Differentially expressed genes for aggressive pecking behaviour in laying hens

BACKGROUND: Aggressive behaviour is an important aspect in the daily lives of animals living in groups. Aggressive animals have advantages, such as better access to food or territories, and they produce more offspring than low ranking animals. The social hierarchy in chickens is measured using the 'pecking order' concept, which counts the number of aggressive pecks given and received. To date, little is known about the underlying genetics of the 'pecking order'. RESULTS: A total of 60 hens from a high feather pecking selection line were divided into three groups: only receivers (R), only peckers (P) and mixed peckers and receivers (P&R). In comparing the R and P groups, we observed that there were 40 differentially expressed genes [false discovery rate (FDR) P < 0.10]. It was not fully clear how the 40 genes regulated aggressive behaviour; however, gene set analysis detected a number of GO identifiers, which were potentially involved in aggressive behavioural processes. These genes code for synaptosomes (GO:0019797), and proteins involved in the regulation of the excitatory postsynaptic membrane potential (GO:0060079), the regulation of the membrane potential (GO:0042391), and glutamate receptor binding (GO:0035254). CONCLUSION: In conclusion, our study provides new insights into which genes are involved in aggressive behaviours in chickens. Pecking and receiving hens exhibited different gene expression profiles in their brains. Following confirmation, the identification of differentially expressed genes may elucidate how the pecking order forms in laying hens at a molecular level

Bayesian genomic selection: the effect of haplotype length and priors

Breeding values for animals with marker data are estimated using a genomic selection approach where data is analyzed using Bayesian multi-marker association models. Fourteen model scenarios with varying haplotype lengths, hyper parameter and prior distributions were compared to find the scenario expected to give the most correct genomic estimated breeding values for animals with marker information only. Five-fold cross validation was performed to assess the ability of models to estimate breeding values for animals in generation 3. In each of the five subsets, 20% of phenotypic records in generation 3 were left out. Correlations between breeding values estimated on full data and on subsets for the "leave-out" animals varied between 0.77–0.99. Regression coefficients of breeding values from full data on breeding values from subsets ranged from 0.78–1.01. Single-SNP marker models didn't perform well. Correlations were 0.77–0.89 and predicted breeding values were biased. In addition the models seemed to over fit the genomic part of the variation. Highest correlations and most unbiased results were obtained when SNP markers were joined into haplotypes. Especially the scenarios with 5-SNP haplotypes gave promising results (distance between adjacent SNPs is 0.1 cM evenly over the genome). All correlations were 0.99 and regression coefficients were 0.99–1.01. Models with 5-SNP markers seemed robust to hyper parameter and prior changes. Haplotypes up to 40 SNPs also gave good results. However, longer haplotypes are expected to have less predictive ability over several generations and therefore the 5-SNP haplotypes are expected to give the best predictions for generations 4–6

From genetical genomics to systems genetics: potential applications in quantitative genomics and animal breeding

This article reviews methods of integration of transcriptomics (and equally proteomics and metabolomics), genetics, and genomics in the form of systems genetics into existing genome analyses and their potential use in animal breeding and quantitative genomic modeling of complex traits. Genetical genomics or the expression quantitative trait loci (eQTL) mapping method and key findings in this research are reviewed. Various procedures and potential uses of eQTL mapping, global linkage clustering, and systems genetics are illustrated using actual analysis on recombinant inbred lines of mice with data on gene expression (for diabetes- and obesity-related genes), pathway, and single nucleotide polymorphism (SNP) linkage maps. Experimental and bioinformatics difficulties and possible solutions are discussed. The main uses of this systems genetics approach in quantitative genomics were shown to be in refinement of the identified QTL, candidate gene and SNP discovery, understanding gene-environment and gene-gene interactions, detection of candidate regulator genes/eQTL, discriminating multiple QTL/eQTL, and detection of pleiotropic QTL/eQTL, in addition to its use in reconstructing regulatory networks. The potential uses in animal breeding are direct selection on heritable gene expression measures, termed "expression assisted selection,” and genetical genomic selection of both QTL and eQTL based on breeding values of the respective genes, termed "expression-assisted evaluation.

