Genome-wide association studies for methane emission and ruminal volatile fatty acids using Holstein cattle sequence data.

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 < 5 × 10- 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.

Genetic parameter estimates for growth traits and prolificacy in Raeini Cashmere goats.

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).

Incorporating Prior Knowledge of Principal Components in Genomic Prediction.

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.

Chromium downregulates the expression of Acetyl CoA Carboxylase 1 gene in lipogenic tissues of domestic goats: a potential strategy for meat quality improvement.

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<0.05). We measured some phenotypic traits of kid's carcasses to detect their probable correlations with chromium-mediated downregulation of ACC1 expression. Interestingly, changes in ACC1 expression were accompanied with decreased accumulation of fats in adipose tissues such that the subcutaneous fat thickness and heart fat percentage decreased in kids feeding on chromium. By contrast, chromium supplemented kids showed higher percentage of muscles despite the fact that their total body weight did not differ from that of non-supplemented kids. Our study suggests that trivalent chromium alters the direction of energy accumulation towards muscles rather than fats and provides insights into application of chromium supplementation as a useful strategy for improvement of meat quality in domestic animals.