Critical assessment of variant prioritization methods for rare disease diagnosis within the rare genomes project.

BACKGROUND: A major obstacle faced by families with rare diseases is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years and causal variants are identified in under 50%, even when capturing variants genome-wide. To aid in the interpretation and prioritization of the vast number of variants detected, computational methods are proliferating. Knowing which tools are most effective remains unclear. To evaluate the performance of computational methods, and to encourage innovation in method development, we designed a Critical Assessment of Genome Interpretation (CAGI) community challenge to place variant prioritization models head-to-head in a real-life clinical diagnostic setting. METHODS: We utilized genome sequencing (GS) data from families sequenced in the Rare Genomes Project (RGP), a direct-to-participant research study on the utility of GS for rare disease diagnosis and gene discovery. Challenge predictors were provided with a dataset of variant calls and phenotype terms from 175 RGP individuals (65 families), including 35 solved training set families with causal variants specified, and 30 unlabeled test set families (14 solved, 16 unsolved). We tasked teams to identify causal variants in as many families as possible. Predictors submitted variant predictions with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on the rank position of causal variants, and the maximum F-measure, based on precision and recall of causal variants across all EPCR values. RESULTS: Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performers recalled causal variants in up to 13 of 14 solved families within the top 5 ranked variants. Newly discovered diagnostic variants were returned to two previously unsolved families following confirmatory RNA sequencing, and two novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant in an unsolved proband with phenotypes consistent with asparagine synthetase deficiency. CONCLUSIONS: Model methodology and performance was highly variable. Models weighing call quality, allele frequency, predicted deleteriousness, segregation, and phenotype were effective in identifying causal variants, and models open to phenotype expansion and non-coding variants were able to capture more difficult diagnoses and discover new diagnoses. Overall, computational models can significantly aid variant prioritization. For use in diagnostics, detailed review and conservative assessment of prioritized variants against established criteria is needed.

Persistence of autozygosity in crossbreds between autochthonous and cosmopolitan breeds of swine: a simulation study.

Crossbreeding might be a valid strategy to valorize local pig breeds. Crossbreeding should reduce homozygosity and, as a consequence, yield hybrid vigor for fitness and production traits. This study aimed to quantify the persistence of autozygosity in terminal crossbred pigs compared with purebreds and, in turn, identify genomic regions where autozygosity's persistence would not be found. The study was based on genotyping data from 20 European local pig breeds and three cosmopolitan pig breeds used to simulate crossbred offspring. This study consisted of two steps. First, one hundred matings were simulated for each pairwise combination of the 23 considered breeds (for a total of 276 combinations), ignoring the sex of the parent individuals in order to generate purebred and crossbred matings leveraging all the germplasm available. Second, a few preselected terminal-maternal breed pairs were used to mimic a realistic terminal crossbreeding system: (i) Mora Romagnola (boars) or Cinta Senese (boars) crossed with Large White (sows) or Landrace (sows); (ii) Duroc (boars) crossed with Mora Romagnola (sows) or Cinta Senese (sows). Runs of homozygosity was used to estimate genome-wide autozygosity (FROH). Observed FROH was higher in purebreds than in crossbreds, although some crossbred combinations showed higher FROH than other purebred combinations. Among the purebreds, the highest FROH values were observed in Mora Romagnola and Turopolje (0.50 and 0.46, respectively). FROH ranged from 0.04 to 0.16 in the crossbreds Alentejana × Large White and Alentejana × Iberian, respectively. Persistence of autozygosity was found in several genomic segments harboring regions where quantitative trait loci (QTLs) were found in the literature. The regions were enriched in QTLs involved in fatty acid metabolism and associated with performance traits. This simulation shows that autozygosity persists in most breed combinations of terminal crosses. Results suggest that a strategy for crossbreeding is implemented when leveraging autochthonous and cosmopolitan breeds to obtain most of the hybrid vigor.

