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Structural equation models allow causal effects between 2 or more variables to be considered and can postulate unidirectional (recursive models; RM) or bidirectional (simultaneous models) causality between variables. This review evaluated the properties of RM in animal breeding and how to interpret the genetic parameters and the corresponding estimated breeding values. In many cases, RM and mixed multitrait models (MTM) are statistically equivalent, although subject to the assumption of variance-covariance matrices and restrictions imposed for achieving model identification. Inference under RM requires imposing some restrictions on the (co)variance matrix or on the location parameters. The estimates of the variance components and the breeding values can be transformed from RM to MTM, although the biological interpretation differs. In the MTM, the breeding values predict the full influence of the additive genetic effects on the traits and should be used for breeding purposes. In contrast, the RM breeding values express the additive genetic effect while holding the causal traits constant. The differences between the additive genetic effect in RM and MTM can be used to identify the genomic regions that affect the additive genetic variation of traits directly or causally mediated for another trait or traits. Furthermore, we presented some extensions of the RM that are useful for modeling quantitative traits with alternative assumptions. The equivalence of RM and MTM can be used to infer causal effects on sequentially expressed traits by manipulating the residual (co)variance matrix under the MTM. Further, RM can be implemented to analyze causality between traits that might differ among subgroups or within the parametric space of the independent traits. In addition, RM can be expanded to create models that introduce some degree of regularization in the recursive structure that aims to estimate a large number of recursive parameters. Finally, RM can be used in some cases for operational reasons, although there is no causality between traits. [ABSTRACT FROM AUTHOR]

Genomic selection has been implemented over the years in several livestock species, due to the achievable higher genetic progress. The use of genomic information in evaluations provides better prediction accuracy than do pedigree-based evaluations, and the makeup of the genotyped population is a decisive point. The aim of this work is to compare the effect of different genotyping strategies (number and type of animals) on the prediction accuracy for dairy sheep Latxa breeds. A simulation study was designed based on the real data structure of each population, and the phenotypic and genotypic data obtained were used in genetic (BLUP) and genomic (single-step genomic BLUP) evaluations of different genotyping strategies. The genotyping of males was beneficial when they were genetically connected individuals and if they had daughters with phenotypic records. Genotyping females with their own lactation records increased prediction accuracy, and the connection level has less relevance. The differences in genotyping females were independent of their estimated breeding value. The combined genotyping of males and females provided intermediate accuracy results regardless of the female selection strategy. Therefore, assuming that genotyping rams is interesting, the incorporation of genotyped females would be beneficial and worthwhile. The benefits of genotyping individuals from various generations were highlighted, although it was also possible to gain prediction accuracy when historic individuals were not considered. Greater genotyped population sizes resulted in more accuracy, even if the increase seems to reach a plateau. [ABSTRACT FROM AUTHOR]

An experiment of divergent selection for intramuscular fat was carried out at Universitat Politècnica de València. The high response of selection in intramuscular fat content, after nine generations of selection, and a multidimensional scaling analysis showed a high degree of genomic differentiation between the two divergent populations. Therefore, local genomic differences could link genomic regions, encompassing selective sweeps, to the trait used as selection criterion. In this sense, the aim of this study was to identify genomic regions related to intramuscular fat through three methods for detection of selection signatures and to generate a list of candidate genes. The methods implemented in this study were Wright's fixation index, cross population composite likelihood ratio and cross population – extended haplotype homozygosity. Genomic data came from the 9th generation of the two populations divergently selected, 237 from Low line and 240 from High line. A high single nucleotide polymorphism (SNP) density array, Affymetrix Axiom OrcunSNP Array (around 200k SNPs), was used for genotyping samples. Several genomic regions distributed along rabbit chromosomes (OCU) were identified as signatures of selection (SNPs having a value above cut-off of 1%) within each method. In contrast, 8 genomic regions, harbouring 80 SNPs (OCU1, OCU3, OCU6, OCU7, OCU16 and OCU17), were identified by at least 2 methods and none by the 3 methods. In general, our results suggest that intramuscular fat selection influenced multiple genomic regions which can be a consequence of either only selection effect or the combined effect of selection and genetic drift. In addition, 73 genes were retrieved from the 8 selection signatures. After functional and enrichment analyses, the main genes into the selection signatures linked to energy, fatty acids, carbohydrates and lipid metabolic processes were ACER2, PLIN2, DENND4C, RPS6, RRAGA (OCU1), ST8SIA6, VIM (OCU16), RORA, GANC and PLA2G4B (OCU17). This genomic scan is the first study using rabbits from a divergent selection experiment. Our results pointed out a large polygenic component of the intramuscular fat content. Besides, promising positional candidate genes would be analysed in further studies in order to bear out their contributions to this trait and their feasible implications for rabbit breeding programmes. [ABSTRACT FROM AUTHOR]

