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Rearing entire pigs may lead to meat quality and welfare problems in relation to pubertal development. A better knowledge of the sources of variation of pubertal development, behaviour and boar taint is needed before generalizing entire male pigs. From 84 days of age, entire male pigs were reared in groups of 10 either in a conventional (C, 1 m²/animal, slatted floor) or an enriched (E, 2.5 m²/animal, straw bedding, outdoor run) housing during spring or autumn and fed ad libitum (n=10/housing/season). Mounting behaviour was observed for 3 h during the third (M3), fourth (M4) and fifth (M5) months of age. The total number of skin lesions was counted on both sides of the pigs 1 day before the behavioural recordings. The time spent in the outdoor run was also recorded during 3 days per month. The animals were slaughtered at 161±1 days of age (122±9 kg live weight). Blood samples were collected at 89 (M3), 119 (M4) and 152 (M5) days of age and at slaughter for the testosterone and oestradiol measurements. The testes were collected at slaughter, freed from the surrounding tissues and weighed. The fat samples were collected for the androstenone and skatole concentration measurement. Plasma testosterone and oestradiol-17β (oestradiol), fat androstenone and skatole and weight of the testes did not differ between the housing systems. Plasma testosterone (8.3 v. 3.9 nmol/l, P<0.05) and oestradiol (12.0 v. 9.2 pmol/l, P<0.1) at M3, fat skatole (0.124 v. 0.043, P<0.03) and weight of the testes (587 v. 512 g, P<0.05) were higher in the autumn than in the spring trial, suggesting that the pubertal development was accelerated. The number of received mounting behaviours was slightly higher in the autumn (P=0.08) trial and was markedly higher in the E than in the C environment (P<0.003). Skin lesions were more numerous in the C than in the E housing at M4 and M5 and in the spring than in the autumn trial at M3 and M4 (P<0.05). Fat androstenone and the number of performed mounting behaviours were significantly correlated between each other and with numerous indicators of the pubertal development (P<0.05). The number of skin lesions was correlated with plasma testosterone and live weight (P<0.05). Overall, this study suggests the effect of season on sexual development, the effect of the housing system on behaviour, and demonstrates the links between sexual hormones, behaviour and boar taint.
In vitro and in vivo experiments were designed to evaluate the effectiveness of laboratory-made di-d-fructose dianhydride (DFA)-enriched caramels. The DFA-enriched caramels were obtained from d-fructose (FC), d-fructose and sucrose (FSC), or d-fructose and β-cyclodextrin (FCDC). In the in vitro experiment, raftilose and all caramels increased (P<0.05) l-lactate concentration and decreased (P<0.05) pH. Total short-chain fatty acid concentration was higher (P<0.05) than controls in tubes containing raftilose, FSC, FCDC and commercial sucrose caramel (CSC). Raftilose, and all caramels tested except FSC and FC (1%), increased (P<0.01) lactobacilli log10 number of copies compared with the non-additive control. FSC, FCDC and CSC increased (P<0.01) the bifidobacteria number of copies as compared with controls. All additives, except FCDC, decreased (P<0.01) Clostridium coccoides/Eubacterium rectale log number of copies. Compared with controls, raftilose, FC and CSC led to lower (P<0.01) Escherichia–Shigella and enterobacteria. For the in vivo experiment, a total of 144 male 1-day-old broiler chickens of the Cobb strain were randomly assigned to one of the three dietary treatments for 21 days. Dietary treatments were control (commercial diet with no additive), inulin (20 g inulin/kg diet) and FC (20 g FC/kg diet). Final BW of birds fed FC diet was higher (P<0.01) than controls or inulin-fed birds, although feed: gain values were not different. Feed intake of chickens fed FC was higher (P<0.01) than that of inulin-fed birds but not statistically different from controls. Crop pH values were lower (P<0.01) in birds fed FC diet as compared with control diet, with inulin-fed chickens showing values not different from control- or FC-fed birds. Lower (P<0.05) lactobacilli number of copies was determined in the crop, ileum and caeca of birds fed the inulin diet compared with the control diet. Inulin supplementation also resulted in lower (P<0.05) C. coccoides/E. rectale, bacteroides and total bacteria in caecal contents. Addition of FC to broiler diets gave place to lower (P<0.05) enterobacteria and Escherichia–Shigella in crop and caecal contents compared with controls. The bacteroides number of copies increased (P<0.05) as compared with controls in the ileum, but decreased (P<0.05) in the caeca of chickens fed the FC diet. Energy, ADF, NDF and non-starch polysaccharides faecal digestibilities were greater (P<0.05) than controls in chickens fed diets containing inulin or FC. Fat digestibility was higher (P<0.05) in FC-fed birds compared with controls or inulin-fed chickens. In conclusion, DFA-enriched caramels tested here, particularly FC, may represent a type of new additives useful in poultry production.
