MicroRNAs (miRNAs) are a class of ∼22 nucleotide-long small noncoding RNAs that target mRNAs for translational repression or degradation. miRNAs target mRNAs by base-pairing with the 3′-untranslated regions (3′-UTRs) of mRNAs. miRNAs are present in various species, from animals to plants. In this review, we summarize the identification, expression, and function of miRNAs in four important farm animal species: cattle, chicken, pig and sheep. In each of these species, hundreds of miRNAs have been identified through homology search, small RNA cloning and next generation sequencing. Real-time RT-PCR and microarray experiments reveal that many miRNAs are expressed in a tissue-specific or spatiotemporal-specific manner in farm animals. Limited functional studies suggest that miRNAs have important roles in muscle development and hypertrophy, adipose tissue growth, oocyte maturation and early embryonic development in farm animals. Increasing evidence suggests that single-nucleotide polymorphisms in miRNA target sites or miRNA gene promoters may contribute to variation in production or health traits in farm animals.

This study investigated some aspects of breeding management in the Italian Heavy Draught Horse breed, aiming at improving its efficiency at stud farm level. A first aim was to evaluate the risk of unsuccessful reproduction in mares after an early (3 years) or normal (4 years) age at first foaling, in interaction with different stud rearing systems. A second objective was the examination of the mean time length in which young 2-year-old stallions maintain a genetic superiority on older proven stallions, identifying a ‘genetic lifespan’ in which young stallions can be safely used for reducing the cost of services. Reproductive performance at first and second foaling of 1513 mares were used. Mares had a normal first foal at 3 (n = 745) or 4 years of age (n = 768) in stud farms on the basis of stable (n = 488), feral (n = 345) or semi-feral (n = 680) rearing systems. Logistic regression analysis was performed by modeling the risk of unsuccessful reproduction in the subsequent season (i.e., results at second foaling), as affected by the interaction of age at first foaling × rearing system (six classes). Genetic lifespan of young stallions was estimated by regressing the least square means from a mixed model analysis for repeated measures of individual differences in ‘total merit’ estimated breeding values (EBVs) between young stallions (mean no. of 45/year) and the mean EBV of all proven stallions in a given year of genetic evaluation (mean no. of 483/year). Young stallions born between 1999 and 2005 were used, following each generation (i.e., birth year) from 2 to 7 subsequent yearly genetic evaluations. In comparison with the best reproductive success of second foaling at 4 years in stable systems, the greatest risk of unsuccessful reproduction was at 3 years in feral (+167%) and 3 years in semi-feral conditions (+91%). Young stallions showed a 0.50 s.d. greater EBV at the first evaluation than proven stallions, with a mean annual decrease in EBV of 0.07 s.d./year on proven stallions. Optimal breeding management could be obtained in stud farms by limiting foaling at 3 years, particularly in feral and semi-feral rearing systems, and using young stallions for 3 to 4 years to maintain a perceptible selection differential with older proven stallions and to reduce cost of services. Later, the selection differential with proven stallions become less consistent and genetic improvement could be slowed down.

