COMPILATION OF A UNIFIED AND HOMOGENEOUS AEROMAGNETIC MAP OF THE GREEK MAINLAND

We present a unified and homogeneous digital aeromagnetic map of the Hellenic mainland, based on the 1:50,000 map series of IGME. These maps cover the areas A1, A2, B, C1, C2, C3, D1 compiled by Hunting Geology and Geophysics Ltd. and measured at nominal ground clearances (flight altitudes) 150m AGL, 150m AGL, 300m AGL, and 2300m AMSL respectively (part of C2 with 3000m AMSL). We also include the entire area of Northern Greece, measured by ABEM AB with nominal ground clearance 275±75m AGL. The original map sheets were digitally imaged, georeferenced, digitized along contour lines and interpolated onto regular 250´250m grids. The unified aeromagnetic map was constructed by collating the mosaic of the resulting gridded data. Using upward/ downward continuation techniques various homogeneous versions of the map, were compiled by referencing of the observed mosaic total magnetic field to a unique constant ground clearance or to a unique constant elevation above mean sea level. This is the first time there is a complete and unified image of the magnetic signature of the isopic zones and rock formations comprising the Hellenic mainland, with particular reference to the ophiolite suites, which provides additional insight into the Alpine and post-alpine tectonics of the area

Genetic basis of Campylobacter colonisation in the broiler chicken and its impact on intestinal health following natural field exposure

Campylobacter is the leading bacterial cause of foodborne diarrheal illness in humans and source attribution studies unequivocally identify handling or consumption of poultry meat as a key risk factor. Campylobacter colonizes the avian intestines in high numbers and rapidly spreads within flocks. A need therefore exists to devise strategies to reduce Campylobacter populations in poultry flocks. There has been a great deal of research aiming to understand the epidemiology and transmission characteristics of Campylobacter in poultry as a means to reduce carriage rates in poultry and reduce infection in humans. One potential strategy for control is the genetic selection of poultry for increased resistance to colonization by Campylobacter. The potential for genetic control of colonization has been demonstrated in inbred populations following experimental challenge with Campylobacter where quantitative trait loci associated with resistance have been identified. Currently in the literature there is no information of the genetic basis of Campylobacter colonization in commercial broiler lines and it is unknown whether these QTL are found in commercial broiler lines. The aim of this study was to estimate genetic parameters associated with Campylobacter load and genetic correlations with gut health and production traits following natural exposure of broiler chickens to Campylobacter. The results from the analysis show a low but significant heritability estimate (0.095 ± 0.037) for Campylobacter load which indicates a limited genetic basis and that non-genetic factors have a greater influence on the level of Campylobacter found in the broiler chicken. Furthermore, through examination of macroscopic intestinal health and absorptive capacity, our study indicated that Campylobacter has no detrimental effects on intestinal health and bird growth following natural exposure in the broiler line under study. These data indicate that whilst there is a genetic component to Campylobacter colonization worthy of further investigation, there is a large proportion of phenotypic variance under the influence of non-genetic effects. As such the control of Campylobacter will require understanding and manipulation of non-genetic host and environmental factors

Comparison of inference methods of genetic parameters with an application to body weight in broilers

REML (restricted maximum likelihood) has become the standard method of variance component estimation in animal breeding. Inference in Bayesian animal models is typically based upon Markov chain Monte Carlo (MCMC) methods, which are generally flexible but time-consuming. Recently, a new Bayesian computational method, integrated nested Laplace approximation (INLA), has been introduced for making fast non-sampling-based Bayesian inference for hierarchical latent Gaussian models. This paper is concerned with the comparison of estimates provided by three representative programs (ASReml, WinBUGS and the R package AnimalINLA) of the corresponding methods (REML, MCMC and INLA), with a view to their applicability for the typical animal breeder. Gaussian and binary as well as simulated data were used to assess the relative efficiency of the methods. Analysis of 2319 records of body weight at 35 days of age from a broiler line suggested a purely additive animal model, in which the heritability estimates ranged from 0.31 to 0.34 for the Gaussian trait and from 0.19 to 0.36 for the binary trait, depending on the estimation method. Although in need of further development, AnimalINLA seems a fast program for Bayesian modeling, particularly suitable for the inference of Gaussian traits, while WinBUGS appeared to successfully accommodate a complicated structure between the random effects. However, ASReml remains the best practical choice for the serious animal breeder

The utility of low-density genotyping for imputation in the Thoroughbred horse

BACKGROUND: Despite the dramatic reduction in the cost of high-density genotyping that has occurred over the last decade, it remains one of the limiting factors for obtaining the large datasets required for genomic studies of disease in the horse. In this study, we investigated the potential for low-density genotyping and subsequent imputation to address this problem. RESULTS: Using the haplotype phasing and imputation program, BEAGLE, it is possible to impute genotypes from low- to high-density (50K) in the Thoroughbred horse with reasonable to high accuracy. Analysis of the sources of variation in imputation accuracy revealed dependence both on the minor allele frequency of the single nucleotide polymorphisms (SNPs) being imputed and on the underlying linkage disequilibrium structure. Whereas equidistant spacing of the SNPs on the low-density panel worked well, optimising SNP selection to increase their minor allele frequency was advantageous, even when the panel was subsequently used in a population of different geographical origin. Replacing base pair position with linkage disequilibrium map distance reduced the variation in imputation accuracy across SNPs. Whereas a 1K SNP panel was generally sufficient to ensure that more than 80% of genotypes were correctly imputed, other studies suggest that a 2K to 3K panel is more efficient to minimize the subsequent loss of accuracy in genomic prediction analyses. The relationship between accuracy and genotyping costs for the different low-density panels, suggests that a 2K SNP panel would represent good value for money. CONCLUSIONS: Low-density genotyping with a 2K SNP panel followed by imputation provides a compromise between cost and accuracy that could promote more widespread genotyping, and hence the use of genomic information in horses. In addition to offering a low cost alternative to high-density genotyping, imputation provides a means to combine datasets from different genotyping platforms, which is becoming necessary since researchers are starting to use the recently developed equine 70K SNP chip. However, more work is needed to evaluate the impact of between-breed differences on imputation accuracy

