In today’s chicken egg industry, maintaining the strength of eggshells in longer laying cycles is pivotal for improving the persistency of egg laying. Eggshell development and mineralization underlie a complex regulatory interplay of various proteins and signaling cascades involving multiple organ systems. Understanding the regulatory mechanisms influencing this dynamic trait over time is imperative, yet scarce. To investigate the temporal changes in the signaling cascades, we considered eggshell strength at two different time points during the egg production cycle and studied the genotype–phenotype associations by employing the Random Forests algorithm on chicken genotypic data. For the analysis of corresponding genes, we adopted a well established systems biology approach to delineate gene regulatory pathways and master regulators underlying this important trait. Our results indicate that, while some of the master regulators (Slc22a1 and Sox11) and pathways are common at different laying stages of chicken, others (e.g., Scn11a, St8sia2, or the TGF- $\beta$ pathway) represent age-specific functions. Overall, our results provide: (i) significant insights into age-specific and common molecular mechanisms underlying the regulation of eggshell strength; and (ii) new breeding targets to improve the eggshell quality during the later stages of the chicken production cycle.

Genome wide association studies (GWAS) are a well established methodology to identify genomic variants and genes that are responsible for traits of interest in all branches of the life sciences. Despite the long time this methodology has had to mature the reliable detection of genotype–phenotype associations is still a challenge for many quantitative traits mainly because of the large number of genomic loci with weak individual effects on the trait under investigation. Thus, it can be hypothesized that many genomic variants that have a small, however real, effect remain unnoticed in many GWAS approaches. Here, we propose a two-step procedure to address this problem. In a first step, cubic splines are fitted to the test statistic values and genomic regions with spline-peaks that are higher than expected by chance are considered as quantitative trait loci (QTL). Then the SNPs in these QTLs are prioritized with respect to the strength of their association with the phenotype using a Random Forests approach. As a case study, we apply our procedure to real data sets and find trustworthy numbers of, partially novel, genomic variants and genes involved in various egg quality traits.

The interactions between SNPs result in a complex interplay with the phenotype, known as epistasis. The knowledge of epistasis is a crucial part of understanding genetic causes of complex traits. However, due to the enormous number of SNP pairs and their complex relationship to the phenotype, identification still remains a challenging problem. Many approaches for the detection of epistasis have been developed using mutual information (MI) as an association measure. However, these methods have mainly been restricted to case–control phenotypes and are therefore of limited applicability for quantitative traits. To overcome this limitation of MI-based methods, here, we present an MI-based novel algorithm, MIDESP, to detect epistasis between SNPs for qualitative as well as quantitative phenotypes. Moreover, by incorporating a dataset-dependent correction technique, we deal with the effect of background associations in a genotypic dataset to separate correct epistatic interaction signals from those of false positive interactions resulting from the effect of single SNP×phenotype associations. To demonstrate the effectiveness of MIDESP, we apply it on two real datasets with qualitative and quantitative phenotypes, respectively. Our results suggest that by eliminating the background associations, MIDESP can identify important genes, which play essential roles for bovine tuberculosis or the egg weight of chickens.

Skeletal disorders, including fractures and osteoporosis, in laying hens cause major welfare and economic problems. Although genetics have been shown to play a key role in bone integrity, little is yet known about the underlying genetic architecture of the traits. This study aimed to identify genes associated with bone breaking strength and bone mineral density of the tibiotarsus and the humerus in laying hens. Potentially informative single nucleotide polymorphisms (SNP) were identified using Random Forests classification. We then searched for genes known to be related to bone stability in close proximity to the SNPs and identified 16 potential candidates. Some of them had human orthologues. Based on our findings, we can support the assumption that multiple genes determine bone strength, with each of them having a rather small effect, as illustrated by our SNP effect estimates. Furthermore, the enrichment analysis showed that some of these candidates are involved in metabolic pathways critical for bone integrity. In conclusion, the identified candidates represent genes that may play a role in the bone integrity of chickens. Although further studies are needed to determine causality, the genes reported here are promising in terms of alleviating bone disorders in laying hens.

Background and Aim: Soybean meal (SBM) is the primary protein source in broiler diets; however, its high cost and import dependency necessitate alternative protein sources. Sunflower meal (SFM) is a viable alternative but contains high fiber and non-starch polysaccharides (NSPs), which can impair nutrient utilization. This study evaluated the effects of partially replacing SBM with SFM, with or without NSP degrading enzymes (NSPase) enzyme supplementation, on growth performance, nutrient digestibility, digesta viscosity, and gut morphology in broilers. Materials and Methods: A total of 588 day-old Ross-308 broiler chicks were randomly assigned to six dietary treatments following a 3 × 2 factorial design, with three levels of SBM replacement (0%, 10%, and 20% SFM) and two levels of NSPase supplementation (0 or 100 g/ton feed). Diets were formulated for the starter (1–10 days), grower (11–21 days), and finisher (22–35 days) phases. Growth performance (body weight gain [BWG], feed intake [FI], feed conversion ratio [FCR]), nutrient digestibility (dry matter, crude protein, ether extract, and crude fiber [CF]), digesta viscosity, and gut morphology (villus height [VH], crypt depth [CD], villus width, and villus-to-crypt ratio) were assessed. Statistical analysis was conducted using two-way analysis of variance with Tukey’s test for mean comparisons (p < 0.05). Results: Replacing SBM with up to 20% SFM did not significantly impact BWG (p > 0.05), FI (p > 0.05), or FCR (p > 0.05). However, digesta viscosity increased significantly with higher SFM levels (p < 0.001), while NSPase supplementation reduced viscosity (p < 0.001). CF digestibility was lower with increasing SFM levels (p < 0.01) but improved with NSPase addition (p < 0.01). Gut morphology parameters, including VH and CD, were negatively affected by higher SFM inclusion but showed improvement with NSPase supplementation. Conclusion: SBM can be partially replaced with up to 20% SFM in broiler diets without compromising growth performance. However, increasing SFM levels can reduce CF digestibility and increase digesta viscosity. The addition of NSPase enzymes mitigates these adverse effects by enhancing fiber digestibility and reducing gut viscosity. These findings support the use of SFM as an economically viable protein alternative in broiler feed formulations, particularly in SBM-importing regions. Keywords: broiler nutrition, feed efficiency, gut morphology, non-starch polysaccharide degrading enzymes, nutrient digestibility, soybean meal replacement, sunflower meal.