Identification of population-informative markers from high-density genotyping data through combined feature selection and machine learning algorithms: Application to European autochthonous and cosmopolitan pig breeds.

Large genotyping datasets, obtained from high-density single nucleotide polymorphism (SNP) arrays, developed for different livestock species, can be used to describe and differentiate breeds or populations. To identify the most discriminating genetic markers among thousands of genotyped SNPs, a few statistical approaches have been proposed. In this study, we applied the Boruta algorithm, a wrapper of the machine learning random forest algorithm, on a database of 23 European pig breeds (20 autochthonous and three cosmopolitan breeds) genotyped with a 70k SNP chip, to pre-select informative SNPs. To identify different sets of SNPs, these pre-selected markers were then ranked with random forest based on their mean decrease accuracy and mean decrease gene indexes. We evaluated the efficiency of these subsets for breed classification and the usefulness of this approach to detect candidate genes affecting breed-specific phenotypes and relevant production traits that might differ among breeds. The lowest overall classification error (2.3%) was reached with a subpanel including only 398 SNPs (ranked based on their mean decrease accuracy), with no classification error in seven breeds using up to 49 SNPs. Several SNPs of these selected subpanels were in genomic regions in which previous studies had identified signatures of selection or genes associated with morphological or production traits that distinguish the analysed breeds. Therefore, even if these approaches have not been originally designed to identify signatures of selection, the obtained results showed that they could potentially be useful for this purpose.

Genome-wide association studies for diarrhoea outcomes identified genomic regions affecting resistance to a severe enteropathy in suckling rabbits.

Selection and breeding strategies to improve resistance to enteropathies are essential to reaching the sustainability of the rabbit production systems. However, disease heterogeneity (having only as major visible symptom diarrhoea) and low disease heritability are two barriers for the implementation of these strategies. Diarrhoea condition can affect rabbits at different life stages, starting from the suckling period, with large negative economic impacts. In this study, from a commercial population of suckling rabbits (derived from 133 litters) that experienced an outbreak of enteropathy, we first selected a few animals that died with severe symptoms of diarrhoea and characterized their microbiota, using 16S rRNA gene sequencing data. Clostridium genus was consistently present in all affected specimens. In addition, with the aim to identify genetic markers in the rabbit genome that could be used as selection tools, we performed genome-wide association studies for symptoms of diarrhoea in the same commercial rabbit population. These studies were also complemented with FST analyses between the same groups of rabbits. A total of 332 suckling rabbits (151 with severe symptoms of diarrhoea, 42 with mild symptoms and 129 without any symptoms till the weaning period), derived from 45 different litters (a subset of the 133 litters) were genotyped with the Affymetrix Axiom OrcunSNP Array. In both genomic approaches, rabbits within litters were paired to constitute two groups (susceptible and resistant, including the mildly affected in one or the other group) and run case and control genome-wide association analyses. Genomic heritability estimated in the designed experimental structure integrated in a commercial breeding scheme was 0.19-0.21 (s.e. 0.09-0.10). A total of eight genomic regions on rabbit chromosome 2 (OCU2), OCU3, OCU7, OCU12, OCU13, OCU16 and in an unassembled scaffold had significant single nucleotide polymorphisms (SNPs) and/or markers that trespassed the FST percentile distribution. Among these regions, three main peaks of SNPs were identified on OCU12, OCU13 and OCU16. The QTL region on OCU13 encompasses several genes that encode members of a family of immunoglobulin Fc receptors (FCER1G, FCRLA, FCRLB and FCGR2A) involved in the immune innate system, which might be important candidate genes for this pathogenic condition. The results obtained in this study demonstrated that resistance to an enteropathy occurring in suckling rabbits is in part genetically determined and can be dissected at the genomic level, providing DNA markers that could be used in breeding programmes to increase resistance to enteropathies in meat rabbits.

Comparative targeted metabolomic profiles of porcine plasma and serum.

