The impact of FSH stimulation and age on the ovarian and uterine traits and histomorphometry of prepubertal gilts.

This study investigated the effect of age and follicle stimulating hormone (FSH) treatment on the estradiol (E2) plasma concentration, ovarian follicle development, endometrial histomorphometry, and ultrasonographic parameters of the ovaries and uterus in prepubertal gilts. Thirty-five prepubertal gilts were grouped according to age (140 or 160 d), and within each age, gilts were allotted to receive 100 mg of FSH (treated; G140 + FSH [n = 10] and G160 + FSH [n = 7]) or saline solution (control; G140 + control [n = 10] and G160 + control [n = 8]). The total dose of FSH was divided into 6 similar doses administered every 8 h (days 0-2). Before and after FSH treatment, blood sample was collected, and transabdominal scanning of the ovaries and uterus was performed. Twenty-four hours after the last FSH injection, the gilts were slaughtered and their ovaries and uterus were processed for histological and histomorphometric analysis. The histomorphometric parameters of the uterus differed (P < 0.05) between prepubertal gilts at 160 d and 140 d of age. Moreover, changes (P < 0.05) in uterine and ovarian ultrasound images occurred between 140 and 160 d of age. Age and FSH treatment did not affect (P > 0.05) E2 plasma concentrations. Follicle stimulating hormone treatment did not affect (P > 0.05) the early stage of folliculogenesis in the prepubertal gilts; however, the number of early atretic follicles decreased (P < 0.05) after the FSH treatment. Follicle stimulating hormone administration increased (P < 0.05) the number of medium follicles and decreased (P < 0.05) the number of small follicles in 140 and 160 d old gilts. In the endometrium, luminal/glandular epithelium height and glandular diameter increased (P < 0.05) after FSH treatment. Thus, injections of 100 mg of FSH stimulate the endometrium epithelium and induce follicular growth to a medium follicle size without affecting the preantral stages in prepubertal gilts; also, the uterine macroscopic morphometry does not change from 140 to 160 d of age.

A natural knockout of the MYO7A gene leads to pre-weaning mortality in pigs.

The pig breeding system provides a unique framework to study recessive defects and the consequence on the phenotype. We examined a commercial synthetic Duroc population for recessive defects and identified a haplotype on chromosome 9 significantly affecting pre-weaning mortality. To identify the causal variant underlying the mortality, we examined sequence data of four carrier animals and 21 non-carrier animals from the same population. The results yield a strong candidate causal stop-gained variant (NM_001099928.1:c.541C>T) affecting the MYO7A gene in complete linkage disequilibrium with the lethal haplotype. The variant leads to an impaired (p.Gln181*) MYO7A protein that truncates 2032 amino acids from the protein. We examined a litter from a carrier sow inseminated by a carrier boar. From the resulting piglets, two confirmed homozygous piglets suffered from severe balance difficulties and the inability to walk properly. The variant segregates at a carrier frequency of 8.2% in the evaluated population and will be gradually purged from the population, improving animal welfare. Finally, this 'natural knockout' will increase our understanding of the functioning of the MYO7A gene and provides a potential model for Usher syndrome in humans.

A genome-wide association study for feed efficiency-related traits in a crossbred pig population.