The objective of the study was to evaluate whether magnetic resonance imaging (MRI) offers the opportunity to reliably analyze body composition of pigs in vivo. Therefore, the relation between areas of loin eye muscle and its back fat based on MRI images were used to predict body composition values measured by dual energy X-ray absorptiometry (DXA). During the study, a total of 77 pigs were studied by MRI and DXA, with a BW ranging between 42 and 102 kg. The pigs originated from different extensive or conventional breeds or crossbreds such as Cerdo Iberico, Duroc, German Landrace, German Large White, Hampshire and Pietrain. A Siemens Magnetom Open was used for MRI in the thorax region between 13th and 14th vertebrae in order to measure the loin eye area (MRI-LA) and the above back fat area (MRI-FA) of both body sides, whereas a whole body scan was performed by DXA with a GE Lunar DPX-IQ in order to measure the amount and percentage of fat tissue (DXA-FM; DXA-%FM) and lean tissue mass (DXA-LM; DXA-%LM). A linear single regression analysis was performed to quantify the linear relationships between MRI- and DXA-derived traits. In addition, a stepwise regression procedure was carried out to calculate (multiple) regression equations between MRI and DXA variables (including BW). Single regression analyses showed high relationships between DXA-%FM and MRI-FA (R2 = 0.89, √MSE = 2.39%), DXA-FM and MRI-FA (R2 = 0.82, √MSE = 2757 g) and DXA-LM and MRI-LA (R2 = 0.82, √MSE = 4018 g). Only DXA-%LM and MRI-LA did not show any relationship (R2 = 0). As a result of the multiple regression analysis, DXA-LM and DXA-FM were both highly related to MRI-LA, MRI-FA and BW (R2 = 0.96; √MSE = 1784 g, and R2 = 0.95, √MSE = 1496 g). Therefore, it can be concluded that the use of MRI-derived images provides exact information about important ‘carcass-traits’ in pigs and may be used to reliably predict the body composition in vivo.

The objective of the present review was (i) to survey different approaches for choosing the complex of traits for which economic values (EVs) are calculated, (ii) to call attention to the proper definition of traits and (iii) to discuss the manner and extent to which relationships among traits have been considered in the calculation of EVs. For this purpose, papers dealing with the estimation of EVs of traits in livestock were reviewed. The most important reasons for incompatibility of EVs for similar traits estimated in different countries and by different authors were found to be inconsistencies in trait definitions and in assumptions being made about relationships among traits. An important problem identified was how to choose the most appropriate criterion to characterise production or functional ability for a particular class of animals. Accordingly, the review covered the following three topics: (i) which trait(s) would best characterise the growth ability of an animal; (ii) how to define traits expressed repeatedly in subsequent reproductive cycles of breeding females and (iii) how to deal with traits that differ in average value between sexes or among animal groups. Various approaches that have been used to solve these problems were discussed. Furthermore, the manner in which diverse authors chose one or more traits from a group of alternatives for describing a specific biological potential were reviewed and commented on. The consequences of including or excluding relationships among economically important traits when estimating the EV for a specific trait were also examined. An important conclusion of the review is that, for a better comparability and interpretability of estimated EVs in the literature, it is desirable that clear and unique definitions of the traits, complete information on assumptions used in analytical models and details on inter-relationships between traits are documented. Furthermore, the method and the model used for the genetic evaluation of specific traits in a certain breeding organisation are important for the exact definition of traits, for which the economic values will be calculated, and for the inclusion or exclusion of relationships among traits in the calculation of the EVs in livestock breeding.

Genomic selection relaxes the requirement of traditional selection tools to have phenotypic measurements on close relatives of all selection candidates. This opens up possibilities to select for traits that are difficult or expensive to measure. The objectives of this paper were to predict accuracy of and response to genomic selection for a new trait, considering that only a cow reference population of moderate size was available for the new trait, and that selection simultaneously targeted an index and this new trait. Accuracy for and response to selection were deterministically evaluated for three different breeding goals. Single trait selection for the new trait based only on a limited cow reference population of up to 10 000 cows, showed that maximum genetic responses of 0.20 and 0.28 genetic standard deviation (s.d.) per year can be achieved for traits with a heritability of 0.05 and 0.30, respectively. Adding information from the index based on a reference population of 5000 bulls, and assuming a genetic correlation of 0.5, increased genetic response for both heritability levels by up to 0.14 genetic s.d. per year. The scenario with simultaneous selection for the new trait and the index, yielded a substantially lower response for the new trait, especially when the genetic correlation with the index was negative. Despite the lower response for the index, whenever the new trait had considerable economic value, including the cow reference population considerably improved the genetic response for the new trait. For scenarios with a zero or negative genetic correlation with the index and equal economic value for the index and the new trait, a reference population of 2000 cows increased genetic response for the new trait with at least 0.10 and 0.20 genetic s.d. per year, for heritability levels of 0.05 and 0.30, respectively. We conclude that for new traits with a very small or positive genetic correlation with the index, and a high positive economic value, considerable genetic response can already be achieved based on a cow reference population with only 2000 records, even when the reliability of individual genomic breeding values is much lower than currently accepted in dairy cattle breeding programs. New traits may generally have a negative genetic correlation with the index and a small positive economic value. For such new traits, cow reference populations of at least 10 000 cows may be required to achieve acceptable levels of genetic response for the new trait and for the whole breeding goal.

