In the era of genome-wide selection (GWS), genotype-by-environment (G×E) interactions can be studied using genomic information, thus enabling the estimation of SNP marker effects and the prediction of genomic estimated breeding values (GEBV) for young candidates for selection in different environments. Although G×E studies in pigs are scarce, the use of artificial insemination has enabled the distribution of genetic material from sires across multiple environments. Given the relevance of reproductive traits, such as the total number born (TNB) and the variation in environmental conditions encountered by commercial dams, understanding G×E interactions can be essential for choosing the best sires for different environments. The present work proposes a two-step reaction norm approach for G×E analysis using genomic information. The first step provided estimates of environmental effects (herd-year-season, HYS), and the second step provided estimates of the intercept and slope for the TNB across different HYS levels, obtained from the first step, using a random regression model. In both steps, pedigree ( A: ) and genomic ( G: ) relationship matrices were considered. The genetic parameters (variance components, h(2) and genetic correlations) were very similar when estimated using the A: and G: relationship matrices. The reaction norm graphs showed considerable differences in environmental sensitivity between sires, indicating a reranking of sires in terms of genetic merit across the HYS levels. Based on the G: matrix analysis, SNP by environment interactions were observed. For some SNP, the effects increased at increasing HYS levels, while for others, the effects decreased at increasing HYS levels or showed no changes between HYS levels. Cross-validation analysis demonstrated better performance of the genomic approach with respect to traditional pedigrees for both the G×E and standard models. The genomic reaction norm model resulted in an accuracy of GEBV for "juvenile" boars varying from 0.14 to 0.44 across different HYS levels, while the accuracy of the standard genomic prediction model, without reaction norms, varied from 0.09 to 0.28. These results show that it is important and feasible to consider G×E interactions in evaluations of sires using genomic prediction models and that genomic information can increase the accuracy of selection across environments.

Thirteen reference genes were investigated to determine their stability to be used as a housekeeping in gene expression studies in skeletal muscle of chickens. Five different algorithms were used for ranking of reference genes and results suggested that individual rankings of the genes differed among them. The stability of the expression of reference genes were validated using samples obtained from the Pectoralis major muscle in chicken. Samples were obtained from chickens in different development periods post hatch and under different nutritional diets. For gene expression calculation the ΔΔCt approach was applied to compare relative expression of pairs of genes within each of 52 samples when normalized to mitochondrially encoded cytochrome c oxidase II (MT-CO2) target gene. Our findings showed that hydroxymethylbilane synthase (HMBS) and hypoxanthine phosphoribosyl transferase 1 (HPRT1) are the most stable reference genes while transferrin receptor (TFRC) and beta-2-microglobulin (B2M) ranked as the least stable genes in the Pectoralis major muscle of chickens. Moreover, our results revealed that HMBS and HPRT1 gene expression did not change due to dietary variations and thus it is recommended for accurate normalization of RT-qPCR data in chicken Pectoralis major muscle.

BackgroundIn tropical countries, losses caused by bovine tick Rhipicephalus (Boophilus) microplus infestation have a tremendous economic impact on cattle production systems. Genetic variation between Bos taurus and Bos indicus to tick resistance and molecular biology tools might allow for the identification of molecular markers linked to resistance traits that could be used as an auxiliary tool in selection programs. The objective of this work was to identify QTL associated with tick resistance/susceptibility in a bovine F2 population derived from the Gyr (Bos indicus) × Holstein (Bos taurus) cross.ResultsThrough a whole genome scan with microsatellite markers, we were able to map six genomic regions associated with bovine tick resistance. For most QTL, we have found that depending on the tick evaluation season (dry and rainy) different sets of genes could be involved in the resistance mechanism. We identified dry season specific QTL on BTA 2 and 10, rainy season specific QTL on BTA 5, 11 and 27. We also found a highly significant genome wide QTL for both dry and rainy seasons in the central region of BTA 23.ConclusionsThe experimental F2 population derived from Gyr × Holstein cross successfully allowed the identification of six highly significant QTL associated with tick resistance in cattle. QTL located on BTA 23 might be related with the bovine histocompatibility complex. Further investigation of these QTL will help to isolate candidate genes involved with tick resistance in cattle.

BackgroundReproductive traits such as number of stillborn piglets (SB) and number of teats (NT) have been evaluated in many genome-wide association studies (GWAS). Most of these GWAS were performed under the assumption that these traits were normally distributed. However, both SB and NT are discrete (e.g. count) variables. Therefore, it is necessary to test for better fit of other appropriate statistical models based on discrete distributions. In addition, although many GWAS have been performed, the biological meaning of the identified candidate genes, as well as their functional relationships still need to be better understood. Here, we performed and tested a Bayesian treatment of a GWAS model assuming a Poisson distribution for SB and NT in a commercial pig line. To explore the biological role of the genes that underlie SB and NT and identify the most likely candidate genes, we used the most significant single nucleotide polymorphisms (SNPs), to collect related genes and generated gene-transcription factor (TF) networks.ResultsComparisons of the Poisson and Gaussian distributions showed that the Poisson model was appropriate for SB, while the Gaussian was appropriate for NT. The fitted GWAS models indicated 18 and 65 significant SNPs with one and nine quantitative trait locus (QTL) regions within which 18 and 57 related genes were identified for SB and NT, respectively. Based on the related TF, we selected the most representative TF for each trait and constructed a gene-TF network of gene-gene interactions and identified new candidate genes.ConclusionsOur comparative analyses showed that the Poisson model presented the best fit for SB. Thus, to increase the accuracy of GWAS, counting models should be considered for this kind of trait. We identified multiple candidate genes (e.g. PTP4A2, NPHP1, and CYP24A1 for SB and YLPM1, SYNDIG1L, TGFB3, and VRTN for NT) and TF (e.g. NF-κB and KLF4 for SB and SOX9 and ELF5 for NT), which were consistent with known newborn survival traits (e.g. congenital heart disease in fetuses and kidney diseases and diabetes in the mother) and mammary gland biology (e.g. mammary gland development and body length).Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-016-0189-x) contains supplementary material, which is available to authorized users.

Seventy-two pigs of three genetic groups (Brazilian indigenous breed Piau, Commercial line and Crossbred) of both sexes were slaughtered at four live weights (30, 60, 90 and 120 kg). Intramuscular fat (IMF) content in Longissimus dorsi muscle of each animal was extracted and correlated with candidate gene mRNA expression (ATN1, EEF1A2, FABP3, LDLR, MGP, OBSCN, PDHB, TRDN and RYR1). Within slaughter weight of 120 kg, Piau and Crossbred pigs showed higher IMF content (p < 0.05) than commercial animals, with 2.48, 2.08 and 1.00% respectively. Barrows presented higher values of IMF (p < 0.05) than gilts (1.54 and 1.30% respectively). Gene expression of EEF1A2, FABP3, LDLR, OBSCN, PDHB, TRDN and RYR1 were correlated with IMF (p < 0.05) using the whole dataset. For Piau data only, expression of FABP3, LDLR, MGP, OBSCN, PDHB, TRDN and RYR1 showed correlation with IMF (p < 0.05). Genes that have important roles in lipid transportation inside the cell (FABP3) and tissues (LDLR) showed correlation with IMF of, respectively, 0.68 and 0.63 using the whole data set, and 0.90 and 0.91 using data from Piau animals. The highly positive correlation of the LDLR and FAPB3 expression with IMF content may confirm that these genes are important for fat deposition in the porcine L. dorsi muscle.