Feed efficiency (FE) is one of the most important traits in pig production. However, it is difficult and costly to measure it, limiting the collection of large amount of data for an accurate selection for better FE. Therefore, the identification of single-nucleotide polymorphisms (SNPs) associated with FE-related traits to be used in the genetic evaluation is of great interest of pig breeding programs for increasing the prediction accuracy and the genetic progress of these traits. The objective of this study was to identify SNPs significantly associated with FE-related traits: average daily gain (ADG), average daily feed intake (ADFI) and feed conversion ratio (FCR). We also aimed to identify potential candidate genes for these traits. Phenotypic information recorded on a population of 2386 three-way crossbreed pigs that were genotyped for 51 468 SNPs was used. We identified three loci of quantitative trait (QTL) regions associated with ADG and three QTL regions associated with ADFI; however, no significant association was found for FCR. A false discovery rate (FDR) ≤ 0.005 was used as the threshold for declaring an association as significant. The QTL regions associated with ADG on Sus scrofa chromosome (SSC) 1 were located between 177.01 and 185.47 Mb, which overlaps with the QTL regions for ADFI on SSC1 (173.26 and 185.47 Mb). The other QTL region for ADG was located on SSC12 (2.87 and 3.22 Mb). The most significant SNPs in these QTL regions explained up to 3.26% of the phenotypic variance of these traits. The non-identification of genomic regions associated with FCR can be explained by the complexity of this trait, which is a ratio between ADG and ADFI. Finally, the genes CDH19, CDH7, RNF152, MC4R, PMAIP1, FEM1B and GAA were the candidate genes found in the 1 Mb window around the QTL regions identified in this study. Among them, the MC4R gene (SSC1) has a well-known function related to ADG and ADFI. In this study, we identified three QTL regions for ADG (SSC1 and SSC12) and three for ADFI (SSC1). These regions were previously described in purebred pig populations; however, to our knowledge, this is the first study to confirm the relevance of these QTL regions in a crossbred pig population. The potential use of the SNPs and genes identified in this study in prediction models that combine genomic selection and marker-assisted selection should be evaluated for increasing the prediction accuracy of these traits in this population.

Genetic correlations between feed efficiency traits, and growth performance and carcass traits in purebred and crossbred pigs.

Selection for feed efficiency (FE) is a strategy to reduce the production costs per unit of animal product, which is one of the major objectives of current animal breeding programs. In pig breeding, selection for FE and other traits traditionally takes place based on purebred pig (PB) performance at the nucleus level, while pork production typically makes use of crossbred animals (CB). The success of this selection, therefore, depends on the genetic correlation between the performance of PB and CB (rpc) and on the genetic correlation (rg) between FE and the other traits that are currently under selection. Different traits are being used to account for FE, but the rpc has been reported only for feed conversion rate. Therefore, this study aimed 1) to estimate the rpc for growth performance, carcass, and FE traits; 2) to estimate rg between traits within PB and CB populations; and 3) to compare three different traits representing FE: feed conversion rate, residual energy intake (REI), and residual feed intake (RFI). Phenotypes of 194,445 PB animals from 23 nucleus farms, and 46,328 CB animals from three farms where research is conducted under near commercial production conditions were available for this study. From these, 22,984 PB and 8,657 CB presented records for feed intake. The PB population consisted of five sire and four dam lines, and the CB population consisted of terminal cross-progeny generated by crossing sires from one of the five PB sire lines with commercially available two-way maternal sow crosses. Estimates of rpc ranged from 0.61 to 0.71 for growth performance traits, from 0.75 to 0.82 for carcass traits, and from 0.62 to 0.67 for FE traits. Estimates of rg between growth performance, carcass, and FE traits differed within PB and CB. REI and RFI showed substantial positive rg estimates in PB (0.84) and CB (0.90) populations. The magnitudes of rpc estimates indicate that genetic progress is being realized in CB at the production level from selection on PB performance at nucleus level. However, including CB phenotypes recorded on production farms, when predicting breeding values, has the potential to increase genetic progress for these traits in CB. Given the genetic correlations with growth performance traits and the genetic correlation between the performance of PB and CB, REI is an attractive FE parameter for a breeding program.

Genetic parameters for semen quality and quantity traits in five pig lines.

We aimed to estimate genetic parameters for semen quality and quantity traits as well as for within-boar variation of these traits to evaluate their inclusion in breeding goals. Genetic parameters were estimated within line using a multiple-trait (4 × 4) repeatability animal model fitted for 5 pig lines, considering 4 semen traits: sperm motility (MOT), sperm progressive motility (PROMOT), log-transformed number of sperm cells per ejaculate (lnN), and total morphological abnormalities (ABN). The within-boar variation of these traits was analyzed based on a multiple-trait (2 × 2) approach for SD and average (AVG) and a single-trait analysis for CV. The average heritabilities across the 5 lines estimated by multiple-trait analysis were 0.18 ± 0.07 (MOT), 0.22 ± 0.08 (PROMOT), 0.16 ± 0.04 (lnN), and 0.20 ± 0.04 (ABN). The average genetic correlations were favorable between MOT and PROMOT (0.86 ± 0.10), between MOT and ABN (-0.66 ± 0.25), and between PROMOT and ABN (-0.65 ± 0.25). As determined by within-boar variation analysis, AVG exhibited the greatest heritabilities followed by SD and CV, respectively, for the traits MOT and ABN. For PROMOT, average SD heritability was lower than CV heritability, whereas for lnN, they were the same. The average genetic correlations between AVG and SD were favorable for MOT (-0.60 ± 0.13), PROMOT (-0.79 ± 0.14), and ABN (0.78 ± 0.17). The moderate heritabilities indicate the possibility of effective selection of boars based on semen traits. Average and SD are proposed as appropriate traits for selection regarding uniformity.

