Background: The laying hen industry aims to extend production for the economic and environmental benefits it offers, while at the same time facing the challenges of declining egg production and quality in aging hens. To explore trait persistence, we studied 998 Rhode Island Red purebred hens from the Novogen nucleus. We recorded daily egg production from 70 to 92 weeks of age and measured individual feed intake twice a week for three weeks, starting at 70, 80, and 90 weeks, as well as body weight at the start and at the end of each feed intake recording period. Random regression models were used to study trait trajectories over time, and PCA and hierarchical clustering were applied to identify groups of hens based on estimated breeding values for the intercept and slope of traits trajectory. Results: Results showed different aging trajectories among traits. Daily body weight variation, Haugh unit and yolk percentage showed persistence (i.e. stability) over the measured period. On the contrary, daily feed intake, residual feed intake, laying rate, egg mass, eggshell breaking strength and stiffness decreased over time, while body weight, mean egg weight, eggshell colour and feed conversion ratio increased. To assess the feasibility of selecting for trait persistence, we estimated the genetic variance of the slope and its correlation with the intercept. We found that, for egg weight and eggshell colour, genetic variance of the slope was negligible, indicating that selection for persistence on these traits requires other means. On the contrary, the slope for other traits such as laying rate and residual feed intake showed significant additive genetic variance. Strong genetic correlations between trait estimates at different ages were also observed and heritabilities estimates were low to high depending of the traits and period. Conclusion: The study explores hens' trait persistence from 70 to 92 weeks, suggesting potential for improved egg production persistence. Challenges arise from low genetic variances impacting the efficiency of the potential selection on persistence. Clustering analysis reveals distinctive response patterns to elongation of production and underlined that selecting for enhanced persistence of different traits will necessitate compromises in breeding goals. ### Competing Interest Statement The authors have declared no competing interest.

Vaccination is one of the most effective strategies for preventing infectious diseases but individual vaccine responses are highly heterogeneous. Host genetics and gut microbiota composition are 2 likely drivers of this heterogeneity. We studied 94 animals belonging to 4 lines of laying hens: a White Leghorn experimental line genetically selected for a high antibody response against the Newcastle Disease Virus (NDV) vaccine (ND3) and its unselected control line (CTR), and 2 commercial lines (White Leghorn [LEG] and Rhode Island Red [RIR]). Animals were reared in the same conditions from hatching to 42 d of age, and animals from different genetic lines were mixed. Animals were vaccinated at 22 d of age and their humoral vaccine response against NDV was assessed by hemagglutination inhibition assay and ELISA from blood samples collected at 15, 19, and 21 d after vaccination. The immune parameters studied were the 3 immunoglobulins subtypes A, M, and Y and the blood cell composition was assessed by flow cytometry. The composition of the cecal microbiota was assessed at the end of the experiment by analyzing amplified 16S rRNA gene sequences to obtain amplicon sequence variants (ASV). The 4 lines showed significantly different levels of NDV vaccine response at the 3 measured points, with, logically, a higher response of the genetically selected ND3 line, and intermediate and low responses for the unselected CTR control line and for the 2 commercial lines, respectively. The ND3 line displayed also a higher proportion of immunoglobulins (IgA, IgM, and IgY). The RIR line showed the most different blood cell composition. The 4 lines showed significantly different microbiota characteristics: composition, abundances at all taxonomic levels, and correlations between genera and vaccine response. The tested genetic lines differ for immune parameters and gut microbiota composition and functions. These phenotypic differences can be attributed to genetic differences between lines. Causal relationships between both types of parameters are discussed and will be investigated in further studies.

To reduce the cost of genomic selection, a low-density (LD) single nucleotide polymorphism (SNP) chip can be used in combination with imputation for genotyping selection candidates instead of using a high-density (HD) SNP chip. Next-generation sequencing (NGS) techniques have been increasingly used in livestock species but remain expensive for routine use for genomic selection. An alternative and cost-efficient solution is to use restriction site-associated DNA sequencing (RADseq) techniques to sequence only a fraction of the genome using restriction enzymes. From this perspective, use of RADseq techniques followed by an imputation step on HD chip as alternatives to LD chips for genomic selection was studied in a pure layer line. Genome reduction and sequencing fragments were identified on reference genome using four restriction enzymes (EcoRI, TaqI, AvaII and PstI) and a double-digest RADseq (ddRADseq) method (TaqI-PstI). The SNPs contained in these fragments were detected from the 20X sequence data of the individuals in our population. Imputation accuracy on HD chip with these genotypes was assessed as the mean correlation between true and imputed genotypes. Several production traits were evaluated using single-step GBLUP methodology. The impact of imputation errors on the ranking of the selection candidates was assessed by comparing a genomic evaluation based on ancestry using true HD or imputed HD genotyping. The relative accuracy of genomic estimated breeding values (GEBVs) was investigated by considering the GEBVs estimated on offspring as a reference. With AvaII or PstI and ddRADseq with TaqI and PstI, more than 10 K SNPs were detected in common with the HD SNP chip, resulting in an imputation accuracy greater than 0.97. The impact of imputation errors on genomic evaluation of the breeders was reduced, with a Spearman correlation greater than 0.99. Finally, the relative accuracy of GEBVs was equivalent. RADseq approaches can be interesting alternatives to low-density SNP chips for genomic selection. With more than 10 K SNPs in common with the SNPs of the HD SNP chip, good imputation and genomic evaluation results can be obtained. However, with real data, heterogeneity between individuals with missing data must be considered.