The objective of this study was to compare the efficiency of transfer of selenium (Se) to plasma and milk from inorganic sodium selenite, either free or microencapsulated, and from selenized yeast in dairy cows. The study consisted of an in situ-nylon bags incubation, and in an in vivo experiment to compare the Se status of cows supplemented with either sodium selenite, microencapsulated sodium selenite, or Se yeast. Thirty dairy cows, divided in five groups, were fed the following diets: the control group (CTR) received a total mixed ration supplemented with sodium selenite in order to have 0.3 mg/kg DM of total Se; 0.3M and 0.5M groups received the same control diet supplemented with lipid microencapsulated sodium selenite to provide 0.3 and 0.5 mg/kg DM of total Se, respectively; 0.3Y and 0.5Y groups received selenized yeast to provide 0.3 and 0.5 mg/kg of total Se, respectively. Cows were fed the supplements for 56 days during which milk, blood, and fecal samples were collected weekly to conduct analysis of Se and glutathione peroxidase (GSH-px) activity. Se concentration in the nylon bags was assessed to 72%, 64%, and 40% of the initial value (time 0) after 4, 8, and 24 h of incubation, respectively. In vivo, cows supplemented with 0.3 mg/kg of microencapsulated Se had higher milk Se concentration compared to CTR. The increment was more pronounced at the highest inclusion rate (0.5 mg/kg, 0.5M group). GSH-px activity was not significantly affected by treatments. The results indicate that lipid microencapsulation has the potential to protect nutrients from complete rumen reduction and that Se from microencapsulated selenite is incorporated in milk more efficiently than the free form. Microencapsulated sodium selenite was shown to be comparable to Se-yeast in terms of availability and incorporation in milk when fed at 0.3 mg/kg DM, whereas the inclusion in the diet at 0.5 mg/kg DM resulted in higher plasma and milk concentrations than selenized yeast.
The purpose of this study was to evaluate the effect of temperature and different levels of available phosphorus (aP) on the expression of nine genes encoding electron transport chain proteins in the Longissimus dorsi (LD) muscle of pigs. Two trials were carried out using 48 high-lean growth pigs from two different growth phases: from 15 to 30 kg (phase 1) and from 30 to 60 kg (phase 2). Pigs from growth phase 1 were fed with three different levels of dietary aP (0.107%, 0.321% or 0.535%) and submitted either to a thermoneutral (24°C and RH at 76%) or to a heat stress (34°C and RH at 70%) environment. Pigs from growth phase 2 were fed with three different levels of dietary aP (0.116%, 0.306% or 0.496%) and submitted either to a thermoneutral (22ºC and RH at 77%) or to a heat stress (32ºC and RH at 73%) environment. Heat stress decreased (P<0.001) average daily feed intake at both growth phases. At 24°C, pigs in phase 1 fed the 0.321% aP diet had greater average daily gain and feed conversion (P<0.05) than those fed the 0.107% or 0.535% while, at 34°C pigs fed the 0.535% aP had the best performance (P<0.05). Pigs from phase 2 fed the 0.306% aP had best performance in both thermal environments. Gene expression profile was analyzed by quantitative real-time polymerase chain reaction. Irrespective of growing phase, the expression of six genes was lower (P<0.05) at high temperature than at thermoneutrality. The lower expression of these genes under high temperatures evidences the effects of heat stress by decreasing oxidative metabolism, through adaptive physiological mechanisms in order to reduce heat production. In pigs from phase 1, six genes were differentially expressed across aP levels (P<0.05) in the thermoneutral and one gene in the heat stress. In pigs from phase 2, two genes were differentially expressed across aP levels (P<0.05) in both thermal environments. These data revealed strong evidence that phosphorus and thermal environments are key factors to regulate oxidative phosphorylation with direct implications on animal performance.