Extensive genetic progress has been achieved in dairy cattle populations on many traits of economic importance because of efficient breeding programmes. Success of these programmes has relied on progeny testing of the best young males to accurately assess their genetic merit and hence their potential for breeding. Over the last few years, the integration of dense genomic information into statistical tools used to make selection decisions, commonly referred to as genomic selection, has enabled gains in predicting accuracy of breeding values for young animals without own performance. The possibility to select animals at an early stage allows defining new breeding strategies aimed at boosting genetic progress while reducing costs. The first objective of this article was to review methods used to model and optimize breeding schemes integrating genomic selection and to discuss their relative advantages and limitations. The second objective was to summarize the main results and perspectives on the use of genomic selection in practical breeding schemes, on the basis of the example of dairy cattle populations. Two main designs of breeding programmes integrating genomic selection were studied in dairy cattle. Genomic selection can be used either for pre-selecting males to be progeny tested or for selecting males to be used as active sires in the population. The first option produces moderate genetic gains without changing the structure of breeding programmes. The second option leads to large genetic gains, up to double those of conventional schemes because of a major reduction in the mean generation interval, but it requires greater changes in breeding programme structure. The literature suggests that genomic selection becomes more attractive when it is coupled with embryo transfer technologies to further increase selection intensity on the dam-to-sire pathway. The use of genomic information also offers new opportunities to improve preservation of genetic variation. However, recent simulation studies have shown that putting constraints on genomic inbreeding rates for defining optimal contributions of breeding animals could significantly reduce achievable genetic gain. Finally, the article summarizes the potential of genomic selection to include new traits in the breeding goal to meet societal demands regarding animal health and environmental efficiency in animal production.

Behavioural adaptation of farm animals to environmental changes contributes to high levels of production under a wide range of farming conditions, from highly controlled indoor systems to harsh outdoor systems. The genetic variation in livestock behaviour is considerable. Animals and genotypes with a larger behavioural capacity for adaptation may cope more readily with varying farming conditions than those with a lower capacity for adaptation. This capacity should be exploited when the aim is to use a limited number of species extensively across the world. The genetics of behavioural traits is understood to some extent, but it is seldom accounted for in breeding programmes. This review summarizes the estimates of genetic parameters for behavioural traits in cattle, pigs, poultry and fish. On the basis of the major studies performed in the last two decades, we focus the review on traits of common interest in the four species. These concern the behavioural responses to both acute and chronic stressors in the physical environment (feed, temperature, etc.) and those in the social environment (other group members, progeny, humans). The genetic strategies used to improve the behavioural capacity for adaptation of animals differ between species. There is a greater emphasis on responses to acute environmental stress in fish and birds, and on responses to chronic social stress in mammals.

Heritability is a central element in quantitative genetics. New molecular markers to assess genetic variance and heritability are continually under development. The availability of molecular single nucleotide polymorphism (SNP) markers can be applied for estimation of variance components and heritability on population, where relationship information is unknown. In this study, we evaluated the capabilities of two Bayesian genomic models to estimate heritability in simulated populations. The populations comprised different family structures of either no or a limited number of relatives, a single quantitative trait, and with one of two densities of SNP markers. All individuals were both genotyped and phenotyped. Results illustrated that the two models were capable of estimating heritability, when true heritability was 0.15 or higher and populations had a sample size of 400 or higher. For heritabilities of 0.05, all models had difficulties in estimating the true heritability. The two Bayesian models were compared with a restricted maximum likelihood (REML) approach using a genomic relationship matrix. The comparison showed that the Bayesian approaches performed equally well as the REML approach. Differences in family structure were in general not found to influence the estimation of the heritability. For the sample sizes used in this study, a 10-fold increase of SNP density did not improve precision estimates compared with set-ups with a less dense distribution of SNPs. The methods used in this study showed that it was possible to estimate heritabilities on the basis of SNPs in animals with direct measurements. This conclusion is valuable in cases when quantitative traits are either difficult or expensive to measure.

