Background: One of the main limitations of many livestock breeding programs is that selection is in pure breeds housed in high-health environments but the aim is to improve crossbred performance under field conditions. Genomic selection (GS) using high-density genotyping could be used to address this. However in crossbred populations, 1) effects of SNPs may be breed specific, and 2) linkage disequilibrium may not be restricted to markers that are tightly linked to the QTL. In this study we apply GS to select for commercial crossbred performance and compare a model with breed-specific effects of SNP alleles (BSAM) to a model where SNP effects are assumed the same across breeds (ASGM). The impact of breed relatedness (generations since separation), size of the population used for training, and marker density were evaluated. Trait phenotype was controlled by 30 QTL and had a heritability of 0.30 for crossbred individuals. A Bayesian method (Bayes-B) was used to estimate the SNP effects in the crossbred training population and the accuracy of resulting GS breeding values for commercial crossbred performance was validated in the purebred population.

BackgroundNew advances in high-throughput technologies have allowed for the massive analysis of genomic data, providing new opportunities for the characterization of the transcriptome architectures. Recent studies in pigs have employed RNA-Seq to explore the transcriptome of different tissues in a reduced number of animals. The main goal of this study was the identification of differentially-expressed genes in the liver of Iberian x Landrace crossbred pigs showing extreme phenotypes for intramuscular fatty acid composition using RNA-Seq.ResultsThe liver transcriptomes of two female groups (H and L) with phenotypically extreme intramuscular fatty acid composition were sequenced using RNA-Seq. A total of 146 and 180 unannotated protein-coding genes were identified in intergenic regions for the L and H groups, respectively. In addition, a range of 5.8 to 7.3% of repetitive elements was found, with SINEs being the most abundant elements. The expression in liver of 186 (L) and 270 (H) lncRNAs was also detected. The higher reproducibility of the RNA-Seq data was validated by RT-qPCR and porcine expression microarrays, therefore showing a strong correlation between RT-qPCR and RNA-Seq data (ranking from 0.79 to 0.96), as well as between microarrays and RNA-Seq (r=0.72). A differential expression analysis between H and L animals identified 55 genes differentially-expressed between groups. Pathways analysis revealed that these genes belong to biological functions, canonical pathways and three gene networks related to lipid and fatty acid metabolism. In concordance with the phenotypic classification, the pathways analysis inferred that linolenic and arachidonic acids metabolism was altered between extreme individuals. In addition, a connection was observed among the top three networks, hence suggesting that these genes are interconnected and play an important role in lipid and fatty acid metabolism.ConclusionsIn the present study RNA-Seq was used as a tool to explore the liver transcriptome of pigs with extreme phenotypes for intramuscular fatty acid composition. The differential gene expression analysis showed potential gene networks which affect lipid and fatty acid metabolism. These results may help in the design of selection strategies to improve the sensorial and nutritional quality of pork meat.

BackgroundHanwoo beef is known for its marbled fat, tenderness, juiciness and characteristic flavor, as well as for its low cholesterol and high omega 3 fatty acid contents. As yet, there has been no comprehensive investigation to estimate genomic selection accuracy for carcass traits in Hanwoo cattle using dense markers. This study aimed at evaluating the accuracy of alternative statistical methods that differed in assumptions about the underlying genetic model for various carcass traits: backfat thickness (BT), carcass weight (CW), eye muscle area (EMA), and marbling score (MS).MethodsAccuracies of direct genomic breeding values (DGV) for carcass traits were estimated by applying fivefold cross-validation to a dataset including 1183 animals and approximately 34,000 single nucleotide polymorphisms (SNPs).ResultsAccuracies of BayesC, Bayesian LASSO (BayesL) and genomic best linear unbiased prediction (GBLUP) methods were similar for BT, EMA and MS. However, for CW, DGV accuracy was 7% higher with BayesC than with BayesL and GBLUP. The increased accuracy of BayesC, compared to GBLUP and BayesL, was maintained for CW, regardless of the training sample size, but not for BT, EMA, and MS. Genome-wide association studies detected consistent large effects for SNPs on chromosomes 6 and 14 for CW.ConclusionsThe predictive performance of the models depended on the trait analyzed. For CW, the results showed a clear superiority of BayesC compared to GBLUP and BayesL. These findings indicate the importance of using a proper variable selection method for genomic selection of traits and also suggest that the genetic architecture that underlies CW differs from that of the other carcass traits analyzed. Thus, our study provides significant new insights into the carcass traits of Hanwoo cattle.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-016-0283-0) contains supplementary material, which is available to authorized users.

Intramuscular fat (IMF) content and fatty acid composition affect the organoleptic quality and nutritional value of pork. A genome-wide association study was performed on 138 Duroc pigs genotyped with a 60k SNP chip to detect biologically relevant genomic variants influencing fat content and composition. Despite the limited sample size, the genome-wide association study was powerful enough to detect the association between fatty acid composition and a known haplotypic variant in SCD (SSC14) and to reveal an association of IMF and fatty acid composition in the LEPR region (SSC6). The association of LEPR was later validated with an independent set of 853 pigs using a candidate quantitative trait nucleotide. The SCD gene is responsible for the biosynthesis of oleic acid (C18:1) from stearic acid. This locus affected the stearic to oleic desaturation index (C18:1/C18:0), C18:1, and saturated (SFA) and monounsaturated (MUFA) fatty acids content. These effects were consistently detected in gluteus medius, longissimus dorsi, and subcutaneous fat. The association of LEPR with fatty acid composition was detected only in muscle and was, at least in part, a consequence of its effect on IMF content, with increased IMF resulting in more SFA, less polyunsaturated fatty acids (PUFA), and greater SFA/PUFA ratio. Marker substitution effects estimated with a subset of 65 animals were used to predict the genomic estimated breeding values of 70 animals born 7 years later. Although predictions with the whole SNP chip information were in relatively high correlation with observed SFA, MUFA, and C18:1/C18:0 (0.48–0.60), IMF content and composition were in general better predicted by using only SNPs at the SCD and LEPR loci, in which case the correlation between predicted and observed values was in the range of 0.36 to 0.54 for all traits. Results indicate that markers in the SCD and LEPR genes can be useful to select for optimum fatty acid profiles of pork.