Fractional passage rates form a fundamental element within modern feed evaluation systems for ruminants, but knowledge on feed-specific fractional passage is largely lacking. Commonly applied tracer techniques based on externally applied markers, such as chromium-mordanted neutral detergent fibre (Cr-NDF), have been criticised for behaving differently to feed particles. This study describes the use of the carbon stable isotope ratio (13C : 12C) as an internal digesta marker to quantify the fractional passage rate of concentrates through the digestive tract of dairy cows. In a crossover study, five dairy cows were fed low (24.6%) and high (52.6%) levels of concentrates (dry matter (DM) basis) and received a pulse-dosed Cr-NDF and 13C isotopes. The latter was administered orally by exchanging part of the dietary concentrates of low 13C natural abundance with a pulse dose of maize bran-based concentrates of high 13C natural abundance. Fractional passage rates from the rumen (K1) and from the large intestine (K2) were determined from faecal marker concentrations of Cr-NDF and of 13C in the DM (13C-DM), NDF (13C-NDF) and neutral detergent soluble (13C-NDS). No differences in K1 estimates were found for the two concentrate levels fed but significant differences between markers (P<0.001) were observed. Faecal Cr-NDF excretions gave lower K1 estimates (0.037–0.039/h) than 13C-DM (0.054–0.056/h) and 13C-NDF (0.061–0.063/h). The 13C-NDS was calculated by the difference of 13C in the DM and NDF, and K1 values (0.039–0.043/h) were comparable to Cr-NDF. Total mean retention time was considerably higher for Cr-NDF (40.9–42.0 h) as compared to 13C-DM and 13C-NDF (32.0–33.5 h; P<0.001). The accuracy of the curve fits for Cr-NDF and 13C-DM and 13C-NDF was overall good (mean prediction error of 9.9–13.9%). Fractional passage rate of Cr-NDF was comparable to studies where this marker was assumed to represent the fractional passage of roughages. However, K1 estimates based on the 13C : 12C ratio varied considerably from studies based on external markers. Our results suggest that the use of 13C isotopes as digesta passage markers can provide feed component-specific K1 estimates for concentrates and provides new insight into passage kinetics of NDF from technologically treated compound feed.
Transport of animals is a stressful procedure often resulting in significant losses for the slaughter plant. This study aimed to determine whether or not pigs would benefit from a loading density (low density (LD)) (179 kg/m2) below the normal EU standard loading density (normal density (ND)) (235 kg/m2). Eight similar, 550-km-long road journeys, were followed in which fattening pigs were transported across Germany from farm to slaughter plant. During each journey all pigs were transported at LD (n=4) or ND (n=4). Twelve female pigs per journey (total n=96) were randomly selected for measurement and monitoring of body temperature, behaviour, heart rate and blood parameters. Throughout the journeys, LD pigs displayed more resting behaviour than ND pigs. Average body temperature was lower (P<0.05) for pigs transported at LD (38.0±0.07°C) than those transported at ND (38.3±0.06°C). During loading heart rate increased in both ND and LD pigs and declined after the vehicle had been closed before departure but remained slightly elevated in ND pigs. Pigs transported at ND displayed signs of stress (elevated HR and body temperatures) during the drivers’ break. Blood parameters were only slightly (not significant) effected by loading density. Results indicate that pigs are more capable of adapting to long (550 km) transport conditions when loaded at a density below the present EU requirement.
