Use of whole-genome sequence data for fine mapping and genomic prediction of sea louse resistance in Atlantic salmon.

Sea louse (Lepeophtheirus salmonis) infestation of Atlantic salmon (Salmo salar) is a significant challenge in aquaculture. Over the years, this parasite has developed immunity to medicinal control compounds, and non-medicinal control methods have been proven to be stressful, hence the need to study the genomic architecture of salmon resistance to sea lice. Thus, this research used whole-genome sequence (WGS) data to study the genetic basis of the trait since most research using fewer SNPs did not identify significant quantitative trait loci. Mowi Genetics AS provided the genotype (50 k SNPs) and phenotype data for this research after conducting a sea lice challenge test on 3,185 salmon smolts belonging to 191 full-sib families. The 50 k SNP genotype was imputed to WGS using the information from 197 closely related individuals with sequence data. The WGS and 50 k SNPs of the challenged population were then used to estimate genetic parameters, perform a genome-wide association study (GWAS), predict genomic breeding values, and estimate its accuracy for host resistance to sea lice. The heritability of host resistance to sea lice was estimated to be 0.21 and 0.22, while the accuracy of genomic prediction was estimated to be 0.65 and 0.64 for array and WGS data, respectively. In addition, the association test using both array and WGS data did not identify any marker associated with sea lice resistance at the genome-wide level. We conclude that sea lice resistance is a polygenic trait that is moderately heritable. The genomic predictions using medium-density SNP genotyping array were equally good or better than those based on WGS data.

Deep learning the cis-regulatory code for gene expression in selected model plants.

Elucidating the relationship between non-coding regulatory element sequences and gene expression is crucial for understanding gene regulation and genetic variation. We explored this link with the training of interpretable deep learning models predicting gene expression profiles from gene flanking regions of the plant species Arabidopsis thaliana, Solanum lycopersicum, Sorghum bicolor, and Zea mays. With over 80% accuracy, our models enabled predictive feature selection, highlighting e.g. the significant role of UTR regions in determining gene expression levels. The models demonstrated remarkable cross-species performance, effectively identifying both conserved and species-specific regulatory sequence features and their predictive power for gene expression. We illustrated the application of our approach by revealing causal links between genetic variation and gene expression changes across fourteen tomato genomes. Lastly, our models efficiently predicted genotype-specific expression of key functional gene groups, exemplified by underscoring known phenotypic and metabolic differences between Solanum lycopersicum and its wild, drought-resistant relative, Solanum pennellii.

Exploring the potential of incremental feature selection to improve genomic prediction accuracy.

BACKGROUND: The ever-increasing availability of high-density genomic markers in the form of single nucleotide polymorphisms (SNPs) enables genomic prediction, i.e. the inference of phenotypes based solely on genomic data, in the field of animal and plant breeding, where it has become an important tool. However, given the limited number of individuals, the abundance of variables (SNPs) can reduce the accuracy of prediction models due to overfitting or irrelevant SNPs. Feature selection can help to reduce the number of irrelevant SNPs and increase the model performance. In this study, we investigated an incremental feature selection approach based on ranking the SNPs according to the results of a genome-wide association study that we combined with random forest as a prediction model, and we applied it on several animal and plant datasets. RESULTS: Applying our approach to different datasets yielded a wide range of outcomes, i.e. from a substantial increase in prediction accuracy in a few cases to minor improvements when only a fraction of the available SNPs were used. Compared with models using all available SNPs, our approach was able to achieve comparable performances with a considerably reduced number of SNPs in several cases. Our approach showcased state-of-the-art efficiency and performance while having a faster computation time. CONCLUSIONS: The results of our study suggest that our incremental feature selection approach has the potential to improve prediction accuracy substantially. However, this gain seems to depend on the genomic data used. Even for datasets where the number of markers is smaller than the number of individuals, feature selection may still increase the performance of the genomic prediction. Our approach is implemented in R and is available at https://github.com/FelixHeinrich/GP_with_IFS/ .