The lipid content and fatty acid (FA) profile have an important impact in human health as well as in the technological transformation and nutritional and organoleptic quality of meat. A genome-wide association study (GWAS) on 144 backcross pigs (25% Iberian × 75% Landrace) was performed for 32 traits associated with intramuscular FA composition and indices of FA metabolism. The GWAS was carried out using Qxpak 5.0 and the genotyping information obtained from the Porcine SNP60K BeadChip (Illumina Inc., San Diego, CA). Signals of significant association considering a false- discovery rate (q-value < 0.05) were observed in 15 of the 32 analyzed traits, and a total of 813 trait-associated SNP (TAS), distributed in 43 chromosomal intervals on almost all autosomes, were annotated. According to the clustering analysis based on functional classification, several of the annotated genes are related to FA composition and lipid metabolism. Some interesting positional concordances among TAS and previously reported QTL for FA compositions and/or other lipid traits were also found. These common genomic regions for different traits suggest pleiotropic effects for FA composition and were found primarily on SSC4, SSC8, and SSC16. These results contribute to our understanding of the complex genetic basis of FA composition and FA metabolism.

BackgroundIn recent years, there has been an increasing interest in the genetic determination of environmental variance. In the case of litter size, environmental variance can be related to the capacity of animals to adapt to new environmental conditions, which can improve animal welfare.ResultsWe developed a ten-generation divergent selection experiment on environmental variance. We selected one line of rabbits for litter size homogeneity and one line for litter size heterogeneity by measuring intra-doe phenotypic variance. We proved that environmental variance of litter size is genetically determined and can be modified by selection. Response to selection was 4.5% of the original environmental variance per generation. Litter size was consistently higher in the Low line than in the High line during the entire experiment.ConclusionsWe conclude that environmental variance of litter size is genetically determined based on the results of our divergent selection experiment. This has implications for animal welfare, since animals that cope better with their environment have better welfare than more sensitive animals. We also conclude that selection for reduced environmental variance of litter size does not depress litter size.

Data from seven generations of a divergent selection experiment designed for environmental variability of birth weight were analysed to estimate genetic parameters and to explore signs of selection response. A total of 10 783 birth weight records from 638 females and 1127 litters in combination with 10 007 pedigree records were used. Each record of birth weight was assigned to the mother of the pup in a heteroscedastic model, and after seven generations of selection, evidence of success in the selection process was shown. A Bayesian analysis showed that success of the selection process started from the first generation for birth weight and from the second generation for its environmental variability. Genetic parameters were estimated across generations. However, only from the third generation onwards were the records useful to consider the results to be reliable. The results showed a consistent positive and low genetic correlation between the birth weight trait and its environmental variability, which could allow an independent selection process. This study has demonstrated that the genetic control of the birth weight environmental variability is possible in mice. Nevertheless, before the results are applied directly in farm animals, it would be worth confirming any other implications on other important traits, such as robustness, longevity and welfare.

BackgroundPorcine fatty acid composition is a key factor for quality and nutritive value of pork. Several QTLs for fatty acid composition have been reported in diverse fat tissues. The results obtained so far seem to point out different genetic control of fatty acid composition conditional on the fat deposits. Those studies have been conducted using simple approaches and most of them focused on one single tissue. The first objective of the present study was to identify tissue-specific and tissue-consistent QTLs for fatty acid composition in backfat and intramuscular fat, combining linkage mapping and GWAS approaches and conducted under single and multitrait models. A second aim was to identify powerful candidate genes for these tissue-consistent QTLs, using microarray gene expression data and following a targeted genetical genomics approach.ResultsThe single model analyses, linkage and GWAS, revealed over 30 and 20 chromosomal regions, 24 of them identified here for the first time, specifically associated to the content of diverse fatty acids in BF and IMF, respectively. The analyses with multitrait models allowed identifying for the first time with a formal statistical approach seven different regions with pleiotropic effects on particular fatty acids in both fat deposits. The most relevant were found on SSC8 for C16:0 and C16:1(n-7) fatty acids, detected by both linkage and GWAS approaches. Other detected pleiotropic regions included one on SSC1 for C16:0, two on SSC4 for C16:0 and C18:2, one on SSC11 for C20:3 and the last one on SSC17 for C16:0. Finally, a targeted eQTL scan focused on regions showing tissue-consistent effects was conducted with Longissimus and fat gene expression data. Some powerful candidate genes and regions were identified such as the PBX1, RGS4, TRIB3 and a transcription regulatory element close to ELOVL6 gene to be further studied.ConclusionsComplementary genome scans have confirmed several chromosome regions previously associated to fatty acid composition in backfat and intramuscular fat, but even more, to identify new ones. Although most of the detected regions were tissue-specific, supporting the hypothesis that the major part of genes affecting fatty acid composition differs among tissues, seven chromosomal regions showed tissue-consistent effects. Additional gene expression analyses have revealed powerful target regions to carry the mutation responsible for the pleiotropic effects.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-14-845) contains supplementary material, which is available to authorized users.