Metabolomics has been used to characterise many biological matrices and obtain detailed pictures of biological systems based on many metabolites. Plasma and serum are two blood-derived biofluids commonly used to assess and monitor the organismal metabolism and obtain information on the physiological and health conditions of an animal. Plasma is the supernatant that is separated from the cellular components after centrifugation of the blood that is first added with an anticoagulant. Serum is obtained after centrifugation of the blood that has been coagulated. The choice of one or the other biofluid for metabolomic analyses is related to specific analytical needs and technical issues, to problems derived by the collection and preparation steps, in particular when specimens are sampled from animals involved in field studies. Thus far, most of the metabolomic studies that compared plasma and serum have been carried out in humans and very little is known on the pigs. In this study, we used a targeted metabolomic platform that can detect about 180 metabolites of five biochemical classes to compare plasma and serum profiles of samples collected from 24 pigs. To also obtain a cross-species comparative metabolomic analysis, information for human plasma and serum derived from the same platform was retrieved from previous studies. Statistical analyses included univariate and multivariate approaches aimed at identifying stable and/or differentially abundant metabolites between the two porcine biofluids. A total of 154 (∼83%) metabolites passed the initial quality control, indicating a good repeatability of the analytical platform in pigs. Discarded metabolites included aspartate and biogenic amines that were already reported to be unstable in human studies. More than 80% of the metabolites had similar profiles in both porcine biofluids (average correlation was 0.75). Concentrations were usually higher in serum than in plasma, in agreement with what was already reported in humans. The univariate analysis identified 44 metabolites that had statistically different concentrations between porcine plasma and serum, of which 28 metabolites were also confirmed by the multivariate analysis. The obtained picture described similarities and differences between these two biofluids in pigs and the related human-pig comparisons. The obtained information can be useful for the choice of one or the other matrix for the implementation of metabolomic studies in this livestock species. The results can also provide useful hints to valuing the pig as animal model, in particular when metabolite-derived physiological states are relevant.

A meta-analysis of genetic and phenotypic diversity of European local pig breeds reveals genomic regions associated with breed differentiation for production traits.

BACKGROUND: Intense selection of modern pig breeds has resulted in genetic improvement of production traits while the performance of local pig breeds has remained lower. As local pig breeds have been bred in extensive systems, they have adapted to specific environmental conditions, resulting in a rich genotypic and phenotypic diversity. This study is based on European local pig breeds that have been genetically characterized using DNA-pool sequencing data and phenotypically characterized using breed level phenotypes related to stature, fatness, growth, and reproductive performance traits. These data were analyzed using a dedicated approach to detect signatures of selection linked to phenotypic traits in order to uncover potential candidate genes that may underlie adaptation to specific environments. RESULTS: Analysis of the genetic data of European pig breeds revealed four main axes of genetic variation represented by the Iberian and three modern breeds (i.e. Large White, Landrace, and Duroc). In addition, breeds clustered according to their geographical origin, for example French Gascon and Basque breeds, Italian Apulo Calabrese and Casertana breeds, Spanish Iberian, and Portuguese Alentejano breeds. Principal component analysis of the phenotypic data distinguished the larger and leaner breeds with better growth potential and reproductive performance from the smaller and fatter breeds with low growth and reproductive efficiency. Linking the signatures of selection with phenotype identified 16 significant genomic regions associated with stature, 24 with fatness, 2 with growth, and 192 with reproduction. Among them, several regions contained candidate genes with possible biological effects on stature, fatness, growth, and reproductive performance traits. For example, strong associations were found for stature in two regions containing, respectively, the ANXA4 and ANTXR1 genes, for fatness in a region containing the DNMT3A and POMC genes and for reproductive performance in a region containing the HSD17B7 gene. CONCLUSIONS: In this study on European local pig breeds, we used a dedicated approach for detecting signatures of selection that were supported by phenotypic data at the breed level to identify potential candidate genes that may have adapted to different living environments and production systems.

