Genetic response of a perennial grass to warm and wet environments interacts and is associated with trait means as well as plasticity.

The potential for rapid evolution is an important mechanism allowing species to adapt to changing climatic conditions. Although such potential has been largely studied in various short-lived organisms, to what extent we can observe similar patterns in long-lived plant species, which often dominate natural systems, is largely unexplored. We explored the potential for rapid evolution in Festuca rubra, a long-lived grass with extensive clonal growth dominating in alpine grasslands. We used a field sowing experiment simulating expected climate change in our model region. Specifically, we exposed seeds from five independent seed sources to novel climatic conditions by shifting them along a natural climatic grid and explored the genetic profiles of established seedlings after 3 years. Data on genetic profiles of plants selected under different novel conditions indicate that different climate shifts select significantly different pools of genotypes from common seed pools. Increasing soil moisture was more important than increasing temperature or the interaction of the two climatic factors in selecting pressure. This can indicate negative genetic interaction in response to the combined effects or that the effects of different climates are interactive rather than additive. The selected alleles were found in genomic regions, likely affecting the function of specific genes or their expression. Many of these were also linked to morphological traits (mainly to trait plasticity), suggesting these changes may have a consequence on plant performance. Overall, these data indicate that even long-lived plant species may experience strong selection by climate, and their populations thus have the potential to rapidly adapt to these novel conditions.

Multiple genomic solutions for local adaptation in two closely related species (sheep and goats) facing the same climatic constraints.

The question of how local adaptation takes place remains a fundamental question in evolutionary biology. The variation of allele frequencies in genes under selection over environmental gradients remains mainly theoretical and its empirical assessment would help understanding how adaptation happens over environmental clines. To bring new insights to this issue we set up a broad framework which aimed to compare the adaptive trajectories over environmental clines in two domesticated mammal species co-distributed in diversified landscapes. We sequenced the genomes of 160 sheep and 161 goats extensively managed along environmental gradients, including temperature, rainfall, seasonality and altitude, to identify genes and biological processes shaping local adaptation. Allele frequencies at putatively adaptive loci were rarely found to vary gradually along environmental gradients, but rather displayed a discontinuous shift at the extremities of environmental clines. Of the 430 candidate adaptive genes identified, only 6 were orthologous between sheep and goats and those responded differently to environmental pressures, suggesting different putative mechanisms involved in local adaptation in these two closely related species. Interestingly, the genomes of the 2 species were impacted differently by the environment, genes related to signatures of selection were most related to altitude, slope and rainfall seasonality for sheep, and summer temperature and spring rainfall for goats. The diversity of candidate adaptive pathways may result from a high number of biological functions involved in the adaptations to multiple eco-climatic gradients, and a differential role of climatic drivers on the two species, despite their co-distribution along the same environmental gradients. This study describes empirical examples of clinal variation in putatively adaptive alleles with different patterns in allele frequency distributions over continuous environmental gradients, thus showing the diversity of genetic responses in adaptive landscapes and opening new horizons for understanding genomics of adaptation in mammalian species and beyond.

Genome-wide mapping of signatures of selection using a high-density array identified candidate genes for growth traits and local adaptation in chickens.

