Circadian regulation of calcium and phosphorus homeostasis during the oviposition cycle in laying hens.

Maintenance of calcium and phosphorus homeostasis in laying hens is crucial for preservation of skeletal integrity and eggshell quality, though physiological regulation of these systems is incompletely defined. To investigate changes in mineral and vitamin D3 homeostasis during the 24-h egg formation cycle, 32-wk-old commercial laying hens were sampled at 1, 3, 4, 6, 7, 8, 12, 15, 18, 21, 23, and 24 h post-oviposition (HPOP; n ≥ 4). Ovum location and egg calcification stage were recorded, and blood chemistry, plasma vitamin D3 metabolites, circulating parathyroid hormone (PTH), and expression of genes mediating uptake and utilization of calcium and phosphorus were evaluated. Elevated levels of renal 25-hydroxylase from 12 to 23 HPOP suggest this tissue might play a role in vitamin D3 25-hydroxylation during eggshell calcification. In shell gland, retinoid-x-receptor gamma upregulation between 6 and 8 HPOP followed by subsequently increased vitamin D receptor indicate that vitamin D3 signaling is important for eggshell calcification. Increased expression of PTH, calcitonin, and fibroblast growth factor 23 (FGF23) receptors in the shell gland between 18 and 24 HPOP suggest elevated sensitivity to these hormones toward the end of eggshell calcification. Shell gland sodium-calcium exchanger 1 was upregulated between 4 and 7 HPOP and plasma membrane calcium ATPase 1 increased throughout eggshell calcification, suggesting the primary calcium transporter may differ according to eggshell calcification stage. Expression in shell gland further indicated that bicarbonate synthesis precedes transport, where genes peaked at 6 to 7 and 12 to 18 HPOP, respectively. Inorganic phosphorus transporter 1 (PiT-1) expression peaked in kidney between 12 and 15 HPOP, likely to excrete excess circulating phosphorus, and in shell gland between 18 and 21 HPOP. Upregulation of FGF23 receptors and PiT-1 during late eggshell calcification suggest shell gland phosphorus uptake is important at this time. Together, these findings identified potentially novel hormonal pathways involved in calcium and phosphorus homeostasis along with associated circadian patterns in gene expression that can be used to devise strategies aimed at improving eggshell and skeletal strength in laying hens.

Correction: Ramzan F. et al. "Combining Random Forests and a Signal Detection Method Leads to the Robust Detection of Genotype-Phenotype Associations" Genes, 2020, 11, 892.

The authors would like to make a correction to the published paper [...].

Combining Random Forests and a Signal Detection Method Leads to the Robust Detection of Genotype-Phenotype Associations.

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.

Transcriptome analysis of the uterus of hens laying eggs differing in cuticle deposition.

BACKGROUND: Avian eggs have a proteinaceous cuticle. The quantity of cuticle varies and the deposition of a good cuticle in the uterus (Shell-gland) prevents transmission of bacteria to the egg contents. RESULTS: To understand cuticle deposition, uterus transcriptomes were compared between hens with i) naturally good and poor cuticle and, ii) where manipulation of the hypothalamo-pituitary-gonadal-oviduct axis produced eggs with or without cuticle. The highest expressed genes encoded eggshell matrix and cuticle proteins, e.g. MEPE (OC-116), BPIFB3 (OVX-36), RARRES1 (OVX-32), WAP (OVX-25), and genes for mitochondrial oxidative phosphorylation, active transport and energy metabolism. Expression of a number of these genes differed between hens laying eggs with or without cuticle. There was also a high expression of clock genes. PER2, CRY2, CRY1, CLOCK and BMAL1 were differentially expressed when cuticle deposition was prevented, and they also changed throughout the egg formation cycle. This suggests an endogenous clock in the uterus may be a component of cuticle deposition control. Cuticle proteins are glycosylated and glycosaminoglycan binding genes had a lower expression when cuticle proteins were deposited on the egg. The immediate early genes, JUN and FOS, were expressed less when the cuticle had not been deposited and changed over the egg formation cycle, suggesting they are important in oviposition and cuticle deposition. The uterus transcriptome of hens with good and poor cuticle deposition did not differ. CONCLUSIONS: We have gained insights into the factors that can affect the production of the cuticle especially clock genes and immediate early genes. We have demonstrated that these genes change their expression over the period of eggshell formation supporting their importance. The lack of differences in expression between the uterus of hens laying eggs with the best and worse cuticle suggest the genetic basis of the trait may lie outside the oviduct.

