This study aims identifying candidate genes and pathways associated with feed efficiency (FE) in pigs. Liver and duodenum transcriptomes of 37 gilts showing high and low residual feed intake (RFI) were analysed by RNA-Seq. Gene expression data was explored through differential expression (DE) and weighted gene co-expression network analyses. DE analysis revealed 55 and 112 differentially regulated genes in liver and duodenum tissues, respectively. Clustering genes according to their connectivity resulted in 23 (liver) and 25 (duodenum) modules of genes with a co-expression pattern. Four modules, one in liver (with 444 co-expressed genes) and three in duodenum (gathering 37, 126 and 41 co-expressed genes), were significantly associated with FE indicators. Intra-module analyses revealed tissue-specific candidate genes; 12 of these genes were also identified as DE between individuals with high and low RFI. Pathways enriched by the list of genes showing DE and/or belonging to FE co-expressed modules included response to oxidative stress, inflammation, immune response, lipid metabolism and thermoregulation. Low overlapping between genes identified in duodenum and liver tissues was observed but heat shock proteins were associated to FE in both tissues. Our results suggest tissue-specific rather than common transcriptome regulatory processes associated with FE in pigs.

In this study, we used the Illumina OvineSNP50 BeadChip to conduct a genome-wide association (GWA) analysis for milk production traits in dairy sheep by analyzing a commercial population of Spanish Churra sheep. The studied population consisted of a total of 1,681 Churra ewes belonging to 16 half-sib families with available records for milk yield (MY), milk protein and fat yields (PY and FY) and milk protein and fat contents (PP and FP). The most significant association identified reached experiment-wise significance for PP and FP and was located on chromosome 3 (OAR3). These results confirm the population-level segregation of a previously reported QTL affecting PP and suggest that this QTL has a significant pleiotropic effect on FP. Further associations were detected at the chromosome-wise significance level on 14 other chromosomal regions. The marker on OAR3 showing the highest significant association was located at the third intron of the alpha-lactalbumin (LALBA) gene, which is a functional and positional candidate underlying this association. Sequencing this gene in the 16 Churra rams of the studied resource population identified additional polymorphisms. One out of the 31 polymorphisms identified was located within the coding gene sequence (LALBA_g.242T>C) and was predicted to cause an amino acid change in the protein (Val27Ala). Different approaches, including GWA analysis, a combined linkage and linkage disequilibrium study and a concordance test with the QTL segregating status of the sires, were utilized to assess the role of this mutation as a putative QTN for the genetic effects detected on OAR3. Our results strongly support the polymorphism LALBA_g.242T>C as the most likely causal mutation of the studied OAR3 QTL affecting PP and FP, although we cannot rule out the possibility that this SNP is in perfect linkage disequilibrium with the true causal polymorphism.

To gain insight into the importance of carefully selecting the sampling area for intestinal microbiota studies, cecal and fecal microbial communities of Caldes meat rabbit were characterized. The animals involved in the study were divided in two groups according to the feed intake level they received during the fattening period; ad libitum (n = 10) or restricted to 75% of ad libitum intake (n = 11). Cecum and internal hard feces were sampled from sacrificed animals. Assessment of bacterial and archaeal populations was performed by means of Illumina sequencing of 16S rRNA gene amplicons in a MiSeq platform. A total of 596 operational taxonomic units (OTUs) were detected using QIIME software. Taxonomic assignment revealed that microbial diversity was dominated by phyla Firmicutes (76.42%), Tenericutes (7.83%), and Bacteroidetes (7.42%); kingdom Archaea was presented at low percentage (0.61%). No significant differences were detected between sampling origins in microbial diversity or richness assessed using two alpha-diversity indexes: Shannon and the observed number of OTUs. However, the analysis of variance at genus level revealed a higher presence of genera Clostridium, Anaerofustis, Blautia, Akkermansia, rc4-4, and Bacteroides in cecal samples. By contrast, genera Oscillospira and Coprococcus were found to be overrepresented in feces, suggesting that bacterial species of these genera would act as fermenters at the end of feed digestion process. At the lowest taxonomic level, 83 and 97 OTUs in feces and cecum, respectively, were differentially represented. Multivariate statistical assessment revealed that sparse partial least squares discriminant analysis (sPLS-DA) was the best approach for this purpose. Interestingly, the majority of the most discriminative OTUs selected by sPLS-DA were found to be differentially represented between sampling origins in univariate analysis. Our study provides evidence that the choice of intestinal sampling area is relevant due to important differences in some taxa’s relative abundance that have been revealed between rabbits’ cecal and fecal microbiota. An appropriate sampling intestinal area should be chosen in each microbiota assessment.