The Iberian pig is one of the pig breeds that has the highest meat quality. Traditionally, producers have bred one of the available varieties, exclusively, and have not used crosses between them, which has contrasted sharply with other populations of commercial pigs for which crossbreeding has been a standard procedure. The objective of this study was to perform an experiment under full diallel design among three contemporary commercial varieties of Iberian pig and estimate the additive genetic variation and the crossbreeding effects (direct, maternal and heterosis) for prolificacy. The data set comprised 18 193 records for total number born and number born alive from 3800 sows of three varieties of the Iberian breed (Retinto, Torbiscal and Entrepelado) and their reciprocal crosses (Retinto × Torbiscal, Torbiscal × Retinto, Retinto × Entrepelado, Entrepelado × Retinto, Torbiscal × Entrepelado and Entrepelado × Torbiscal), and a pedigree of 4609 individuals. The analysis was based on a multiple population repeatability model, and we developed a model comparison test that indicated the presence of direct line, maternal and heterosis effects. The results indicated the superiorities of the direct line effect of the Retinto and the maternal effect of the Entrepelado populations. All of the potential crosses produced significant heterosis, and additive genetic variation was higher in the Entrepelado than it was in the other two populations. The recommended cross for the highest yield in prolificacy is a Retinto father and an Entrepelado mother to generate a hybrid commercial sow. [ABSTRACT FROM AUTHOR]

This study investigated the potential application of genomic selection under a multi-breed scheme in the Spanish autochthonous beef cattle populations using a simulation study that replicates the structure of linkage disequilibrium obtained from a sample of 25 triplets of sire/dam/offspring per population and using the BovineHD Beadchip. Purebred and combined reference sets were used for the genomic evaluation and several scenarios of different genetic architecture of the trait were investigated. The single-breed evaluations yielded the highest within-breed accuracies. Across breed accuracies were found low but positive on average confirming the genetic connectedness between the populations. If the same genotyping effort is split in several populations, the accuracies were lower when compared with single-breed evaluation, but showed a small advantage over small-sized purebred reference sets over the accuracies of subsequent generations. Besides, the genetic architecture of the trait did not show any relevant effect on the accuracy with the exception of rare variants, which yielded slightly lower results and higher loss of predictive ability over the generations. [ABSTRACT FROM AUTHOR]

Ranking trait was used as a selection criterion for competition horses to estimate racing performance. In the literature the most common approaches to estimate breeding values are the linear or threshold statistical models. However, recent studies have shown that a Thurstonian approach was able to fix the race effect (competitive level of the horses that participate in the same race), thus suggesting a better prediction accuracy of breeding values for ranking trait. The aim of this study was to compare the predictability of linear, threshold and Thurstonian approaches for genetic evaluation of ranking in endurance horses. For this purpose, eight genetic models were used for each approach with different combinations of random effects: rider, rider-horse interaction and environmental permanent effect. All genetic models included gender, age and race as systematic effects. The database that was used contained 4065 ranking records from 966 horses and that for the pedigree contained 8733 animals (47% Arabian horses), with an estimated heritability around 0.10 for the ranking trait. The prediction ability of the models for racing performance was evaluated using a cross-validation approach. The average correlation between real and predicted performances across genetic models was around 0.25 for threshold, 0.58 for linear and 0.60 for Thurstonian approaches. Although no significant differences were found between models within approaches, the best genetic model included: the rider and rider-horse random effects for threshold, only rider and environmental permanent effects for linear approach and all random effects for Thurstonian approach. The absolute correlations of predicted breeding values among models were higher between threshold and Thurstonian: 0.90, 0.91 and 0.88 for all animals, top 20% and top 5% best animals. For rank correlations these figures were 0.85, 0.84 and 0.86. The lower values were those between linear and threshold approaches (0.65, 0.62 and 0.51). In conclusion, the Thurstonian approach is recommended for the routine genetic evaluations for ranking in endurance horses. [ABSTRACT FROM AUTHOR]

