Towards Uniform Gene Bank Documentation In Europe – The Experience From The EFABISnet Project

In the EFABISnet project, a collaborative effort of EAAP, FAO and partners from 14 European countries, in cooperation with the European Regional Focal Point for Animal Genetic Resources (ERFP), national information systems for monitoring the animal genetic resources on breed level were established in Austria, Cyprus, Estonia, Georgia, Iceland, Ireland, Italy, Netherlands, Slovakia, Slovenia, Switzerland, and United Kingdom. The network was soon extended beyond the project plans, with the establishment of EFABIS databases in Finland, Greece, and Hungary. The network was then complemented by a set of inventories of national gene bank collections to strengthen the documentation of ex situ conservation programmes. These documentation systems were established by the National Focal Points for management of farm animal genetic resources. Here we present the experience gained in establishment of these national inventories of gene banks and their relevance to the Strategic Priority Areas of the Global Plan of Action which could be useful for other areas in the world

Clustering of farms based on slaughterhouse health aberration data

Large amounts of data from meat inspections can be used as a tool to support interventions for improvement of herd health. We applied time-series analyses on 3.5 years of meat inspection data of two pig slaughterhouses to identify differences in health aberration patterns over time at farm-level. A negligibly evidence of seasonality and a substantial trend pattern in percentage aberrations over time were identified. Differences exist in percentage health aberrations between the farms and months. This distinction is more elaborate than just grouping farms on basis of aberration incidence

Biochemical pathways analysis of microarray results: regulation of myogenesis in pigs

<p>Abstract</p> <p>Background</p> <p>Combining microarray results and biological pathway information will add insight into biological processes. Pathway information is widely available in databases through the internet.</p> <p>Mammalian muscle formation has been previously studied using microarray technology in pigs because these animals are an interesting animal model for muscle formation due to selection for increased muscle mass. Results indicated regulation of the expression of genes involved in proliferation and differentiation of myoblasts, and energy metabolism. The aim of the present study was to analyse microarrays studying myogenesis in pigs. It was necessary to develop methods to search biochemical pathways databases.</p> <p>Results</p> <p>PERL scripts were developed that used the names of the genes on the microarray to search databases. Synonyms of gene names were added to the list by searching the Gene Ontology database. The KEGG database was searched for pathway information using this updated gene list. The KEGG database returned 88 pathways. Most genes were found in a single pathway, but others were found in up to seven pathways. Combining the pathways and the microarray information 21 pathways showed sufficient information content for further analysis. These pathways were related to regulation of several steps in myogenesis and energy metabolism. Pathways regulating myoblast proliferation and muscle fibre formation were described. Furthermore, two networks of pathways describing the formation of the myoblast cytoskeleton and regulation of the energy metabolism during myogenesis were presented.</p> <p>Conclusion</p> <p>Combining microarray results and pathways information available through the internet provide biological insight in how the process of porcine myogenesis is regulated.</p

DNA-onderzoek beantwoordt veel vragen over het Roodbont Fries Vee

De SZH en het CGN ondersteunen de fokkerij van het Roodbont Fries Vee. Met hulp van de rundveefokkerijorganisatie CRV is er op kosten van de SZH een DNA-onderzoek op de Universiteit van Luik uitgevoerd. Het DNA-onderzoek geeft duidelijke antwoorden over de verschillen in genetische samenstelling in het Roodbont Fries Vee en de verschillen met de andere Nederlandse rassen

Unieke genetische variatie in een bijzondere Nederlandse rundveestapel met zeldzame kleuren en aftekeningen

The herd of van der Veen family consists of about 40 cattle with the color-sided pattern and the rare diluted and roan color. Rare colors and pattern that are brought together through years of targeted breeding and conservation. The exclusive herd of small size therefore has unique combinations of rare alleles and genotypes. Because there are no registration papers present, the genetic make-up of this herd was investigated through DNA analysis. The herd of van der Veen family does not cluster with any one of the local Dutch cattle breeds and therefore consists of unique combinations of breeds and genetic diversity. The observed rare colors were verified through DNA analysis. The DNA, expect for one individual, matched the observed color for red/black and the absence or presence of the diluted color. F or the color-sided pattern, and the spotted and roan color no conclusions could be made as the mutation itself was not genotyped. Based on DNA all known mother-offspring relationships were verified and DNA also provided insights concerning other relationships between the individuals. The herd consists of unique combinations of rare colors and pattern and the animals are genetically unique. It is therefore important to conserve the genetic diversity within this herd

Met DNA-onderzoek aantonen tot welk ras een rund behoort

Een Lakenvelder en een Groninger blaarkop zijn makkelijk te herkennen aan hun specifieke kleurpatronen. Maar dat is niet het enige wat hen onderscheidt. Met behulp van DNA-onderzoek kan men nu van een dier zonder stamboomgegevens op basis van zijn DNA zien tot welk ras het behoort

Globaltest and GOEAST: two different approaches for Gene Ontology analysis

Background Gene set analysis is a commonly used method for analysing microarray data by considering groups of functionally related genes instead of individual genes. Here we present the use of two gene set analysis approaches: Globaltest and GOEAST. Globaltest is a method for testing whether sets of genes are significantly associated with a variable of interest. GOEAST is a freely accessible web-based tool to test GO term enrichment within given gene sets. The two approaches were applied in the analysis of gene lists obtained from three different contrasts in a microarray experiment conducted to study the host reactions in broilers following Eimeria infection. Results The Globaltest identified significantly associated gene sets in one of the three contrasts made in the microarray experiment whereas the functional analysis of the differentially expressed genes using GOEAST revealed enriched GO terms in all three contrasts. Conclusion Globaltest and GOEAST gave different results, probably due to the different algorithms and the different criteria used for evaluating the significance of GO terms