The bone degenerative processes in senile fishes from Holocene Brazilian shell mounds.

Zooarchaeological collections from shell mounds in Rio de Janeiro (2,470-4,632 cal BP) contain a high prevalence of swollen fish bones belonging to the Atlantic spadefish (Chaetodipterus faber), crevalle jack (Caranx hippos) and fat snook (Centropomus parallelus). Given the lack of knowledge of the bone degenerative process in senile fishes, this study analysed hyperostotic bone in zooarchaeological and modern specimens to obtain high-resolution morphology and microstructure reconstruction. We used microCT as well as X-ray diffraction to characterize the crystallographic changes associated with fish senility. Our results showed that trabecular microstructures in hyperostotic bones were consistent with estimated values of the per cent bone volume-to-total volume ratio (BV/TV) and were greater than 60% in cortical bone. Hyperostotic bones indicated a high radiograph density, and X-ray diffractograms showed a decrease in hydroxyapatite [Ca10 (PO4 )6 (OH)2 ] and calcite (CaCO3 ) neocrystallization. These crystalline and density changes revealed an advanced stage of fish senile and indicate the vulnerability of ageing fish populations.

A major gene for host response to porcine reproductive and respiratory syndrome is not unfavorably associated with overall performance under nonchallenging conditions in commercial pig lines.

A QTL for host response to porcine reproductive and respiratory syndrome (PRRS) was identified in a previous study. The SNP WUR10000125 (WUR) is used as a tag SNP for this QTL. The favorable (B) allele at this SNP is in low frequency in commercial populations, possibly because this allele is unfavorably associated with an important trait under nonchallenging conditions and, therefore, may have been selected against. Therefore, objectives of this study were to estimate the effect of WUR on traits under selection in commercial lines under nonchallenging conditions and to estimate the effect of WUR genotype of parents on performance of crossbred progeny in a commercial-like environment. Data were collected on 4 purebred lines: a Landrace dam line (D1), a Large White dam line (D2), a synthetic sire line (S1), and a Pietrain sire line (S2). Traits analyzed included total number born, number stillborn, farrowing survival, lactation survival, litter mortality, daily feed intake (DFI), backfat, average test daily gain (TGR), average lifetime daily gain (LGR), and Topigs Norsvin selection index (TSI) value, indicative of overall economic value. Deregressed EBV were calculated for each trait (except TSI) and analyzed within line. In the S1 line, AB and BB pigs had significantly lower TGR ( = 0.002) and LGR ( = 0.001) than AA pigs but also lower DFI ( = 0.004). Conversely, AB and BB pigs had significantly higher DFI ( < 0.001) and AB pigs had significantly higher TGR ( = 0.03) than AA pigs in the S2 line. The effect of WUR on TSI was not significant for any line ( ≥ 0.15). Analyses of phenotypic records collected on crossbred progeny of S1 sires and D1 × D2 F females showed no significant effect of parent WUR genotype on DFI, backfat, TGR, or LGR ( ≥ 0.07). In conclusion, the effect of WUR was nonsignificant for most traits but the magnitude and direction of the effect differed by trait and by line. The favorable allele for host response to PRRS was associated with greater DFI and a tendency for greater TGR in the S2 line, but the opposite direction of effect was detected for the S1 line. Regardless of the effect on individual traits, no significant effect of WUR on TSI was detected for any line. Therefore, selecting for the B allele is expected to result in progeny with increased resistance to PRRS without compromising overall economic value under normal, nonchallenging conditions.

Using markers with large effect in genetic and genomic predictions.

