Mammalian Genome最新文献

Congenital idiopathic megaesophagus (CIM) is a gastrointestinal disorder of dogs wherein the esophagus is dilated and swallowing activity is reduced, causing regurgitation of ingesta. Affected individuals experience weight loss and malnourishment and are at risk for aspiration pneumonia, intussusception, and euthanasia. Great Danes have among the highest incidences of CIM across dog breeds, suggesting a genetic predisposition. We generated low-pass sequencing data for 83 Great Danes and used variant calls to impute missing whole genome single-nucleotide variants (SNVs) for each individual based on haplotypes phased from 624 high-coverage dog genomes, including 21 Great Danes. We validated the utility of our imputed data set for genome-wide association studies (GWASs) by mapping loci known to underlie coat phenotypes with simple and complex inheritance patterns. We conducted a GWAS for CIM with 2,010,300 SNVs, identifying a novel locus on canine chromosome 1 (P-val = 2.76 × 10^-10). Associated SNVs are intergenic or intronic and are found in two clusters across a 1.7-Mb region. Inspection of coding regions in high-coverage genomes from affected Great Danes did not reveal candidate causal variants, suggesting that regulatory variants underlie CIM. Further studies are necessary to assess the role of these non-coding variants.

Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focussed measurable trait data. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

Protein coding genes exhibit different degrees of intolerance to loss-of-function variation. The most intolerant genes, whose function is essential for cell or/and organism survival, inform on fundamental biological processes related to cell proliferation and organism development and provide a window on the molecular mechanisms of human disease. Here we present a brief overview of the resources and knowledge gathered around gene essentiality, from cancer cell lines to model organisms to human development. We outline the implications of using different sources of evidence and definitions to determine which genes are essential and highlight how information on the essentiality status of a gene can inform novel disease gene discovery and therapeutic target identification.

Experiments in which data are collected by multiple independent resources, including multicentre data, different laboratories within the same centre or with different operators, are challenging in design, data collection and interpretation. Indeed, inconsistent results across the resources are possible. In this paper, we propose a statistical solution for the problem of multi-resource consensus inferences when statistical results from different resources show variation in magnitude, directionality, and significance. Our proposed method allows combining the corrected p-values, effect sizes and the total number of centres into a global consensus score. We apply this method to obtain a consensus score for data collected by the International Mouse Phenotyping Consortium (IMPC) across 11 centres. We show the application of this method to detect sexual dimorphism in haematological data and discuss the suitability of the methodology.

The laboratory mouse is the foremost mammalian model used for studying human diseases and is closely anatomically related to humans. Whilst knowledge about human anatomy has been collected throughout the history of mankind, the first comprehensive study of the mouse anatomy was published less than 60 years ago. This has been followed by the more recent publication of several books and resources on mouse anatomy. Nevertheless, to date, our understanding and knowledge of mouse anatomy is far from being at the same level as that of humans. In addition, the alignment between current mouse and human anatomy nomenclatures is far from being as developed as those existing between other species, such as domestic animals and humans. To close this gap, more in depth mouse anatomical research is needed and it will be necessary to extent and refine the current vocabulary of mouse anatomical terms.

Over the last decade, INFRAFRONTIER has positioned itself as a world-class Research Infrastructure for the generation, phenotyping, archiving, and distribution of mouse models in Europe. The INFRAFRONTIER network consists of 22 partners from 15 countries, and is continuously enhancing and broadening its portfolio of resources and services that are offered to the research community on a non-profit basis. By bringing together European rodent model expertise and providing valuable disease model services to the biomedical research community, INFRAFRONTIER strives to push the accessibility of cutting-edge human disease modelling technologies across the European research landscape. This article highlights the latest INFRAFRONTIER developments and informs the research community about its extensively utilised services, resources, and technical developments, specifically the intricacies of the INFRAFRONTIER database, use of Curated Disease Models, overview of the INFRAFRONTIER Cancer and Rare Disease resources, and information about its main state-of-the-art services.

Artificial and natural selective breeding of goats has resulted in many different goat breeds all around the world. Norduz goat is one of these breeds, and it is a local goat breed of Turkey. The goats are favorable due to pre-weaning viability and reproduction values compared to the regional breeds. Development in sequencing technologies has let to understand huge genomic structures and complex phenotypes. Until now, such a comprehensive study has not been carried out to understand the genomic structure of the Norduz goats, yet. In the study, the next-generation sequencing was carried out to understand the genomic structure of Norduz goat. Real-time PCR was used to evaluate prominent CNVs in the Norduz goat individuals. Whole genome of the goat was constructed with an average of 33.1X coverage level. In the stringent filtering condition, 9,757,980 SNPs, 1,536,715 InDels, and 290 CNVs were detected in the Norduz goat genome. Functional analysis of high-impact SNP variations showed that the classical complement activation biological process was affected significantly in the goat. CNVs in the goat genome were found in genes related to defense against viruses, immune response, and cell membrane transporters. It was shown that GBP2, GBP5, and mammalian ortholog GBP1, which are INF-stimulated GTPases, were found to be high copy numbers in the goats. To conclude, genetic variations mainly in immunological response processes suggest that Norduz goat is an immunologically improved goat breed and natural selection could take an important role in the genetical improvements of the goats.