Genetic selection for milking speed is feasible. The existence of a correlation structure between milking speed and milk yield, however, necessitates a selection strategy to increase milking speed with no repercussion on genetic merit for milk yield. Residual milking duration (RMD) and residual milking duration including somatic cell score (RMDS), defined as the residuals from a regression model of milking duration on milk yield or milk yield plus somatic cell score (SCS) have been advocated. The objective of this study was to undertake a first ever genetic analysis of these novel traits. Data on electronically recorded milking duration and other milking characteristics from 235 005 test-day records on 74 608 cows in 1075 Irish dairy herds were available. Variance components for the milking characteristic traits were estimated using animal linear mixed models and covariances with other performance traits, including udder-related type traits, were estimated using sire models. The heritability of milking duration, RMD and RMDS was 0.20, 0.22 and 0.18, respectively. There were little differences in the heritability of RMD or RMDS when defined using genetic regression. The genetic standard deviation of RMDS defined on the phenotypic or genetic level was 36.8 s and 37.6 s, respectively, clearly indicating considerable exploitable genetic variation in milking duration independent of both milk yield and SCS. The genetic correlation between phenotypically derived RMDS and milk yield was favourable (−0.43), but RMDS was unfavourably genetically correlated with SCS (−0.30); the genetic correlations with both traits when RMDS was defined at a genetic level were zero. RMDS defined at the phenotypic level was negatively (i.e. unfavourable) genetically correlated (−0.35; s.e. = 0.15) with mastitis; however, when defined using genetic regression, shorter RMDS was not associated with greater expected incidence of mastitis. RMDS, defined at the genetic level, is a useful heritable trait with ample genetic variation for inclusion in a national breeding strategy without influencing genetic gain in either milk yield or udder health.
In cancer research, high-throughput profiling studies have been extensively conducted, searching for markers associated with prognosis. Owing to the ‘large d, small n’ characteristic, results generated from the analysis of a single dataset can be unsatisfactory. Recent studies have shown that integrative analysis, which simultaneously analyses multiple datasets, can be more effective than single-dataset analysis and classic meta-analysis. In most of existing integrative analysis, the homogeneity model has been assumed, which postulates that different datasets share the same set of markers. Several approaches have been designed to reinforce this assumption. In practice, different datasets may differ in terms of patient selection criteria, profiling techniques, and many other aspects. Such differences may make the homogeneity model too restricted. In this study, we assume the heterogeneity model, under which different datasets are allowed to have different sets of markers. With multiple cancer prognosis datasets, we adopt the accelerated failure time model to describe survival. This model may have the lowest computational cost among popular semiparametric survival models. For marker selection, we adopt a sparse group minimax concave penalty approach. This approach has an intuitive formulation and can be computed using an effective group coordinate descent algorithm. Simulation study shows that it outperforms the existing approaches under both the homogeneity and heterogeneity models. Data analysis further demonstrates the merit of heterogeneity model and proposed approach.
A recent E-Rare workshop reviewed the ethical aspects of whole exome and whole genome-sequencing studies (WES and WGS, respectively) in rare diseases. Leveraging new genomic technologies, which output vast amounts of known and novel genetic variants, researchers are learning more about the genetic basis and mechanisms involved in rare diseases. In some cases, these findings are translated into diagnostic tools for the benefit of rare disease patients. Among the disclosed data, which can assist in treatment management, incidental findings await, bringing with them ethical concerns for the clinicians, researchers and patients.
Gene expression profiling of peroxisome-proliferator-activated receptor α (PPARα) has been used in several studies, but there were no consistent results on gene expression patterns involved in PPARα activation in genome-wide due to different sample sizes or platforms. Here, we employed two published microarray datasets both PPARα dependent in mouse liver and applied meta-analysis on them to increase the power of the identification of differentially expressed genes and significantly enriched pathways. As a result, we have improved the concordance in identifying many biological mechanisms involved in PPARα activation. We suggest that our analysis not only leads to more identified genes by combining datasets from different resources together, but also provides some novel hepatic tissue-specific marker genes related to PPARα according to our re-analysis.
