Genome-wide association study identifies quantitative trait loci regions involved in muscle acidic profile in Large White heavy pigs.

The widespread use of genome-wide association studies resulted in the discovery of genomic regions associated with fatty acid (FA) composition in different porcine tissues, but little information exists about the genes involved in FA composition of meat obtained from heavy pigs selected for the production of Italian dry-cured hams. To this objective, we genotyped with a single nucleotide polymorphism (SNP) panel 795 Italian Large White heavy pigs to identify the markers and genomic regions associated with Semimembranosus muscle FA profile. Heritability estimates for intramuscular fat FA profile were of low-to-moderate magnitude, suggesting that these traits may be improved with genomic selection. On the whole, 45 SNPs were significantly associated with 14 FAs, and 4 of them (ALGA008109, ALGA0081097, CASI0010164 and SIRI0000267) were associated with more than 1 FA. The palmitoleic : palmitic and oleic : stearic ratios displayed the highest number of significant markers and the most significant associations (Bonferroni adjusted P < 5.00E-07). Of particular interest, the palmitoleic : palmitic ratio was strongly associated with markers located at 111 to 114 Mb on chromosome 14, in the same chromosomal region where Stearoyl-CoA desaturase Δ9 (SCD) gene is located. Several significant chromosomal regions were found; some of them harbour key genes playing pivotal roles in FA desaturation and elongation, such as SCD and some members of the Elongation of Very Long-Chain FA (ELOVL) gene family. The results suggest that the identification of causal mutations in these regions may provide a set of markers useful for selection schemes aimed at improving FA composition in pork products.

Genetic parameters of backfat fatty acids and carcass traits in Large White pigs.

Subcutaneous fat thickness and fatty acid composition (FAC) play an important role on seasoning loss and organoleptic characteristics of seasoned hams. Dry-cured ham industry prefers meats with low contents of polyunsaturated fatty acids (PUFA) because these negatively affect fat firmness and ham quality, whereas consumers require higher contents in those fatty acids (FA) for their positive effect on human health. A population of 950 Italian Large White pigs from the Italian National Sib Test Selection Programme was investigated with the aim to estimate heritabilities, genetic and phenotypic correlations of backfat FAC, Semimembranosus muscle intramuscular fat (IMF) content and other carcass traits. The pigs were reared in controlled environmental condition at the same central testing station and were slaughtered at reaching 150 kg live weight. Backfat samples were collected to analyze FAC by gas chromatography. Carcass traits showed heritability levels from 0.087 for estimated carcass lean percentage to 0.361 for hot carcass weight. Heritability values of FA classes were low-to-moderate, all in the range 0.245 for n-3 PUFA to 0.264 for monounsaturated FA (MUFA). Polyunsaturated fatty acids showed a significant genetic correlation with loin thickness (0.128), backfat thickness (-0.124 for backfat measured by Fat-O-Meat'er and -0.175 for backfat measured by calibre) and IMF (-0.102). Obviously, C18:2(n-6) shows similar genetic correlations with the same traits (0.211 with loin thickness, -0.206 with backfat measured by Fat-O-Meat'er, -0.291 with backfat measured by calibre and -0.171 with IMF). Monounsaturated FA, except with the backfat measured by calibre (0.068; P<0.01), do not show genetic correlations with carcass characteristics, whereas a negative genetic correlation was found between MUFA and saturated FA (SFA; -0.339; P<0.001). These results suggest that MUFA/SFA ratio could be increased without interfering with carcass traits. The level of genetic correlations between FA and carcass traits should be taken into account in dealing with the development of selection schemes addressed to modify carcass composition and/or backfat FAC.

Genome-wide association study for longevity in the Holstein cattle population.

Longevity is one of the most important traits determining dairy cow profitability. In the last decades dairy cows suffered a lowering in the age at culling. With the aim to identify the genes involved in longevity, dates of birth, yields, dates of calving during lifespan and culling dates were collected for 946 culled cows which had been genotyped with the Bovine High Density panel. Using the GenABEL package in R, genome-wide association analysis was performed on three potential traits of longevity: (1) 'days in production,' (2) 'days in herd,' (3) number of calvings over lifespan.' Five genome-wide significant single nucleotide polymorphisms (SNPs) associated with all three longevity traits were detected. Several consecutive SNPs identified on chromosomes 16 and 30 indicated the presence of two suggestive quantitative trait loci (QTL). The genes comprised in the QTL regions had biological functions related to fertility, reproductive disorders, heat stress and welfare of cows. These findings might contribute to improving breeding strategies to improve longevity.