Signatures of Admixture and Genetic Uniqueness in the Autochthonous Greek Black Pig Breed Deduced from Gene Polymorphisms Affecting Domestication-Derived Traits.

The Greek Black Pig (or Greek Pig) is the only recognized autochthonous pig breed raised in Greece, usually in extensive or semi-extensive production systems. According to its name, the characteristic breed coat color is solid black. In this study, with the aim to start a systematic genetic characterization of the Greek Black Pig breed, we investigated polymorphisms in major genes well known to affect exterior and production traits (MC1R, KIT, NR6A1, VRTN and IGF2) and compared these data with population genetic information available in other Mediterranean and Western Balkan pig breeds and wild boars. None of the investigated gene markers were fixed for one allele, suggesting that, in the past, this breed experienced introgression from wild boars and admixture from cosmopolitan pig breeds, enriching the breed genetic pool that should be further investigated to design appropriate conservation genetic strategies. We identified a new MC1R allele, containing two missense mutations already reported in two other independent alleles, but here present in the same haplotype. This allele might be useful to disclose biological information that can lead to better understanding the cascade transmission of signals to produce melanin pigments. This study demonstrated that autochthonous genetic resources can be an interesting reservoir of unexpected genetic variants.

CAGI6 ID-Challenge: Assessment of phenotype and variant predictions in 415 children with Neurodevelopmental Disorders (NDDs).

In the context of the Critical Assessment of the Genome Interpretation, 6th edition (CAGI6), the Genetics of Neurodevelopmental Disorders Lab in Padua proposed a new ID-challenge to give the opportunity of developing computational methods for predicting patient's phenotype and the causal variants. Eight research teams and 30 models had access to the phenotype details and real genetic data, based on the sequences of 74 genes (VCF format) in 415 pediatric patients affected by Neurodevelopmental Disorders (NDDs). NDDs are clinically and genetically heterogeneous conditions, with onset in infant age. In this study we evaluate the ability and accuracy of computational methods to predict comorbid phenotypes based on clinical features described in each patient and causal variants. Finally, we asked to develop a method to find new possible genetic causes for patients without a genetic diagnosis. As already done for the CAGI5, seven clinical features (ID, ASD, ataxia, epilepsy, microcephaly, macrocephaly, hypotonia), and variants (causative, putative pathogenic and contributing factors) were provided. Considering the overall clinical manifestation of our cohort, we give out the variant data and phenotypic traits of the 150 patients from CAGI5 ID-Challenge as training and validation for the prediction methods development.

Critical assessment of variant prioritization methods for rare disease diagnosis within the Rare Genomes Project.

Background: A major obstacle faced by rare disease families is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years, and causal variants are identified in under 50%. The Rare Genomes Project (RGP) is a direct-to-participant research study on the utility of genome sequencing (GS) for diagnosis and gene discovery. Families are consented for sharing of sequence and phenotype data with researchers, allowing development of a Critical Assessment of Genome Interpretation (CAGI) community challenge, placing variant prioritization models head-to-head in a real-life clinical diagnostic setting. Methods: Predictors were provided a dataset of phenotype terms and variant calls from GS of 175 RGP individuals (65 families), including 35 solved training set families, with causal variants specified, and 30 test set families (14 solved, 16 unsolved). The challenge tasked teams with identifying the causal variants in as many test set families as possible. Ranked variant predictions were submitted with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on rank position of true positive causal variants and maximum F-measure, based on precision and recall of causal variants across EPCR thresholds. Results: Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performing teams recalled the causal variants in up to 13 of 14 solved families by prioritizing high quality variant calls that were rare, predicted deleterious, segregating correctly, and consistent with reported phenotype. In unsolved families, newly discovered diagnostic variants were returned to two families following confirmatory RNA sequencing, and two prioritized novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant, in an unsolved proband with phenotype overlap with asparagine synthetase deficiency. Conclusions: By objective assessment of variant predictions, we provide insights into current state-of-the-art algorithms and platforms for genome sequencing analysis for rare disease diagnosis and explore areas for future optimization. Identification of diagnostic variants in unsolved families promotes synergy between researchers with clinical and computational expertise as a means of advancing the field of clinical genome interpretation.