BACKGROUND: Availability of single nucleotide polymorphism (SNP) genotyping arrays and progress in statistical analyses have allowed the identification of genomic regions and genes under selection in chicken. In this study, SNP data from the 600 K Affymetrix chicken array were used to detect signatures of selection in 23 local Italian chicken populations. The populations were categorized into four groups for comparative analysis based on live weight (heavy vs light) and geographical area (Northern vs Southern Italy). Putative signatures of selection were investigated by combining three extended haplotype homozygosity (EHH) statistical approaches to quantify excess of haplotype homozygosity within (iHS) and between (Rsb and XP-EHH) groups. Presence of runs of homozygosity (ROH) islands was also analysed for each group. RESULTS: After editing, 541 animals and 313,508 SNPs were available for statistical analyses. In total, 15 candidate genomic regions that are potentially under selection were detected among the four groups: eight within a group by iHS and seven by combining the results of Rsb and XP-EHH, which revealed divergent selection between the groups. The largest overlap between genomic regions identified to be under selection by the three approaches was on chicken chromosome 8. Twenty-one genomic regions were identified with the ROH approach but none of these overlapped with regions identified with the three EHH-derived statistics. Some of the identified regions under selection contained candidate genes with biological functions related to environmental stress, immune responses, and disease resistance, which indicate local adaptation of these chicken populations. CONCLUSIONS: Compared to commercial lines, local populations are predominantly reared as backyard chickens, and thus, may have developed stronger resistance to environmental challenges. Our results indicate that selection can play an important role in shaping signatures of selection in local chicken populations and can be a starting point to identify gene mutations that could have a useful role with respect to climate change.

Oral ecological environment modifications by hard-cheese: from pH to microbiome: a prospective cohort study based on 16S rRNA metabarcoding approach.

BACKGROUND: The oral ecosystem conditions dental health, and is known to be positively modified by oral hygiene which cannot always be performed between meals, especially outside home. It is therefore important to identify the practices to be adopted to influence the oral environment in an anticariogenic direction. Milk and cheese are considered functional foods and have a role on oral health. There are several mechanisms by which cheese exerts its beneficial effects on teeth. The aim of the present study was to examine whether short term consumption of hard cheese would affect the oral pH and microbial flora of healthy adults modifying ecological oral environment. The Next Generation Sequencing (NGS) approach was applied to study the effect of Italian Grana Padano (GP), as a prototype of typical hard cheese, on the oral microbiota composition. Finally, we explored Streptococcus mutans/sanguinis ratio as a marker of protective biofilm composition. METHODS: Nine oral-healthy adults were instructed to eat 25 gr of GP cheese for 5 consecutive days. Three time points were chosen for supragingival samples collection and pH measurement. 16S rRNA-gene sequences were obtained both from oral samples and GP cheese using the MiSeq platform and analyzed against the expanded Human Oral Microbiome Database (eHOMD). ProgPerm was used to perform statistical analyses to investigate strain differential representation after cheese consumption. RESULTS: Taxonomic analyses of the oral microbiota revealed that Firmicutes was the most abundant phylum, followed by Proteobacteria and Actinobacteria. GP cheese significantly modifies oral pH, causing a shift toward basic conditions which are kept for a few hours. The Streptococcus mutans/Streptococcus sanguinis ratio lowers in the last observed timepoint. CONCLUSION: Our results reveal that a portion of GP cheese eaten after dinner provides important micronutrients (i.e. calcium, vitamins and some aminoacids such as arginine) and changes oral pH toward basic conditions, resulting in a light modification of the oral microbiome towards the reduction of the overall amount of acidophilic bacteria. Furthermore, the S. mutans/S. sanguinis ratio is reduced, contributing to obtain a more protecting environment towards caries establishment and evolution.

Integrating heterogeneous across-country data for proxy-based random forest prediction of enteric methane in dairy cattle.