Identification of Differentially Expressed Genes after Endurance Runs in Karbadian Horses to Determine Candidates for Stress Indicators and Performance Capability.

RNA sequencing makes it possible to uncover genetic mechanisms that underlie certain performance traits. In order to gain a deeper insight into the genetic background and biological processes involved in endurance performance in horses, the changes in the gene expression profiles induced by endurance runs over long (70 km) and short (15 km) distances in the blood of Kabardian horses (Equus caballus) were analyzed. For the long-distance runs, we identified 1484 up- and 691 downregulated genes, while after short-distance runs, only 13 up- and 8 downregulated genes (FC > |1.5|; p < 0.05) were found. These differentially expressed genes (DEGs) are involved in processes and pathways that are primarily related to stress response (interleukin production, activation of inflammatory system) but also to metabolism (carbohydrate catabolic process, lipid biosynthesis, NADP metabolic process). The most important genes involved in these processes therefore represent good candidates for the monitoring and evaluation of the performance of horses in order to avoid excessive demands when endurance performance is required, like ACOD1, CCL5, CD40LG, FOS, IL1R2, IL20RA, and IL22RA2, on the one hand, and, on the other hand, for assessing the suitability of a horse for endurance races, like GATA2, GYG1, HIF1A, MOGAT1, PFKFB3, PLIN5, SIK1, and STBD1.

Ice holes microrefugia harbor genetically and functionally distinct populations of Vaccinium vitis-idaea (Ericaceae).

In the mountain terrain, ice holes are little depressions between rock boulders that are characterized by the exit of cold air able to cool down the rock surface even in summer. This cold air creates cold microrefugia in warmer surroundings that preserve plant species probably over thousands of years under extra-zonal climatic conditions. We hypothesized that ice hole populations of the model species Vaccinium vitis-idaea (Ericaceae) show genetic differentiation from nearby zonal subalpine populations, and high functional trait distinctiveness, in agreement with genetic patterns. We genotyped almost 30,000 single nucleotide polymorphisms using restriction site-associated DNA sequencing and measured eight functional traits indicative of individual performance and ecological strategies. Genetic results showed high differentiation among the six populations suggesting isolation. On siliceous bedrock, ice hole individuals exhibited higher levels of admixture than those from subalpine populations which could have experienced more bottlenecks during demographic fluctuations related to glacial cycles. Ice hole and subalpine calcareous populations clearly separated from siliceous populations, indicating a possible effect of bedrock in shaping genetic patterns. Trait analysis reflected the bedrock effect on populations' differentiation. The significant correlation between trait and genetic distances suggests the genetic contribution in shaping intraspecific functional differentiation. In conclusion, extra-zonal populations reveal a prominent genetic and phenotypic differentiation determined by history and ecological contingency. Therefore, microrefugia populations can contribute to the overall variability of the species and lead to intraspecific-driven responses to upcoming environmental changes.

Comparative Investigation of Coincident Single Nucleotide Polymorphisms Underlying Avian Influenza Viruses in Chickens and Ducks.

Avian influenza is a severe viral infection that has the potential to cause human pandemics. In particular, chickens are susceptible to many highly pathogenic strains of the virus, resulting in significant losses. In contrast, ducks have been reported to exhibit rapid and effective innate immune responses to most avian influenza virus (AIV) infections. To explore the distinct genetic programs that potentially distinguish the susceptibility/resistance of both species to AIV, the investigation of coincident SNPs (coSNPs) and their differing causal effects on gene functions in both species is important to gain novel insight into the varying immune-related responses of chickens and ducks. By conducting a pairwise genome alignment between these species, we identified coSNPs and their respective effect on AIV-related differentially expressed genes (DEGs) in this study. The examination of these genes (e.g., CD74, RUBCN, and SHTN1 for chickens and ABCA3, MAP2K6, and VIPR2 for ducks) reveals their high relevance to AIV. Further analysis of these genes provides promising effector molecules (such as IκBα, STAT1/STAT3, GSK-3ß, or p53) and related key signaling pathways (such as NF-κB, JAK/STAT, or Wnt) to elucidate the complex mechanisms of immune responses to AIV infections in both chickens and ducks.