Ancient DNA re-opens the question of the phylogenetic position of the Sardinian pika Prolagus sardus (Wagner, 1829), an extinct lagomorph.

Palaeogenomics is contributing to refine our understanding of many major evolutionary events at an unprecedented resolution, with relevant impacts in several fields, including phylogenetics of extinct species. Few extant and extinct animal species from Mediterranean regions have been characterised at the DNA level thus far. The Sardinian pika, Prolagus sardus (Wagner, 1829), was an iconic lagomorph species that populated Sardinia and Corsica and became extinct during the Holocene. There is a certain scientific debate on the phylogenetic assignment of the extinct genus Prolagus to the family Ochotonidae (one of the only two extant families of the order Lagomorpha) or to a separated family Prolagidae, or to the subfamily Prolaginae within the family Ochotonidae. In this study, we successfully reconstructed a portion of the mitogenome of a Sardinian pika dated to the Neolithic period and recovered from the Cabaddaris cave, an archaeological site in Sardinia. Our calibrated phylogeny may support the hypothesis that the genus Prolagus is an independent sister group to the family Ochotonidae that diverged from the Ochotona genus lineage about 30 million years ago. These results may contribute to refine the phylogenetic interpretation of the morphological peculiarities of the Prolagus genus already described by palaeontological studies.

Population genomic structures and signatures of selection define the genetic uniqueness of several fancy and meat rabbit breeds.

Following the recent domestication process of the European rabbit (Oryctolagus cuniculus), many different breeds and lines, distinguished primarily by exterior traits such as coat colour, fur structure and body size and shape, have been constituted. In this study, we genotyped, with a high-density single-nucleotide polymorphism panel, a total of 645 rabbits from 10 fancy breeds (Belgian Hare, Champagne d'Argent, Checkered Giant, Coloured Dwarf, Dwarf Lop, Ermine, Giant Grey, Giant White, Rex and Rhinelander) and three meat breeds (Italian White, Italian Spotted and Italian Silver). ADMIXTURE analysis indicated that breeds with similar phenotypic traits (e.g. coat colour and body size) shared common ancestries. Signatures of selection using two haplotype-based approaches (iHS and XP-EHH), combined with the results obtained with other methods previously reported that we applied to the same breeds, we identified a total of 5079 independent genomic regions with some signatures of selection, covering about 1777 Mb of the rabbit genome. These regions consistently encompassed many genes involved in pigmentation processes (ASIP, EDNRA, EDNRB, KIT, KITLG, MITF, OCA2, TYR and TYRP1), coat structure (LIPH) and body size, including two major genes (LCORL and HMGA2) among many others. This study revealed novel genomic regions under signatures of selection and further demonstrated that population structures and signatures of selection, left into the genome of these rabbit breeds, may contribute to understanding the genetic events that led to their constitution and the complex genetic mechanisms determining the broad phenotypic variability present in these untapped rabbit genetic resources.

Whole genome sequencing identifies candidate genes and mutations that can explain diluted and other colour varieties of domestic canaries (Serinus canaria).

