Milk urea concentration (MUC) is an indicator of dietary protein utilization and nitrogen use efficiency in dairy cows. We performed genome-wide association studies (GWAS) on MUC in early, mid, and late lactation in the Holstein (HOL) and Brown Swiss (BSW) dairy cattle breeds using imputed sequence variants. We identified 11 and 17 independent quantitative trait loci (QTL) for MUC across the 3 lactation stages in BSW and HOL, respectively. While many of these QTL have previously been reported for MUC and other dairy traits, our study provides evidence that some QTL exert lactation-stage specific effects. Our findings suggest that variants at the DGAT1 locus on BTA14 have pleiotropic effects on MUC and other dairy traits. This QTL showed an early lactation-specific association with MUC but impacted milk and fat yield across the entire lactation. We fine-mapped 2 QTL for MUC in early and mid-lactation in BSW on BTA9 (lead SNP: 9:21392941, Pcorrected = 1.1E-17, β = 0.6510) and BTA28 (lead SNP: 28:6518357; Pcorrected = 3E-11, β = −0.5020). We identified lncRNA ENSBTAG00000058688 and IBTK as positional and functional candidate genes for the BTA9 QTL, and KCNK1 as positional and functional candidate gene that harbors a highly significant missense variant for the BTA28 QTL. In conclusion, our results shed light on the genetic architecture of MUC and highlighted QTL harboring potential functional variants underpinning milk urea variation within and across breeds.

The Resilient Dairy Genome Project (RDGP) is an international large-scale applied research project that aims to generate genomic tools to breed more resilient dairy cows. In this context, improving feed efficiency and reducing greenhouse gases from dairy is a high priority. The inclusion of traits related to feed efficiency (e.g., dry matter intake [DMI]) or greenhouse gases (e.g., methane emissions [CH4]) relies on available genotypes as well as high quality phenotypes. Currently, 7 countries, i.e., Australia [AUS], Canada [CAN], Denmark [DNK], Germany [DEU], Spain [ESP], Switzerland [CHE], and United States of America [USA] contribute with genotypes and phenotypes including DMI and CH4. However, combining data is challenging due to differences in recording protocols, measurement technology, genotyping, and animal management across sources. In this study, we provide an overview of how the RDGP partners address these issues to advance international collaboration to generate genomic tools for resilient dairy. Specifically, we describe the current state of the RDGP database, data collection protocols in each country, and the strategies used for managing the shared data. As of February 2022, the database contains 1,289,593 DMI records from 12,687 cows and 17,403 CH4 records from 3,093 cows and continues to grow as countries upload new data over the coming years. No strong genomic differentiation between the populations was identified in this study, which may be beneficial for eventual across-country genomic predictions. Moreover, our results reinforce the need to account for the heterogeneity in the DMI and CH4 phenotypes in genomic analysis.

Feed efficiency has become an increasingly important research topic in recent years. As feed costs rise and the environmental impacts of agriculture become more apparent, improving the efficiency with which dairy cows convert feed to milk is increasingly important. However, feed intake is expensive to measure accurately on large populations, making the inclusion of this trait in breeding programs difficult. Understanding how the genetic parameters of feed efficiency and traits related to feed efficiency vary throughout the lactation period is valuable to gain understanding into the genetic nature of feed efficiency. This study used 121,226 dry matter intake (DMI) records, 120,500 energy -corrected milk (ECM) records, and 98,975 metabolic body weight (MBW) records, collected on 7,440 first -lactation Holstein cows from 6 countries (Canada, Denmark, Germany, Spain, Switzerland, and the United States), from January 2003 to February 2022. Genetic parameters were estimated using a multiple -trait random regression model with a fourth -order Legendre polynomial for all traits. Weekly phenotypes for DMI were re -parameterized using linear regressions of DMI on ECM and MBW, creating a measure of feed efficiency that was genetically corrected for ECM and MBW, referred to as genomic residual feed intake (gRFI). Heritability (SE) estimates varied from 0.15 (0.03) to 0.29 (0.02) for DMI, 0.24 (0.01) to 0.29 (0.03) for ECM, 0.55 (0.03) to 0.83 (0.05) for MBW, and 0.12 (0.03) to 0.22 (0.06) for gRFI. In general, heritability estimates were lower in the first stage of lactation compared with the later stages of lactation. Additive genetic correlations between weeks of lactation varied, with stronger correlations between weeks of lactation that were close together. The results of this study contribute to a better understanding of the change in genetic parameters across the first lactation, providing insight into potential selection strategies to include feed efficiency in breeding programs.