The first attempts of applying marker-assisted selection (MAS) in animal breeding were not very successful because the identification of markers closely linked to QTL using low-density microsatellite panels was difficult. More recently, the use of high-density SNP panels in genome-wide association studies (GWAS) have increased the power and precision of identifying markers linked to QTL, which offer new possibilities for MAS. However, when GWAS started to be performed, the focus of many breeders had already shifted from the use of MAS to the application of genomic selection (using all available markers without any preselection of markers linked to QTL). In this study, we aimed to evaluate the prediction accuracy of a MAS approach that accounts for GWAS findings in the prediction models by including the most significant SNP from GWAS as a fixed effect in the marker-assisted BLUP (MA-BLUP) and marker-assisted genomic BLUP (MA-GBLUP) prediction models. A second aim was to compare the prediction accuracies from the marker-assisted models with those obtained from a Bayesian variable selection (BVS) model. To compare the prediction accuracies of traditional BLUP, MA-BLUP, genomic BLUP (GBLUP), MA-GBLUP, and BVS, we applied these models to the trait "number of teats" in 4 distinct pig populations, for validation of the results. The most significant SNP in each population was located at approximately 103.50 Mb on chromosome 7. Applying MAS by accounting for the most significant SNP in the prediction models resulted in improved prediction accuracy for number of teats in all evaluated populations compared with BLUP and GBLUP. Using MA-BLUP instead of BLUP, the increase in prediction accuracy ranged from 0.021 to 0.124, whereas using MA-GBLUP instead of GBLUP, the increase in prediction accuracy ranged from 0.003 to 0.043. The BVS model resulted in similar or higher prediction accuracies than MA-GBLUP. For the trait number of teats, BLUP resulted in the lowest prediction accuracies whereas the highest were observed when applying MA-GBLUP or BVS. In the same data set, MA-BLUP can yield similar or superior accuracies compared with GBLUP. The superiority of MA-GBLUP over traditional GBLUP is more pronounced when training populations are smaller and when relationships between training and validation populations are smaller. Marker-assisted GBLUP did not outperform BVS but does have implementation advantages in large-scale evaluations.

Including crossbred pigs in the genomic relationship matrix through utilization of both linkage disequilibrium and linkage analysis.

In pig breeding, the final product is a crossbred (CB) animal, while selection is performed at the purebred (PB) level using mainly PB data. However, incorporating CB data in genetic evaluations is expected to result in greater genetic progress at the CB level. Currently, there is no optimal way to include CB genotypes into the genomic relationship matrix. This is because, in single-step genomic BLUP, which is the most commonly used method, genomic and pedigree relationships must refer to the same base. This may not be the case when several breeds and CB are included. An alternative to overcome this issue may be to use a genomic relationship matrix (G matrix) that accounts for both linkage disequilibrium (LD) and linkage analysis (LA), called G. The objectives of this study were to further develop the G matrix approach to utilize both PB and CB genotypes simultaneously, to investigate its performance, and the general added value of including CB genotypes in genomic evaluations. Data were available on Dutch Landrace, Large White, and the F1 cross of those breeds. In total, 7 different G matrix compositions (PB alone, PB together, each PB with the CB, all genotypes across breeds, and G) were tested on 3 maternal traits: total number born (TNB), live born (LB), and gestation length (GL). Results show that G gave the greatest prediction accuracy of all the relationship matrices tested for PB prediction, but not for CB prediction. Including CB genotypes in general increased prediction accuracy for all breeds. However, in some cases, these increases in prediction accuracy were not significant (at < 0.05). To conclude, CB genotypes increased prediction accuracy for some of the traits and breeds, but not for all. The G matrix had significantly greater prediction accuracy in PB than the other G matrix with both PB and CB genotypes, except in one case. While for CB, the G matrix with genotypes across all breeds gave the greatest accuracy, though this was not significantly different from G. Computation time was high for G, and research will be needed to reduce its computational costs to make it feasible for use in routine evaluations. The main conclusion is that inclusion of CB genotypes is beneficial for both PB and CB animals.

Accounting for genetic architecture in single- and multipopulation genomic prediction using weights from genomewide association studies in pigs.

