Feather pecking and aggressive pecking is a well-known problem in egg production. In the present study, genetic parameters for 4 feather-pecking-related traits were estimated using generalized linear mixed models. The traits were bouts of feather pecking delivered (FPD), bouts of feather pecking received (FPR), bouts of aggressive pecking delivered (APD), and bouts of aggressive pecking received (APR). An F2-design was established from 2 divergent selected founder lines. The lines were selected for low or high feather pecking for 10 generations. The number of F2 hens was 910. They were housed in pens with around 40 birds. Each pen was observed in 21 sessions of 20 min, distributed over 3 consecutive days. An animal model was applied that treated the bouts observed within 20 min as repeated observations. An over-dispersed Poisson distribution was assumed for observed counts and the link function was a log link. The model included a random animal effect, a random permanent environment effect, and a random day-by-hen effect. Residual variance was approximated on the link scale by the delta method. The results showed a heritability around 0.10 on the link scale for FPD and APD and of 0.04 for APR. The heritability of FPR was zero. For all behavior traits, substantial permanent environmental effects were observed. The approximate genetic correlation between FPD and APD (FPD and APR) was 0.81 (0.54). Egg production and feather eating records were collected on the same hens as well and were analyzed with a generalized linear mixed model, assuming a binomial distribution and using a probit link function. The heritability on the link scale for egg production was 0.40 and for feather eating 0.57. The approximate genetic correlation between FPD and egg production was 0.50 and between FPD and feather eating 0.73. Selection might help to reduce feather pecking, but this might result in an unfavorable correlated selection response reducing egg production. Feather eating and feather pecking are genetically correlated and this needs further investigation.

Improvement in growth and meat quality is one of the main objectives in sire line pig breeding programmes. Mapping quantitative trait loci for these traits using experimental crosses and a linkage-based approach has been performed frequently in the past. The Piétrain breed often was involved as a founder breed to establish the experimental crosses. This breed was selected for muscularity and leanness but shows relatively poor meat quality. It is frequently used as a sire line breed. With the advent of genome-wide and dense SNP chips in pig genomic research, it is possible to also conduct genome-wide association studies within the Piétrain breed. In this study, around 500 progeny-tested sires were genotyped with 60k SNPs. Data filtering showed that around 48k SNPs were useable in this sample. These SNPs were used to conduct a genome-wide association study for growth, muscularity and meat quality traits. Because it is known that a mutation in the RYR1 gene located on chromosome 6 shows a major effect on meat quality, this mutation was included in the models. Single-marker and multimarker association analyses were performed. The results revealed between zero and eight significant associations per trait with P < 5 × 10(-5) . Of special interest are SNPs located on SSC6, SSC10 and SSC15.

BackgroundFeather pecking and aggressive pecking in laying hens are serious economic and welfare issues. In spite of extensive research on feather pecking during the last decades, the motivation for this behavior is still not clear. A small to moderate heritability has frequently been reported for these traits. Recently, we identified several single-nucleotide polymorphisms (SNPs) associated with feather pecking by mapping selection signatures in two divergent feather pecking lines. Here, we performed a genome-wide association analysis (GWAS) for feather pecking and aggressive pecking behavior, then combined the results with those from the recent selection signature experiment, and linked them to those obtained from a differential gene expression study.MethodsA large F2 cross of 960 F2 hens was generated using the divergent lines as founders. Hens were phenotyped for feather pecks delivered (FPD), aggressive pecks delivered (APD), and aggressive pecks received (APR). Individuals were genotyped with the Illumina 60K chicken Infinium iSelect chip. After data filtering, 29,376 SNPs remained for analyses. Single-marker GWAS was performed using a Poisson model. The results were combined with those from the selection signature experiment using Fisher’s combined probability test.ResultsNumerous significant SNPs were identified for all traits but with low false discovery rates. Nearly all significant SNPs were located in clusters that spanned a maximum of 3 Mb and included at least two significant SNPs. For FPD, four clusters were identified, which increased to 13 based on the meta-analysis (FPDmeta). Seven clusters were identified for APD and three for APR. Eight genes (of the 750 investigated genes located in the FPDmeta clusters) were significantly differentially-expressed in the brain of hens from both lines. One gene, SLC12A9, and the positional candidate gene for APD, GNG2, may be linked to the monomanine signaling pathway, which is involved in feather pecking and aggressive behavior.ConclusionsCombining the results from the GWAS with those of the selection signature experiment substantially increased the statistical power. The behavioral traits were controlled by many genes with small effects and no single SNP had effects large enough to justify its use in marker-assisted selection.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-017-0287-4) contains supplementary material, which is available to authorized users.

Feather pecking is a major welfare problem in egg production. It may be caused by genetic, physiological and environmental factors. The main aim of this study was to uncover gene expression variability in brain tissue between individuals from high feather pecking and low feather pecking groups using the Chicken Gene Expression Microarray. In total, 313 signals were initially identified as significant (P ≤ 0.05) for the fold change higher than two. A subset of functional candidate genes including downregulated (GLUL, TSPO, MAOA) and upregulated (HTR1B, SIP1, PSEN1) transcripts was subjected to quantitative PCR validation. The significance level and direction of the fold change in gene expression observed by the microarray analysis were confirmed for four genes (HTR1B, SIP1, PSEN1 and GLUL). Newly identified candidate genes play an important role in neurotransmission and psychopathological disorders and can be considered as potential genetic components involved in complex feather pecking behavior. It can be concluded that this study has revealed some interesting differences in gene expression between high and low feather pecking groups and helped to approach elucidation of the genetic foundations of feather pecking.