Single nucleotide polymorphisms (SNPs) are the most widespread source of variation in genomes. While the very large number of SNPs allows for a very precise description of genetic variation, it impedes data processing and significantly increases analysis time. Many of the SNPs located close to each other frequently carry the same or similar information. This problem can be solved by selecting the most informative SNPs (tagSNPs) using linkage disequilibrium information by identifying a set of tagSNPs representative for a chromosome fragment. The goal of this study is to check whether the genetic structure of a population, expressed by relationship and inbreeding coefficients, affects tagSNP selection. Six subsets of 450 bulls are selected out of the 1228 Polish Holstein-Friesian bulls genotyped by the Illumina BovineSNP50 Bead Chip. TagSNPs are selected for each of the subsets, as well as for the whole data set. The average reduction of the SNP number is 77.2% and is very similar in each sub-population. Differences in tagSNP selection between sub-populations are small. On average, 93.88% of the tagSNPs overlap between subsets. The study showed that differences in the genetic structure of the reference population have little influence on tagSNP selection.

Predictive ability of models for litter size in swine on the basis of different sources of genetic information was investigated. Data represented average litter size on 2598, 1604 and 1897 60K genotyped sows from two purebred and one crossbred line, respectively. The average correlation (r) between observed and predicted phenotypes in a 10-fold cross-validation was used to assess predictive ability. Models were: pedigree-based mixed-effects model (PED), Bayesian ridge regression (BRR), Bayesian LASSO (BL), genomic BLUP (GBLUP), reproducing kernel Hilbert spaces regression (RKHS), Bayesian regularized neural networks (BRNN) and radial basis function neural networks (RBFNN). BRR and BL used the marker matrix or its principal component scores matrix (UD) as covariates; RKHS employed a Gaussian kernel with additive codes for markers whereas neural networks employed the additive genomic relationship matrix (G) or UD as inputs. The non-parametric models (RKHS, BRNN, RNFNN) gave similar predictions to the parametric counterparts (average r ranged from 0.15 to 0.23); most of the genome-based models outperformed PED (r = 0.16). Predictive abilities of linear models and RKHS were similar over lines, but BRNN varied markedly, giving the best prediction (r = 0.31) when G was used in crossbreds, but the worst (r = 0.02) when the G matrix was used in one of the purebred lines. The r values for RBFNN ranged from 0.16 to 0.23. Predictive ability was better in crossbreds (0.26) than in purebreds (0.15 to 0.22). This may be related to family structure in the purebred lines.

The objectives of this study were to compare the in situ ruminal degradation of CP and amino acids (AAs) of dried distillers’ grains with solubles (DDGS), and to estimate intestinal digestibility (ID) of undegradable crude protein (UDP) with the in vitro pepsin–pancreatin solubility of CP (PPS), using either DDGS samples (DDGS-s) or DDGS residues (DDGS-r) obtained after 16 h ruminal incubation. Thirteen samples originating from wheat, corn, barley and blends were studied. Lysine and methionine content of DDGS-s varied from 1.4 to 4.0 and 1.3 to 2.0 g/16 g N, respectively. The milk protein score (MPS) of DDGS-s was low and ranged from 0.36 to 0.51, and lysine and isoleucine were estimated to be the most limiting AAs in DDGS-s and DDGS-r. DDGS-r contained slightly more essential AAs (EAAs) than did the DDGS-s. Rumen degradation after 16 h varied from 44% to 94% for CP, from 39% to 90% for lysine and from 35% to 92% for methionine. Linear regressions showed that the ruminal degradation of individual AAs can be predicted from CP degradation. The PPS of DDGS-s was higher than that of DDGS-r and it varied from 70% to 89% and from 47% to 81%, respectively. There was no significant correlation between the PPS of DDGS-s and PPS of DDGS-r (R2=0.31). The estimated intestinally absorbable dietary protein (IADP) averaged 21%. Moderate correlation was found between the crude fibre (CF) content and PPS of DDGS-r (R2=0.43). This study suggests an overestimation of the contribution of UDP of DDGS to digestible protein supply in the duodenum in some currently used protein evaluation systems. More research is required and recommended to assess the intestinal digestibility of AAs from DDGS.

