Rudolf Preisinger

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

BackgroundHigh 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.ResultsWe 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.ConclusionsThis 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.

Breeding value prediction for production traits in layer chickens using pedigree or genomic relationships in a reduced animal model

BackgroundGenomic selection involves breeding value estimation of selection candidates based on high-density SNP genotypes. To quantify the potential benefit of genomic selection, accuracies of estimated breeding values (EBV) obtained with different methods using pedigree or high-density SNP genotypes were evaluated and compared in a commercial layer chicken breeding line.MethodsThe following traits were analyzed: egg production, egg weight, egg color, shell strength, age at sexual maturity, body weight, albumen height, and yolk weight. Predictions appropriate for early or late selection were compared. A total of 2,708 birds were genotyped for 23,356 segregating SNP, including 1,563 females with records. Phenotypes on relatives without genotypes were incorporated in the analysis (in total 13,049 production records).The data were analyzed with a Reduced Animal Model using a relationship matrix based on pedigree data or on marker genotypes and with a Bayesian method using model averaging. Using a validation set that consisted of individuals from the generation following training, these methods were compared by correlating EBV with phenotypes corrected for fixed effects, selecting the top 30 individuals based on EBV and evaluating their mean phenotype, and by regressing phenotypes on EBV.ResultsUsing high-density SNP genotypes increased accuracies of EBV up to two-fold for selection at an early age and by up to 88% for selection at a later age. Accuracy increases at an early age can be mostly attributed to improved estimates of parental EBV for shell quality and egg production, while for other egg quality traits it is mostly due to improved estimates of Mendelian sampling effects. A relatively small number of markers was sufficient to explain most of the genetic variation for egg weight and body weight.

Genome-wide association analysis and genetic architecture of egg weight and egg uniformity in layer chickens.

The pioneering work by Professor Soller et al., among others, on the use of genetic markers to analyze quantitative traits has provided opportunities to discover their genetic architecture in livestock by identifying quantitative trait loci (QTL). The recent availability of high-density single nucleotide polymorphism (SNP) panels has advanced such studies by capitalizing on population-wide linkage disequilibrium at positions across the genome. In this study, genomic prediction model Bayes-B was used to identify genomic regions associated with the mean and standard deviation of egg weight at three ages in a commercial brown egg layer line. A total of 24,425 segregating SNPs were evaluated simultaneously using over 2900 genotyped individuals or families. The corresponding phenotypic records were represented as individual measurements or family means from full-sib progeny. A novel approach using the posterior distribution of window variances from the Monte Carlo Markov Chain samples was used to describe genetic architecture and to make statistical inferences about regions with the largest effects. A QTL region on chromosome 4 was found to explain a large proportion of the genetic variance for the mean (30%) and standard deviation (up to 16%) of the weight of eggs laid at specific ages. Additional regions with smaller effects on chromosomes 2, 5, 6, 8, 20, 23, 28 and Z showed suggestive associations with mean egg weight and a region on chromosome 13 with the standard deviation of egg weight at 26-28 weeks of age. The genetic architecture of the analyzed traits was characterized by a limited number of genes or genomic regions with large effects and many regions with small polygenic effects. The region on chromosome 4 can be used to improve both the mean and standard deviation of egg weight by marker-assisted selection.

Persistence of accuracy of genomic estimated breeding values over generations in layer chickens

BackgroundThe predictive ability of genomic estimated breeding values (GEBV) originates both from associations between high-density markers and QTL (Quantitative Trait Loci) and from pedigree information. Thus, GEBV are expected to provide more persistent accuracy over successive generations than breeding values estimated using pedigree-based methods. The objective of this study was to evaluate the accuracy of GEBV in a closed population of layer chickens and to quantify their persistence over five successive generations using marker or pedigree information.MethodsThe training data consisted of 16 traits and 777 genotyped animals from two generations of a brown-egg layer breeding line, 295 of which had individual phenotype records, while others had phenotypes on 2,738 non-genotyped relatives, or similar data accumulated over up to five generations. Validation data included phenotyped and genotyped birds from five subsequent generations (on average 306 birds/generation). Birds were genotyped for 23,356 segregating SNP. Animal models using genomic or pedigree relationship matrices and Bayesian model averaging methods were used for training analyses. Accuracy was evaluated as the correlation between EBV and phenotype in validation divided by the square root of trait heritability.ResultsPedigree relationships in outbred populations are reduced by 50% at each meiosis, therefore accuracy is expected to decrease by the square root of 0.5 every generation, as observed for pedigree-based EBV (Estimated Breeding Values). In contrast the GEBV accuracy was more persistent, although the drop in accuracy was substantial in the first generation. Traits that were considered to be influenced by fewer QTL and to have a higher heritability maintained a higher GEBV accuracy over generations. In conclusion, GEBV capture information beyond pedigree relationships, but retraining every generation is recommended for genomic selection in closed breeding populations.

