Three-step Bayesian factor analysis applied to QTL detection in crosses between outbred pig populations

AbstractMarker assisted selection (MAS) can be used to improve the efficiency of genetic selection of traits for which phenotypic measurements are expensive or cannot be obtained on selection candidates, such as carcass traits. Marker information required for MAS may be acquired through the identification of QTLs. Generally, univariate models are used for QTL detection, although multiple-trait models (MTM) may enhance QTL detection and breeding value estimation. In MTM, however, the number of parameters can be large and, if traits are highly correlated, such as carcass traits, estimates of (co)variance matrices may be close to singular. Because of this, dimension reduction techniques such as Factor Analysis (FA) may be useful. The aim of our project is to evaluate the use of FA for structuring (co)variance matrices in the context of Bayesian models for QTL detection in crosses between outbred populations. In our method, QTL effects are postulated at the level of common factors (CF) rather than the original traits, using a three-step approach. In a first step, a MTM is fitted to arrive at estimates of systematic effects and prediction of breeding values (procedure A) and only systematic effect (procedure B). These estimates/predictions are then used to generate an adjusted phenotype that is further analyzed with a Bayesian FA model. This step yields estimates of factor scores for each animal and CF. In the last step, the scores relative to each CF are analyzed independently using probabilities for the line of origin combination. To illustrate the methodology, data on 416 F2 pigs (Brazilian Piau X commercial) with ten traits (5 fat-related, 2 loin measurements, and 3 carcass classification systems) were analyzed. For each of the three resulting CFs, an independent QTL scan was performed on chromosome 7 considering three models: I) null (i.e., absence of QTL); II) additive effect QTL, and III) additive and dominance effect QTL. The posterior probability (PP) of each model was calculated from Bayes factor for each considered procedures (A and B). A Three-step Bayesian factor analysis allowed us to calculate the probability of QTLs that simultaneously affect a group of carcass traits for each position of SSC 7. The removal of systematic effects in the first step of the evaluation (procedure B) allowed that the factor analysis, which was performed in the second step, identify three distinct factors that explained 85% of the total traits variation. For the common factor that represented fat-related traits (bacon depth, midline lower backfat thickness, higher backfat thickness on the shoulder; midline backfat thickness after the last rib; midline backfat thickness on the last lumbar vertebrae) the third step of the analysis showed that the highest probability of an additive QTL effect at the 65cM position was 86%

Use of multivariate analysis to evaluate genetic groups of pigs for dry-cured ham production

AbstractRecords of a pig population used for dry-cured ham production were used to evaluate genetic groups by multivariate analysis. The investigated genetic groups were as follows: DULL=Duroc×(Landrace×Large White), DULA=Duroc×Landrace, DUWI=Duroc×Large White, WIWI=Large White and DUDU=Duroc. Two groups were obtained for the carcass traits hot carcass weight (HCW), backfat thickness (BT) and loin depth (LD), with the groups including 597 and 341 animals harvested at 130kg and at 160kg weights, respectively. Two groups were also found for ham traits gross ham weight (GHW), trimmed ham weight (THW), ham inner layer fat thickness (HIFT), ham outer layer fat thickness (HOFT), pH (PH), and Göfo value, with 393 and 91 animals harvested at 130kg and 160kg weights, respectively. The analysis was performed within each group of traits and harvest weights, and the animals without records were excluded. The first and the second canonical variables explained 97.5% and 93.6% of the total variation for the carcass traits at 130kg and 160kg, respectively, and 88.8% of ham traits at 130kg. In the dispersion graph concerning the canonical means, a significant distance was observed between the genetic groups DUDU and WIWI for the carcass traits at 130kg and 160kg and the ham traits at 130kg. The 50% Duroc animals exhibited little dispersion regarding the carcass traits at 130kg and 160kg and were not divergent from the DUDU genetic group for the ham traits at 130kg. In a cluster analysis using the single linkage method, DULL, DULA and DUWI were grouped with a high similarity level for the carcass traits at 130kg and 160kg and ham traits at 130kg. Using the Tocher optimization method, 50% Duroc crossbred and 100% Duroc purebred animals were grouped for the ham traits at 130kg, suggesting that for ham traits, 50% Duroc animals were similar to 100% Duroc purebred animals. In this context, the genetic groups Duroc×Large White, Duroc×Landrace and Duroc×(Landrace×Large White) are recommended for use in producing dry-cured ham