Estimates of Water Consumption of Growing Pigs under Practical Conditions Based on Climate and Performance Data.

The water consumption of fattening pigs was recorded under practical conditions and compared with calculated water consumption. The experiment was carried out in the summer of 2020 with 79 fattening pigs. Data loggers were used to record the climate data, such as temperature and relative humidity. These data were used to calculate the temperature-humidity index (THI). It was found that there were sometimes considerable discrepancies between the measured and the calculated water consumption. One possible reason for this discrepancy could be the age of the existing water requirement equations, as in recent decades there has been a clear breeding development and thus a strong increase in pig performance. Based on these deviations, six new water consumption equations were established, which considered the variables body weight (BW), temperature, THI and feed consumption. It was found that the THI and BW should be included in one equation as predictor variables and the evaluation also showed good results. Its use, in practice, should also be considered. Overall, it became apparent that there is still a need for further research to make water consumption equations more precise. This would require a larger database.

Does the Implementation of an Animal Welfare Programme on a Farm Yield a Demonstrable Improvement in Fattening Pig Welfare?

In the course of social criticism of fattening pig farming, an animal welfare programme called "Initiative Animal Welfare" (ITW) was founded to increase animal welfare in pig farming in Germany. Furthermore, there is a legal obligation to record animal welfare parameters as a self-monitoring measure. The "German Association for Technology and Structures in Agriculture" published a guideline on the applicable animal welfare criteria. This guide formed the basis of this study's data collection. The aim was to apply the animal welfare parameters on farms by comparing the results between farms participating in ITW with those not participating. A cumulative score was calculated by evaluating the collected data. In addition, the relative risk was calculated in order to estimate the risk of finding a negative expression of a parameter. Our data show that ITW farms did not perform significantly better than the farms without ITW in terms of both the cumulative score and the relative risk. Overall, it must be considered that in both farm variants the occurrence of negative evaluations was very rare and the visited farms thus certainly can be considered to be well-managed farms. Climate parameters were recorded in each compartment and showed no significant differences in most cases.

Middle Jurassic fossils document an early stage in salamander evolution.

Salamanders are an important group of living amphibians and model organisms for understanding locomotion, development, regeneration, feeding, and toxicity in tetrapods. However, their origin and early radiation remain poorly understood, with early fossil stem-salamanders so far represented by larval or incompletely known taxa. This poor record also limits understanding of the origin of Lissamphibia (i.e., frogs, salamanders, and caecilians). We report fossils from the Middle Jurassic of Scotland representing almost the entire skeleton of the enigmatic stem-salamander Marmorerpeton. We use computed tomography to visualize high-resolution three-dimensional anatomy, describing morphologies that were poorly characterized in early salamanders, including the braincase, scapulocoracoid, and lower jaw. We use these data in the context of a phylogenetic analysis intended to resolve the relationships of early and stem-salamanders, including representation of important outgroups alongside data from high-resolution imaging of extant species. Marmorerpeton is united with Karaurus, Kokartus, and others from the Middle Jurassic-Lower Cretaceous of Asia, providing evidence for an early radiation of robustly built neotenous stem-salamanders. These taxa display morphological specializations similar to the extant cryptobranchid "giant" salamanders. Our analysis also demonstrates stem-group affinities for a larger sample of Jurassic species than previously recognized, highlighting an unappreciated diversity of stem-salamanders and cautioning against the use of single species (e.g., Karaurus) as exemplars for stem-salamander anatomy. These phylogenetic findings, combined with knowledge of the near-complete skeletal anatomy of Mamorerpeton, advance our understanding of evolutionary changes on the salamander stem-lineage and provide important data on early salamanders and the origins of Batrachia and Lissamphibia.

agReg-SNPdb-Plants: A Database of Regulatory SNPs for Agricultural Plant Species.