Direct measurements of methane (CH4) from individual animals are difficult and expensive. Predictions based on proxies for CH4 are a viable alternative. Most prediction models are based on multiple linear regressions (MLR) and predictor variables that are not routinely available in commercial farms, such as dry matter intake (DMI) and diet composition. The use of machine learning (ML) algorithms to predict CH4 emissions from across-country heterogeneous data sets has not been reported. The objectives were to compare performances of ML ensemble algorithm random forest (RF) and MLR models in predicting CH4 emissions from proxies in dairy cows, and assess effects of imputing missing data points on prediction accuracy. Data on CH4 emissions and proxies for CH4 from 20 herds were provided by 10 countries. The integrated data set contained 43,519 records from 3,483 cows, with 18.7% missing data points imputed using k-nearest neighbor imputation. Three data sets were created, 3k (no missing records), 21k (missing DMI imputed from milk, fat, protein, body weight), and 41k (missing DMI, milk fat, and protein records imputed). These data sets were used to test scenarios (with or without DMI, imputed vs. nonimputed DMI, milk fat, and protein), and prediction models (RF vs. MLR). Model predictive ability was evaluated within and between herds through 10-fold cross-validation. Prediction accuracy was measured as correlation between observed and predicted CH4, root mean squared error (RMSE) and mean normalized discounted cumulative gain (NDCG). Inclusion of DMI in the model improved within and between-herd prediction accuracy to 0.77 (RMSE = 23.3%) and 0.58 (RMSE = 31.9%) in RF and to 0.50 (RMSE = 0.327) and 0.13 (RMSE = 42.71) in MLR, respectively than when DMI was not included in the predictive model. When missing DMI records were imputed, within and between-herd accuracy increased to 0.84 (RMSE = 18.5%) and 0.63 (RMSE = 29.9%), respectively. In all scenarios, RF models out-performed MLR models. Results suggest routinely measured variables from dairy farms can be used in developing globally robust prediction models for CH4 if coupled with state-of-the-art techniques for imputation and advanced ML algorithms for predictive modeling.

Technical note: Development of multiplex PCR assays for the molecular characterization of Streptococcus uberis strains isolated from bovine mastitis.

Streptococcus uberis is an important causative agent for clinical and subclinical mastitis in dairy cattle. The aim of this study was to develop 2 multiplex PCR assays (mPCR) for the simultaneous detection of virulence factors and housekeeping genes for use when investigating the genetic variability and distribution of Strep. uberis virulence factors. The tuf, cpn60, pauA, sodA, sua, oppF, and gapC genes were grouped in assay 1 (mPCR1) and the hasA, hasB, and hasC genes were included in assay 2 (mPCR2). The detection limits were 11.8 pg and 5.9 pg of DNA for mPCR1 and mPCR2, respectively. The 2 mPCR assays were validated with 56 Strep. uberis strains isolated from mastitis milk samples collected from different bovine herds in northern Italy. Results revealed that gapC and oppF were detected in 98.2% of the strains, whereas sua and hasC genes were detected in 94.6 and 89.2% of the strains, respectively. The most common pattern was gapC+, oppF+, cpn60+, sua+, sodA+, pauA+, tuf+, hasA+, hasB+, and hasC+, which appeared in 59% of the strains analyzed. The molecular assays developed in the present study represent a powerful tool for the evaluation of virulence pattern distribution in Strep. uberis strains associated with intramammary infections.

Dscam1 web server: online prediction of Dscam1 self- and hetero-affinity.

MOTIVATION: Formation of homodimers by identical Dscam1 protein isomers on cell surface is the key factor for the self-avoidance of growing neurites. Dscam1 immense diversity has a critical role in the formation of arthropod neuronal circuit, showing unique evolutionary properties when compared to other cell surface proteins. Experimental measures are available for 89 self-binding and 1722 hetero-binding protein samples, out of more than 19 thousands (self-binding) and 350 millions (hetero-binding) possible isomer combinations. RESULTS: We developed Dscam1 Web Server to quickly predict Dscam1 self- and hetero- binding affinity for batches of Dscam1 isomers. The server can help the study of Dscam1 affinity and help researchers navigate through the tens of millions of possible isomer combinations to isolate the strong-binding ones. AVAILABILITY AND IMPLEMENTATION: Dscam1 Web Server is freely available at: http://bioinformatics.tecnoparco.org/Dscam1-webserver . Web server code is available at https://gitlab.com/ne1s0n/Dscam1-binding . CONTACT: simone.marini@unipv.it or guangzhong.wang@picb.ac.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Gut Microbiome in BALB/c and C57BL/6J Mice Undergoing Experimental Thyroid Autoimmunity Associate with Differences in Immunological Responses and Thyroid Function.