The Spanish local beef cattle breeds have most likely common origin followed by a process of differentiation. This particular historical evolution has most probably left detectable signatures in the genome. The objective of this study was to identify genomic regions associated with differentiation processes in seven Spanish autochthonous populations (Asturiana de los Valles (AV), Avileña-Negra Ibérica (ANI), Bruna dels Pirineus (BP), Morucha (Mo), Pirenaica (Pi), Retinta (Re) and Rubia Gallega (RG)). The BovineHD 777K BeadChip was used on 342 individuals (AV, n=50; ANI, n=48; BP, n=50; Mo, n=50; Pi, n=48; Re, n=48; RG, n=48) chosen to be as unrelated as possible. We calculated the fixation index (FST) and performed a Bayesian analysis named SelEstim. The output of both procedures was very similar, although the Bayesian analysis provided a richer inference and allowed us to calculate significance thresholds by generating a pseudo-observed data set from the estimated posterior distributions. We identified a very large number of genomic regions, but when a very restrictive significance threshold was applied these regions were reduced to only 10. Among them, four regions can be highlighted because they comprised a large number of single nucleotide polymorphisms and showed extremely high signals (Kullback–Leiber divergence (KLD)>6). They are located in BTA 2 (5 575 950 to 10 152 228 base pairs (bp)), BTA 5 (17 596 734 to 18 850 702 bp), BTA 6 (37 853 912 to 39 441 548 bp) and BTA 18 (13 345 515 to 15 243 838 bp) and harbor, among others, the MSTN (Myostatin), KIT-LG (KIT Ligand), LAP3 (leucine aminopeptidase 3), NAPCG (non-SMC condensing I complex, subunit G), LCORL (ligand dependent nuclear receptor corepressor-like) and MC1R (Melanocortin 1 receptor) genes. Knowledge on these genomic regions allows to identify potential targets of recent selection and helps to define potential candidate genes associated with traits of interest, such as coat color, muscle development, fertility, growth, carcass and immunological response. [ABSTRACT FROM PUBLISHER]

An experiment of a 2×2 full diallelic cross between two contemporary Iberian pig strains (Retinto: RR, and Torbiscal: TT) was conducted to estimate the crossbreeding effects for growth and carcass traits. Phenotypic records were obtained under intensive management and consisted of two different data sets. The first set comprised growth traits until weaning and was collected at two different farms (6236 and 1208 records, respectively). Specific data included individual piglet weight at birth and at weaning at 28 days and average daily gain from birth to weaning at 28 days (ADG28) for both RR and TT and their reciprocal crosses. The second set comprised growth data from birth to slaughter (~340 days and ~160 kg) and carcass traits from 349 individuals (randomly) sampled at weaning from the first dataset. Data were analyzed through a Bayesian analysis by using a reparameterization of Dickerson’s model that allowed estimation of the posterior distributions of the following crossbreeding effects: average maternal breed effect (gM), average paternal breed effect (gP) and individual heterosis (hI). Results showed that the relative magnitude of crossbreeding effects depends on the trait analyzed. Crosses where Torbiscal strain was used as mother (RT and TT) achieved the greatest performance for all growth traits at weaning, leading to remarkable gM effects. The most outstanding example is the case of ADG28 where the probability of relevance was one. In contrast, TR cross showed the greatest differences from RR cross for all growth at slaughter and carcass traits. These differences were mainly due to hI and gP crossbreeding parameters. In particular, the posterior mean of hI was more noticeable for live weight at slaughter, average daily gain at slaughter and carcass length, while gP was more relevant for hams (kg) and loins (kg) representing from 3% to10% of average performance of traits. Hence, growth traits at weaning did not reveal any notable advantage of the crossbreeding scheme because of the superiority of the Torbiscal strain with respect to its mothering ability and the small hI. However, results from growth and carcass traits at slaughter would support the implementation of a TR crossbred system. It would allow exploitation of both the gP of the Torbiscal strain and the hI between these two Iberian pig strains. Additionally, gP estimates and phenotypic differences between reciprocal crosses might suggest signs of the presence of paternal genetic imprinting in primal cuts traits. [ABSTRACT FROM PUBLISHER]