Single nucleotide polymorphisms (SNPs) that are located in the promoter regions of genes and affect the binding of transcription factors (TFs) are called regulatory SNPs (rSNPs). Their identification can be highly valuable for the interpretation of genome-wide association studies (GWAS), since rSNPs can reveal the biologically causative variant and decipher the regulatory mechanisms behind a phenotype. In our previous work, we presented agReg-SNPdb, a database of regulatory SNPs for agriculturally important animal species. To complement this previous work, in this study we present the extension agReg-SNPdb-Plants storing rSNPs and their predicted effects on TF-binding for 13 agriculturally important plant species and subspecies (Brassica napus, Helianthus annuus, Hordeum vulgare, Oryza glaberrima, Oryza glumipatula, Oryza sativa Indica, Oryza sativa Japonica, Solanum lycopersicum, Sorghum bicolor, Triticum aestivum, Triticum turgidum, Vitis vinifera, and Zea mays). agReg-SNPdb-Plants can be queried via a web interface that allows users to search for SNP IDs, chromosomal regions, or genes. For a comprehensive interpretation of GWAS results or larger SNP-sets, it is possible to download the whole list of SNPs and their impact on transcription factor binding sites (TFBSs) from the website chromosome-wise.

Deciphering the Molecular Mechanism Underlying African Animal Trypanosomiasis by Means of the 1000 Bull Genomes Project Genomic Dataset.

African Animal Trypanosomiasis (AAT) is a neglected tropical disease and spreads by the vector tsetse fly, which carries the infectious Trypanosoma sp. in their saliva. Particularly, this parasitic disease affects the health of livestock, thereby imposing economic constraints on farmers, costing billions of dollars every year, especially in sub-Saharan African countries. Mainly considering the AAT disease as a multistage progression process, we previously performed upstream analysis to identify transcription factors (TFs), their co-operations, over-represented pathways and master regulators. However, downstream analysis, including effectors, corresponding gene expression profiles and their association with the regulatory SNPs (rSNPs), has not yet been established. Therefore, in this study, we aim to investigate the complex interplay of rSNPs, corresponding gene expression and downstream effectors with regard to the AAT disease progression based on two cattle breeds: trypanosusceptible Boran and trypanotolerant N'Dama. Our findings provide mechanistic insights into the effectors involved in the regulation of several signal transduction pathways, thereby differentiating the molecular mechanism with regard to the immune responses of the cattle breeds. The effectors and their associated genes (especially MAPKAPK5, CSK, DOK2, RAC1 and DNMT1) could be promising drug candidates as they orchestrate various downstream regulatory cascades in both cattle breeds.

Non-linear transformation of enzyme-linked immunosorbent assay (ELISA) measurements allows usage of linear models for data analysis.

BACKGROUND: In research questions such as in resistance breeding against the Beet necrotic yellow vein virus it is of interest to compare the virus concentrations of samples from different groups. The enzyme-linked immunosorbent assay (ELISA) counts as the standard tool to measure virus concentrations. Simple methods for data analysis such as analysis of variance (ANOVA), however, are impaired due to non-normality of the resulting optical density (OD) values as well as unequal variances in different groups. METHODS: To understand the relationship between the OD values from an ELISA test and the virus concentration per sample, we used a large serial dilution and modelled its non-linear form using a five parameter logistic regression model. Furthermore, we examined if the quality of the model can be increased if one or several of the model parameters are defined beforehand. Subsequently, we used the inverse of the best model to estimate the virus concentration for every measured OD value. RESULTS: We show that the transformed data are essentially normally distributed but provide unequal variances per group. Thus, we propose a generalised least squares model which allows for unequal variances of the groups to analyse the transformed data. CONCLUSIONS: ANOVA requires normally distributed data as well as equal variances. Both requirements are not met with raw OD values from an ELISA test. A transformation with an inverse logistic function, however, gives the possibility to use linear models for data analysis of virus concentrations. We conclude that this method can be applied in every trial where virus concentrations of samples from different groups are to be compared via OD values from an ELISA test. To encourage researchers to use this method in their studies, we provide an R script for data transformation as well as the data from our trial.

