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Background Methane emission by ruminants has contributed considerably to the global warming and understanding the genomic architecture of methane production may help livestock producers to reduce the methane emission from the livestock production system. The goal of our study was to identify genomic regions affecting the predicted methane emission (PME) from volatile fatty acids (VFAs) indicators and VFA traits using imputed whole-genome sequence data in Iranian Holstein cattle. Results Based on the significant-association threshold (p − 8), 33 single nucleotide polymorphisms (SNPs) were detected for PME per kg milk (n = 2), PME per kg fat (n = 14), and valeric acid (n = 17). Besides, 69 genes were identified for valeric acid (n = 18), PME per kg milk (n = 4) and PME per kg fat (n = 47) that were located within 1 Mb of significant SNPs. Based on the gene ontology (GO) term analysis, six promising candidate genes were significantly clustered in organelle organization (GO:0004984, p = 3.9 × 10− 2) for valeric acid, and 17 candidate genes significantly clustered in olfactory receptors activity (GO:0004984, p = 4 × 10− 10) for PME traits. Annotation results revealed 31 quantitative trait loci (QTLs) for milk yield and its components, body weight, and residual feed intake within 1 Mb of significant SNPs. Conclusions Our results identified 33 SNPs associated with PME and valeric acid traits, as well as 17 olfactory receptors activity genes for PME traits related to feed intake and preference. Identified SNPs were close to 31 QTLs for milk yield and its components, body weight, and residual feed intake traits. In addition, these traits had high correlations with PME trait. Overall, our findings suggest that marker-assisted and genomic selection could be used to improve the difficult and expensive-to-measure phenotypes such as PME. Moreover, prediction of methane emission by VFA indicators could be useful for increasing the size of reference population required in genome-wide association studies and genomic selection.

Genomic prediction using a large number of markers is challenging, due to the curse of dimensionality as well as multicollinearity arising from linkage disequilibrium between markers. Several methods have been proposed to solve these problems such as Principal Component Analysis (PCA) that is commonly used to reduce the dimension of predictor variables by generating orthogonal variables. Usually, the knowledge from PCA is incorporated in genomic prediction, assuming equal variance for the PCs or a variance proportional to the eigenvalues, both treat variances as fixed. Here, three prior distributions including normal, scaled-t and double exponential were assumed for PC effects in a Bayesian framework with a subset of PCs. These developed PCR models (dPCRm) were compared to routine genomic prediction models (RGPM) i.e., ridge and Bayesian ridge regression, BayesA, BayesB, and PC regression with a subset of PCs but PC variances predefined as proportional to the eigenvalues (PCR-Eigen). The performance of methods was compared by simulating a single trait with heritability of 0.25 on a genome consisted of 3,000 SNPs on three chromosomes and QTL numbers of 15, 60, and 105. After 500 generations of random mating as the historical population, a population was isolated and mated for another 15 generations. The generations 8 and 9 of recent population were used as the reference population and the next six generations as validation populations. The accuracy and bias of predictions were evaluated within the reference population, and each of validation populations. The accuracies of dPCRm were similar to RGPM (0.536 to 0.664 vs. 0.542 to 0.671), and higher than the accuracies of PCR-Eigen (0.504 to 0.641) within reference population over different QTL numbers. Decline in accuracies in validation populations were from 0.633 to 0.310, 0.639 to 0.313, and 0.617 to 0.298 using dPCRm, RGPM and PCR-Eigen, respectively. Prediction biases of dPCRm and RGPM were similar and always much less than biases of PCR-Eigen. In conclusion assuming PC variances as random variables via prior specification yielded higher accuracy than PCR-Eigen and same accuracy as RGPM, while fewer predictors were used.

Acetyl CoA Carboxylase 1 (ACC1) is a biotin-dependent enzyme that catalyzes the carboxylation of Acetyl CoA to form Malonyl CoA, the key intermediate metabolite in fatty acid synthesis. In this study, the mRNA expression of the ACC1 gene was evaluated in four different tissues (liver, visceral fat, subcutaneous fat, and longissimus muscle) of the domestic goat (Capra hircus) kids feeding on four different levels of trivalent chromium (0, 0.5, 1, and 1.5mg/day) as food supplementation. RT-qPCR technique was used for expression analyses and heat shock protein 90 gene (HSP-90) was considered as reference gene for data normalization. Our results revealed that 1.5mg/day chromium significantly reduced the expression of the ACC1 gene in liver, visceral fat, and subcutaneous fat tissues, but not in longissimus muscles (P

Genetic parameters and genetic trends for birth weight (BW), weaning weight (WW), 6-month weight (6MW), and yearling weight (YW) traits were estimated by using records of 5,634 Makooei lambs, descendants of 289 sires and 1,726 dams, born between 1996 and 2009 at the Makooei sheep breeding station, West Azerbaijan, Iran. The (co)variance components were estimated with different animal models using a restricted maximum likelihood procedure and the most appropriate model for each trait was determined by Akaike's Information Criterion. Breeding values of animals were predicted with best linear unbiased prediction methodology under multi-trait animal models and genetic trends were estimated by regression mean breeding values on birth year. The most appropriate model for BW was a model including direct and maternal genetic effects, regardless of their covariance. The model for WW and 6MW included direct additive genetic effects. The model for YW included direct genetic effects only. Direct heritabilities based on the best model were estimated 0.15 ± 0.04, 0.16 ± 0.03, 0.21 ± 0.04, and 0.22 ± 0.06 for BW, WW, 6MW, and YW, respectively, and maternal heritability obtained 0.08 ± 0.02 for BW. Genetic correlations among the traits were positive and varied from 0.28 for BW-YW to 0.66 for BW-WW and phenotypic correlations were generally lower than the genetic correlations. Genetic trends were 8.1 ± 2, 67.4 ± 5, 38.7 ± 4, and 47.6 ± 6 g per year for BW, WW, 6MW, and YW, respectively.

The main objectives of this study were to estimate genetic and phenotypic parameters for growth traits and prolificacy in the Raeini Cashmere goat. Traits included, birth weight (BWT), weaning weight (WWT), 6-month weight (6WT), 9-month weight (9WT), 12-month weight (12WT), average daily gain from birth to weaning (ADG1), average daily gain from weaning to 6WT (ADG2), average daily gain from 6WT to 12WT (ADG3), survival rate (SR), litter size at birth (LSB) and litter size at weaning (LSW) and total litter weight at birth (LWB). Data were collected over a period of 28 years (1982–2009) at the experimental breeding station of Raeini goat, southeast of Iran. Genetic parameters were estimated with univariate models using restricted maximum likelihood (REML) procedures. In addition to an animal model, sire and threshold models, using a logit link function, were used for analyses of SR. Age of dam, birth of type, sex and of kidding had significant influence (p < 0.05 or 0.01) all the traits. Direct heritability estimates were low for prolificacy traits (0.04 ± 0.01 for LSB, 0.09 ± 0.02 for LSW, 0.16 ± 0.02 for LWB and 0.05 ± 0.02 for SR) and average daily gain (0.12 ± 0.03 for ADG1, 0.08 ± 0.02 for ADG2, and 0.07 ± 0.03 for ADG3) to moderate for production traits (0.22 ± 0.02 for BWT, 0.25 ± 0.02 for WWT, 0.29 ± 0.04 for 6WT, 0.30 ± 0.02 for 9WT, 0.32 ± 0.05 for 12WT). The estimates for the maternal additive genetic variance ratios were lower than direct heritability for BWT (0.17 ± 0.03) and WWT (0.07 ± 0.02).