One of the main goals of selection schemes in beef cattle populations is to increase carcass weight at slaughter. Live weights at different growth stages are frequently used as selection criteria under the hypothesis that they usually have a high and positive genetic correlation with weight at slaughter. However, the presence of compensatory growth may bias the prediction ability of early weights for selection purposes. Recursive models may represent an interesting alternative for understanding the genetic and phenotypic relationship between weight traits during growth. For the purposes of this study, the analysis was performed for three different set of data from the Pirenaica beef cattle breed: weight at 120 days (W120) and at 210 days (W210); W120 and carcass weight at slaughter at 365 days (CW365); W210 and CW365. The number of records for each analysis was 8592, 4648 and 3234, respectively. A pedigree composed of 56323 individuals was also included. The statistical model comprised sex, year-season of birth, herd and slaughterhouse, plus a non-linear recursive dependency between traits. The dependency was modeled as a polynomial up to the 4th degree and models were compared using a Logarithm of Conditional Predictive Ordinates. The results of model comparison suggest that the best models were the 3rd degree polynomial for W120-W210 and W120-CW365 and the 2nd degree polynomial for W210-CW365. The posterior mean estimates for heritabilities ranged between 0.29 and 0.44 and the posterior mean estimates of the genetic correlations were null or very low, indicating that the relationship between traits is fully captured by the recursive dependency. The results imply that the predictive ability of the performance of future growth is low if it is only based on records of early weights. The usefulness of slaughterhouse records in beef cattle breeding evaluation is confirmed. [ABSTRACT FROM PUBLISHER]

This research investigated two sources of sire-specific genetic effects on the birth weight (BWT) and weaning weight (WWT) of Bruna dels Pirineus beef calves. More specifically, we focused on the influence of genes located in the non-autosomal region of the Y chromosome and the contribution of paternal imprinting. Our analyses were performed on 8130 BWT and 1245 WWT records from 12 and 2 purebred herds, respectively, they being collected between years 1986 and 2010. All animals included in the study were registered in the Yield Recording Scheme of the Bruna dels Pirineus breed. Both BWT and WWT were analyzed using a univariate linear animal model, and the relevance of paternal imprinting and Y chromosome-linked effects were checked by the deviance information criterion (DIC). In addition to sire-specific and direct genetic effects, our model accounted for random permanent effects (dam and herd-year-season) and three systematic sources of variation, that is, sex of the calf (male or female), age of the dam at calving (six levels) and birth type (single or twin). Both weight traits evidenced remarkable effects from the Y chromosome, whereas paternal imprinting was only revealed in WWT. Note that differences in DIC between the preferred model and the remaining ones exceed 39 000 and 2 800 000 DIC units for BWT and WWT, respectively. It is important to highlight that Y chromosome accounted for ∼2% and ∼6% of the total phenotypic variance for BWT and WWT, respectively, and paternal imprinting accounted for ∼13% of the phenotypic variance for WWT. These results revealed two relevant sources of sire-specific genetic variability with potential contributions to the current breeding scheme of the Bruna dels Pirineus beef cattle breed; moreover, these sire-specific effects could be included in other beef cattle breeding programs or, at least, they must be considered and appropriately analyzed. [ABSTRACT FROM PUBLISHER]

Survival or longevity is an economically relevant trait in cattle. However, it is not currently included in cattle selection criteria because of the delayed recording of phenotypic data and the high computational demand of survival techniques under proportional hazard models. The identification of longevity-correlated traits that can be early registered in lifetime would therefore be very useful for beef cattle selection processes. The aim of this study was to estimate the genetic correlation of survival (SURV) with: growth – birth weight (BW), weight at 120 days (W120), weight at 210 days (W210); carcass – cold carcass weight (CCW), conformation (CON), fatness (FAT) and meat colour (COL); teat morphology – teat thickness (TT), teat length (TL) and udder depth (UD); leg morphology – forward (FL) and backward legs (BL); milk production (MILK) and docility (DOC). In the statistical analysis, SURV was measured in discrete-time intervals and modelled via a sequential threshold model. A series of independent bivariate Bayesian analyses between cow survival and each recorded trait were carried out. The posterior mean estimates (and posterior standard deviation) for the heritability of SURV was 0.05 (0.01); and for the relevant genetic correlations with SURV were 0.07 (0.04), 0.12 (0.05), 0.10 (0.05), 0.15 (0.05), −0.18 (0.06), 0.33 (0.06) and 0.27 (0.15) for BW, W120, W210, CCW, CON, FAT and COL, respectively. [ABSTRACT FROM PUBLISHER]