In order to formulate the Fundamental Theorem of Natural Selection, Fisher defined the average excess and average effect of a gene substitution. Finding these notions to be somewhat opaque, some authors have recommended reformulating Fisher's ideas in terms of covariance and regression, which are classical concepts of statistics. We argue that Fisher intended his two averages to express a distinction between correlation and causation. On this view, the average effect is a specific weighted average of the actual phenotypic changes that result from physically changing the allelic states of homologous genes. We show that the statistical and causal conceptions of the average effect, perceived as inconsistent by Falconer, can be reconciled if certain relationships between the genotype frequencies and non-additive residuals are conserved. There are certain theory-internal considerations favouring Fisher's original formulation in terms of causality; for example, the frequency-weighted mean of the average effects equaling zero at each locus becomes a derivable consequence rather than an arbitrary constraint. More broadly, Fisher's distinction between correlation and causation is of critical importance to gene-trait mapping studies and the foundations of evolutionary biology.
In most countries, male pigs are physically castrated soon after birth to reduce the risk of boar taint and to avoid behaviours such as fighting and mounting. However, entire male pigs are more feed efficient and deposit less fat than barrows. In addition, many animal welfare organizations are lobbying for a cessation of castration, with a likelihood that this could lead to inferior pork unless an alternative method is used to control boar taint. An alternative to physical castration is immunization against gonadotrophin releasing factor (GnRF) which allows producers to capitalize on the superior feed efficiency and carcass characteristics of boars without the risk of boar taint. From a physiological perspective, immunized pigs are entire males until shortly after the second dose, typically given 4 to 6 weeks before slaughter. Following full immunization, there is a temporary suppression of testicular function and a hormonal status that resembles that of a barrow. Nutrient requirements will be different in these two phases, before and after full immunization. Given that there have been few published studies comparing the lysine requirements of entire males and barrows in contemporary genotypes, it is useful to use gilt requirements as a benchmark. A series of meta-analyses comparing anti-GnRF immunized boars and physical castrates and use of nutritional models suggest that the lysine requirement of entire males before the second immunization is 5% higher than for gilts, from 25 to 50 kg BW, and by 8% from 50 to 95 kg. Given that the penalty in growth performance for having inadequate dietary lysine is greater in males than in gilts or barrows, it is important to ensure that lysine requirements are met to obtain the maximum benefits of entire male production during this phase. After the second immunization, the lysine requirement of immunized males decreases and may become more like that of barrows. In addition, a consistent effect of full immunization is a marked increase in voluntary feed intake from about 10 days after the second dose. Putting these together, the estimated lysine requirement, expressed in terms of diet composition, falls to 94% of the gilt level. Although general principles can be described now, further research is needed to fully define the lysine requirements of immunized boars. It is important that the temporal pattern of tissue deposition rates and feed intake be explored to be incorporated into models to predict nutrient requirements over the period of rapidly changing metabolism.
Genes and markers on the same chromosome have a linear arrangement, which can be described by a (linear) map. Genes and markers on different, non-homologous chromosomes are inherited independently and therefore there is no linear arrangement between them. This is the reason why genetic linkage maps are estimated separately for each chromosome. Determining which genes and markers belong to the same chromosome is therefore a necessary preparation for map construction. Sets of linked loci are called linkage groups. Ideally, the number of linkage groups is the same as the haploid number of chromosomes. In practice, this is not always the case. Sometimes, the set of loci studied does not cover the entire genome or is not distributed evenly across the genome. On other occasions, spurious linkage causes loci from separate chromosomes to end up in a single linkage group, which is a more common problem to solve.
Why determine linkage groups and how?
Chromosomes are very large linear molecules. Genes and genetic markers are, or represent, tiny parts of the chromosomes. They are referred to as loci. Their linear arrangement on the chromosomes can be described with mutual distance measures, for instance in base pairs or in recombination units. A linkage map is such a description. Because a linear arrangement exists only for loci of the same chromosome, determining which loci reside on the same chromosome is the first step in the construction of a linkage map. Loci on the same chromosome are physically linked, whereas those on different (non-homologous) chromosomes are physically unlinked. Genetic linkage is the phenomenon whereby traits have a tendency to be inherited together. Due to independent assortment of the chromosomes in meiosis, genetic linkage is the result of physical linkage. Determining whether two loci are genetically linked is the starting point of lumping loci into groups. It is preferable to identify such groups of linked genes and markers as linkage groups rather than as chromosomes, as long as the physical relationship between the loci and the chromosomes is not established (e.g. through cytogenetics). This is even more relevant if there are more groups than homologous chromosome pairs. Such situations often occur in experiments where the genome is not covered completely with markers.
