L. Varona

Fine mapping of porcine chromosome 6 QTL and LEPR effects on body composition in multiple generations of an Iberian by Landrace intercross.

The leptin receptor gene (LEPR) is a candidate for traits related to growth and body composition, and is located on SSC6 in a region where fatness and meat composition quantitative trait loci (QTL) have previously been detected in several F2 experimental designs. The aims of this work were: (i) to fine map these QTL on a larger sample of animals and generations (F3 and backcross) of an Iberian x Landrace intercross and (ii) to examine the effects of LEPR alleles on body composition traits. Eleven single nucleotide polymorphisms (SNPs) were detected by sequencing LEPR coding regions in Iberian and Landrace pig samples. Three missense polymorphisms were genotyped by pyrosequencing in 33 F0, 70 F1, 418 F2, 86 F3 and 128 individuals coming from the backcross of four F2 males with 24 Landrace females. Thirteen microsatellites and one SNP were also genotyped. Traits analysed were: backfat thickness at different locations (BF(T)), intramuscular fat percentage (IMF(P)), eye muscle area (EM(A)), loin depth (LO(D)), weight of shoulder (SH(W)), weight of ribs (RIB(W)) and weight of belly bacon (BB(W)). Different statistical models were applied in order to evaluate the number and effects of QTL on chromosome 6 and the possible causality of the LEPR gene variants with respect to the QTL. The results support the presence of two QTL on SSC6. One, at position 60-100 cM, affects BF(T) and RIB(W). The other and more significant maps in a narrow region (130-132 cM) and affects BF(T), IMF(P), EM(A), LO(D), SH(W), RIB(W) and BB(W). Results also support the association between LEPR alleles and BF(T) traits. The possible functional implications of the analysed polymorphisms are considered.

Test for positional candidate genes for body composition on pig chromosome 6

One QTL affecting backfat thickness (BF), intramuscular fat content (IMF) and eye muscle area (MA) was previously localized on porcine chromosome 6 in an F2 cross between Iberian and Landrace pigs. This work was done to study the effect of two positional candidate genes on these traits: H-FABP and LEPR genes. The QTL mapping analysis was repeated with a regression method using genotypes for seven microsatellites and two PCR-RFLPs in the H-FABP and LEPR genes. H-FABP and LEPR genes were located at 85.4 and 107 cM respectively, by linkage analysis. The effects of the candidate gene polymorphisms were analyzed in two ways. When an animal model was fitted, both genes showed significant effects on fatness traits, the H-FABP polymorphism showed significant effects on IMF and MA, and the LEPR polymorphism on BF and IMF. But when the candidate gene effect was included in a QTL regression analysis these associations were not observed, suggesting that they must not be the causal mutations responsible for the effects found. Differences in the results of both analyses showed the inadequacy of the animal model approach for the evaluation of positional candidate genes in populations with linkage disequilibrium, when the probabilities of the parental origin of the QTL alleles are not included in the model.

On the Additive and Dominant Variance and Covariance of Individuals Within the Genomic Selection Scope

Genomic evaluation models can fit additive and dominant SNP effects. Under quantitative genetics theory, additive or “breeding” values of individuals are generated by substitution effects, which involve both “biological” additive and dominant effects of the markers. Dominance deviations include only a portion of the biological dominant effects of the markers. Additive variance includes variation due to the additive and dominant effects of the markers. We describe a matrix of dominant genomic relationships across individuals, D, which is similar to the G matrix used in genomic best linear unbiased prediction. This matrix can be used in a mixed-model context for genomic evaluations or to estimate dominant and additive variances in the population. From the “genotypic” value of individuals, an alternative parameterization defines additive and dominance as the parts attributable to the additive and dominant effect of the markers. This approach underestimates the additive genetic variance and overestimates the dominance variance. Transforming the variances from one model into the other is trivial if the distribution of allelic frequencies is known. We illustrate these results with mouse data (four traits, 1884 mice, and 10,946 markers) and simulated data (2100 individuals and 10,000 markers). Variance components were estimated correctly in the model, considering breeding values and dominance deviations. For the model considering genotypic values, the inclusion of dominant effects biased the estimate of additive variance. Genomic models were more accurate for the estimation of variance components than their pedigree-based counterparts.