Experimental models of hyperthyroid Graves' disease (GD) and Graves' orbitopathy (GO) are efficiently developed by genetic immunisation by electroporation with human thyrotropin hormone receptor (hTSHR) A-subunit plasmid in female BALB/c (H-2d) mice. We investigated susceptibility in C57BL/6 J (H-2b) animals to allow studies on disease mechanisms in transgenic and immune response gene knock-out mice. Higher numbers of female C57BL/6 J were positive for pathogenic thyroid stimulating antibodies, but induced hyperthyroidism remained at a low frequency compared to BALB/c animals. Assessment of hTSHR specific T cells showed reduced proliferation in C57BL/6 J animals accompanied with anti-inflammatory IL-10, with less pro-inflammatory IFN-γ compared to BALB/c. Whilst the orbital tissue from immune BALB/c mice showed inflammation and adipogenesis, in contrast C57BL/6 J animals showed normal pathology. We characterised the gut microbiota using 16 S ribosomal RNA gene sequencing to explore its possible pathogenic role in the model. Despite being housed under identical conditions, we observed significantly different organisation of the microbiota (beta-diversity) in the two strains. Taxonomic differences were also noted, with C57BL/6 J showing an enrichment of Operational Taxonomic Units (OTUs) belonging to the Paludibacter and Allobaculum, followed by Limibacter, Anaerophaga and Ureaplasma genera. A higher number of genera significantly correlating with clinical features was observed in C57BL/6 J compared to BALB/c; for example, Limibacter OTUs correlated negatively with thyroid-stimulating antibodies in C57BL/6 J mice. Thus, our data suggest gut microbiota may play a pivotal immunomodulatory role that differentiates the thyroid function and orbital pathology outcome in these two inbred strains undergoing experimental GO.

Genome-enabled predictions for fruit weight and quality from repeated records in European peach progenies.

BACKGROUND: Highly polygenic traits such as fruit weight, sugar content and acidity strongly influence the agroeconomic value of peach varieties. Genomic Selection (GS) can accelerate peach yield and quality gain if predictions show higher levels of accuracy compared to phenotypic selection. The available IPSC 9K SNP array V1 allows standardized and highly reliable genotyping, preparing the ground for GS in peach. RESULTS: A repeatability model (multiple records per individual plant) for genome-enabled predictions in eleven European peach populations is presented. The analysis included 1147 individuals derived from both commercial and non-commercial peach or peach-related accessions. Considered traits were average fruit weight (FW), sugar content (SC) and titratable acidity (TA). Plants were genotyped with the 9K IPSC array, grown in three countries (France, Italy, Spain) and phenotyped for 3-5 years. An analysis of imputation accuracy of missing genotypic data was conducted using the software Beagle, showing that two of the eleven populations were highly sensitive to increasing levels of missing data. The regression model produced, for each trait and each population, estimates of heritability (FW:0.35, SC:0.48, TA:0.53, on average) and repeatability (FW:0.56, SC:0.63, TA:0.62, on average). Predictive ability was estimated in a five-fold cross validation scheme within population as the correlation of true and predicted phenotypes. Results differed by populations and traits, but predictive abilities were in general high (FW:0.60, SC:0.72, TA:0.65, on average). CONCLUSIONS: This study assessed the feasibility of Genomic Selection in peach for highly polygenic traits linked to yield and fruit quality. The accuracy of imputing missing genotypes was as high as 96%, and the genomic predictive ability was on average 0.65, but could be as high as 0.84 for fruit weight or 0.83 for titratable acidity. The estimated repeatability may prove very useful in the management of the typical long cycles involved in peach productions. All together, these results are very promising for the application of genomic selection to peach breeding programmes.

Use of SNP genotypes to identify carriers of harmful recessive mutations in cattle populations.