The chicken as a natural model for extraintestinal infections caused by avian pathogenic Escherichia coli (APEC)

E. coli infections in avian species have become an economic threat to the poultry industry worldwide. Several factors have been associated with the virulence of E. coli in avian hosts, but no specific virulence gene has been identified as being entirely responsible for the pathogenicity of avian pathogenic E. coli (APEC). Needless to say, the chicken would serve as the best model organism for unravelling the pathogenic mechanisms of APEC, an extraintestinal pathogen. Five-week-old white leghorn SPF chickens were infected intra-tracheally with a well characterized APEC field strain IMT5155 (O2:K1:H5) using different doses corresponding to the respective models of infection established, that is, the lung colonization model allowing re-isolation of bacteria only from the lung but not from other internal organs, and the systemic infection model. These two models represent the crucial steps in the pathogenesis of APEC infections, including the colonization of the lung epithelium and the spread of bacteria throughout the bloodstream. The read-out system includes a clinical score, pathomorphological changes and bacterial load determination. The lung colonization model has been established and described for the first time in this study, in addition to a comprehensive account of a systemic infection model which enables the study of severe extraintestinal pathogenic E. coli (ExPEC) infections. These in vivo models enable the application of various molecular approaches to study host-pathogen interactions more closely. The most important application of such genetic manipulation techniques is the identification of genes required for extraintestinal virulence, as well as host genes involved in immunity in vivo. The knowledge obtained from these studies serves the dual purpose of shedding light on the nature of virulence itself, as well as providing a route for rational attenuation of the pathogen for vaccine construction, a measure by which extraintestinal infections, including those caused by APEC, could eventually be controlled and prevented in the field.

A High Resolution Genome-Wide Scan for Significant Selective Sweeps: An Application to Pooled Sequence Data in Laying Chickens

In most studies aimed at localizing footprints of past selection, outliers at tails of the empirical distribution of a given test statistic are assumed to reflect locus-specific selective forces. Significance cutoffs are subjectively determined, rather than being related to a clear set of hypotheses. Here, we define an empirical p-value for the summary statistic by means of a permutation method that uses the observed SNP structure in the real data. To illustrate the methodology, we applied our approach to a panel of 2.9 million autosomal SNPs identified from re-sequencing a pool of 15 individuals from a brown egg layer line. We scanned the genome for local reductions in heterozygosity, suggestive of selective sweeps. We also employed a modified sliding window approach that accounts for gaps in the sequence and increases scanning resolution by moving the overlapping windows by steps of one SNP only, and suggest to call this a “creeping window” strategy. The approach confirmed selective sweeps in the region of previously described candidate genes, i.e. TSHR, PRL, PRLHR, INSR, LEPR, IGF1, and NRAMP1 when used as positive controls. The genome scan revealed 82 distinct regions with strong evidence of selection (genome-wide p-value<0.001), including genes known to be associated with eggshell structure and immune system such as CALB1 and GAL cluster, respectively. A substantial proportion of signals was found in poor gene content regions including the most extreme signal on chromosome 1. The observation of multiple signals in a highly selected layer line of chicken is consistent with the hypothesis that egg production is a complex trait controlled by many genes.

Signature-Tagged Mutagenesis in a Chicken Infection Model Leads to the Identification of a Novel Avian Pathogenic Escherichia coli Fimbrial Adhesin

The extraintestinal pathogen, avian pathogenic E. coli (APEC), known to cause systemic infections in chickens, is responsible for large economic losses in the poultry industry worldwide. In order to identify genes involved in the early essential stages of pathogenesis, namely adhesion and colonization, Signature-tagged mutagenesis (STM) was applied to a previously established lung colonization model of infection by generating and screening a total of 1,800 mutants of an APEC strain IMT5155 (O2:K1:H5; Sequence type complex 95). The study led to the identification of new genes of interest, including two adhesins, one of which coded for a novel APEC fimbrial adhesin (Yqi) not described for its role in APEC pathogenesis to date. Its gene product has been temporarily designated ExPEC Adhesin I (EA/I) until the adhesin-specific receptor is identified. Deletion of the ExPEC adhesin I gene resulted in reduced colonization ability by APEC strain IMT5155 both in vitro and in vivo. Furthermore, complementation of the adhesin gene restored its ability to colonize epithelial cells in vitro. The ExPEC adhesin I protein was successfully expressed in vitro. Electron microscopy of an afimbriate strain E. coli AAEC189 over-expressed with the putative EA/I gene cluster revealed short fimbrial-like appendages protruding out of the bacterial outer membrane. We observed that this adhesin coding gene yqi is prevalent among extraintestinal pathogenic E. coli (ExPEC) isolates, including APEC (54.4%), uropathogenic E. coli (UPEC) (65.9%) and newborn meningitic E. coli (NMEC) (60.0%), and absent in all of the 153 intestinal pathogenic E. coli strains tested, thereby validating the designation of the adhesin as ExPEC Adhesin I. In addition, prevalence of EA/I was most frequently associated with the B2 group of the EcoR classification and ST95 complex of the multi locus sequence typing (MLST) scheme, with evidence of a positive selection within this highly pathogenic complex. This is the first report of the newly identified and functionally characterized ExPEC adhesin I and its significant role during APEC infection in chickens.