We studied the effect of including GWAS results on the accuracy of single- and multipopulation genomic predictions. Phenotypes (backfat thickness) and genotypes of animals from two sire lines (SL1, n = 1146 and SL3, n = 1264) were used in the analyses. First, GWAS were conducted for each line and for a combined data set (both lines together) to estimate the genetic variance explained by each SNP. These estimates were used to build matrices of weights (D), which was incorporated into a GBLUP method. Single population evaluated with traditional GBLUP had accuracies of 0.30 for SL1 and 0.31 for SL3. When weights were employed in GBLUP, the accuracies for both lines increased (0.32 for SL1 and 0.34 for SL3). When a multipopulation reference set was used in GBLUP, the accuracies were higher (0.36 for SL1 and 0.32 for SL3) than in single-population prediction. In addition, putting together the multipopulation reference set and the weights from the combined GWAS provided even higher accuracies (0.37 for SL1, and 0.34 for SL3). The use of multipopulation predictions and weights estimated from a combined GWAS increased the accuracy of genomic predictions.

After genome-wide association studies: Gene networks elucidating candidate genes divergences for number of teats across two pig populations.

Number of teats (NT) is an important trait affecting both piglet's welfare and the production level of pig farms. Biologically, embryonic mammary gland development requires the coordination of many signaling pathways necessary for the proper development of teats. Several QTL for NT have been identified; however, further analysis is still lacking. Therefore, gene networks derived from genomewide association study (GWAS) results can be used to examine shared pathways and functions of putative candidate genes. Besides, such analyses may also be helpful to understand the genetic diversity between populations for the same trait or traits. In this study, we identified significant SNP for Landrace-based (line C) and Large White-based (line D) dam lines. Besides, gene-transcription factor (TF) networks were constructed aiming to obtain the most likely candidate genes for NT in each line followed by a comparative analysis between both lines to access similarities or dissimilarities at the marker and gene level. We identified 24 and 19 significant SNP (Bayes factor ≥ 100) for lines C and D, respectively. Only 1 significant SNP overlapped both lines. Network analysis illustrated gene interactions consistent with known mammal's breast biology and captured known TF. We observed different sets of putative candidate genes for NT in each line evaluated that may have common effects on the phenotype. Based on these results, we demonstrated the importance of post-GWAS analyses increasing the biological understanding of relevant genes for a complex trait. Moreover, we believe that this genomic diversity across lines should be taken into account, considering breed-specific reference populations for genomic selection.

Accuracy of genomic prediction of purebreds for cross bred performance in pigs.

In pig breeding, as the final product is a cross bred (CB) animal, the goal is to increase the CB performance. This goal requires different strategies for the implementation of genomic selection from what is currently implemented in, for example dairy cattle breeding. A good strategy is to estimate marker effects on the basis of CB performance and subsequently use them to select pure bred (PB) breeding animals. The objective of our study was to assess empirically the predictive ability (accuracy) of direct genomic values of PB for CB performance across two traits using CB and PB genomic and phenotypic data. We studied three scenarios in which genetic merit was predicted within each population, and four scenarios where PB genetic merit for CB performance was predicted based on either CB or a PB training data. Accuracy of prediction of PB genetic merit for CB performance based on CB training data ranged from 0.23 to 0.27 for gestation length (GLE), whereas it ranged from 0.11 to 0.22 for total number of piglets born (TNB). When based on PB training data, it ranged from 0.35 to 0.55 for GLE and from 0.30 to 0.40 for TNB. Our results showed that it is possible to predict PB genetic merit for CB performance using CB training data, but predictive ability was lower than training using PB training data. This result is mainly due to the structure of our data, which had small-to-moderate size of the CB training data set, low relationship between the CB training and the PB validation populations, and a high genetic correlation (0.94 for GLE and 0.90 for TNB) between the studied traits in PB and CB individuals, thus favouring selection on the basis of PB data.

Genomic prediction of growth in pigs based on a model including additive and dominance effects.

