Effect of genotyping density on the detection of runs of homozygosity and heterozygosity in cattle.

Runs of homozygosity (ROHom) are contiguous stretches of homozygous regions of the genome. In contrast, runs of heterozygosity (ROHet) are heterozygosity-rich regions. The detection of these two types of genomic regions (ROHom and ROHet) is influenced by the parameters involved in their identification and the number of available single nucleotide polymorphisms (SNP). The present study aimed to test the effect of chip density in detecting ROHom and ROHet in the Italian Simmental cattle breed. A sample of 897 animals were genotyped at low-density (50k SNP; 397 individuals), medium-density (140k SNP; 348 individuals), or high-density (800k SNP; 152 individuals). The number of ROHom and ROHet per animal (nROHom and nROHet, respectively) and their average length were calculated. ROHom or ROHet shared by more than one animal and the number of times a particular SNP was inside a run were also computed (SNPROHom and SNPROHet). As the chip density increased, the nROHom increased, whereas their average length decreased. In contrast, the nROHet decreased and the average length increased as the chip density increased. The most repeated ROHom harbored no genes, whereas in the most repeated ROHet four genes (SNRPN, SNURF, UBE3A and ATP10A) previously associated with reproductive traits were found. Across the three datasets, 31 SNP, located on Bos taurus autosome (BTA) 6, and 37 SNP (located on BTA21) exceeded the 99th percentile in the distribution of the SNPROHom and SNPROHet, respectively. The genomic region on BTA6 mapped the SLIT2, PACRGL, and KCNIP4 genes, whereas 19 and 18 genes were mapped on BTA16 and BTA21, respectively. Interestingly, most of genes found through the ROHet analysis were previously reported to be related to health, reproduction, and fitness traits. The results of the present study confirm that the detection of ROHom is more reliable when the chip density increases, whereas the ROHet trend seems to be the opposite. Genes and Quantitative Trait Loci (QTL) mapped in the highlighted regions confirm that ROHet can be due to balancing selection, thus related to fitness traits, health, and reproduction, whereas ROHom are mainly involved in production traits. The results of the present study strengthened the usefulness of these parameters in analyzing the genomes of livestock and their biological meaning.

Investigation of phenotypic, genetic and genomic background of Milk spectra in Sarda dairy sheep.

Aim of this study was to analyse the genetic background of milk Fourier transform infrared (FTIR) spectra in dairy sheep. Individual milk FTIR spectra, with 1060 wavenumbers each, were available for 793 adult Sarda breed ewes genotyped at 45,813 SNP. The absorbance values of each wavenumber was analysed using a linear mixed model that included dim class, parity and lambing month as fixed effects and flock-test date and animal as random effects. The model was applied to estimate variance components and heritability and to perform a genome-wide association study for each wavenumber. Average h2 of wavenumbers absorbance was 0.13 ± 0.08, with the largest values observed in the regions associated with the characteristic bonds of carbonylic and methylenic groups of milk fat (h2 = 0.57 at 1724-1728 cm-1; and h2 = 0.34 at 2811-2834 cm-1, respectively). The absorbance values of wavenumbers were moderately correlated with the estimated heritabilities. After the Bonferroni correction, a total of nine markers were found to be significantly associated with 32 different wavenumbers. Of particular interest was the SNP s63269.1, mapped on chromosome 2, that was found to be associated with 27 wavenumbers. Genes previously found to be related to traits of interest (e.g. disease resistance, milk yield and quality, cheese firmness) are located close to the significant SNP. As expected, the heritability estimated for the absorbance of each wavenumbers seems to be associated with the related milk components.

Effects of Age, Fulton's Condition Index (K) and Muscle Fat on Total Mercury Content in Raw, Pre-Canning and Canned Samples of Atlantic Bluefin Tuna (Thunnus thynnus).

A total of 30 samples of Atlantic bluefin tuna were analysed for total mercury concentration. Relationships between total mercury content and age, Fulton's condition index (K) and fat content were statistically evaluated. The effect of muscle status (raw, pre-canning, canned) on mercury content was also investigated. The average total mercury content was: 1.185 ± 0.662 mg/kg in raw, 1.481 ± 0.893 mg/kg in pre-canning and 1.393 ± 0.882 mg/kg in canned samples, respectively. Canning promotes a statistically significant increase in the concentration of mercury. The weight of fish, K and fat content are useful tools to estimate the mercury accumulation in Atlantic bluefin tuna. The results of the present study represent a contribution to the assessment of the EU mercury levels in Atlantic bluefin tuna.

Dry Matter Intake Prediction from Milk Spectra in Sarda Dairy Sheep.