Linkage disequilibrium reveals different demographic history in egg laying chickens.

BackgroundThe availability of larger-scale SNP data sets in the chicken genome allows to achieve a higher resolution of the pattern of linkage disequilibrium (LD). In this study, 36 k and 57 k genotypes from two independent genotyping chips were used to systematically characterize genome-wide extent and structure of LD in the genome of four chicken populations. In total, we analyzed genotypes of 454 animals from two commercial and two experimental populations of white and brown layers which allows to some extent a generalization of the results.ResultsThe number of usable SNPs in this study was 19 k to 37 k in brown layers and 8 k to 19 k in white layers. Our analyzes showed a large difference of LD between the lines of white and brown layers. A mean value of r2 = 0.73 ± 0.36 was observed in pair-wise distances of < 25 Kb for commercial white layers, and it dropped to 0.60 ± 0.38 with distances of 75 to 120 Kb, the interval which includes the average inter-marker space in this line. In contrast, an overall mean value of r2= 0.32 ± 0.33 was observed for SNPs less than 25 Kb apart from each other and dropped to 0.21 ± 0.26 at a distance of 100 kb in commercial brown layers. There was a remarkable similarity of the LD patterns among the two populations of white layers. The same was true for the two populations of brown layers, while the LD pattern between white and brown layers was clearly different. Inferring the population demographic history from LD data resulted in a larger effective population size in brown than white populations, reflecting less inbreeding among brown compared to white egg layers.ConclusionsWe report comprehensive LD map statistics for the genome of egg laying chickens with an up to 3 times higher resolution compared to the maps available so far. The results were found to be consistent between analyzes based on the parallel SNP chips and across different populations (commercial vs. experimental) within the brown and the white layers. It is concluded that the current density of usable markers in this study is sufficient for association mapping and the implementation of genomic selection in these populations to achieve a similar accuracy as in implementations of association mapping and genomic selection in mammalian farm animals.

Breeding to reduce susceptibility to Escherichia coli in layers

Colibacillosis is a bacterial disease of great concern in the layer industry causing substantial animal and economic losses worldwide. Breeding for resistance to colibacillosis is an important control strategy that would complement traditional management strategies such as vaccinations and therapeutic treatments. Because antibiotic use in animal production is expected to substantially decrease in the future to meet not only consumer demands but also regulations, it is expected that bacterial diseases such as colibacillosis will become even more important. A challenge test was carried out on 353 pedigreed White Leghorn female chickens of 1 pure line. The birds were inoculated with Escherichia coli at an age of 93 to 95 wk (3 hatches) and symptoms of disease and mortality were constantly monitored for up to 6 d after inoculation. The cumulative mortality was 64%, the majority of which took place in the first 2 d. In the present study, genetic parameters of resistance to colibacillosis were estimated to judge the value of genetic selection. The heritability for survival rate was 0.17+/-0.07. Both for quantitative as well as for qualitative traits of economic interest, there was no remarkable difference between survivors and hens that died. Data showed that it is in principle possible to breed for colibacillosis resistance without penalizing the improvement in other key traits. The B21 marker for MHC in this study was associated with a lower colibacillosis susceptibility. In this study, it was demonstrated that it is possible to breed for Escherichia coli resistance. However, due to the inconveniences of a challenge test, it would be very interesting to make use of MAS.

A QTL for osteoporosis detected in an F2 population derived from White Leghorn chicken lines divergently selected for bone index

Osteoporosis, resulting from progressive loss of structural bone during the period of egg-laying in hens, is associated with an increased susceptibility to bone breakage. To study the genetic basis of bone strength, an F(2) cross was produced from lines of hens that had been divergently selected for bone index from a commercial pedigreed White Leghorn population. Quantitative trait loci (QTL) affecting the bone index and component traits of the index (tibiotarsal and humeral strength and keel radiographic density) were mapped using phenotypic data from 372 F(2) individuals in 32 F(1) families. Genotypes for 136 microsatellite markers in 27 linkage groups covering approximately 80% of the genome were analysed for association with phenotypes using within-family regression analyses. There was one significant QTL on chromosome 1 for bone index and the component traits of tibiotarsal and humeral breaking strength. Additive effects for tibiotarsal breaking strength represented 34% of the trait standard deviation and 7.6% of the phenotypic variance of the trait. These QTL for bone quality in poultry are directly relevant to commercial populations.