A Pathway Analysis Tool for Analyzing Microarray Data of Species with Low Physiological Information

Pathway information provides insight into the biological processes underlying microarray data. Pathway information is widely available for humans and laboratory animals in databases through the internet, but less for other species, for example, livestock. Many software packages use species-specific gene IDs that cannot handle genomics data from other species. We developed a species-independent method to search pathways databases to analyse microarray data. Three PERL scripts were developed that use the names of the genes on the microarray. (1) Add synonyms of gene names by searching the Gene Ontology (GO) database. (2) Search the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database for pathway information using this GO-enriched gene list. (3) Combine the pathway data with the microarray data and visualize the results using color codes indicating regulation. To demonstrate the power of the method, we used a previously reported chicken microarray experiment investigating line-specific reactions to Salmonella infection as an example

Methods for interpreting lists of affected genes obstained in a DNA microarray experiment

Background - The aim of this paper was to describe and compare the methods used and the results obtained by the participants in a joint EADGENE (European Animal Disease Genomic Network of Excellence) and SABRE (Cutting Edge Genomics for Sustainable Animal Breeding) workshop focusing on post analysis of microarray data. The participating groups were provided with identical lists of microarray probes, including test statistics for three different contrasts, and the normalised log-ratios for each array, to be used as the starting point for interpreting the affected probes. The data originated from a microarray experiment conducted to study the host reactions in broilers occurring shortly after a secondary challenge with either a homologous or heterologous species of Eimeria. Results - Several conceptually different analytical approaches, using both commercial and public available software, were applied by the participating groups. The following tools were used: Ingenuity Pathway Analysis, MAPPFinder, LIMMA, GOstats, GOEAST, GOTM, Globaltest, TopGO, ArrayUnlock, Pathway Studio, GIST and AnnotationDbi. The main focus of the approaches was to utilise the relation between probes/genes and their gene ontology and pathways to interpret the affected probes/genes. The lack of a well-annotated chicken genome did though limit the possibilities to fully explore the tools. The main results from these analyses showed that the biological interpretation is highly dependent on the statistical method used but that some common biological conclusions could be reached. Conclusion - It is highly recommended to test different analytical methods on the same data set and compare the results to obtain a reliable biological interpretation of the affected genes in a DNA microarray experimen

Genomic prediction of dry matter intake in dairy cattle from an international data set consisting of research herds in Europe, North America, and Australasia

peer-reviewedFinancial support for gDMI from CRV (Arnhem, the Netherlands), ICBF (Cork, Ireland), CONAFE (Madrid, Spain), DairyCo (Warwickshire, UK) directly to the gDMI consortium, and The Natural Science and Engineering Research Council of Canada and DairyGen Council of Canadian Dairy Network (Guelph, ON, Canada) is gratefully appreciated, as well as the EU FP7 IRSES SEQSEL (Grant no. 317697).With the aim of increasing the accuracy of genomic estimated breeding values for dry matter intake (DMI) in Holstein-Friesian dairy cattle, data from 10 research herds in Europe, North America, and Australasia were combined. The DMI records were available on 10,701 parity 1 to 5 records from 6,953 cows, as well as on 1,784 growing heifers. Predicted DMI at 70 d in milk was used as the phenotype for the lactating animals, and the average DMI measured during a 60- to 70-d test period at approximately 200 d of age was used as the phenotype for the growing heifers. After editing, there were 583,375 genetic markers obtained from either actual high-density single nucleotide polymorphism (SNP) genotypes or imputed from 54,001 marker SNP genotypes. Genetic correlations between the populations were estimated using genomic REML. The accuracy of genomic prediction was evaluated for the following scenarios: (1) within-country only, by fixing the correlations among populations to zero, (2) using near-unity correlations among populations and assuming the same trait in each population, and (3) a sharing data scenario using estimated genetic correlations among populations. For these 3 scenarios, the data set was divided into 10 sub-populations stratified by progeny group of sires; 9 of these sub-populations were used (in turn) for the genomic prediction and the tenth was used for calculation of the accuracy (correlation adjusted for heritability). A fourth scenario to quantify the benefit for countries that do not record DMI was investigated (i.e., having an entire country as the validation population and excluding this country in the development of the genomic predictions). The optimal scenario, which was sharing data, resulted in a mean prediction accuracy of 0.44, ranging from 0.37 (Denmark) to 0.54 (the Netherlands). Assuming near-unity among-country genetic correlations, the mean accuracy of prediction dropped to 0.40, and the mean within-country accuracy was 0.30. If no records were available in a country, the accuracy based on the other populations ranged from 0.23 to 0.53 for the milking cows, but were only 0.03 and 0.19 for Australian and New Zealand heifers, respectively; the overall mean prediction accuracy was 0.37. Therefore, there is a benefit in collaboration, because phenotypic information for DMI from other countries can be used to augment the accuracy of genomic evaluations of individual countries.financial support for gDMI from CRV (Arnhem, the Netherlands), ICBF (Cork, Ireland), CONAFE (Madrid, Spain), DairyCo (Warwickshire, UK) directly to the gDMI consortium, and The Natural Science and Engineering Research Council of Canada and DairyGen Council of Canadian Dairy Network (Guelph, ON, Canada) is gratefully appreciated, as well as the EU FP7 IRSES SEQSEL (Grant no. 317697).financial support for gDMI from CRV (Arnhem, the Netherlands), ICBF (Cork, Ireland), CONAFE (Madrid, Spain), DairyCo (Warwickshire, UK) directly to the gDMI consortium, and The Natural Science and Engineering Research Council of Canada and DairyGen Council of Canadian Dairy Network (Guelph, ON, Canada) is gratefully appreciated, as well as the EU FP7 IRSES SEQSEL (Grant no. 317697)