In the present study, data from four F2 crosses were analysed and used to study the linkage disequilibrium (LD) structure within and across the crosses. Genome-wide association analyses (GWASes) for conductivity and dressing out meat traits were conducted using single-marker and Bayesian multi-marker models using the pooled data from all F2 crosses. Porcine F2 crosses generated from the distantly related founder breeds Wild Boar, Piétrain and Meishan, as well as from a porcine F2 cross from the closely related founder breed Piétrain and an F1 Large White × Landrace cross were pooled. A total of 2572 F2 animals were genotyped using a 62K SNP chip. The positions of the SNPs were based on genome assembly Sscrofa11.1. After post-alignment and genotype filtering, approximately 50K SNPs were usable for LD studies and GWASes. The main findings of the present study are that the breakdown of LD was faster in crosses from closely related founder breeds compared to crosses from distantly related founders. The fastest breakdown of LD was observed by pooling the data. Based on the single-marker results and LD structure, clusters and windows were built for 1-Mb intervals. For conductivity and dressing out, 183 and 191 nominal significant associations respectively and six and five clusters respectively were found. Dominance was important for conductivity, and considering dominance in GWASes improved the mapping signals. Most clear signals were found for conductivity on SSC6, 8 and 15 and for dressing out on SSC2 and 7. Considering dominance might contribute to the accuracy of genomic selection and serve as a guide for choosing mating pairs with good combining abilities. However, further research is needed to investigate if dominance is also important in crossbreed pig breeding schemes.

The aim of this study was to study the population structure, to characterize the LD structure and to define core regions based on low recombination rates among SNP pairs in the genome of Piétrain pigs using data from the PorcineSNP60 BeadChip. This breed is a European sire line and was strongly selected for lean meat content during the last decades. The data were used to map signatures of selection using the REHH test. In the first step, selection signatures were searched genome-wide using only core haplotypes having a frequency above 0.25. In the second step, the results from the selection signature analysis were matched with the results from the recently conducted genome-wide association study for economical relevant traits to investigate putative overlaps of chromosomal regions. A small subdivision of the population with regard to the geographical origin of the individuals was observed. The extent of LD was determined genome-wide using r(2) values for SNP pairs with a distance ≤5 Mb and was on average 0.34. This comparable low r(2) value indicates a high genetic diversity in the Piétrain population. Six REHH values having a p-value < 0.001 were genome-wide detected. These were located on SSC1, 2, 6 and 17. Three positional candidate genes with potential biological roles were suggested, called LOC100626459, LOC100626014 and MIR1. The results imply that for genome-wide analysis especially in this population, a higher marker density and higher sample sizes are required. For a number of nine SNPs, which were successfully annotated to core regions, the REHH test was applied. However, no selection signatures were found for those regions (p-value < 0.1).

The aim of this study was to map QTL for meat quality traits in three connected porcine F(2) crosses comprising around 1000 individuals. The three crosses were derived from the founder breeds Chinese Meishan, European Wild Boar and Pietrain. The animals were genotyped genomewide for approximately 250 genetic markers, mostly microsatellites. They were phenotyped for seven meat quality traits (pH at 45 min and 24 h after slaughter, conductivity at 45 min and 24 h after slaughter, meat colour, drip loss and rigour). QTL mapping was conducted using a two-step procedure. In the first step, the QTL were mapped using a multi-QTL multi-allele model that was tailored to analyse multiple connected F(2) crosses. It considered additive, dominance and imprinting effects. The major gene RYR1:g.1843C>T affecting the meat quality on SSC6 was included as a cofactor in the model. The mapped QTL were tested for pairwise epistatic effects in the second step. All possible epistatic effects between additive, dominant and imprinting effects were considered, leading to nine orthogonal forms of epistasis. Numerous QTL were found. The most interesting chromosome was SSC6. Not all genetic variance of meat quality was explained by RYR1:g.1843C>T. A small confidence interval was obtained, which facilitated the identification of candidate genes underlying the QTL. Epistasis was significant for the pairwise QTL on SSC12 and SSC14 for pH24 and for the QTL on SSC2 and SSC5 for rigour. Some evidence for additional pairwise epistatic effects was found, although not significant. Imprinting was involved in epistasis.

In the present study 3 connected F(2) crosses were used to map QTL for classical fat traits as well as fat-related metabolic and cytological traits in pigs. The founder breeds were Chinese Meishan, European Wild Boar, and Pietrain with to some extent the same founder animals in the different crosses. The different selection history of the breeds for fatness traits as well as the connectedness of the crosses led to a high statistical power. The total number of F(2) animals varied between 694 and 966, depending on the trait. The animals were genotyped for around 250 genetic markers, mostly microsatellites. The statistical model was a multi-allele, multi-QTL model that accounted for imprinting. The model was previously introduced from plant breeding experiments. The traits investigated were backfat depth and fat area as well as relative number of fat cells with different sizes and 2 metabolic traits (i.e., soluble protein content as an indicator for the level of metabolic turnover and NADP-malate dehydrogenase as an indicator for enzyme activity). The results revealed in total 37 significant QTL on chromosomes 1, 2, 4, 5, 6, 7, 8, 9, 14, 17, and 18, with often an overlap of confidence intervals of several traits. These confidence intervals were in some cases remarkably small, which is due to the high statistical power of the design. In total, 18 QTL showed significant imprinting effects. The small and overlapping confidence intervals for the classical fatness traits as well as for the cytological and metabolic traits enabled positional and functional candidate gene identification for several mapped QTL.