The domestic canary (Serinus canaria) is one of the most common pet birds and has been extensively selected and bred over the last few centuries to constitute many different varieties. Plumage pigmentation is one of the main phenotypic traits that distinguish canary breeds and lines. Feather colours in these birds, similarly to other avian species, are mainly depended on the presence of two major types of pigments: carotenoids and melanins. In this study, we exploited whole genome sequencing (WGS) datasets produced from five canary lines or populations (Black Frosted Yellow, Opal, Onyx, Opal × Onyx and Mogno, some of which carrying different putative dilute alleles), complemented with other WGS datasets retrieved from previous studies, to identify candidate genes that might explain pigmentation variability across canary breeds and varieties. Sequencing data were obtained using a DNA pool-seq approach and genomic data were compared using window-based FST analyses. We identified signatures of selection in genomic regions harbouring genes involved in carotenoid-derived pigmentation variants (CYP2J19, EDC, BCO2 and SCARB1), confirming the results reported by previous works, and identified several other signatures of selection in the correspondence of melanogenesis-related genes (AGRP, ASIP, DCT, EDNRB, KITLG, MITF, MLPH, SLC45A2, TYRP1 and ZEB2). Two putative causative mutations were identified in the MLPH gene that may explain the Opal and Onyx dilute mutant alleles. Other signatures of selection were also identified that might explain additional phenotypic differences between the investigated canary populations.

Whole Genome Sequencing Provides Information on the Genomic Architecture and Diversity of Cultivated Gilthead Seabream (Sparus aurata) Broodstock Nuclei.

The gilthead seabream (Sparus aurata) is a species of relevance for the Mediterranean aquaculture industry. Despite the advancement of genetic tools for the species, breeding programs still do not often include genomics. In this study, we designed a genomic strategy to identify signatures of selection and genomic regions of high differentiation among populations of farmed fish stocks. A comparative DNA pooling sequencing approach was applied to identify signatures of selection in gilthead seabream from the same hatchery and from different nuclei that had not been subjected to genetic selection. Identified genomic regions were further investigated to detect SNPs with predicted high impact. The analyses underlined major genomic differences in the proportion of fixed alleles among the investigated nuclei. Some of these differences highlighted genomic regions, including genes involved in general metabolism and development already detected in QTL for growth, size, skeletal deformity, and adaptation to variation of oxygen levels in other teleosts. The obtained results pointed out the need to control the genetic effect of breeding programs in this species to avoid the reduction of genetic variability within populations and the increase in inbreeding level that, in turn, might lead to an increased frequency of alleles with deleterious effects.

Investigation of ABO Gene Variants across More Than 60 Pig Breeds and Populations and Other Suidae Species Using Whole-Genome Sequencing Datasets.

Polymorphisms in the human ABO gene determine the major blood classification system based on the three well-known forms: A; B; and O. In pigs that carry only two main alleles in this gene (A and O), we still need to obtain a more comprehensive distribution of variants, which could also impact its function. In this study, we mined more than 500 whole-genome sequencing datasets to obtain information on the ABO gene in different Suidae species, pig breeds, and populations and provide (i) a comprehensive distribution of the A and O alleles, (ii) evolutionary relationships of ABO gene sequences across Suidae species, and (iii) an exploratory evaluation of the effect of the different ABO gene variants on production traits and blood-related parameters in Italian Large White pigs. We confirmed that allele O is likely under balancing selection, present in all Sus species investigated, without being fixed in any of them. We reported a novel structural variant in perfect linkage disequilibrium with allele O that made it possible to estimate the evolutionary time window of occurrence of this functional allele. We also identified two single nucleotide polymorphisms that were suggestively associated with plasma magnesium levels in pigs. Other studies can also be constructed over our results to further evaluate the effect of this gene on economically relevant traits and basic biological functions.

A genotyping by sequencing approach can disclose Apis mellifera population genomic information contained in honey environmental DNA.

Awareness has been raised over the last years on the genetic integrity of autochthonous honey bee subspecies. Genomic tools available in Apis mellifera can make it possible to measure this information by targeting individual honey bee DNA. Honey contains DNA traces from all organisms that contributed or were involved in its production steps, including the honey bees of the colony. In this study, we designed and tested a genotyping by sequencing (GBS) assay to analyse single nucleotide polymorphisms (SNPs) of A. mellifera nuclear genome using environmental DNA extracted from honey. A total of 121 SNPs (97 SNPs informative for honey bee subspecies identification and 24 SNPs associated with relevant traits of the colonies) were used in the assay to genotype honey DNA, which derives from thousands of honey bees. Results were integrated with information derived from previous studies and whole genome resequencing datasets. This GBS method is highly reliable in estimating honey bee SNP allele frequencies of the whole colony from which the honey derived. This assay can be used to identify the honey bee subspecies of the colony that produced the honey and, in turn, to authenticate the entomological origin of the honey.