Individual dry matter intake (DMI) is a relevant factor for evaluating feed efficiency in livestock. However, the measurement of this trait on a large scale is difficult and expensive. DMI, as well as other phenotypes, can be predicted from milk spectra. The aim of this work was to predict DMI from the milk spectra of 24 lactating Sarda dairy sheep ewes. Three models (Principal Component Regression, Partial Least Squares Regression, and Stepwise Regression) were iteratively applied to three validation schemes: records, ewes, and days. DMI was moderately correlated with the wavenumbers of the milk spectra: the largest correlations (around ±0.30) were observed at ~1100-1330 cm-1 and ~2800-3000 cm-1. The average correlations between real and predicted DMI were 0.33 (validation on records), 0.32 (validation on ewes), and 0.23 (validation on days). The results of this preliminary study, even if based on a small number of animals, demonstrate that DMI can be routinely estimated from the milk spectra.

Study of the Fatty Acid Profile of Milk in Different Sheep Breeds: Evaluation by Multivariate Factorial Analysis.

A multivariate analysis was used to investigate the fatty acid (FA) profile in three different Italian sheep breeds: Comisana, Massese, and Sarda. A sample of 852 animals was considered: 118 Massese, 303 Comisana, 431 Sarda. Sarda sheep were divided into two groups, based on their breeding origin (298 and 133 reared in Sardinia and Tuscany, respectively). Sarda sheep, bred both in Sardinia and in Tuscany, were considered in different groups, both because in these two regions most of the sheep of this breed are reared, and because they differ in geographical characteristics and in the farming system. The individual milk FA composition of dairy ewes was analyzed with multivariate factor analysis. The extracted factors were representative of the following eight groups of fatty acids or functions: factor 1 (odd branched fatty acids and long-chain fatty acids), factor 2 (sn3_position), factor 3 (alternative biohydrogenation), factor 4 (SCD_1), factor 5 (SCD_2), factor 6 (SCD_3), factor 7 (fat secretion) and factor 8 (omega-3). A factor analysis suggested the presence of different metabolic pathways for de novo short- and medium-chain fatty acids and Δ9-desaturase products. The ANOVA of factor scores highlighted the significant effects of the breed. The results of the present study showed that breed is an important factor in defining the fatty acid profile of milk, combined with the effect of the diet. Breeds reared in the same farming system (Comisana, Massese and Sarda reared in Tuscany) showed significant differences for all the factors extracted. At the same time, we found differences between the Sarda sheep reared in Sardinia and Tuscany, two different regions of Italy.

Genomic prediction for latent variables related to milk fatty acid composition in Holstein, Simmental and Brown Swiss dairy cattle breeds.

Genomic selection (GS) reports on milk fatty acid (FA) profiles have been published quite recently and are still few despite this trait represents the most important aspect of milk nutritional and sensory quality. Reasons for this can be found in the high costs of phenotype recording but also in issues related to its nature of complex trait constituted by multiple genetically correlated variables with low heritabilities. One possible strategy to deal with such constraint is represented by the use of dimension reduction methods. We analysed 40 individual FAs from Italian Brown Swiss, Holstein and Simmental milk through multivariate factor analysis (MFA) to study the genetics of milk FA-related latent variables (factors) and assess their potential use in breeding. A total of nine factors were obtained, and their genetic parameters were inferred under a Bayesian framework using two statistical approaches: the classical pedigree best linear unbiased prediction (ABLUP) and the single-step genomic BLUP (ssGBLUP). The resulting factorial solutions were able to represent groups of FAs with common origin and function and can be considered concise pathway-level phenotypes. The heritability (h2 ) values showed relevant variations across different factors in each breed (0.03 ≤ h2  ≤ 0.38). The accuracies of breeding values predicted were low to high, ranging from 0.13 to 0.72 and from 0.18 to 0.74 considering the pedigree and the genomic model, respectively. The gain in accuracy in genetic prediction due to the addition of genomic information was ~30% and ~5% in validation and training groups respectively, confirming the contribution of genomic information in yielding more accurate predictions compared to the traditional ABLUP methodology. Our results suggest that MFA in combination with GS can be a valuable tool in dairy cattle breeding and deserves to be further investigated for use in future breeding programs to improve cow's milk FA-related traits.

Use of multivariate factor analysis of detailed milk fatty acid profile to perform a genome-wide association study in Italian Simmental and Italian Holstein.

Milk fatty acid (FA) profile is a clear example of complex and multiple correlated traits whose genetic basis is difficult to assess. Although genome-wide association (GWA) studies have been successful in the identification of significant genetic variants for complex traits, when correlated phenotypes are analysed separately, the outcomes are difficult to compare and interpret in a metabolic context. Here, we performed a multivariate factor analysis (MFA) on Italian Simmental and Italian Holstein milk fat profiles to extract latent unobserved factors able to explain correlation structure and common metabolic function among different FAs. Individual factor scores obtained by MFA were used to perform a single-SNP based GWA. In both breeds, MFA was able to extract ten latent factors with specific biological meaning, notably: de novo synthesis, desaturation activity and biohydrogenation. The GWA result confirmed the increased power of joint association analysis on multiple correlated traits and allowed us to identify major candidate genes with well-documented function consistent with the metabolic classification of factors obtained, such as DGAT1, FASN and SCD.