BACKGROUND: SNP (single nucleotide polymorphisms) genotype data are increasingly available in cattle populations and, among other things, can be used to predict carriers of specific mutations. It is therefore convenient to have a practical statistical method for the accurate classification of individuals into carriers and non-carriers. In this paper, we compared - through cross-validation- five classification models (Lasso-penalized logistic regression -Lasso, Support Vector Machines with either linear or radial kernel -SVML and SVMR, k-nearest neighbors -KNN, and multi-allelic gene prediction -MAG), for the identification of carriers of the TUBD1 recessive mutation on BTA19 (Bos taurus autosome 19), known to be associated with high calf mortality. A population of 3116 Fleckvieh and 392 Brown Swiss animals genotyped with the 54K SNP-chip was available for the analysis. RESULTS: In general, the use of SNP genotypes proved to be very effective for the identification of mutation carriers. The best predictive models were Lasso, SVML and MAG, with an average error rate, respectively, of 0.2 %, 0.4 % and 0.6 % in Fleckvieh, and 1.2 %, 0.9 % and 1.7 % in Brown Swiss. For the three models, the false positive rate was, respectively, 0.1 %, 0.1 % and 0.2 % in Fleckvieh, and 3.0 %, 2.4 % and 1.6 % in Brown Swiss; the false negative rate was 4.4 %, 7.6 %1.0 % in Fleckvieh, and 0.0 %, 0.1% and 0.8 % in Brown Swiss. MAG appeared to be more robust to sample size reduction: with 25 % of the data, the average error rate was 0.7 % and 2.2 % in Fleckvieh and Brown Swiss, compared to 2.1 % and 5.5 % with Lasso, and 2.6 % and 12.0 % with SVML. CONCLUSIONS: The use of SNP genotypes is a very effective and efficient technique for the identification of mutation carriers in cattle populations. Very few misclassifications were observed, overall and both in the carriers and non-carriers classes. This indicates that this is a very reliable approach for potential applications in cattle breeding.

A new polymorphism on chromosome 6 associated with bolting tendency in sugar beet.

BACKGROUND: Premature flowering or bolting is an undesirable characteristic that causes severe sugar yield losses and interferes with harvesting. Vernalization is a prerequisite for the floral induction, achieved by exposure to low temperatures for 10-14 weeks. This process is also controlled by other environmental factors, such as long daylight photoperiods and a combination of genetic factors. The objective of this study was the identification of new genetic polymorphisms linked to bolting tendency in sugar beet. RESULTS: Two pollinators characterized by low and high bolting tendency were subjected to RAD-sequencing in order to detect discriminating SNPs between lines. 6,324 putative SNPs were identified. Of these, 192 were genotyped in a set of 19 pollinators, each comprising bolted and non-bolted individuals, for a total of 987 samples. Among the 192 candidate SNPs, the strongest overall association was found for SNP183 on chromosome 6 (p-value = 1.246 10(-13)). The association between SNP183 and bolting tendency was then confirmed in an independent population of 730 plants from 11 breeding lines (p-value = 0.0061). SNP183 is located in the intron of Bv_22330_orky, a sugar beet homolog of a matrix metalloproteinase (MMP) gene that could be implied in flowering in Arabidopsis thaliana. CONCLUSION: Our data support a significant association between an intronic SNP in the MMP gene located on chromosome 6 and the regulation of bolting tendency in sugar beet. The newly identified locus supports the polygenic nature of flowering control. The associated marker can be used to design SNP panels for the discrimination of bolters and non-bolters, to be used in sugar beet breeding programs for the development of improved germplasm with low bolting tendency.

Predicting haplotype carriers from SNP genotypes in Bos taurus through linear discriminant analysis.