Identification of Age-Specific and Common Key Regulatory Mechanisms Governing Eggshell Strength in Chicken Using Random Forests.

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- ß 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.

Improving Imputation Quality in BEAGLE for Crop and Livestock Data.

Imputation is one of the key steps in the preprocessing and quality control protocol of any genetic study. Most imputation algorithms were originally developed for the use in human genetics and thus are optimized for a high level of genetic diversity. Different versions of BEAGLE were evaluated on genetic datasets of doubled haploids of two European maize landraces, a commercial breeding line and a diversity panel in chicken, respectively, with different levels of genetic diversity and structure which can be taken into account in BEAGLE by parameter tuning. Especially for phasing BEAGLE 5.0 outperformed the newest version (5.1) which in turn also lead to improved imputation. Earlier versions were far more dependent on the adaption of parameters in all our tests. For all versions, the parameter ne (effective population size) had a major effect on the error rate for imputation of ungenotyped markers, reducing error rates by up to 98.5%. Further improvement was obtained by tuning of the parameters affecting the structure of the haplotype cluster that is used to initialize the underlying Hidden Markov Model of BEAGLE. The number of markers with extremely high error rates for the maize datasets were more than halved by the use of a flint reference genome (F7, PE0075 etc.) instead of the commonly used B73. On average, error rates for imputation of ungenotyped markers were reduced by 8.5% by excluding genetically distant individuals from the reference panel for the chicken diversity panel. To optimize imputation accuracy one has to find a balance between representing as much of the genetic diversity as possible while avoiding the introduction of noise by including genetically distant individuals.

Genetic variation and potential for genetic improvement of cuticle deposition on chicken eggs.

BACKGROUND: The cuticle is an invisible glycosylated protein layer that covers the outside of the eggshell and forms a barrier to the transmission of microorganisms. Cuticle-specific staining and in situ absorbance measurements have been used to quantify cuticle deposition in several pure breeds of chicken. For brown eggs, a pre-stain and a post-stain absorbance measurement is required to correct for intrinsic absorption by the natural pigment. For white eggs, a post-stain absorbance measurement alone is sufficient to estimate cuticle deposition. The objective of the research was to estimate genetic parameters and provide data to promote adoption of the technique to increase cuticle deposition and reduce vertical transmission of microorganisms. RESULTS: For all pure breeds examined here, i.e. Rhode Island Red, two White Leghorns, White Rock and a broiler breed, the estimate of heritability for cuticle deposition from a meta-analysis was moderately high (0.38 ± 0.04). In the Rhode Island Red breed, the estimate of the genetic correlation between measurements recorded at early and late times during the egg-laying period was ~ 1. There was no negative genetic correlation between cuticle deposition and production traits. Estimates of the genetic correlation of cuticle deposition with shell color ranged from negative values or 0 in brown-egg layers to positive values in white- or tinted-egg layers. Using the intrinsic fluorescence of tryptophan in the cuticle proteins to quantify the amount of cuticle deposition failed because of complex quenching processes. Tryptophan fluorescence intensity at 330 nm was moderately heritable, but there was no evidence of a non-zero genetic correlation with cuticle deposition. This was complicated furthermore by a negative genetic correlation of fluorescence with color in brown eggs, due to the quenching of tryptophan fluorescence by energy transfer to protoporphyrin pigment. We also confirmed that removal of the cuticle increased reflection of ultraviolet wavelengths from the egg. CONCLUSIONS: These results provide additional evidence for the need to incorporate cuticle deposition into breeding programs of egg- and meat-type birds in order to reduce vertical and horizontal transmission of potentially pathogenic organisms and to help improve biosecurity in poultry.

A Simple Test Identifies Selection on Complex Traits.