The effectiveness of low cost breeding scheme designs for small aquaculture breeding programmes were assessed for their ability to achieve genetic gain while managing inbreeding using stochastic simulation. Individuals with trait data were simulated over 15 generations with selection on a single trait. Combinations of selection methods, mating strategies and genetic evaluation options were evaluated with and without the presence of common environmental effects. An Optimal Parent Selection (OPS) method using semi-definite programming was compared with a truncation selection (TS) method. OPS constrains the rate of inbreeding while maximising genetic gain. For either selection method, mating pairs were assigned from the selected parents by either random mating (RM) or Minimum Inbreeding Mating (MIM), which used integer programming to determine mating pairs. Offspring were simulated for each mating pair with equal numbers of offspring per pair and these offspring were the candidates for selection of parents of the next generation. Inbreeding and genetic gain for each generation were averaged over 25 replicates. Combined OPS and MIM led to a similar level of genetic gain to TS and RM, but inbreeding levels were around 75% lower than TS and RM after 15 generations. Results demonstrate that it would be possible to manage inbreeding over 15 generations within small breeding programmes comprised of 30 to 40 males and 30 to 40 females with the use of OPS and MIM. Selection on breeding values computed using Best Linear Unbiased Prediction (BLUP) with all individuals genotyped to obtain pedigree information resulted in an 11% increase in genetic merit and a 90% increase in the average inbreeding coefficient of progeny after 15 generations compared with selection on raw phenotype. Genetic evaluation strategies using BLUP wherein elite individuals by raw phenotype are genotyped to obtain parentage along with a range of different samples of remaining individuals did not increase genetic progress in comparison to selection on raw phenotype. When common environmental effects on full-sib families were simulated, performance of small breeding scheme designs was little affected. This was because the majority of selection must anyway be applied within family due to inbreeding constraints.

SWOT (Strengths, Weaknesses, Opportunities and Threats) analysis is a tool widely used to help in decision making in complex systems. It suits to exploring the issues and measures related to the conservation and development of local breeds, as it allows the integration of many driving factors influencing breed dynamics. We developed a quantified SWOT method as a decision-making tool for identification and ranking of conservation and development strategies of local breeds, and applied it to a set of 13 cattle breeds of six European countries. The method has four steps: definition of the system, identification and grouping of the driving factors, quantification of the importance of driving factors and identification and prioritization of the strategies. The factors were determined following a multi-stakeholder approach and grouped with a three-level structure. Animal genetic resources expert groups ranked the factors, and a quantification process was implemented to identify and prioritize strategies. The proposed SWOT methodology allows analyzing the dynamics of local cattle breeds in a structured and systematic way. It is a flexible tool developed to assist different stakeholders in defining the strategies and actions. The quantification process allows the comparison of the driving factors and the prioritization of the strategies for the conservation and development of local cattle breeds. We identified 99 factors across the breeds. Although the situation is very heterogeneous, the future of these breeds may be promising. The most important strengths and weaknesses were related to production systems and farmers. The most important opportunities were found in marketing new products, whereas the most relevant threats were found in selling the current products. The across-breed strategies utility decreased as they gained specificity. Therefore, the strategies at European level should focus on general aspects and be flexible enough to be adapted to the country and breed specificities.