BACKGROUND: SNP (single nucleotide polymorphisms) genotype data are increasingly available in cattle populations and, among other things, can be used to predict carriers of specific haplotypes. It is therefore convenient to have a practical statistical method for the accurate classification of individuals into carriers and non-carriers. In this paper, we present a procedure combining variable selection (i.e. the selection of predictive SNPs) and linear discriminant analysis for the identification of carriers of a haplotype on BTA19 (Bos taurus autosome 19) known to be associated with reduced cow fertility. A population of 3645 Brown Swiss cows and bulls genotyped with the 54K SNP-chip was available for the analysis. RESULTS: The overall error rate for the prediction of haplotype carriers was on average very low (∼≤1%). The error rate was found to depend on the number of SNPs in the model and their density around the region of the haplotype on BTA19. The minimum set of SNPs to still achieve accurate predictions was 5, with a total test error rate of 1.59. CONCLUSIONS: The paper describes a procedure to accurately identify haplotype carriers from SNP genotypes in cattle populations. Very few misclassifications were observed, which indicates that this is a very reliable approach for potential applications in cattle breeding.

Relative extended haplotype homozygosity signals across breeds reveal dairy and beef specific signatures of selection.

BACKGROUND: A number of methods are available to scan a genome for selection signatures by evaluating patterns of diversity within and between breeds. Among these, "extended haplotype homozygosity" (EHH) is a reliable approach to detect genome regions under recent selective pressure. The objective of this study was to use this approach to identify regions that are under recent positive selection and shared by the most representative Italian dairy and beef cattle breeds. RESULTS: A total of 3220 animals from Italian Holstein (2179), Italian Brown (775), Simmental (493), Marchigiana (485) and Piedmontese (379) breeds were genotyped with the Illumina BovineSNP50 BeadChip v.1. After standard quality control procedures, genotypes were phased and core haplotypes were identified. The decay of linkage disequilibrium (LD) for each core haplotype was assessed by measuring the EHH. Since accurate estimates of local recombination rates were not available, relative EHH (rEHH) was calculated for each core haplotype. Genomic regions that carry frequent core haplotypes and with significant rEHH values were considered as candidates for recent positive selection. Candidate regions were aligned across to identify signals shared by dairy or beef cattle breeds. Overall, 82 and 87 common regions were detected among dairy and beef cattle breeds, respectively. Bioinformatic analysis identified 244 and 232 genes in these common genomic regions. Gene annotation and pathway analysis showed that these genes are involved in molecular functions that are biologically related to milk or meat production. CONCLUSIONS: Our results suggest that a multi-breed approach can lead to the identification of genomic signatures in breeds of cattle that are selected for the same production goal and thus to the localisation of genomic regions of interest in dairy and beef production.

Genome-enabled predictions for binomial traits in sugar beet populations.

BACKGROUND: Genomic information can be used to predict not only continuous but also categorical (e.g. binomial) traits. Several traits of interest in human medicine and agriculture present a discrete distribution of phenotypes (e.g. disease status). Root vigor in sugar beet (B. vulgaris) is an example of binomial trait of agronomic importance. In this paper, a panel of 192 SNPs (single nucleotide polymorphisms) was used to genotype 124 sugar beet individual plants from 18 lines, and to classify them as showing "high" or "low" root vigor. RESULTS: A threshold model was used to fit the relationship between binomial root vigor and SNP genotypes, through the matrix of genomic relationships between individuals in a genomic BLUP (G-BLUP) approach. From a 5-fold cross-validation scheme, 500 testing subsets were generated. The estimated average cross-validation error rate was 0.000731 (0.073%). Only 9 out of 12326 test observations (500 replicates for an average test set size of 24.65) were misclassified. CONCLUSIONS: The estimated prediction accuracy was quite high. Such accurate predictions may be related to the high estimated heritability for root vigor (0.783) and to the few genes with large effect underlying the trait. Despite the sparse SNP panel, there was sufficient within-scaffold LD where SNPs with large effect on root vigor were located to allow for genome-enabled predictions to work.