Genetic Parameters for Tolerance to Heat Stress in Crossbred Swine Carcass Traits.

Data for loin and backfat depth, as well as carcass growth of 126,051 three-way crossbred pigs raised between 2015 and 2019, were combined with climate records of air temperature, relative humidity, and temperature-humidity index. Environmental covariates with the largest impact on the studied traits were incorporated in a random regression model that also included genomic information. Genetic control of tolerance to heat stress and the presence of genotype by environment interaction were detected. Its magnitude was more substantial for loin depth and carcass growth, but all the traits studied showed a different impact of heat stress and different magnitude of genotype by environment interaction. For backfat depth, heritability was larger under comfortable conditions (no heat stress), as compared to heat stress conditions. Genetic correlations between extreme values of environmental conditions were lower (∼0.5 to negative) for growth and loin depth. Based on the solutions obtained from the model, sires were ranked on their breeding value for general performance and tolerance to heat stress. Antagonism between overall performance and tolerance to heat stress was moderate. Still, the models tested can provide valuable information to identify genetic material that is resilient and can perform equally when environmental conditions change. Overall, the results obtained from this study suggest the existence of genotype by environment interaction for carcass traits, as a possible genetic contributor to heat tolerance in swine.

Prediction of Milk Coagulation Properties and Individual Cheese Yield in Sheep Using Partial Least Squares Regression.

The objectives of this study were (i) the prediction of sheep milk coagulation properties (MCP) and individual laboratory cheese yield (ILCY) from mid-infrared (MIR) spectra by using partial least squares (PLS) regression, and (ii) the comparison of different data pre-treatments on prediction accuracy. Individual milk samples of 970 Sarda breed ewes were analyzed for rennet coagulation time (RCT), curd-firming time (k20), and curd firmness (a30) using the Formagraph instrument; ILCY was measured by micro-manufacturing assays. An Furier-transform Infrared (FTIR) milk-analyzer was used for the estimation of the milk gross composition and the recording of MIR spectrum. The dataset (n = 859, after the exclusion of 111 noncoagulating samples) was divided into two sub-datasets: the data of 700 ewes were used to estimate prediction model parameters, and the data of 159 ewes were used to validate the model. Four prediction scenarios were compared in the validation, differing for the use of whole or reduced MIR spectrum and the use of raw or corrected data (locally weighted scatterplot smoothing). PLS prediction statistics were moderate. The use of the reduced MIR spectrum yielded the best results for the considered traits, whereas the data correction improved the prediction ability only when the whole MIR spectrum was used. In conclusion, PLS achieves good accuracy of prediction, in particular for ILCY and RCT, and it may enable increasing the number of traits to be included in breeding programs for dairy sheep without additional costs and logistics.

Use of canonical discriminant analysis to study signatures of selection in cattle.

BACKGROUND: Cattle include a large number of breeds that are characterized by marked phenotypic differences and thus constitute a valuable model to study genome evolution in response to processes such as selection and domestication. Detection of "signatures of selection" is a useful approach to study the evolutionary pressures experienced throughout history. In the present study, signatures of selection were investigated in five cattle breeds farmed in Italy using a multivariate approach. METHODS: A total of 4094 bulls from five breeds with different production aptitudes (two dairy breeds: Italian Holstein and Italian Brown Swiss; two beef breeds: Piemontese and Marchigiana; and one dual purpose breed: Italian Simmental) were genotyped using the Illumina BovineSNP50 v.1 beadchip. Canonical discriminant analysis was carried out on the matrix of single nucleotide polymorphisms (SNP) genotyping data, separately for each chromosome. Scores for each canonical variable were calculated and then plotted in the canonical space to quantify the distance between breeds. SNPs for which the correlation with the canonical variable was in the 99th percentile for a specific chromosome were considered to be significantly associated with that variable. Results were compared with those obtained using an FST-based approach. RESULTS: Based on the results of the canonical discriminant analysis, a large number of signatures of selection were detected, among which several had strong signals in genomic regions that harbour genes known to have an impact on production and morphological bovine traits, including MSTN, LCT, GHR, SCD, NCAPG, KIT, and ASIP. Moreover, new putative candidate genes were identified, such as GCK, B3GALNT1, MGAT1, GALNTL1, PRNP, and PRND. Similar results were obtained with the FST-based approach. CONCLUSIONS: The use of canonical discriminant analysis on 50 K SNP genotypes allowed the extraction of new variables that maximize the separation between breeds. This approach is quite straightforward, it can compare more than two groups simultaneously, and relative distances between breeds can be visualized. The genes that were highlighted in the canonical discriminant analysis were in concordance with those obtained using the FST index.