The central idea of linkage analysis is that the rate of recombination between two loci is a reflection of their mutual distance on the chromosomes. Combining recombination frequencies between multiple loci on the same chromosome allows determination of the relative positions of these loci on the chromosome, thus creating a linkage map. A linkage map is defined by the order of its loci and by their mutual distances. This chapter is about obtaining the map distances for a given order.
Recombination frequencies and distances
One of the functions of a linkage map is to allow easy calculation of recombination frequencies between loci from their positions on the map. The linearity of a linkage map implies that distances can be added or subtracted. The question is whether recombination frequencies can be used as map distances. This problem was initially met by Sturtevant (1913), who was the first person to produce a genetic linkage map. At the time, it was becoming clear that genetic factors were located on the chromosomes. Sturtevant realized that it should be possible to determine the spatial arrangement of the genetic factors on the linear structure of a chromosome using recombination frequencies between factors as measures of their mutual distances. However, he found a discrepancy between the sum of adjacent distances and the direct measurement of the overall distance. He claimed that shorter distances were measured more accurately. He attributed the discrepancy to double recombinations and to a phenomenon now known as crossover interference. The essence of the problem is that recombination frequencies are not additive. The problem was solved by Haldane (1919), who derived a function that translates recombination frequencies into additive distances.
Around 1900, scientists tried to understand the relationship between the inheritance of simple traits and observations of meiotic cell division under a microscope. It was the time that Mendel's laws on the inheritance of traits (Mendel, 1865) were rediscovered. Mendel did not have any idea of the biological mechanisms underlying his laws. However, some 35 years later, after studying Boveri's 1902 paper, Sutton (1902) realized that chromosomes and their behaviour in meiotic cell division could very well explain Mendel's results. However, in the many experimental crosses carried out to study the simultaneous inheritance of two traits, occasionally large deviations from Mendel's Law of Independent Assortment were observed. Bateson et al. (1905) described these deviations in terms of coupling of the heritable factors determining the traits. In subsequent work by Morgan and others, it became clear that heritable factors could be grouped with respect to the law of independent assortment: if two factors belonged to the same group, then their inheritance showed some interdependence; otherwise, the law would hold. In other words, they started realizing that these groups corresponded with chromosomes. By 1911, Morgan showed the possibility of recombination between factors lying on the same chromosome (Morgan, 1911a). Morgan assumed that this was due to an interchange (as he called the crossing over) between homologous chromosomes during meiosis. This corresponded very well with the detailed cytological observations of Janssens (1909). Later, Morgan (1911b) suggested that the heritable factors should be located in a linear fashion on the chromosomes and that the degree of coupling between traits would depend on the distance between the factors on the chromosomes. Sturtevant (1913), a student of Morgan, tested the hypothesis that the proportion of crossovers (as he called the recombinants) could be used as an index of the distance between two factors. He argued that, after determining distances from A to B and from B to C, it should be possible to predict the distance from A to C and to determine the order of the factors on the chromosome. Sturtevant successfully analysed six factors located on the sex chromosome of Drosophila and thus became the first ever person producing a genetic map. With his work, the chromosome theory of inheritance became really established.
Genetic linkage maps play an important role in many branches of biological research. The specific calculations involved in the construction of genetic maps ask for dedicated computer software. When using this software, the construction of genetic maps looks simple: just press a few buttons and you get a map. However, things are not as simple as they look. The various computations are based on a fair amount of theory from genetics, probability, statistics and optimization. We felt the need to explain this theory and write a special textbook dedicated to the construction of genetic maps. We think it is essential reading for those who want to construct reliable genetic maps.
Ever since the late 1980s, when the first RFLP projects started in Wageningen, we have been involved in linkage analysis with molecular genetic markers. By the mid-1990s, we started teaching short courses on the construction of genetic maps. Currently, our course covers 3 days of lectures and practicals. This book is a complete rewrite of our course reader. The book is aimed at the biologist with an interest in the construction of genetic maps. For a proper understanding, knowledge of genetics, probability and statistics at the undergraduate level is required.