Independent of whether prediction is based on pedigree or genomic information, the focus of animal breeders has been on additive genetic effects or 'breeding values'. However, when predicting phenotypes rather than breeding values of an animal, models that account for both additive and dominance effects might be more accurate. Our aim with this study was to compare the accuracy of predicting phenotypes using a model that accounts for only additive effects (MA) and a model that accounts for both additive and dominance effects simultaneously (MAD). Lifetime daily gain (DG) was evaluated in three pig populations (1424 Pietrain, 2023 Landrace, and 2157 Large White). Animals were genotyped using the Illumina SNP60K Beadchip and assigned to either a training data set to estimate the genetic parameters and SNP effects, or to a validation data set to assess the prediction accuracy. Models MA and MAD applied random regression on SNP genotypes and were implemented in the program Bayz. The additive heritability of DG across the three populations and the two models was very similar at approximately 0.26. The proportion of phenotypic variance explained by dominance effects ranged from 0.04 (Large White) to 0.11 (Pietrain), indicating that importance of dominance might be breed-specific. Prediction accuracies were higher when predicting phenotypes using total genetic values (sum of breeding values and dominance deviations) from the MAD model compared to using breeding values from both MA and MAD models. The highest increase in accuracy (from 0.195 to 0.222) was observed in the Pietrain, and the lowest in Large White (from 0.354 to 0.359). Predicting phenotypes using total genetic values instead of breeding values in purebred data improved prediction accuracy and reduced the bias of genomic predictions. Additional benefit of the method is expected when applied to predict crossbred phenotypes, where dominance levels are expected to be higher.

Genome-wide association study reveals regions associated with gestation length in two pig populations.

Reproduction traits, such as gestation length (GLE), play an important role in dam line breeding in pigs. The objective of our study was to identify single nucleotide polymorphisms (SNPs) that are associated with GLE in two pig populations. Genotypes and deregressed breeding values were available for 2081 Dutch Landrace-based (DL) and 2301 Large White-based (LW) pigs. We identified two QTL regions for GLE, one in each population. For DL, three associated SNPs were detected in one QTL region spanning 0.52 Mbp on Sus scrofa chromosome (SSC) 2. For LW, four associated SNPs were detected in one region of 0.14 Mbp on SSC5. The region on SSC2 contains the heparin-binding EGF-like growth factor (HBEGF) gene, which promotes embryo implantation and has been described to be involved in embryo survival throughout gestation. The associated SNP can be used for marker-assisted selection in the studied populations, and further studies of the HBEGF gene are warranted to investigate its role in GLE.

Genome-wide association study reveals novel loci for litter size and its variability in a Large White pig population.

BACKGROUND: In many traits, not only individual trait levels are under genetic control, but also the variation around that level. In other words, genotypes do not only differ in mean, but also in (residual) variation around the genotypic mean. New statistical methods facilitate gaining knowledge on the genetic architecture of complex traits such as phenotypic variability. Here we study litter size (total number born) and its variation in a Large White pig population using a Double Hierarchical Generalized Linear model, and perform a genome-wide association study using a Bayesian method. RESULTS: In total, 10 significant single nucleotide polymorphisms (SNPs) were detected for total number born (TNB) and 9 SNPs for variability of TNB (varTNB). Those SNPs explained 0.83 % of genetic variance in TNB and 1.44 % in varTNB. The most significant SNP for TNB was detected on Sus scrofa chromosome (SSC) 11. A possible candidate gene for TNB is ENOX1, which is involved in cell growth and survival. On SSC7, two possible candidate genes for varTNB are located. The first gene is coding a swine heat shock protein 90 (HSPCB = Hsp90), which is a well-studied gene stabilizing morphological traits in Drosophila and Arabidopsis. The second gene is VEGFA, which is activated in angiogenesis and vasculogenesis in the fetus. Furthermore, the genetic correlation between additive genetic effects on TNB and on its variation was 0.49. This indicates that the current selection to increase TNB will also increase the varTNB. CONCLUSIONS: To the best of our knowledge, this is the first study reporting SNPs associated with variation of a trait in pigs. Detected genomic regions associated with varTNB can be used in genomic selection to decrease varTNB, which is highly desirable to avoid very small or very large litters in pigs. However, the percentage of variance explained by those regions was small. The SNPs detected in this study can be used as indication for regions in the Sus scrofa genome involved in maintaining low variability of litter size, but further studies are needed to identify the causative loci.

Accuracy of genome-enabled prediction exploring purebred and crossbred pig populations.