Genome-wide association study for morphological and hunting-behavior traits in Braque Français Type Pyrénées dogs: A preliminary study.

High-throughput genotyping offers great potential to increase our understanding of the genomic basis of canid variation. Braque Français Type Pyrénées (BRA) are smart, agile, and friendly dogs originally developed for tracking, hunting, and retrieving feathered game. On a population of 44 unrelated BRA dogs, single nucleotide polymorphism (SNP) genotype data from the CanineHD Whole-Genome Genotyping BeadChip and evaluation scores for 12 traits related to morphology and hunting performance were available. After quality filtering, 95,859 SNPs on the 38 dog autosomes (CFA) were retained. Phenotypic scores were expressed on a scale from 1 (worst) to 6 (best) and were mostly poorly to moderately correlated except for some morphological traits (e.g. r = 0.81 between the conformation of the head and that of the eye). From GWAS, a total of 378 SNP-phenotype associations with posterior odds of association > 1 have been detected. The strongest associations were found for the eye conformation, for the skull/muzzle ratio, and for connection to the hunter. These included both new and previously identified markers and genes potentially involved with type and behavior traits in BRA. Six of the significant markers mapped within SETDB2, a gene known to be related to pointing behavior in dogs. These results advance our understanding of the genetic basis for morphology and hunting behavior in dogs and identify new variants which are potential targets for further research.

Biologically meaningful genome interpretation models to address data underdetermination for the leaf and seed ionome prediction in Arabidopsis thaliana.

Genome interpretation (GI) encompasses the computational attempts to model the relationship between genotype and phenotype with the goal of understanding how the first leads to the second. While traditional approaches have focused on sub-problems such as predicting the effect of single nucleotide variants or finding genetic associations, recent advances in neural networks (NNs) have made it possible to develop end-to-end GI models that take genomic data as input and predict phenotypes as output. However, technical and modeling issues still need to be fixed for these models to be effective, including the widespread underdetermination of genomic datasets, making them unsuitable for training large, overfitting-prone, NNs. Here we propose novel GI models to address this issue, exploring the use of two types of transfer learning approaches and proposing a novel Biologically Meaningful Sparse NN layer specifically designed for end-to-end GI. Our models predict the leaf and seed ionome in A.thaliana, obtaining comparable results to our previous over-parameterized model while reducing the number of parameters by 8.8 folds. We also investigate how the effect of population stratification influences the evaluation of the performances, highlighting how it leads to (1) an instance of the Simpson's Paradox, and (2) model generalization limitations.

Characterization of heterozygosity-rich regions in Italian and worldwide goat breeds.

Heterozygosity-rich regions (HRR) are genomic regions of high heterozygosity, which may harbor loci related to key functional traits such as immune response, survival rate, fertility, and other fitness traits. This study considered 30 Italian and 19 worldwide goat breeds genotyped with the Illumina GoatSNP50k BeadChip. The aim of the work was to study inter-breed relationships and HRR patterns using Sliding Window (SW) and Consecutive Runs (CR) detection methods. Genetic relationships highlighted a clear separation between non-European and European breeds, as well as the north-south geographic cline within the latter. The Pearson correlation coefficients between the descriptive HRR parameters obtained with the SW and CR methods were higher than 0.9. A total of 166 HRR islands were detected. CHI1, CHI11, CHI12 and CHI18 were the chromosomes harboring the highest number of HRR islands. The genes annotated in the islands were linked to various factors such as productive, reproductive, immune, and environmental adaptation mechanisms. Notably, the Montecristo feral goat showed the highest number of HRR islands despite the high level of inbreeding, underlining potential balancing selection events characterizing its evolutionary history. Identifying a species-specific HRR pattern could provide a clearer view of the mechanisms regulating the genome modelling following anthropogenic selection combined with environmental interaction.

A genome-wide association study for survival from a multi-centre European study identified variants associated with COVID-19 risk of death.