Floor eggs, which are defined as eggs that hens lay off-nest, are a major issue in cage-free layer poultry systems. They create additional work for farmers because they must be collected by hand. They are also usually soiled or broken, which results in economic losses. Nonetheless, knowledge about the genetics of nesting behavior is limited. The aim of this study was to estimate genetic parameters for traits related to nest preference for laying and to time spent in the nests used for laying (laying duration). Two pure lines of laying hens were studied: 927 Rhode Island Red and 980 White Leghorn. Electronic nests were used to record the nesting behavior of these hens in floor pens from 24 to 64 weeks of age. Nest preference was studied based on the mean distance between nests used for laying and the percentage of nests used for laying. Laying duration was studied based on mean laying duration, mean duration in the nest before laying, and mean duration in the nest after laying. Genetic parameters were estimated for each line using a restricted maximum-likelihood method applied to a pedigree-based multi-trait animal model. Estimates of genetic parameters were similar for the two lines. Estimates of heritability ranged from 0.18 to 0.37 for nest preference traits and from 0.54 to 0.70 for laying duration traits. Estimates of genetic correlations of these traits with clutch number or mean oviposition time were favorable. Positive genetic correlations were estimated between nest preference and laying rate in the nests or nest acceptance for laying (+ 0.06 to + 0.37). These results show that genetics influences traits related to nest preference and laying duration. Selecting hens that have no preference for particular nests and spend little time laying in the nests could help optimize nest use, reduce their occupation rate, and thus decrease the incidence of floor eggs in cage-free systems. Genetic correlations of these traits with other traits of interest related to hen welfare and egg quality have yet to be estimated.

In cage-free systems, laying hens must lay their eggs in the nests. Selecting layers based on nesting behavior would be a good strategy for improving egg production in these breeding systems. However, little is known about the genetic determinism of nest-related traits. Laying rate in the nests (LRN), clutch number (CN), oviposition traits (OT), and nest acceptance for laying (NAL) of 1,430 Rhode Island Red (RIR) hens and 1,008 White Leghorn (WL) hens were recorded in floor pens provided with individual electronic nests. Heritability and genetic and phenotypic correlations of all traits were estimated over two recording periods-the peak (24-43 weeks of age) and the middle (44-64 weeks of age) of production-by applying the restricted maximum likelihood method to an animal model. The mean oviposition time (MOT) ranged from 2 h 5 min to 3 h and from 3 h 35 min to 3 h 44 min after turning on the lights for RIR and WL hens, respectively. The mean oviposition interval ranged from 24 h 3 min to 24 h 16 min. All heritability and correlation estimates were similar for RIR and WL. Low to moderate heritability coefficients were estimated for LRN (0.04-0.25) and moderate to high heritability coefficients for CN and OT (0.27-0.68). CN and OT were negatively genetically correlated with LRN (-0.92 to -0.39) except during peak production for RIR (-0.30 to +0.43). NAL was weakly to moderately heritable (0.13-0.26). Genetic correlations between NAL and other traits were low to moderate (-0.41 to +0.44). In conclusion, CN and OT are promising selection criteria to improve egg production in cage-free systems. NAL can be also used to reduce the number of eggs laid off-nest in these breeding systems. However, variability in MOT must be maintained to limit competition for the nests.