Development of a high density 600K SNP genotyping array for chicken

Background: High density (HD) SNP genotyping arrays are an important tool for genetic analyses of animals and plants. Although the chicken is one of the most important farm animals, no HD array is yet available for high resolution genetic analysis of this species.Results: We report here the development of a 600 K Affymetrix® Axiom® HD genotyping array designed using SNPs segregating in a wide variety of chicken populations. In order to generate a large catalogue of segregating SNPs, we re-sequenced 243 chickens from 24 chicken lines derived from diverse sources (experimental, commercial broiler and layer lines) by pooling 10-15 samples within each line. About 139 million (M) putative SNPs were detected by mapping sequence reads to the new reference genome (Gallus_gallus_4.0) of which ~78 M appeared to be segregating in different lines. Using criteria such as high SNP-quality score, acceptable design scores predicting high conversion performance in the final array and uniformity of distribution across the genome, we selected ~1.8 M SNPs for validation through genotyping on an independent set of samples (n = 282). About 64% of the SNPs were polymorphic with high call rates (>98%), good cluster separation and stable Mendelian inheritance. Polymorphic SNPs were further analysed for their population characteristics and genomic effects. SNPs with extreme breach of Hardy-Weinberg equilibrium (P < 0.00001) were excluded from the panel. The final array, designed on the basis of these analyses, consists of 580,954 SNPs and includes 21,534 coding variants. SNPs were selected to achieve an essentially uniform distribution based on genetic map distance for both broiler and layer lines. Due to a lower extent of LD in broilers compared to layers, as reported in previous studies, the ratio of broiler and layer SNPs in the array was kept as 3:2. The final panel was shown to genotype a wide range of samples including broilers and layers with over 100 K to 450 K informative SNPs per line. A principal component analysis was used to demonstrate the ability of the array to detect the expected population structure which is an important pre-investigation step for many genome-wide analyses.Conclusions: This Affymetrix® Axiom® array is the first SNP genotyping array for chicken that has been made commercially available to the public as a product. This array is expected to find widespread usage both in research and commercial application such as in genomic selection, genome-wide association studies, selection signature analyses, fine mapping of QTLs and detection of copy number variants

Applications of Genomic Selection in Poultry

Here we describe the application of genomic selection in both layer and broiler breeder populations. A brown egg layer line was partitioned into two sub-lines, one used for genomic selection and the other as a pedigree selected control. Generation interval in the genomic sub-line was halved and the sub-line size was reduced compared to the traditionally-selected control. The genomic sub-line outperformed pedigree-selected contemporaries in 12 of 16 traits evaluated, and genomic estimated breeding values were more accurate and persistent than pedigree-based estimates. Genome wide association studies for all available traits identified several regions associated with economically important traits. Similar improvements in prediction accuracy were observed in broilers. Estimation of the Mendelian sampling term for full sibs without own phenotypic information contributed to this gain. The development of robust imputation methods enabled the implementation of genomic selection into the routine evaluations to accelerate genetic progress

Use and optimization of different sources of information for genomic prediction

Abstract Background Molecular data is now commonly used to predict breeding values (BV). Various methods to calculate genomic relationship matrices (GRM) have been developed, with some studies proposing regression of coefficients back to the reference matrix of pedigree-based relationship coefficients (A). The objective was to compare the utility of two GRM: a matrix based on linkage analysis (LA) and anchored to the pedigree, i.e. $${\mathbf{G}}_{{{\mathbf{LA}}}} ,$$ G LA , and a matrix based on linkage disequilibrium (LD), i.e. $${\mathbf{G}}_{{{\mathbf{LD}}}}$$ G LD , using genomic and phenotypic data collected on 5416 broiler chickens. Furthermore, the effects of regressing the coefficients of $${\mathbf{G}}_{{{\mathbf{LD}}}}$$ G LD back to A (LDA) and to $${\mathbf{G}}_{{{\mathbf{LA}}}}$$ G LA (LDLA) were evaluated, using a range of weighting factors. The performance of the matrices and their composite products was assessed by the fit of the models to the data, and the empirical accuracy and bias of the BV that they predicted. The sensitivity to marker choice was examined by using two chips of equal density but including different single nucleotide polymorphisms (SNPs). Results The likelihood of models using GRM and composite matrices exceeded the likelihood of models based on pedigree alone and was highest with intermediate weighting factors for both the LDA and LDLA approaches. For these data, empirical accuracies were not strongly affected by the weighting factors, although they were highest when different sources of information were combined. The optimum weighting factors depended on the type of matrices used, as well as on the choice of SNPs from which the GRM were constructed. Prediction bias was strongly affected by the chip used and less by the form of the GRM. Conclusions Our findings provide an empirical comparison of the efficacy of pedigree and genomic predictions in broiler chickens and examine the effects of fitting GRM with coefficients regressed back to a reference anchored to the pedigree, either A or $${\mathbf{G}}_{{{\mathbf{LA}}}}$$ G LA . For the analysed dataset, the best results were obtained when $${\mathbf{G}}_{{{\mathbf{LD}}}}$$ G LD was combined with relationships in A or $${\mathbf{G}}_{{{\mathbf{LA}}}}$$ G LA , with optimum weighting factors that depended on the choice of SNPs used. The optimum weighting factor for broiler body weight differed from weighting factors that were based on the density of SNPs and theoretically derived using generalised assumptions