Pig breeding companies keep relatively small populations of pure sire and dam lines that are selected to improve the performance of crossbred animals. This design of the pig breeding industry presents challenges to the implementation of genomic selection, which requires large data sets to obtain highly accurate genomic breeding values. The objective of this study was to evaluate the impact of different reference sets (across population and multipopulation) on the accuracy of genomic breeding values in 3 purebred pig populations and to assess the potential of using crossbreed performance in genomic prediction. Data consisted of phenotypes and genotypes on animals from 3 purebred populations (sire line [SL] 1, = 1,146; SL2, = 682; and SL3, = 1,264) and 3 crossbred pig populations (Terminal cross [TER] 1, = 183; TER2, = 106; and TER3, = 177). Animals were genotyped using the Illumina Porcine SNP60 Beadchip. For each purebred population, within-, across-, and multipopulation predictions were considered. In addition, data from the paternal purebred populations were used as a reference set to predict the performance of crossbred animals. Backfat thickness phenotypes were precorrected for fixed effects and subsequently included in the genomic BLUP model. A genomic relationship matrix that accounted for the differences in allele frequencies between lines was implemented. Accuracies of genomic EBV obtained within the 3 different sire lines varied considerably. For within-population prediction, SL1 showed higher values (0.80) than SL2 (0.61) and SL3 (0.67). Multipopulation predictions had accuracies similar to within-population accuracies for the validation in SL1. For SL2 and SL3, the accuracies of multipopulation prediction were similar to the within-population prediction when the reference set was composed by 900 animals (600 of the target line plus 300 of another line). For across-population predictions, the accuracy was mostly close to zero. The accuracies of predicting crossbreed performance were similar for the 3 different crossbred populations (ranging from 0.25 to 0.29). In summary, the differences in accuracy of the within-population scenarios may be due to line divergences in heritability and genetic architecture of the trait. Within- and multipopulation predictions yield similar accuracies. Across-population prediction accuracy was negligible. The moderate accuracy of prediction of crossbreed performance appears to be a result of the relationship between the crossbreed and its parental lines.

Accuracy of genomic prediction using deregressed breeding values estimated from purebred and crossbred offspring phenotypes in pigs.

Genomic selection is applied to dairy cattle breeding to improve the genetic progress of purebred (PB) animals, whereas in pigs and poultry the target is a crossbred (CB) animal for which a different strategy appears to be needed. The source of information used to estimate the breeding values, i.e., using phenotypes of CB or PB animals, may affect the accuracy of prediction. The objective of our study was to assess the direct genomic value (DGV) accuracy of CB and PB pigs using different sources of phenotypic information. Data used were from 3 populations: 2,078 Dutch Landrace-based, 2,301 Large White-based, and 497 crossbreds from an F1 cross between the 2 lines. Two female reproduction traits were analyzed: gestation length (GLE) and total number of piglets born (TNB). Phenotypes used in the analyses originated from offspring of genotyped individuals. Phenotypes collected on CB and PB animals were analyzed as separate traits using a single-trait model. Breeding values were estimated separately for each trait in a pedigree BLUP analysis and subsequently deregressed. Deregressed EBV for each trait originating from different sources (CB or PB offspring) were used to study the accuracy of genomic prediction. Accuracy of prediction was computed as the correlation between DGV and the DEBV of the validation population. Accuracy of prediction within PB populations ranged from 0.43 to 0.62 across GLE and TNB. Accuracies to predict genetic merit of CB animals with one PB population in the training set ranged from 0.12 to 0.28, with the exception of using the CB offspring phenotype of the Dutch Landrace that resulted in an accuracy estimate around 0 for both traits. Accuracies to predict genetic merit of CB animals with both parental PB populations in the training set ranged from 0.17 to 0.30. We conclude that prediction within population and trait had good predictive ability regardless of the trait being the PB or CB performance, whereas using PB population(s) to predict genetic merit of CB animals had zero to moderate predictive ability. We observed that the DGV accuracy of CB animals when training on PB data was greater than or equal to training on CB data. However, when results are corrected for the different levels of reliabilities in the PB and CB training data, we showed that training on CB data does outperform PB data for the prediction of CB genetic merit, indicating that more CB animals should be phenotyped to increase the reliability and, consequently, accuracy of DGV for CB genetic merit.