A healthy sucking reflex is essential for newborn calves to ensure sufficient colostrum intake in the first few hours postpartum. In recent decades, European Brown Swiss breeders have repeatedly reported that some calves lack the ability to consume colostrum directly after birth due to an absent sucking reflex. In this study, we collected the phenotypes of more than 5,500 German Brown Swiss calves and performed variance component estimation with sire threshold models using Markov chain Monte Carlo algorithms. The 50K (777K) genotypes of nearly 2,000 (200) calves were collected, and an imputation was performed for all 50K genotypes up to 777K. Genome-wide association studies (GWAS) for the trait sucking reflex were conducted for all 777K genotypes. Depending on the trait coding, a low heritability was estimated to range from 0.08 to 0.11. The GWAS results identified 34 trait-associated SNP on 6 different chromosomes. Post-GWAS analyses showed significant overrepresentation of Gene Ontologies for central nervous development and several regulative processes. Functional annotation clustering and pathway analysis revealed relations to lipid metabolism, immune and endocrine systems, and signal transduction. The results of this study suggest that breeding for an improved sucking reflex is possible but requires large data sets for the estimation of reliable breeding values (either large progeny testing groups or a large reference genome in a genomic selection program).

In this study, genetic parameters of nine growth, carcass, and meat quality (MQ) traits were estimated, and targeted association studies were conducted using mixed models. Phenotypic information was collected on 1,599 lambs, including both purebred Merinoland (ML) animals and five different F1 crosses. The F1 lambs were produced by mating rams of the meat-type breeds Charollais, Ile de France, German Blackheaded Mutton (Deutsches Schwarzköpfiges Fleischschaf), Suffolk, and Texel with ML ewes. Between four and six sires were used per sire breed. In total, 29 sires and 298 purebred ML sheep were genotyped with the Illumina OvineSNP50 BeadChip. All F1 individuals were genotyped for 289 SNPs located on the chromosomes 1, 2, 3, 18, and 21. These SNPs were used to impute SNPs on five chromosomes of the Illumina Ovine chip in the F1 individuals. Several Bonferroni-corrected significant associations were identified for shoulder width. A number of additional significant associations were found for other traits. Genetic parameters were estimated and single-marker association analyses were performed with breed-specific effects. Moderate heritability estimates were found for average daily gain (0.23), kidney fat weight (0.19), carcass length (0.15), shoulder width (0.33), subcutaneous fat thickness (0.22), and cutlet area (0.36). While heritability for cooking loss was found to be low (0.07), shear force (0.17) and dressing percentage (0.20) showed moderate heritability, and thus might be candidate traits to be included in the selection index in the population. In general, low phenotypic and low or moderate genetic correlations were detected between the traits.

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.

Background Feather 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. Methods A 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. Results Numerous 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 (FPD meta ). Seven clusters were identified for APD and three for APR. Eight genes (of the 750 investigated genes located in the FPD meta 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. Conclusions Combining 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.

The improvement of the postpartum survival rate of piglets is important for pig breeding nowadays. For the improvement, breeding for good mothering abilities of sows offer a promising strategy. The aim of the following study was to estimate genetic parameters for mothering ability traits recorded on an five-point ordinal scale (1=“best” to 5=“worst”). Information of 48,028 litters and 15,688 sows belonging to different lines and crosses was available. Genetic parameters were estimated by applying a linear mixed- and a threshold model. Pairwise bivariate analyses were conducted for the linear mixed model (LMM) in order to calculate correlations.The estimates for the heritability (repeatability) using the LMM are between 0.02 and 0.07 (0.05 and 0.09). With the threshold model the heritability (repeatability) estimates are between 0.05 and 0.15 (0.08 and 0.17). The difference between repeatability and heritability points to a huge impact of the permanent environment. The threshold model seems to be more suitable for analysing the data. The heritability estimated for group – and nursing behaviour with the threshold model was highest (0.15 and 0.10). All the correlations were positive and the genetic correlations were in accordance with the phenotypic correlation. The genetic correlation for piglet vitality and estrus behaviour was zero. The highest genetic correlations were estimated between nursing – (group behaviour) and litter balance and piglet weaning weight. For nursing behaviour and piglet weaning weight the genetic correlation was 0.86.Accelerating genetic gain for good mothering abilities of sows will be possible. Especially nursing – and group behaviour seem to be promising traits. For the implementation in breeding programs, clearly defined traits are required and correlations between mothering ability and common production traits (e.g. daily weight gain) should be investigated.