Genomic selection for boar taint compounds and carcass traits in a commercial pig population

AbstractThis study aimed to compare two different Genome-Wide Selection (GWS) methods (Ridge Regression BLUP − RR-BLUP and Bayesian LASSO − BL) to predict the genomic estimated breeding values (GEBV) of four phenotypes, including two boar taint compounds, i.e., the concentrations of androstenone (andro) and skatole (ska), and two carcass traits, i.e., backfat thickness (fat) and loin depth (loin), which were measured in a commercial male pig line. Six hundred twenty-two boars were genotyped for 2,500 previously selected single nucleotide polymorphisms (SNPs). The accuracies of the GEBV using both methods were estimated based on Jack-knife cross-validation. The BL showed the best performance for the andro, ska and loin traits, which had accuracy values of 0.65, 0.58 and 0.33, respectively; for the fat trait, the RR-BLUP accuracy of 0.61 outperformed the BL accuracy of 0.56. Considering that BL was more accurate for the majority of the traits, this method is the most favoured for GWS under the conditions of this study. The most relevant SNPs for each trait were located in the chromosome regions that were previously indicated as QTL regions in other studies, i.e., SSC6 for andro and ska, SSC2 for fat, and SSC11, SSC15 and SSC17 for loin

Expression profile of genes associated with mastitis in dairy cattle

In order to characterize the expression of genes associated with immune response mechanisms to mastitis, we quantified the relative expression of the IL-2, IL-4, IL-6, IL-8, IL-10, IFN-γ and TNF- α genes in milk cells of healthy cows and cows with clinical mastitis. Total RNA was extracted from milk cells of six Black and White Holstein (BW) cows and six Gyr cows, including three animals with and three without mastitis per breed. Gene expression was analyzed by real-time PCR. IL-10 gene expression was higher in the group of BW and Gyr cows with mastitis compared to animals free of infection from both breeds (p < 0.05). It was also higher in BW Holstein animals with clinical mastitis (p < 0.001), but it was not significant when Gyr cows with and without mastitis were compared (0.05 < p < 0.10). Among healthy cows, BW Holstein animals tended to present a higher expression of all genes studied, with a significant difference for the IL-2 and IFN- γ genes (p < 0.001). For animals with mastitis no significant difference in gene expression was observed between the two breeds. These findings suggest that animals with mastitis develop a preferentially cell-mediated immune response. Further studies including larger samples are necessary to better characterize the gene expression profile in cows with mastitis

A canonical correlation analysis of the association between carcass and ham traits in pigs used to produce dry-cured ham

The association between carcass and ham traits in a pig population used to produce dry-cured ham was studied using canonical correlation analysis. The carcass traits examined were hot carcass weight (HCW), backfat thickness (BT) and loin depth (LD), and the ham traits studied were gross ham weight (GHW), trimmed ham weight (THW), ham inner layer fat thickness (HIFT), ham outer layer fat thickness (HOFT), pH (pH) and the Göfo value. Carcass and ham traits are not independent. The canonical correlations (r) between the carcass and ham traits at 130 kg were 0.77, 0.24 and 0.20 for the first, second and third canonical pair, respectively, and were all significant (p < 0.01) by the Wilks test. The corresponding canonical correlations between the three canonical variate pairs for the carcass and ham traits at 160 kg were 0.88, 0.42 and 0.14, respectively (p < 0.05 for all, except the third). The correlations between the traits and their canonical variate showed an association among HCW, GHW and THW, and between BT and HOFT. These results indicate that carcass traits should be used to cull pigs that are not suitable for dry-cured ham production