Genetically reducing boar taint using low-taint lines is considered the most sustainable and economic long-term alternative to surgical castration of male pigs. Owing to the high heritability of the main boar taint components (androstenone, skatole and indole), breeding is an excellent tool for reducing the number of tainted carcasses. To incorporate boar taint into breeding programmes, standardized performance testing is required. The objective of this study was to develop and formally present a performance test for the main boar taint compounds on live breeding candidates. First, a standardized performance test for boar taint was established. A biopsy device was developed to extract small tissue samples (200 to 300 mg) from breeding candidates. Quantification of boar taint components from these small samples using specialized chemical extraction methods proved accurate and repeatable (r = 0.938). Following establishment of the method, biopsy samples of 516 live boars (100 to 130 kg live weight) were collected in the second step. Various mixed linear models were tested for each boar taint compound; models were ranked in terms of their information content. Pedigree information of 2245 ancestors of biopsied animals was included, and genetic parameters were estimated using univariate and multivariate models. Androstenone (in μg/g liquid fat (LF): mean = 0.578, σ = 0.527), skatole (in μg/g LF: mean = 0.033, σ = 0.002) and indole (in μg/g LF: mean = 0.032, σ = 0.002) levels obtained by biopsy were plausible. Heritability estimates for androstenone calculated with univariate (0.453) and multivariate (0.452) analyses were comparable to those in the literature. Heritabilities for skatole (0.495) and indole (0.550) were higher than that for androstenone. Genetic and phenotypic correlations were similar to those published previously. Our results show that data on boar taint compounds from small adipose samples obtained by biopsy provide similar genetic parameters as that described in the literature for larger samples and are therefore a reliable performance test for boar taint in live breeding candidates.

This study aimed to investigate the impact of repeated acidosis challenges (ACs) and the effect of live yeast supplementation (Saccharomyces cerevisiae I-1077, SC) on rumen fermentation, microbial ecosystem and inflammatory response. The experimental design involved two groups (SC, n=6; Control, n=6) of rumen fistulated wethers that were successively exposed to three ACs of 5 days each, preceded and followed by resting periods (RPs) of 23 days. AC diets consisted of 60% wheat-based concentrate and 40% hay, whereas RPs diets consisted of 20% concentrate and 80% hay. ACs induced changes in rumen fermentative parameters (pH, lactate and volatile fatty-acid concentrations and proportions) as well as in microbiota composition and diversity. The first challenge drove the fermentation pattern towards propionate. During successive challenges, rumen pH measures worsened in the control group and the fermentation profile was characterised by a higher butyrate proportion and changes in the microbiota. The first AC induced a strong release of rumen histamine and lipopolysaccharide that triggered the increase of acute-phase proteins in the plasma. This inflammatory status was maintained during all AC repetitions. Our study suggests that the response of sheep to an acidosis diet is greatly influenced by the feeding history of individuals. In live yeast-supplemented animals, the first AC was as drastic as in control sheep. However, during subsequent challenges, yeast supplementation contributed to stabilise fermentative parameters, promoted protozoal numbers and decreased lactate producing bacteria. At the systemic level, yeast helped normalising the inflammatory status of the animals.

The objectives of this study were to analyze the trend of within-breed genetic diversity and identify major causes leading to loss of genetic diversity in Guernsey breed in three countries. Pedigree files of Canadian (GCN), South African (GSA) and American (GUS) Guernsey populations containing 130 927, 18 593 and 1 851 624 records, respectively, were analyzed. Several parameters derived from the in-depth pedigree analyses were used to measure trends and current levels of genetic diversity. Pedigree completeness index of GCN, GSA and GUS populations, in the most recent year (2007), was 97%, 74% and 79%, respectively, considering four generations back in the analysis. The rate of inbreeding in each population was 0.19%, 0.16% and 0.17% between 2002 and 2007, respectively. For the same period, the estimated effective population size for GCN, GSA and GUS was 46, 57 and 46, respectively. The estimated percentage of genetic diversity lost within each population over the last four decades was 8%, 3% and 5%, respectively. The relative proportion of genetic diversity lost due to random genetic drift in the three populations was 93%, 91% and 86%, respectively. In conclusion, the results suggested that GCN and GUS have lost more genetic diversity than GSA over the past four decades, and this loss is gaining momentum due to increasing rates of inbreeding. Therefore, strategies such as optimum contribution selection and migration of genetic material are advised to increase effective population size, particularly in GCN and GUS.