Selection against genetic defects in conservation schemes while controlling inbreeding

We studied different genetic models and evaluation systems to select against a genetic disease with additive, recessive or polygenic inheritance in genetic conservation schemes. When using optimum contribution selection with a restriction on the rate of inbreeding (ΔF) to select against a disease allele, selection directly on DNA-genotypes is, as expected, the most efficient strategy. Selection for BLUP or segregation analysis breeding value estimates both need 1–2 generations more to halve the frequency of the disease allele, while these methods do not require knowledge of the disease mutation at the DNA level. BLUP and segregation analysis methods were equally efficient when selecting against a disease with single gene or complex polygene inheritance, i.e. knowledge about the mode of inheritance of the disease was not needed for efficient selection against the disease. Smaller schemes or schemes with a more stringent restriction on ΔF needed more generations to halve the frequency of the disease alleles or the fraction of diseased animals. Optimum contribution selection maintained ΔF at its predefined level, even when selection of females was at random. It is argued that in the investigated small conservation schemes with selection against a genetic defect, control of ΔF is very important

Bayesian Variable Selection to identify QTL affecting a simulated quantitative trait

Background Recent developments in genetic technology and methodology enable accurate detection of QTL and estimation of breeding values, even in individuals without phenotypes. The QTL-MAS workshop offers the opportunity to test different methods to perform a genome-wide association study on simulated data with a QTL structure that is unknown beforehand. The simulated data contained 3,220 individuals: 20 sires and 200 dams with 3,000 offspring. All individuals were genotyped, though only 2,000 offspring were phenotyped for a quantitative trait. QTL affecting the simulated quantitative trait were identified and breeding values of individuals without phenotypes were estimated using Bayesian Variable Selection, a multi-locus SNP model in association studies. Results Estimated heritability of the simulated quantitative trait was 0.30 (SD = 0.02). Mean posterior probability of SNP modelled having a large effect ( pˆi) was 0.0066 (95%HPDR: 0.0014-0.0132). Mean posterior probability of variance of second distribution was 0.409 (95%HPDR: 0.286-0.589). The genome-wide association analysis resulted in 14 significant and 43 putative SNP, comprising 7 significant QTL on chromosome 1, 2 and 3 and putative QTL on all chromosomes. Assigning single or multiple QTL to significant SNP was not obvious, especially for SNP in the same region that were more or less in LD. Correlation between the simulated and estimated breeding values of 1,000 offspring without phenotypes was 0.91. Conclusions Bayesian Variable Selection using thousands of SNP was successfully applied to genome-wide association analysis of a simulated dataset with unknown QTL structure. Simulated QTL with Mendelian inheritance were accurately identified, while imprinted and epistatic QTL were only putatively detected. The correlation between simulated and estimated breeding values of offspring without phenotypes was high

Efficiency of population structures for mapping of Mendelian and imprinted quantitative trait loci in outbred pigs using variance component methods

In a simulation study different designs for a pure line pig population were compared for efficiency of mapping QTL using the variance component method. Phenotypes affected by a Mendelian QTL, a paternally expressed QTL, a maternally expressed QTL or by a QTL without an effect were simulated. In all alternative designs 960 progeny were phenotyped. Given the limited number of animals there is an optimum between the number of families and the family size. Estimation of Mendelian and parentally expressed QTL is more efficient in a design with large family sizes. Too small a number of sires should be avoided to minimize chances of sires to be non-segregating. When a large number of families is used, the number of haplotypes increases which reduces the accuracy of estimating the QTL effect and thereby reduces the power to show a significant QTL and to correctly position the QTL. Dense maps allow for smaller family size due to exploitation of LD-information. Given the different possible modes of inheritance of the QTL using 8 to16 boars, two litters per dam was optimal with respect to determining significance and correct location of the QTL for a data set consisting of 960 progeny. The variance component method combining linkage disequilibrium and linkage analysis seems to be an appropriate choice to analyze data sets which vary in marker density and which contain complex family structures

A Bayesian approach to detect QTL affecting a simulated binary and quantitative trait

Background - We analyzed simulated data from the 14th QTL-MAS workshop using a Bayesian approach implemented in the program iBay. The data contained individuals genotypes for 10,031 SNPs and phenotyped for a quantitative and a binary trait. Results - For the quantitative trait we mapped 8 out of 30 additive QTL, 1 out of 3 imprinted QTL and both epistatic pairs of QTL successfully. For the binary trait we mapped 11 out of 22 additive QTL successfully. Four out of 22 pleiotropic QTL were detected as such. Conclusions - The Bayesian variable selection method showed to be a successful method for genome-wide association. This method was reasonably fast using dense marker map

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