In addition to their common usages to study gene expression, RNA-seq data accumulated over the last 10 years are a yet-unexploited resource of SNPs in numerous individuals from different populations. SNP detection by RNA-seq is particularly interesting for livestock species since whole genome sequencing is expensive and exome sequencing tools are unavailable. These SNPs detected in expressed regions can be used to characterize variants affecting protein functions, and to study cis-regulated genes by analyzing allele-specific expression (ASE) in the tissue of interest. However, gene expression can be highly variable, and filters for SNP detection using the popular GATK toolkit are not yet standardized, making SNP detection and genotype calling by RNA-seq a challenging endeavor. We compared SNP calling results using GATK suggested filters, on two chicken populations for which both RNA-seq and DNA-seq data were available for the same samples of the same tissue. We showed, in expressed regions, a RNA-seq precision of 91% (SNPs detected by RNA-seq and shared by DNA-seq) and we characterized the remaining 9% of SNPs. We then studied the genotype (GT) obtained by RNA-seq and the impact of two factors (GT call-rate and read number per GT) on the concordance of GT with DNA-seq; we proposed thresholds for them leading to a 95% concordance. Applying these thresholds to 767 multi-tissue RNA-seq of 382 birds of 11 chicken populations, we found 9.5 M SNPs in total, of which ∼550,000 SNPs per tissue and population with a reliable GT (call rate ≥ 50%) and among them, ∼340,000 with a MAF ≥ 10%. We showed that such RNA-seq data from one tissue can be used to (i) detect SNPs with a strong predicted impact on proteins, despite their scarcity in each population (16,307 SIFT deleterious missenses and 590 stop-gained), (ii) study, on a large scale, cis-regulations of gene expression, with ∼81% of protein-coding and 68% of long non-coding genes (TPM ≥ 1) that can be analyzed for ASE, and with ∼29% of them that were cis-regulated, and (iii) analyze population genetic using such SNPs located in expressed regions. This work shows that RNA-seq data can be used with good confidence to detect SNPs and associated GT within various populations and used them for different analyses as GTEx studies.

Additional file 5 Slopes of regression for (G)EBVs in candidate populations with respect to cC traits. Traits: EW: Egg Weight; ESC: Eggshell Colour; ESS: Eggshell Strength; AH: Albumen Height; ESshape: Eggshell shape index. Candidates: C1: male candidates from G1; C2: male candidates from G1 and G2; F: female candidates from G2. Multi-trait evaluations were performed for the five traits using BLUP (EBV) and ssGBLUP (GEBV) methodologies. Case 1: evaluation at birth; Case 2: evaluation at 60 weeks of age; Case 3: evaluation at 140 weeks of age.

With the availability of the 600K Affymetrix Axiom high-density (HD) single nucleotide polymorphism (SNP) chip, genomic selection has been implemented in broiler and layer chicken. However, the cost of this SNP chip is too high to genotype all selection candidates. A solution is to develop a low-density SNP chip, at a lower price, and to impute all missing markers. But to routinely implement this solution, the impact of imputation on genomic evaluation accuracy must be studied. It is also interesting to study the consequences of the use of low-density SNP chips in genomic evaluation accuracy. In this perspective, the interest of using imputation in genomic selection was studied in a pure layer line. Two low-density SNP chip designs were compared: an equidistant methodology and a methodology based on linkage disequilibrium. Egg weight, egg shell color, egg shell strength, and albumen height were evaluated with single-step genomic best linear unbiased prediction methodology. The impact of imputation errors or the absence of imputation on the ranking of the male selection candidates was assessed with a genomic evaluation based on ancestry. Thus, genomic estimated breeding values (GEBV) obtained with imputed HD genotypes or low-density genotypes were compared with GEBV obtained with the HD SNP chip. The relative accuracy of GEBV was also investigated by considering as reference GEBV estimated on the offspring. A limited reordering of the breeders, selected on a multitrait index, was observed. Spearman correlations between GEBV on HD genotypes and GEBV on low-density genotypes (with or without imputation) were always higher than 0.94 with more than 3K SNP. For the genetically closer, top 150 individuals for a specific trait, with imputation, the reordering was reduced with correlation higher than 0.94 with more than 3K SNP. Without imputation, the correlations remained lower than 0.85 with less than 3K and 16K SNP for equidistant and linkage disequilibrium methodology, respectively. The differences in GEBV correlations between both methodologies were never significant. The conclusions were the same for all studied traits.

Additional file 2 Estimates of (G)EBVs relative accuracy in candidate populations with respect to iC traits. Traits: EW: Egg Weight; ESC: Eggshell Colour; ESS: Eggshell Strength; AH: Albumen Height; ESshape: Eggshell shape index. Candidates: C1: male candidates from G1; C2: male candidates from G1 and G2; F: female candidates from G2. Multi-trait evaluations were performed for the five traits using BLUP (EBV) and ssGBLUP (GEBV) methodologies. Case 1: evaluation at birth; Case 2: evaluation at 80 weeks of age; Case 3: evaluation at 160 weeks of age. In parentheses are groups determined by the Hotelling-Williams test at a confidence level of 95%. Groups a, b and c are used when EBVs correlations are compared to other EBVs correlations; d, e, f are used when GEBVs correlations are compared to other GEBVs.