Comparative analysis of genomic inbreeding parameters and runs of homozygosity islands in several fancy and meat rabbit breeds.

Runs of homozygosity (ROH) are defined as long stretches of DNA homozygous at each polymorphic position. The proportion of genome covered by ROH and their length are indicators of the level and origin of inbreeding. In this study, we analysed SNP chip datasets (obtained using the Axiom OrcunSNP Array) of a total of 702 rabbits from 12 fancy breeds and four meat breeds to identify ROH with different approaches and calculate several genomic inbreeding parameters. The highest average number of ROH per animal was detected in Belgian Hare (~150) and the lowest in Italian Silver (~106). The average length of ROH ranged from 4.001 ± 0.556 Mb in Italian White to 6.268 ± 1.355 Mb in Ermine. The same two breeds had the lowest (427.9 ± 86.4 Mb, Italian White) and the highest (921.3 ± 179.8 Mb, Ermine) average values of the sum of all ROH segments. More fancy breeds had a higher level of genomic inbreeding (as defined by ROH) than meat breeds. Several ROH islands contain genes involved in body size, body length, pigmentation processes, carcass traits, growth, and reproduction traits (e.g.: AOX1, GPX5, IFRD1, ITGB8, NELL1, NR3C1, OCA2, TRIB1, TRIB2). Genomic inbreeding parameters can be useful to overcome the lack of information in the management of rabbit genetic resources. ROH provided information to understand, to some extent, the genetic history of rabbit breeds and to identify signatures of selection in the rabbit genome.

Admixture and breed traceability in European indigenous pig breeds and wild boar using genome-wide SNP data.

Preserving diversity of indigenous pig (Sus scrofa) breeds is a key factor to (i) sustain the pork chain (both at local and global scales) including the production of high-quality branded products, (ii) enrich the animal biobanking and (iii) progress conservation policies. Single nucleotide polymorphism (SNP) chips offer the opportunity for whole-genome comparisons among individuals and breeds. Animals from twenty European local pigs breeds, reared in nine countries (Croatia: Black Slavonian, Turopolje; France: Basque, Gascon; Germany: Schwabisch-Hällisches Schwein; Italy: Apulo Calabrese, Casertana, Cinta Senese, Mora Romagnola, Nero Siciliano, Sarda; Lithuania: Indigenous Wattle, White Old Type; Portugal: Alentejana, Bísara; Serbia: Moravka, Swallow-Bellied Mangalitsa; Slovenia: Krskopolje pig; Spain: Iberian, Majorcan Black), and three commercial breeds (Duroc, Landrace and Large White) were sampled and genotyped with the GeneSeek Genomic Profiler (GGP) 70 K HD porcine genotyping chip. A dataset of 51 Wild Boars from nine countries was also added, summing up to 1186 pigs (~ 49 pigs/breed). The aim was to: (i) investigate individual admixture ancestries and (ii) assess breed traceability via discriminant analysis on principal components (DAPC). Albeit the mosaic of shared ancestries found for Nero Siciliano, Sarda and Moravka, admixture analysis indicated independent evolvement for the rest of the breeds. High prediction accuracy of DAPC mark SNP data as a reliable solution for the traceability of breed-specific pig products.

One Health and Cattle Genetic Resources: Mining More than 500 Cattle Genomes to Identify Variants in Candidate Genes Potentially Affecting Coronavirus Infections.