Existence of individual variation in the onset of heat stress for daily milk yield of dairy cows was assessed. Data included 353,376 test-day records of 38,383 first-parity Holsteins from a random sample of US herds. Three hierarchical models were investigated. Model 1 inferred the value of a temperature-humidity index (THI) at which mean yield began to decline as well as the extent of that decline. Model 2 assumed individual variation in yield decline beyond a common THI threshold. Model 3 additionally assumed individual variation for the onset of heat stress. Deviance information criteria indicated the superiority of model 3 over model 2. For model 2, genetic correlation between milk yield in the absence of heat stress and the THI threshold for heat stress was -0.4 (0.11) [marginal posterior mean (marginal posterior standard deviation)]. For model 3, genetic correlations were -0.53 (0.05) between milk yield and THI threshold and -0.62 (0.08) between milk yield and yield decay beyond the THI threshold. Total standard deviation (sum of additive genetic and permanent environmental standard deviations) for the THI threshold was 3.95 (0.06), and more than half of that variation had an additive genetic origin [56% (5%)]. Because of the high genetic correlation [0.95 (0.03)] between yield decay and THI threshold with model 3, using only one of them as a selection criterion for heat tolerance would modify the other in the desired direction.

The objective of this study was to describe genetic variability of pig carcass weight as a function of heat stress. Data included carcass weights of 23,556 crossbred pigs [Duroc × (Landrace × Large White)] raised on 2 farms in North Carolina and harvested from May 2005 through December 2006. Weather data were obtained from a weather station located about 20 km from the furthest farm. Weekly heat load was calculated as degrees of average temperature-humidity index (THI) in excess of 18°C. The total heat load (H) was the sum of heat loads for 10 wk before harvest. Variance components were estimated with 3 models: univariate (UNI)—not accounting for heat stress, 2-trait (MT2), and random regression (RR). In all of the models, effects included contemporary group, sex, age at harvest, sire, and litter. In MT2, observations in months in which heat stress was observed (“hot”) and not observed (“cold”) were treated as separate traits. Heat stress was observed in the months of August to November 2005, as well as July to October 2006. No heat stress was observed in the months of May to July 2005, January to June 2006, and November to December 2006. The RR model added a random regression on heat load for the sire effect. Heat load was adjusted to a scale ranging from 0 (no heat stress) to 5 (greatest heat stress). The heritability estimate ± SE of carcass weight in UNI was 0.17 ± 0.01. In MT2, the estimates were 0.14 ± 0.01 for “cold” and 0.28 ± 0.01 for “hot”; the genetic correlation between carcass weight in “hot” and “cold” months was 0.42 ± 0.13. The heritability estimates obtained with RR were 0.20 ± 0.11, 0.19 ± 0.15, and 0.51 ± 0.17 for H = 0, 2.5, and 5, respectively. The genetic correlation between the performance in “cold” months (H = 0), and performance under maximum heat load (H = 5) was 0.02, between H = 0 and intermediate heat load (H = 2.5) was 0.52, and between H = 2.5 and H = 5 was 0.86. Rank correlations between EPD derived from the different models ranged from 0.82 to 0.94 between carcass weights under similar H, 0.18 to 0.54 between carcass weights under high and low H, and 0.66 to 0.91 between carcass weights of intermediate and high/low H. Heritability for growth was greater under heat stress. Selection for crossbred performance would be optimal when data for periods both in the absence and presence of heat stress were considered. Copyright © 2008. . Copyright 2008 Journal of Animal Science

The objective of this study was to quantify the effect of heat stress during the life of a pig on its final weight, as a first step toward a genetic evaluation for heat tolerance. Data included carcass weights of 23,556 crossbred pigs [Duroc × (Landrace × Large White)] raised on 2 farms in North Carolina and slaughtered from May 2005 through December 2006. Weather data were available from a nearby weather station. Lifetime of a pig was assumed to be partitioned into 2 periods. During an initial period, the effect of heat stress was assumed to be negligible or compensated for later. During the second period ending in slaughtering, the ADG was assumed to be affected linearly by heat load. Weekly heat load was calculated as degrees of average temperature-humidity index in excess of a threshold (18°C). The total heat load (H) was the sum of weekly heat loads during the second period. During the months of January to May H was 0; H reached a peak in September. The final BW during the peak of heat stress decreased about 6 kg compared with BW during months of non-heat stress. Weekly and monthly averages of carcass weight generally moved similarly to H. However, there were large fluctuations unrelated to H; the fluctuations were different on the 2 farms. The model included the effects of farm-year of slaughter, sex, age at slaughter, and H, where age at slaughter and H were linear regressions. In analyses, the threshold was varied from 16 to 20°C, and the second period was varied from 8 to 16 wk. The greatest R² (10.4%) was at the threshold of temperature-humidity index = 18°C for a period of 10 wk. Varying the threshold and the length of time reduced R² less than 1%. Least squares means of year-month and year-week of carcass weight were calculated using a model with the fixed effects farm-year-month or farm-year-week of slaughter, sex, and age at slaughter (linear covariate), and the random effect of birth litter. Changes in BW of finisher pigs due to heat stress can be quantified by H during the last 10 wk of the life of the pig. Copyright © 2008. . Copyright 2008 Journal of Animal Science