Deciphering Pleiotropic Signatures of Regulatory SNPs in Zea mays L. Using Multi-Omics Data and Machine Learning Algorithms.

Maize is one of the most widely grown cereals in the world. However, to address the challenges in maize breeding arising from climatic anomalies, there is a need for developing novel strategies to harness the power of multi-omics technologies. In this regard, pleiotropy is an important genetic phenomenon that can be utilized to simultaneously enhance multiple agronomic phenotypes in maize. In addition to pleiotropy, another aspect is the consideration of the regulatory SNPs (rSNPs) that are likely to have causal effects in phenotypic development. By incorporating both aspects in our study, we performed a systematic analysis based on multi-omics data to reveal the novel pleiotropic signatures of rSNPs in a global maize population. For this purpose, we first applied Random Forests and then Markov clustering algorithms to decipher the pleiotropic signatures of rSNPs, based on which hierarchical network models are constructed to elucidate the complex interplay among transcription factors, rSNPs, and phenotypes. The results obtained in our study could help to understand the genetic programs orchestrating multiple phenotypes and thus could provide novel breeding targets for the simultaneous improvement of several agronomic traits.

Comparative Investigation of Gene Regulatory Processes Underlying Avian Influenza Viruses in Chicken and Duck.

The avian influenza virus (AIV) mainly affects birds and not only causes animals' deaths, but also poses a great risk of zoonotically infecting humans. While ducks and wild waterfowl are seen as a natural reservoir for AIVs and can withstand most virus strains, chicken mostly succumb to infection with high pathogenic avian influenza (HPAI). To date, the mechanisms underlying the susceptibility of chicken and the effective immune response of duck have not been completely unraveled. In this study, we investigate the transcriptional gene regulation underlying disease progression in chicken and duck after AIV infection. For this purpose, we use a publicly available RNA-sequencing dataset from chicken and ducks infected with low-pathogenic avian influenza (LPAI) H5N2 and HPAI H5N1 (lung and ileum tissues, 1 and 3 days post-infection). Unlike previous studies, we performed a promoter analysis based on orthologous genes to detect important transcription factors (TFs) and their cooperation, based on which we apply a systems biology approach to identify common and species-specific master regulators. We found master regulators such as EGR1, FOS, and SP1, specifically for chicken and ETS1 and SMAD3/4, specifically for duck, which could be responsible for the duck's effective and the chicken's ineffective immune response.

Detecting Animal Contacts-A Deep Learning-Based Pig Detection and Tracking Approach for the Quantification of Social Contacts.

The identification of social interactions is of fundamental importance for animal behavioral studies, addressing numerous problems like investigating the influence of social hierarchical structures or the drivers of agonistic behavioral disorders. However, the majority of previous studies often rely on manual determination of the number and types of social encounters by direct observation which requires a large amount of personnel and economical efforts. To overcome this limitation and increase research efficiency and, thus, contribute to animal welfare in the long term, we propose in this study a framework for the automated identification of social contacts. In this framework, we apply a convolutional neural network (CNN) to detect the location and orientation of pigs within a video and track their movement trajectories over a period of time using a Kalman filter (KF) algorithm. Based on the tracking information, we automatically identify social contacts in the form of head-head and head-tail contacts. Moreover, by using the individual animal IDs, we construct a network of social contacts as the final output. We evaluated the performance of our framework based on two distinct test sets for pig detection and tracking. Consequently, we achieved a Sensitivity, Precision, and F1-score of 94.2%, 95.4%, and 95.1%, respectively, and a MOTA score of 94.4%. The findings of this study demonstrate the effectiveness of our keypoint-based tracking-by-detection strategy and can be applied to enhance animal monitoring systems.

MIDESP: Mutual Information-Based Detection of Epistatic SNP Pairs for Qualitative and Quantitative Phenotypes.