Genetic parameters and genome-wide associations of twinning rate in a local breed, the Maremmana cattle.

This study seeks to verify the feasibility of increasing twinning in a herd of the Italian autochtonous Maremmana breed. The data set included 1260 individuals born from 1963 to 2014, 527 males and 733 females, 402 of them calving at least once from 1983 through 2015. Breeding values for twinning were estimated by a single-trait linear animal model. However, since twinning is a dichotomous trait and the frequency of twins is far smaller than the frequency of single births, breeding values were also estimated by a single-trait animal threshold model. Heritability of twinning was 0.014±0.018 and 0.062±0.093 for the linear and the threshold models, respectively. Repeatability was 0.071±0.004 and 0.286± 0.012, respectively, for the two models. Genotyping with the Illumina BovineSNP54 BeadChip was performed for cows living on farm in 2012 (119 cows) and a genome-wide association analysis was performed on the corrected phenotype of all calving during the lifespan of each cow, using the GenABEL package in R and a three step GRAMMAR-GC approach. Genomic heritability, calculated from the genomic kinship matrix estimated through genomic marker data, was 0.29±0.021. The most significant detected single nucleotide polymorphisms (Hapmap22923-BTA-129564) was located in proximity of two genes, ARHGAP8 and TMEM200C, which might be potential functional candidates for twinning rate in cattle.

Use of the canonical discriminant analysis to select SNP markers for bovine breed assignment and traceability purposes.

Several market research studies have shown that consumers are primarily concerned with the provenance of the food they eat. Among the available identification methods, only DNA-based techniques appear able to completely prevent frauds. In this study, a new method to discriminate among different bovine breeds and assign new individuals to groups was developed. Bulls of three cattle breeds farmed in Italy - Holstein, Brown, and Simmental - were genotyped using the 50K SNP Illumina BeadChip. Multivariate canonical discriminant analysis was used to discriminate among breeds, and discriminant analysis (DA) was used to assign new observations. This method was able to completely identify the three groups at chromosome level. Moreover, a genome-wide analysis developed using 340 linearly independent SNPs yielded a significant separation among groups. Using the reduced set of markers, the DA was able to assign 30 independent individuals to the proper breed. Finally, a set of 48 high discriminant SNPs was selected and used to develop a new run of the analysis. Again, the procedure was able to significantly identify the three breeds and to correctly assign new observations. These results suggest that an assay with the selected 48 SNP could be used to routinely track monobreed products.

Analysis of lactation shapes in extended lactations.

In order to describe the temporal evolution of milk yield (MY) and composition in extended lactations, 21 658 lactations of Italian Holstein cows were analyzed. Six empirical mathematical models currently used to fit 305 standard lactations (Wood, Wilmink, Legendre, Ali and Schaeffer, quadratic and cubic splines) and one function developed specifically for extended lactations (a modification of the Dijkstra model) were tested to identify a suitable function for describing patterns until 1000 days in milk (DIM). Comparison was performed on individual patterns and on average curves grouped according to parity (primiparous and multiparous) and lactation length (standard ≤305 days, and extended from 600 to 1000 days). For average patterns, polynomial models showed better fitting performances when compared with the three or four parameters models. However, LEG and spline regression, showed poor prediction ability at the extremes of the lactation trajectory. The Ali and Schaeffer polynomial and Dijkstra function were effective in modelling average curves for MY and protein percentage, whereas a reduced fitting ability was observed for fat percentage and somatic cell score. When individual patterns were fitted, polynomial models outperformed nonlinear functions. No detectable differences were observed between standard and extended patterns in the initial phase of lactation, with similar values of peak production and time at peak. A considerable difference in persistency was observed between 200 and 305 DIM. Such a difference resulted in an estimated difference between standard and extended cycle of about 7 and 9 kg/day for daily yield at 305 DIM and of 463 and 677 kg of cumulated milk production at 305 DIM for the first- and second-parity groups, respectively. For first and later lactation animals, peak yield estimates were nearly 31 and 38 kg, respectively, and occurred at around 65 and 40 days. The asymptotic level of production was around 9 kg for multiparous cows, whereas the estimate was negative for first parity.

A pirinixic acid derivative (LP105) inhibits murine 5-lipoxygenase activity and attenuates vascular remodelling in a murine model of aortic aneurysm.