Epidemiological and biological characteristics of coronaviruses and their ability to cross species barriers are a matter of increasing concerns for these zoonotic agents. To prevent their spread, One Health approaches should be designed to include the host (animal) genome variability as a potential risk factor that might confer genetic resistance or susceptibility to coronavirus infections. At present, there is no example that considers cattle genetic resources for this purpose. In this study, we investigated the variability of six genes (ACE2, ANPEP, CEACAM1 and DPP4 encoding for host receptors of coronaviruses; FURIN and TMPRSS2 encoding for host proteases involved in coronavirus infection) by mining whole genome sequencing datasets from more than 500 cattle of 34 Bos taurus breeds and three related species. We identified a total of 180 protein variants (44 already known from the ARS-UCD1.2 reference genome). Some of them determine altered protein functions or the virus-host interaction and the related virus entry processes. The results obtained in this study constitute a first step towards the definition of a One Health strategy that includes cattle genetic resources as reservoirs of host gene variability useful to design conservation and selection programs to increase resistance to coronavirus diseases.

Genetic diversity and population structure of six autochthonous pig breeds from Croatia, Serbia, and Slovenia.

BACKGROUND: The importance of local breeds as genetic reservoirs of valuable genetic variation is well established. Pig breeding in Central and South-Eastern Europe has a long tradition that led to the formation of several local pig breeds. In the present study, genetic diversity parameters were analysed in six autochthonous pig breeds from Slovenia, Croatia and Serbia (Banija spotted, Black Slavonian, Turopolje pig, Swallow-bellied Mangalitsa, Moravka and Krskopolje pig). Animals from each of these breeds were genotyped using microsatellites and single nucleotide polymorphisms (SNPs). The results obtained with these two marker systems and those based on pedigree data were compared. In addition, we estimated inbreeding levels based on the distribution of runs of homozygosity (ROH) and identified genomic regions under selection pressure using ROH islands and the integrated haplotype score (iHS). RESULTS: The lowest heterozygosity values calculated from microsatellite and SNP data were observed in the Turopolje pig. The observed heterozygosity was higher than the expected heterozygosity in the Black Slavonian, Moravka and Turopolje pig. Both types of markers allowed us to distinguish clusters of individuals belonging to each breed. The analysis of admixture between breeds revealed potential gene flow between the Mangalitsa and Moravka, and between the Mangalitsa and Black Slavonian, but no introgression events were detected in the Banija spotted and Turopolje pig. The distribution of ROH across the genome was not uniform. Analysis of the ROH islands identified genomic regions with an extremely high frequency of shared ROH within the Swallow-bellied Mangalitsa, which harboured genes associated with cholesterol biosynthesis, fatty acid metabolism and daily weight gain. The iHS approach to detect signatures of selection revealed candidate regions containing genes with potential roles in reproduction traits and disease resistance. CONCLUSIONS: Based on the estimation of population parameters obtained from three data sets, we showed the existence of relationships among the six pig breeds analysed here. Analysis of the distribution of ROH allowed us to estimate the level of inbreeding and the extent of homozygous regions in these breeds. The iHS analysis revealed genomic regions potentially associated with phenotypic traits and allowed the detection of genomic regions under selection pressure.

Mining livestock genome datasets for an unconventional characterization of animal DNA viromes.

Whole genome sequencing (WGS) datasets, usually generated for the investigation of the individual animal genome, can be used for additional mining of the fraction of sequencing reads that remains unmapped to the respective reference genome. A significant proportion of these reads contains viral DNA derived from viruses that infected the sequenced animals. In this study, we mined more than 480 billion sequencing reads derived from 1471 WGS datasets produced from cattle, pigs, chickens and rabbits. We identified 367 different viruses among which 14, 11, 12 and 1 might specifically infect the cattle, pig, chicken and rabbit, respectively. Some of them are ubiquitous, avirulent, highly or potentially damaging for both livestock and humans. Retrieved viral DNA information provided a first unconventional and opportunistic landscape of the livestock viromes that could be useful to understand the distribution of some viruses with potential deleterious impacts on the animal food production systems.