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.

agReg-SNPdb: A Database of Regulatory SNPs for Agricultural Animal Species.

Transcription factors (TFs) govern transcriptional gene regulation by specifically binding to short DNA motifs, known as transcription factor binding sites (TFBSs), in regulatory regions, such as promoters. Today, it is well known that single nucleotide polymorphisms (SNPs) in TFBSs can dramatically affect the level of gene expression, since they can cause a change in the binding affinity of TFs. Such SNPs, referred to as regulatory SNPs (rSNPs), have gained attention in the life sciences due to their causality for specific traits or diseases. In this study, we present agReg-SNPdb, a database comprising rSNP data of seven agricultural and domestic animal species: cattle, pig, chicken, sheep, horse, goat, and dog. To identify the rSNPs, we constructed a bioinformatics pipeline and identified a total of 10,623,512 rSNPs, which are located within TFBSs and affect the binding affinity of putative TFs. Altogether, we implemented the first systematic analysis of SNPs in promoter regions and their impact on the binding affinity of TFs for livestock and made it usable via a web interface.

In Silico Prediction of Transcription Factor Collaborations Underlying Phenotypic Sexual Dimorphism in Zebrafish (Danio rerio).

The transcriptional regulation of gene expression in higher organisms is essential for different cellular and biological processes. These processes are controlled by transcription factors and their combinatorial interplay, which are crucial for complex genetic programs and transcriptional machinery. The regulation of sex-biased gene expression plays a major role in phenotypic sexual dimorphism in many species, causing dimorphic gene expression patterns between two different sexes. The role of transcription factor (TF) in gene regulatory mechanisms so far has not been studied for sex determination and sex-associated colour patterning in zebrafish with respect to phenotypic sexual dimorphism. To address this open biological issue, we applied bioinformatics approaches for identifying the predicted TF pairs based on their binding sites for sex and colour genes in zebrafish. In this study, we identified 25 (e.g., STAT6-GATA4; JUN-GATA4; SOX9-JUN) and 14 (e.g., IRF-STAT6; SOX9-JUN; STAT6-GATA4) potentially cooperating TFs based on their binding patterns in promoter regions for sex determination and colour pattern genes in zebrafish, respectively. The comparison between identified TFs for sex and colour genes revealed several predicted TF pairs (e.g., STAT6-GATA4; JUN-SOX9) are common for both phenotypes, which may play a pivotal role in phenotypic sexual dimorphism in zebrafish.

Fossilized cell structures identify an ancient origin for the teleost whole-genome duplication.

Teleost fishes comprise one-half of all vertebrate species and possess a duplicated genome. This whole-genome duplication (WGD) occurred on the teleost stem lineage in an ancient common ancestor of all living teleosts and is hypothesized as a trigger of their exceptional evolutionary radiation. Genomic and phylogenetic data indicate that WGD occurred in the Mesozoic after the divergence of teleosts from their closest living relatives but before the origin of the extant teleost groups. However, these approaches cannot pinpoint WGD among the many extinct groups that populate this 50- to 100-million-y lineage, preventing tests of the evolutionary effects of WGD. We infer patterns of genome size evolution in fossil stem-group teleosts using high-resolution synchrotron X-ray tomography to measure the bone cell volumes, which correlate with genome size in living species. Our findings indicate that WGD occurred very early on the teleost stem lineage and that all extinct stem-group teleosts known so far possessed duplicated genomes. WGD therefore predates both the origin of proposed key innovations of the teleost skeleton and the onset of substantial morphological diversification in the clade. Moreover, the early occurrence of WGD allowed considerable time for postduplication reorganization prior to the origin of the teleost crown group. This suggests at most an indirect link between WGD and evolutionary success, with broad implications for the relationship between genomic architecture and large-scale evolutionary patterns in the vertebrate Tree of Life.

Identification and Functional Annotation of Genes Related to Bone Stability in Laying Hens Using Random Forests.

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.