Genomic selection relies on the whole-genome evaluation of single nucleotide polymorphisms (SNP), some of them linked to quantitative trait loci (QTL). Although statistical methodology has been developed for the analysis of genomic data, little is known about the performance of SNP association studies when trying to capture variability from QTL mutations of different ages. Within this context, the influence of mutation age was analyzed under a simulation design, assuming presence or absence of selection on mutant QTL alleles. Focusing on a unique chromosome with a single QTL located in the proximal end, the performance of the genomic selection analyses was evaluated in terms of standardized mean square error (MSE). For all simulation scenarios, MSE was highest for the youngest mutations. The MSE was progressively reduced with mutation age under random mating and soft selection, and reached its maximum performance with the oldest mutations. On the other hand, moderate and strong selection caused a quick reduction of the MSE from youngest mutations to mutations arising in generations 920 to 939, thus resulting in a progressive increase for older mutations. In both cases, very young mutations escaped from genomic selection analyses, releasing a relevant amount of genetic variability that could not be captured and used in genomic selection programs. This demonstrated the need for new analytical approaches to model relevant and recent sources of variation; if captured, these young mutations could substantially contribute to current breeding schemes. [ABSTRACT FROM AUTHOR]

Abstract: The male progeny of nine bulls used in artificial insemination were sampled to determine the effects of selection for weight at 210 days of age (W 210) on carcass and meat quality in the Pirenaica cattle breed. For each of the 125 male offspring raised in experimental conditions, we measured 38 variables (y) defining carcass and meat quality. In turn, the breeding values of those animals for the W 210 trait (û 210) were calculated without their own record and without data of maternal siblings. Indirect responses were estimated from the slope of the covariate between the selection index (û 210) and the phenotype of those variables (b(y, û 210)). We derived that the covariate depends on the genetic correlation and the genetic variances of the selection index and the trait. Consequently, this method provides an unbiased estimator of the correlated response, without requiring an estimate of the genetic correlation. We conclude that, in the Pirenaica breed, selection for W 210 produces animals with higher live- and carcass weights at slaughter, wider carcasses, deeper and longer legs with a greater perimeter, and greater loin surface. Genetic changes were not detected in dressing percentage and physicochemical or sensorial parameters of meat quality. [Copyright &y& Elsevier]

