We investigated 2 alternative methods to control for peak shifts in the NMR spectra, binning and aligning, to determine which approach was the most efficient for assessing genetic variance. Series of univariate analyses were implemented to elucidate the heritability of each MF. Furthermore, records on BW and ADG from 154 to 294 d of age (ADG154–294), 294 to 336 d of age (ADG294–336), and 154 to 336 d of age (ADG154–336) were used in a series of bivariate analyses to establish the genetic and phenotypic correlations with MFs. Bivariate analyses were only performed for MFs that had a heritability significantly different from zero. The heritabilities obtained in the univariate analyses for the MFs in the binned data set were low (<0.2). In contrast, in the aligned data set, we obtained moderate heritability (0.2 to 0.5) for 3.5% of MFs and high heritability (more than 0.5) for 1% of MFs. The bivariate analyses showed that ~12%, ~3%, ~9%, and ~9% of MFs had significant additive genetic correlations with BW, ADG154–294, ADG294–336, and ADG154–336, respectively. In all of the bivariate analyses, the percentage of significant additive genetic correlations was higher than the percentage of significant phenotypic correlations of the corresponding trait. Our results provided insights into the influence of the underlying genetic mechanisms on MFs. Further investigations in this field are needed for better understanding of the genetic relationship among the MFs and quantitative traits. [ABSTRACT FROM AUTHOR]