Important traits in agricultural, natural, and human populations are increasingly being shown to be under the control of many genes that individually contribute only a small proportion of genetic variation. However, the majority of modern tools in quantitative and population genetics, including genome-wide association studies and selection-mapping protocols, are designed to identify individual genes with large effects. We have developed an approach to identify traits that have been under selection and are controlled by large numbers of loci. In contrast to existing methods, our technique uses additive-effects estimates from all available markers, and relates these estimates to allele-frequency change over time. Using this information, we generate a composite statistic, denoted [Formula: see text] which can be used to test for significant evidence of selection on a trait. Our test requires pre- and postselection genotypic data but only a single time point with phenotypic information. Simulations demonstrate that [Formula: see text] is powerful for identifying selection, particularly in situations where the trait being tested is controlled by many genes, which is precisely the scenario where classical approaches for selection mapping are least powerful. We apply this test to breeding populations of maize and chickens, where we demonstrate the successful identification of selection on traits that are documented to have been under selection.

Characterization of Enterococcus faecalis isolates by chicken embryo lethality assay and ERIC-PCR.

Enterococcus faecalis is the major causative agent of amyloid arthropathy in chickens. Given the difficulty of estimating the risk from field strains, the embryo lethality assay (ELA) is proposed in this study as a model to predict the virulence of 68 avian E. faecalis strains. Additionally, Enterobacterial Repetitive Intergenic Consensus Polymerase Chain Reaction (ERIC-PCR) was used to characterize the genetic diversity of the E. faecalis strains. The ELA was performed 10 times with subsets of 7-8 E. faecalis strains each on a sample of 9987 eggs, including control groups. An estimated 3-24 colony-forming units were inoculated into the allantoic cavity of 10-day-old embryos. The embryonic mortality rate (EMR) was determined by means of candling the eggs over a period of seven days. The ELA was able to distinguish the virulence of the E. faecalis strains. Twenty-six strains were considered as avirulent strains with an EMR of below 40%. Five strains were highly virulent with an EMR above 80%. The remaining 37 strains were classified as strains of moderate virulence, causing an EMR between 40% and 80%. The highest EMR occurred three and four days post-inoculation (p.i.). From the fourth day p.i., almost no embryonic mortality was observed. Therefore, the ELA could be optimized by reducing experiment duration to four days p.i. ERIC-PCR did not cluster the strains according to its virulence, although ERIC banding patterns revealed a considerable genetic diversity. In conclusion, the ELA can be considered a reliable and useful tool to predict the virulence of avian E. faecalis strains.

Chicken embryo lethality assay for determining the lethal dose and virulence of Enterococcus faecalis.

Enterococcus faecalis is the major pathogen found in field cases of amyloid arthropathy in chickens. Given the need for a better understanding of the virulence mechanisms of the causative strains, the embryo lethality assay (ELA) is proposed in the present study as a model to evaluate the virulence of E. faecalis strains, specifically the pathogenic avian strain K923/96, which was previously related with amyloid arthropathy. Hence, 0.2 ml of five doses of the cited strain (from 2.5 to 2500 colony-forming units (CFU) per ml) were inoculated into the allantoic cavity of 10-day-old embryos. The embryo mortality rate (EMR) was determined by daily candling of the eggs over a period of seven days and based on this information the median lethal dose (LD50) was calculated. The ELA was repeated four times on a sample of 3443 eggs. The infectious dose showed a significant effect on the EMR. The EMR with the doses of 2.5, 5, 25, 250 and 2500 CFU/ml was 43%, 45%, 63%, 90% and 93%, respectively. The estimated dose at LD50 was 6.6 CFU/ml. As expected, the higher the infectious dose, the greater the EMR and the lower the embryo survival time. The highest EMR was recorded after three and four days post-inoculation in all doses. In conclusion, these results can be used as a basis for further researches on the E. faecalis virulence. In order to corroborate its model capacity to predict the virulence of this bacterium, more ELAs with different E. faecalis strains are required.

Subgroup detection in genotype data using invariant coordinate selection.

BACKGROUND: The current gold standard in dimension reduction methods for high-throughput genotype data is the Principle Component Analysis (PCA). The presence of PCA is so dominant, that other methods usually cannot be found in the analyst's toolbox and hence are only rarely applied. RESULTS: We present a modern dimension reduction method called 'Invariant Coordinate Selection' (ICS) and its application to high-throughput genotype data. The more commonly known Independent Component Analysis (ICA) is in this framework just a special case of ICS. We use ICS on both, a simulated and a real dataset to demonstrate first some deficiencies of PCA and how ICS is capable to recover the correct subgroups within the simulated data. Second, we apply the ICS method on a chicken dataset and also detect there two subgroups. These subgroups are then further investigated with respect to their genotype to provide further evidence of the biological relevance of the detected subgroup division. Further, we compare the performance of ICS also to five other popular dimension reduction methods. CONCLUSION: The ICS method was able to detect subgroups in data where the PCA fails to detect anything. Hence, we promote the application of ICS to high-throughput genotype data in addition to the established PCA. Especially in statistical programming environments like e.g. R, its application does not add any computational burden to the analysis pipeline.