A method based on the analysis of recursive multiple-trait models was used to 1) estimate genetic and phenotypic relationships of calving ease (CE) with fertility traits and 2) analyze whether dystocia negatively affects reproductive performance in the next reproductive cycle. Data were collected from 1995 through 2002, and contained 33,532 records of CE and reproductive data of 17,558 Holstein cows distributed across 560 herds in official milk recording from the Basque Country Autonomous Community (Spain). The following fertility traits were considered: days open (DO), days to first service, number of services per pregnancy (NINS), and outcome of first insemination (OFI). Four bivariate sire and sirematernal grandsire models were used for the analyses. Censoring existed in DO (26.49% of the data) and NINS (12.22% of the data) because of cows having been sold or culled before reaching the next parturition. To avoid bias, a data augmentation technique was applied to censored data. Threshold models were used for CE and OFI. To consider that CE affects fertility and the genetic determination of CE and fertility traits, recursive models were applied, which simultaneously considered CE as a fixed effect on fertility performance and the existence of a genetic correlation between CE and fertility traits. The effects of CE score 3 (difficult birth) with respect to score 1 (no problem) for days to first service, DO, NINS, and OFI were 8 d, 31 d, 0.5 services, and -12% success at first insemination, respectively. These results showed poorer fertility after dystocia. Genetic correlations between genetic effects of fertility traits and CE were close to zero, except for the genetic correlations between direct effects of DO and CE, which were positive, moderate, and statistically different from 0 (0.47 ± 0.24), showing that genes associated with difficult births also reduce reproductive success. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the genetic and putative causal relationships between fertility traits [i.e., days open (DO) and calving rate (CR)] and milk quality, composition, and fatty acid contents (milk composition traits) in Holstein-Friesian, Brown Swiss, and Simmental cattle, using recursive models within a Bayesian framework. Trivariate animal models were run, each including one fertility trait, one milk composition trait, and, in all models, milk yield. The DO and CR data were merged with the test days closest to the insemination date for milk composition traits. After editing, 16,468 to 23,424 records for Holstein-Friesian, 23,424 to 46,660 for Brown Swiss, and 26,105 to 35,574 for Simmental were available for the analyses. Recursive animal models were applied to investigate the possible causal influences of milk composition traits on fertility and the genetic relationships among these traits. The results suggested a potential cause-and-effect relationship between milk composition traits and fertility traits, with the first trait influencing the latter. We also found greater recursive effects of milk composition traits on DO than on CR, the latter with some putative differences among breeds in terms of sensitivity. For instance, the putative causal effects of somatic cell score on CR (on the observed scale, %) varied from −0.96 to −1.39%, depending on the breed. Concerning fatty acids, we found relevant putative effects of C18:0 on CR, with estimates varying from −7.8 to −9.9%. Protein and casein percentages, and short-chain fatty acid showed larger recursive effects on CR, whereas fat, protein, and casein percentages, C16:0, C18:0, and long-chain fatty acid had larger effects on DO. The results obtained suggested that these milk traits could be considered as effective indicators of the effects of animal metabolic and physiological status on fertility. [ABSTRACT FROM AUTHOR]

In this study, we aimed to investigate differences in the genetics of fertility traits (heritability of traits and correlations between traits in divergent environments) in dairy cows of different production levels defined on the basis of the herd-average daily milk energy output (herd-dMEO). Data were obtained from Holstein-Friesian (n = 37,359 for fertility traits, 381,334 for dMEO), Brown Swiss (n = 79,638 for fertility traits, 665,697 for dMEO), and Simmental cows (n = 63,048 for fertility traits, 448,445 for dMEO) reared in northeastern Italy. Fertility traits under study were interval from calving to first service, interval from first service to conception, days open, calving interval, calving rate, and nonreturn rate at d 56. We classified herds into low and high productivity based on the herd-average dMEO (inferred using mixed effects models). We estimated genetic parameters using Bayesian bivariate animal models, where expressions of a phenotype in the low and high dMEO herds were taken as being different—albeit correlated—traits. Fertility traits were more favorable in Simmental than in Holstein-Friesian cows, whereas for all traits, Holstein-Friesian had the highest estimates of intraherd heritability [ranging from 0.021 (0.006–0.038) to 0.126 (0.10–0.15)] and Simmental the lowest [ranging from 0.008 (0.001–0.017) to 0.101 (0.08–0.12)]. The genetic correlations between fertility traits and dMEO were moderate and unfavorable, ranging, in absolute values, from 0.527 (0.37–0.68) to 0.619 (0.50–0.73) in Holstein-Friesian; from 0.339 (0.20–0.47) to 0.556 (0.45–0.66) in Brown Swiss; and from 0.340 (0.10–0.60) to 0.475 (0.33–0.61) in Simmental cattle. The only exception was the nonreturn rate at d 56, which had weak genetic correlations with dMEO in all 3 breeds. The herd correlations between fertility and dMEO tended to be modest and favorable and the residual correlations modest and variable. The heritability of fertility traits tended to be greater in the low dMEO than in the high dMEO herds in the case of the Holstein-Friesians, but not in the case of the Brown Swiss or Simmentals. The additive genetic correlations between fertility traits in the low and high dMEO herds were always lower than 1 [0.329 (−0.17 to 0.85) to 0.934 (0.86 to 0.99)] for all traits considered in all breeds. The correlation was particularly low for the threshold characters and the interval from first service to conception in Holstein-Friesian, suggesting that the relative performances of genotypes vary significantly between herds of different dMEO levels. Although there was large variability in the estimates, results might support making separate genetic evaluations of fertility in the different herd production groups. Our results also indicate that Simmental, a dual-purpose breed, has higher fertility and lower environmental sensitivity than Holstein-Friesian, with Brown Swiss being intermediate. [ABSTRACT FROM AUTHOR]