Japanese quail (Coturnix japonica) is an important model animal in poultry studies. In the present study a large F2-cross design to map QTL for PU and other correlated traits on 14 chromosomes was used. Next to PU, the traits calcium utilization, body weight gain, and feed per gain ratio were investigated. Because the marker density was only sparse, QTL linkage interval mapping was performed. A line cross model and a paternal half-sib model was applied. Both models revealed several QTL for the traits investigated. In the line cross model QTL were identified on CJA04, CJA09 and CJA10. The dominance effect was important for the calcium utilization QTL on CJA04 and CJA09 and imprinting was important for the calcium utilization and PU QTL on CJA04 as well as for the PU QTL on CJA10. In the paternal half-sib model, QTL were identified on CJA1, CJA14, CJA17 and CJA20. There was no overlap in detected QTL between the models. A likely explanation is the different assumption made by the two models.

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.

To reduce the costs of genomic selection, selection candidates can be genotyped with an SNP panel of reduced density. Two strategies for the selection of SNPs to be considered in a very low density SNP panel (384 SNPs) are investigated using 895 progeny tested and genotyped German Piétrain boars. In the first strategy equal spaced SNPs were selected, which were used to impute the high density genotypes. In the second strategy SNPs were selected based on results of association analysis. Accuracies of DGV for the two strategies were obtained from cross validation. A regression approach to correct for the upward bias of the cross validation accuracy of the DGV was used. The first strategy resulted in more accurate DGV. This implies that imputation is beneficial even if only 384 SNPs are genotyped for the selection candidates.

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).

This study reports the results of targeted association analysis in multiple F1 Merinoland crossbred lambs. A number of 384 SNPs in chromosomal regions with reported QTL for growth, carcass and meat quality were genotyped at 1493 crossbred lambs. These lambs were produced from Merinoland ewes and rams from five different meat-type breeds (Charollais, Ile de France, German Blackheaded Mutton, Suffolk, and Texel). Single SNP association analysis was conducted across the crosses or nested within the crosses. The traits daily gain, carcass yield, drip loss, haunch circumference, and fat layer were considered. Modeling SNP effect across the crosses identified weak associations with the same effect sign across the crosses. The nested analysis revealed significant associations with different effects signs in the crosses, which were not detected in the model where SNP effect was fitted across the crosses. Positional and functional candidate genes were identified and discussed.

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.

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 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.

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.

QTL experiments in pigs are often analysed separately, although similar or same founder breeds are frequently used to establish the experimental design. The aim of the present study was to jointly analyse three porcine F-2-crosses for six growth and four muscling traits. The crosses were a Meishan x Pietrain cross, a Wild Boar x Pietrain cross, and a Wild Boar x Meishan cross. In some cases, same founder animals were used to establish the crosses. 966 F-2-individuals were genotyped for 242 genetic markers (mostly microsatellites) and phenotyped for birth weight, 21 and 35 day weight, slaughter weight, carcass length, food conversion ratio, ham meat weight, shoulder meat weight, loin and neck meat weight, and meat area. A multi-allele multi-QTL model was applied that estimated an additive QTL effect for each founder breed and parental origin (either paternally or maternally derived), and a dominant QTL effect for each cross. This model was previously introduced in plant breeding. Numerous QTL were mapped on the autosomes. Most QTL were localised on SSC1, 2, 3, 4, 6 and 8, and no QTL were on SSC9, 11, 13, 15, 17 and 18. The confidence intervals were short in many cases. QTL with an exceptionally high test statistic were found for carcass length on SSC1, 4, 7 and 17. The coefficient of variation was remarkably small for this trait, which suggests that carcass length is affected by only a few genes with large effects. Positional and functional candidates underlying promising QTL are suggested for further study.