With the human population predicted to reach nine billion by 2050, demand for food is predicted to more than double over this time period, a trend which will lead to increased greenhouse gas (GHG) emissions from agriculture. Furthermore, expansion in food production is predicted to occur primarily in the developing world, where adaptation to climate change may be more difficult and opportunities to mitigate emissions limited. In the establishment of the United Nations Framework Convention on Climate Change (UNFCCC), ‘ensuring that food production is not threatened’ is explicitly mentioned in the objective of the Convention. However, the focus of negotiations under the Convention has largely been on reducing GHG emissions from energy, and industrial activities and realizing the potential of forestry as a carbon sink. There has been little attention by the UNFCCC to address the challenges and opportunities for the agriculture sector. Since 2006, concerted efforts have been made to raise the prominence of agriculture within the negotiations. The most recent The Intergovernmental Panel on Climate Change report and ‘The Emissions Gap Report’ by the UNEP highlighted the significant mitigation potential of agriculture, which can help contribute towards keeping global temperature rises below the 2°C limit agreed in Cancun. Agriculture has to be a part of the solution to address climate change, but this will also require a focus on how agriculture systems can adapt to climate change in order to continue to increase food output. However, to effectively realize this potential, systematic and dedicated discussion and decisions within the UNFCCC are needed. UNFCCC discussions on a specific agriculture agenda item started in 2012, but are currently inconclusive. However, Parties are generally in agreement on the importance of agriculture in contributing to food security and employment as well as the need to improve understanding of agriculture and how it can contribute to realizing climate objectives. Discussions on agriculture are continuing with a view to finding an acceptable approach to address the climate change related challenges faced by agriculture worldwide and to ensure that ‘food production is not threatened’.

Differential gene expression analyses typically focus on departures across mathematical expectations (i.e. mean) from two or more groups of microarrays, without considering alternative patterns of departure. Nevertheless, recent studies in humans and great apes have suggested that differential gene expression could also be characterized in terms of heterogeneous dispersion patterns. This must be viewed as a very interesting genetic phenomenon clearly linked to the regulation mechanisms of gene transcription. Unfortunately, we completely lack information about the incidence and relevance of dispersion-specific differential gene expression in livestock species, although a specific Bayes factor (BF) for testing this kind of differential gene expression (i.e. within-probe heteroskedasticity) has been recently developed. Within this context, our main objective was to characterize the incidence of dispersion-specific differential gene expression in pigs and, if possible, providing the first evidence of this phenomenon in a livestock species. We evaluated dispersion-specific differential gene expression on ovary, uterus and hypophysis samples from 22 F2 Iberian × Meishan sows, where a total of 15 252 probes were interrogated. For each tissue, heteroskedasticity of probe-specific residual variances was evaluated by three pairwise comparisons involving three physiological stages, that is, heat, 15 days of pregnancy and 45 days of pregnancy. Between 2.9% and 37.4% of the analyzed probes provided statistical evidence of within-tissue across-physiological stages dispersion-specific differential gene expression (BF >1), and between 0.1% and 3.0% of them reported decisive evidence (BF >100). It is important to highlight that <8% of the heteroskedastic probes were also linked to differential gene expression in terms of departures among the probe-specific mathematical expectation of each physiological stage. This discarded the disturbance of scale effects in a high percentage of probes and suggested that probe-specific heteroskedasticity must be viewed as an independent phenomenon within the context of differential gene expression. As a whole, our results report a remarkable incidence of dispersion-specific differential gene expression across the whole genome of the pig, establishing a very interesting starting point for further studies focused on deciphering the genetic mechanisms underlying heteroskedasticity.