This study analyzed the haplotype diversity along the genome of seven Spanish Beef Cattle populations within regions of 500 kb using the information provided by the BovineHD Beadchip. The results of the analysis pointed out a strong variability of the haplotype diversity across the genome, which is greatly conserved across populations. This strong concordance between populations suggests that the reasons behind it are intrinsic to the structure of the bovine genome and caused probably by the mutation or recombination rate. Nevertheless, some of the genomic regions with very large haplotype diversity are also due of genome assembly errors. [ABSTRACT FROM AUTHOR]

The objective of this study was to estimate the genetic variation of ovine milk fatty acid (FA) composition. We collected 4,100 milk samples in 14 herds from 976 Churra ewes sired mostly by 15 AI rams and analyzed them by gas-liquid chromatography for milk fatty acid composition. The studied traits were 12 individual FA contents (proportion in relation to the total amount of FA), 3 groups of fatty acids [saturated fatty acids (SFA), monounsaturated FA (MUFA), and polyunsaturated FA (PUFA)], and 2 FA ratios (n-6:n-3 and C18:2 cis-9,trans-11:C18:1 trans-11). In addition, percentages of fat and protein and daily milk yield were studied. For the analysis, repeatability animal models were implemented using Bayesian methods. In an initial step, univariate methods were conducted to test the hypothesis of the traits showing additive genetic determination. Deviance information criterion and Bayes factor were employed as model choice criteria. All the studied SFA showed additive genetic variance, but the estimated heritabilities were low. Among unsaturated FA (UFA), only C18:1 trans-11 and C18:2 cis-9,cis-12 showed additive genetic variation, their estimated heritabilities being [marginal posterior mean (marginal posterior SD)] 0.02(0.01) and 0.11(0.04), respectively. For the FA groups, only PUFA showed significant additive genetic variation. None of the studied ratios of FA showed additive genetic variation. In second multitrait analyses, genetic correlations between individual FA and production traits, and between groups of FA and ratios of FA and production traits, were investigated. Positive genetic correlations were estimated among medium-chain SFA, ranging from 0 to 0.85, but this parameter was close to zero between long-chain SFA (C16:0 and C18:0). Between long- and medium-chain SFA, estimated genetic correlations were negative, around -0.6. Among those UFA showing significant additive genetic variance, genetic correlations were close to zero. The estimated genetic correlations among all the investigated FA, milk yield, and fat and protein percentages were not different from zero. Our results suggest that low additive genetic variation is involved in the determination of the FA composition of milk fat in Churra sheep under current production conditions, which results in low values of heritabilities. [ABSTRACT FROM AUTHOR]

Abstract: The duration of nocturnal melatonin secretion reaches its minimum in summer, a physiological event that is likely related with the diminished sow fertility and delayed puberty typically observed in this season. Melatonin exerts its function by binding two different receptors named as MTNR1A and MTNR1B. Interestingly, the MTNR1A gene is located on a chromosome SSC17 region where QTL for prolificacy traits have been detected in previous studies. In this work, we have found a synonymous T162C polymorphism at exon 2 of the pig MTNR1A gene. An association analysis between this polymorphism and sow prolificacy in an Iberian× Meishan intercross was performed. The utilization of four statistical models of increasing complexity demonstrated that the MTNR1A gene has both additive and dominant effects on total number of born piglets (TNB) and number of piglets born alive (NBA). Additive effects were significant in summer (TNB, P <0.01; NBA, P <0.001), whereas dominant effects reached significance both in fall (TNB, P <0.01; NBA, P <0.05) and in winter (TNB, P <0.001; NBA, P <0.05). The seasonal variation observed for MTNR1A additive and dominant effects might be produced by the influence of photoperiod on the pattern and duration of melatonin secretion. These results illustrate that the complex interaction between genotype and environment can be an important source of phenotypic variation of reproductive traits. [Copyright &y& Elsevier]