BACKGROUND AND PURPOSE Arachidonic acid derivatives play a central role in inflammation processes. Arachidonic acid is metabolized by several enzymes, particularly cyclooxygenases (COX), 5-lipoxygenase (5-LOX) and microsomal prostaglandin E-synthase-1 (mPGES-1) to pro-inflammatory mediators. EXPERIMENTAL APPROACH We determined the effect of LP105, a pirinixic acid derivative which acts as inhibitor of 5-LOX, COX and mPGES-1, on aortic aneurysm development in mice and on 5-LOX activity in murine monocytes. KEY RESULTS In a monocyte cell line (RAW264.7), LP105 inhibited 5-LOX in whole cells (IC(50) : 1-3 µM) and in supernatants (IC(50) : ∼10 µM). Oral administration of LP105 to mice resulted in therapeutic tissue and plasma levels. Aortic aneurysms were induced in ApoE(-/-) mice by angiotensin II (AngII) and LP105 (5 mg·day(-1) per animal) was co-administered to a subgroup. Compared with animals receiving AngII alone, the LP105+AngII group showed a lower heart rate, a trend towards reduced heart to body weight ratio but similar hypertensive responses. AngII alone significantly increased aortic weight and diameter but co-treatment with LP105+AngII prevented these changes. LC/MS-MS studies revealed increased 15-hydroxytetraenoic acid (15-HETE) and 14,15-epoxyeicosatrienoic acid (14,15-EET) plasma levels in LP105-treated animals. In the murine kidney, mRNAs of EET-generating or metabolizing enzymes and of 5-LOX and 15-LOX were unaffected by LP105. LP105 also did not inhibit the EET-metabolizing soluble epoxide hydrolase. CONCLUSIONS AND IMPLICATIONS LP105 was a potent inhibitor of monocyte 5-LOX and reduced AngII-induced vascular remodelling in mice. A shift of arachidonic acid metabolism to the protective EET pathway may contribute to the beneficial effects of LP105.

Use of partial least squares regression to predict single nucleotide polymorphism marker genotypes when some animals are genotyped with a low-density panel.

High-density single nucleotide polymorphism (SNP) platforms are currently used in genomic selection (GS) programs to enhance the selection response. However, the genotyping of a large number of animals with high-throughput platforms is rather expensive and may represent a constraint for a large-scale implementation of GS. The use of low-density marker (LDM) platforms could overcome this problem, but different SNP chips may be required for each trait and/or breed. In this study, a strategy of imputation independent from trait and breed is proposed. A simulated population of 5865 individuals with a genome of 6000 SNP equally distributed on six chromosomes was considered. First, reference and prediction populations were generated by mimicking high- and low-density SNP platforms, respectively. Then, the partial least squares regression (PLSR) technique was applied to reconstruct the missing SNP in the low-density chip. The proportion of SNP correctly reconstructed by the PLSR method ranged from 0.78 to 0.97 when 90% and 50%, respectively, of genotypes were predicted. Moreover, data sets consisting of a mixture of actual and PLSR-predicted SNP or only actual SNP were used to predict genomic breeding values (GEBVs). Correlations between GEBV and true breeding values varied from 0.74 to 0.76, respectively. The results of the study indicate that the PLSR technique can be considered a reliable computational strategy for predicting SNP genotypes in an LDM platform with reasonable accuracy.

Using eigenvalues as variance priors in the prediction of genomic breeding values by principal component analysis.

Genome-wide selection aims to predict genetic merit of individuals by estimating the effect of chromosome segments on phenotypes using dense single nucleotide polymorphism (SNP) marker maps. In the present paper, principal component analysis was used to reduce the number of predictors in the estimation of genomic breeding values for a simulated population. Principal component extraction was carried out either using all markers available or separately for each chromosome. Priors of predictor variance were based on their contribution to the total SNP correlation structure. The principal component approach yielded the same accuracy of predicted genomic breeding values obtained with the regression using SNP genotypes directly, with a reduction in the number of predictors of about 96% and computation time of 99%. Although these accuracies are lower than those currently achieved with Bayesian methods, at least for simulated data, the improved calculation speed together with the possibility of extracting principal components directly on individual chromosomes may represent an interesting option for predicting genomic breeding values in real data with a large number of SNP. The use of phenotypes as dependent variable instead of conventional breeding values resulted in more reliable estimates, thus supporting the current strategies adopted in research programs of genomic selection in livestock.