Disinfection of corrugated tubing by ozone and ultrasound in mechanically ventilated tracheostomized patients.

BACKGROUND: Medical equipment coming into contact with non-intact skin or mucous membranes is classified as semi-critical material. This equipment requires at least high-level disinfection, as the major risk in all invasive procedures is the introduction of pathogenic microbes causing hospital-associated infections. AIM: To evaluate the capacity of ozone gas and ultrasound to disinfect semi-critical, thermally sensitive material. METHODS: Used corrugated tubing from mechanically ventilated tracheostomized patients in the intensive care unit was obtained. Enzymatic detergent was applied for 15min before different disinfection techniques were evaluated as follows: Group A (0.2% peracetic acid); Group B (ultrasound for 60min); Group C (application of ozone gas at a concentration of 33mg/L for 15min); Group D (ultrasound for 30min and ozone for 15min); Group E (ultrasound for 60min and ozone for 15min). FINDINGS: Application of ultrasound for 60min reduced the level of microbial contamination by 4 log10, whereas ozone alone and the other two combined techniques (ultrasound and ozone) and the peracetic acid reduced the level of microbial contamination by 5 log10. CONCLUSION: Ozone was the most advantageous technique taking into consideration processing time, ease of use, effectiveness, and cost. The use of ozone gas to disinfect semi-critical material proved to be technically feasible and extremely promising.

Genetic characterization of the wheat association mapping initiative (WAMI) panel for dissection of complex traits in spring wheat.

KEY MESSAGE: The wheat association mapping initiative is appropriate for gene discovery without the confounding effects of phenology and plant height. The wheat association mapping initiative (WAMI) population is a set of 287 diverse advanced wheat lines with a narrow range of variation for days to heading (DH) and plant height (PH). This study aimed to characterize the WAMI and showed that this diverse panel has a favorable genetic background in which stress adaptive traits and their alleles contributing to final yield can be identified with reduced confounding major gene effects through genome-wide association studies (GWAS). Using single nucleotide polymorphism (SNP) markers, we observed lower gene diversity on the D genome, compared with the other genomes. Population structure was primarily related to the distribution of the 1B.1R rye translocation. The narrow range of variation for DH and PH in the WAMI population still entailed segregation for a few markers associated with the former traits, while Rht genes were associated with grain yield (GY). Genotype by environment (G × E) interaction for GY was primarily explained by Rht-B1, Vrn-A1 and markers on chromosomes 2D and 3A when running GWAS with genotype scores from the G × E biplot. The use of PC scores from the G × E biplot seems a promising tool to determine genes and markers associated with complex interactions across environments. The WAMI panel lends itself to GWAS for complex trait dissection by avoiding the confounding effects of DH and PH which were reduced to a minimum (using Rht-B1 and Vrn-A1 scores as covariables), with significant associations with GY on chromosomes 2D, 3A and 3B.

A genome-wide association study reveals a novel candidate gene for sperm motility in pigs.

Sperm motility is one of the most widely used parameters in order to evaluate boar semen quality. However, this trait can only be measured after puberty. Thus, the use of genomic information appears as an appealing alternative to evaluate and improve selection for boar fertility traits earlier in life. With this study we aimed to identify SNPs with significant association with sperm motility in two different commercial pig populations and to identify possible candidate genes within the identified QTL regions. We performed a single-SNP genome-wide association study using genotyped animals from a Landrace-based (L1) and a Large White-based (L2) pig populations. For L1, a total of 602 animals genotyped for 42,551 SNPs were used in the association analysis. For L2, a total of 525 animals genotyped for 40,890 SNPs were available. After the association analysis, a false discovery rate q-value ≤0.05 was used as the threshold for significant association. No SNPs were significantly associated with sperm motility in L1, while six SNPs on Sus scrofa chromosome 1 (position 117.26-119.56Mb) were significant in L2. The mitochondrial methionyl-tRNA formyltransferase (MTFMT) gene, which affects translation efficiency of proteins in sperm cells, was identified as a putative candidate gene. The significant markers identified in this study may be useful to enhance the genetic improvement of sperm motility by selection of boars at an earlier age under a marker assisted selection strategy.