A Bayesian analysis of the effect of selection for growth rate on growth curves in rabbits

Gompertz growth curves were fitted to the data of 137 rabbits from control (C) and selected (S) lines. The animals came from a synthetic rabbit line selected for an increased growth rate. The embryos from generations 3 and 4 were frozen and thawed to be contemporary of rabbits born in generation 10. Group C was the offspring of generations 3 and 4, and group S was the contemporary offspring of generation 10. The animals were weighed individually twice a week during the first four weeks of life, and once a week thereafter, until 20 weeks of age. Subsequently, the males were weighed weekly until 40 weeks of age. The random samples of the posterior distributions of the growth curve parameters were drawn by using Markov Chain Monte Carlo (MCMC) methods. As a consequence of selection, the selected animals were heavier than the C animals throughout the entire growth curve. Adult body weight, estimated as a parameter of the Gompertz curve, was 7% higher in the selected line. The other parameters of the Gompertz curve were scarcely affected by selection. When selected and control growth curves are represented in a metabolic scale, all differences disappear.

Detection of QTL affecting fatty acid composition in the pig

We present a QTL genome scan for fatty acid composition in pigs. An F2 cross between Iberian × Landrace pigs and a regression approach fitting the carcass weight as a covariate for QTL identification was used. Chromosomes (Chrs) 4, 6, 8, 10, and 12 showed highly significant effects. The Chr 4 QTL influenced the linoleic content and both the fatty acid double-bond index and peroxidability index. In Chr 6 we found significant associations with the double-bond index and the unsaturated index of fatty acids. Chr 8 showed clear effects on the percentages of palmitic and palmitoleic fatty acids as well as the average chain length of fatty acids. In Chr 10 we detected a significant QTL for the percentage of myristic fatty acid, with an F value that was slightly above the genomewide threshold. The percentage of linolenic fatty acid was affected by a region on Chr 12. A nearly significant QTL for the content of gadoleic fatty acid was also detected in Chr 12. We also analyzed the genomic QTL distribution by a regression model that fits the backfat thickness as a covariate. Some of the QTL that were detected in our analysis could not be detected when the data were corrected by backfat thickness. This work shows how critical the selection of a covariate can be in the interpretation of results. This is the first report of a genome scan detection of QTL directly affecting fatty acid composition in pigs.

QTL mapping for growth and carcass traits in an Iberian by Landrace pig intercross: additive, dominant and epistatic effects

Results from a QTL experiment on growth and carcass traits in an experimental F2 cross between Iberian and Landrace pigs are reported. Phenotypic data for growth, length of carcass and muscle mass, fat deposition and carcass composition traits from 321 individuals corresponding to 58 families were recorded. Animals were genotyped for 92 markers covering the 18 porcine autosomes (SSC). The results from the genomic scan show genomewide significant QTL in SSC2 (longissimus muscle area and backfat thickness), SSC4 (length of carcass, backfat thickness, loin, shoulder and belly bacon weights) and SSC6 (longissimus muscle area, backfat thickness, loin, shoulder and belly bacon weights). Suggestive QTL were also found on SSC1, SSC5, SSC7, SSC8, SSC9, SSC13, SCC14, SSC16 and SSC17. A bidimensional genomic scan every 10 cM was performed to detect interaction between QTL. The joint action of two suggestive QTL in SSC2 and SSC17 led to a genome-wide significant effect in live weight. The results of the bidimensional genomic scan showed that the genetic architecture was mainly additive or the experimental set-up did not have enough power to detect epistatic interactions.

A note on mate allocation for dominance handling in genomic selection

Estimation of non-additive genetic effects in animal breeding is important because it increases the accuracy of breeding value prediction and the value of mate allocation procedures. With the advent of genomic selection these ideas should be revisited. The objective of this study was to quantify the efficiency of including dominance effects and practising mating allocation under a whole-genome evaluation scenario. Four strategies of selection, carried out during five generations, were compared by simulation techniques. In the first scenario (MS), individuals were selected based on their own phenotypic information. In the second (GSA), they were selected based on the prediction generated by the Bayes A method of whole-genome evaluation under an additive model. In the third (GSD), the model was expanded to include dominance effects. These three scenarios used random mating to construct future generations, whereas in the fourth one (GSD + MA), matings were optimized by simulated annealing. The advantage of GSD over GSA ranges from 9 to 14% of the expected response and, in addition, using mate allocation (GSD + MA) provides an additional response ranging from 6% to 22%. However, mate selection can improve the expected genetic response over random mating only in the first generation of selection. Furthermore, the efficiency of genomic selection is eroded after a few generations of selection, thus, a continued collection of phenotypic data and re-evaluation will be required.