The clinical manifestations of SARS-CoV-2 infection vary widely among patients, from asymptomatic to life-threatening. Host genetics is one of the factors that contributes to this variability as previously reported by the COVID-19 Host Genetics Initiative (HGI), which identified sixteen loci associated with COVID-19 severity. Herein, we investigated the genetic determinants of COVID-19 mortality, by performing a case-only genome-wide survival analysis, 60 days after infection, of 3904 COVID-19 patients from the GEN-COVID and other European series (EGAS00001005304 study of the COVID-19 HGI). Using imputed genotype data, we carried out a survival analysis using the Cox model adjusted for age, age2, sex, series, time of infection, and the first ten principal components. We observed a genome-wide significant (P-value < 5.0 × 10-8) association of the rs117011822 variant, on chromosome 11, of rs7208524 on chromosome 17, approaching the genome-wide threshold (P-value = 5.19 × 10-8). A total of 113 variants were associated with survival at P-value < 1.0 × 10-5 and most of them regulated the expression of genes involved in immune response (e.g., CD300 and KLR genes), or in lung repair and function (e.g., FGF19 and CDH13). Overall, our results suggest that germline variants may modulate COVID-19 risk of death, possibly through the regulation of gene expression in immune response and lung function pathways.

Effect of the Selective Dry Cow Therapy on Udder Health and Milk Microbiota.

Recently, the use of antimicrobials on dairy farms has been significantly limited from both the legislative and consumer points of view. This study aims to check the efficacy of selective dry cow therapy (SDCT) versus blanket dry cow therapy (BDCT) on bovine udder in healthy animals. SDTC is when an antibiotic is administered only to infected cows, compared with BDCT, where all cows receive an antimicrobial, regardless of their infection status. The milk samples were collected from enrolled Holstein Friesian cows 7 days before dry-off (T0) and 10 days after calving (T1) to assess somatic cell count (SCC), intramammary infections (IMIs), and milk microbiota variation. After pre-drying sampling, cows are randomly assigned to the following treatments: internal teat sealant alone (ITS; 24 cows), which is a treatment in a cow that does not receive antibiotics in SDTC, or in combination with intramammary antibiotic treatment (A+ITS; 22 cows). Non-statistically significant results are found between the two treatment groups at T1 for SCC, milk yield, and alpha diversity in milk microbiota. A statistically (p < 0.033) T1 IMI decrease is reported in the A+ITS group, and a significant beta diversity analysis is shown between the two timepoints (p = 0.009). This study confirms the possibility of selective drying without new IMI risk or increased SCC at calving, considering healthy cows without contagious infections and SCC values >200,000 cells/mL in the previous lactation.

Effects of Thymbra capitata essential oil on in vitro fermentation end-products and ruminal bacterial communities.

An in vitro trial was carried out to investigate the effects of natural Thymbra capitata essential oil (NEO) and its main compounds [including carvacrol, p-cymene, γ-terpinene given alone or in a synthetic combination (SEO)] on ruminal fermentation and the bacterial community using batch cultures inoculated with ruminal digesta and incubating two different basal diets [high-forage (F) and high-concentrate (C) diet]. After 24 h of incubation, primary fermentation end-products [gas, methane, volatile fatty acids (VFAs) and ammonia] and rumen microbial diversity were determined. NEO reduced the total VFA concentration (P < 0.05) only in the C diet. In contrast, SEO and carvacrol decreased the total VFA concentration (P < 0.05) only in the F diet. Methane production was not affected (P > 0.05) by any of the experimental treatments or diets evaluated. Microbial diversity analysis showed only a moderate effect of carvacrol and SEO on 13 genera, including, mainly, Atopobium and Blautia (involved in subacute ruminal acidosis) or Candidatus Saccharimonas (related to laminitis). In conclusion, T. capitata EO has a limited potential to attain nutritional or environmental benefits, but further research should be carried out to clarify its effects on animal health and microbial food safety.