Multiple trait genetic analysis of underlying biological variables of production functions

Abstract A Bayesian procedure to analyze performance data from production functions is presented. The method implies the consideration of each parameter of the production function as a different trait. The systematic effects and genetic relationship between animals are taken into account in the adjustment of production for each animal. The method weights all possible sources of information. In this sense, it allows estimation of the parameters of production functions with only one data along the production cycle and to reduce the influence of outliers. Under analysis, marginal posterior distributions of the function parameters, breeding values, systematic effects and (co)variance components were obtained using the Gibbs sampling algorithm. The procedure requires the definition of the joint posterior distribution to be generalized to any production function. From this joint posterior distribution, the full set of posterior conditional distributions needed for the Gibbs sampling algorithm can be easily obtained.

Comparative study of equine bone marrow and adipose tissue‐derived mesenchymal stromal cells

REASONS FOR PERFORMING STUDY Mesenchymal stromal cells (MSCs) represent an attractive source for regenerative medicine. However, prior to their application, fundamental questions regarding molecular characterisation, growth and differentiation of MSCs must be resolved. OBJECTIVES To compare and better understand the behaviour of equine MSCs obtained from bone marrow (BM) and adipose tissue (AT) in culture. METHODS Five horses were included in this study. Proliferation rate was measured using MTT assay and cell viability; apoptosis, necrosis and late apoptosis and necrosis were evaluated by flow cytometry. The mRNA expression levels of 7 surface marker genes were quantified using RT-qPCR and CD90 was also analysed by flow cytometry. Differentiation was evaluated using specific staining, measurement of alkaline phosphatase activity and analysis of the mRNA expression. RESULTS High interindividual differences were observed in proliferation in both cell types, particularly during the final days. Statistically significant differences in viability and early apoptosis of cultured AT- and BM-MSCs were found. The highest values of early apoptosis were observed during the first days of culture, while the highest percentage of necrosis and late apoptosis and lowest viability was observed in the last days. Surface marker expression pattern observed is in accordance to other studies in horse and other species. Osteogenic differentiation was evident after 7 days, with an increasing of ALP activity and mRNA expression of osteogenic markers. Adipogenic differentiation was achieved in BM-MSCs from 2 donors with one of the 16 media tested. Chondrogenic differentiation was also observed. CONCLUSIONS Proliferation ability is different in AT-MSCs and BM-MSCs. Differences in viability and early apoptosis were observed between both sources and CD34 was only found in AT-MSCs. Differences in their osteogenic and adipogenic potential were detected by staining and quantification of specific tissue markers. POTENTIAL RELEVANCE To provide data to better understand AT-MSCs and BM-MSCs behaviour in vitro.

Mapping of quantitative trait loci for cholesterol, LDL, HDL, and triglyceride serum concentrations in pigs

The fine mapping of polymorphisms influencing cholesterol (CT), triglyceride (TG), and lipoprotein serum levels in human and mouse has provided a wealth of knowledge about the complex genetic architecture of these traits. The extension of these genetic analyses to pigs would be of utmost importance since they constitute a valuable biological and clinical model for the study of coronary artery disease and myocardial infarction. In the present work, we performed a whole genome scan for serum lipid traits in a half-sib Duroc pig population of 350 individuals. Phenotypic registers included total CT, TG, and low (LDL)- and high (HDL)-density lipoprotein serum concentrations at 45 and 190 days of age. This approach allowed us to identify two genomewide significant quantitative trait loci (QTL) for HDL-to-LDL ratio at 45 days (SSC6, 84 cM) and for TG at 190 days (SSC4, 23 cM) as well as a number of chromosomewide significant QTL. The comparison of QTL locations at 45 and 190 days revealed a notable lack of concordance at these two time points, suggesting that the effects of these QTL are age specific. Moreover, we have observed a considerable level of correspondence among the locations of the most significant porcine lipid QTL and those identified in humans. This finding might suggest that, in mammals, diverse polymorphisms located in a common set of genes are involved in the genetic variation of serum lipid levels.