Whole-genome sequence-based genomic prediction in laying chickens with different genomic relationship matrices to account for genetic architecture.

BACKGROUND: With the availability of next-generation sequencing technologies, genomic prediction based on whole-genome sequencing (WGS) data is now feasible in animal breeding schemes and was expected to lead to higher predictive ability, since such data may contain all genomic variants including causal mutations. Our objective was to compare prediction ability with high-density (HD) array data and WGS data in a commercial brown layer line with genomic best linear unbiased prediction (GBLUP) models using various approaches to weight single nucleotide polymorphisms (SNPs). METHODS: A total of 892 chickens from a commercial brown layer line were genotyped with 336 K segregating SNPs (array data) that included 157 K genic SNPs (i.e. SNPs in or around a gene). For these individuals, genome-wide sequence information was imputed based on data from re-sequencing runs of 25 individuals, leading to 5.2 million (M) imputed SNPs (WGS data), including 2.6 M genic SNPs. De-regressed proofs (DRP) for eggshell strength, feed intake and laying rate were used as quasi-phenotypic data in genomic prediction analyses. Four weighting factors for building a trait-specific genomic relationship matrix were investigated: identical weights, -(log10 P) from genome-wide association study results, squares of SNP effects from random regression BLUP, and variable selection based weights (known as BLUP|GA). Predictive ability was measured as the correlation between DRP and direct genomic breeding values in five replications of a fivefold cross-validation. RESULTS: Averaged over the three traits, the highest predictive ability (0.366 ± 0.075) was obtained when only genic SNPs from WGS data were used. Predictive abilities with genic SNPs and all SNPs from HD array data were 0.361 ± 0.072 and 0.353 ± 0.074, respectively. Prediction with -(log10 P) or squares of SNP effects as weighting factors for building a genomic relationship matrix or BLUP|GA did not increase accuracy, compared to that with identical weights, regardless of the SNP set used. CONCLUSIONS: Our results show that little or no benefit was gained when using all imputed WGS data to perform genomic prediction compared to using HD array data regardless of the weighting factors tested. However, using only genic SNPs from WGS data had a positive effect on prediction ability.

Efficiency of genomic selection in an established commercial layer breeding program.

BACKGROUND: In breeding programs for layers, selection of hens and cocks is based on recording phenotypic data from hens in different housing systems. Genomic information can provide additional information for selection and/or allow for a strong reduction in the generation interval. In this study, a typical conventional layer breeding program using a four-line cross was modeled and the expected genetic progress was derived deterministically with the software ZPLAN+. This non-genomic reference scenario was compared to two genomic breeding programs to determine the best strategy for implementing genomic information in layer breeding programs. RESULTS: In scenario I, genomic information was used in addition to all other information available in the conventional breeding program, so the generation interval was the same as in the reference scenario, i.e. 14.5 months. Here, we assumed that either only young cocks or young cocks and hens were genotyped as selection candidates. In scenario II, we assumed that breeders of both sexes were used at the biologically earliest possible age, so that at the time of selection only performance data of the parent generation and genomic information of the selection candidates were available. In this case, the generation interval was reduced to eight months. In both scenarios, the number of genotyped male selection candidates was varied between 800 and 4800 males and two sizes of the calibration set (500 or 2000 animals) were considered. All genomic scenarios increased the expected genetic gain and the economic profit of the breeding program. In scenario II, the increase was much more pronounced and even in the most conservative implementation led to a 60% improvement in genetic gain and economic profit. This increase was in all cases associated with higher breeding costs. CONCLUSIONS: While genomic selection is shown to have the potential to improve genetic gain in layer breeding programs, its implementation remains a business decision of the breeding company; the possible extra profit for the breeding company depends on whether the customers of breeding stock are willing to pay more for improved genetic quality.

Mapping of QTL affecting incidence of blood and meat inclusions in egg layers.

BACKGROUND: Occurrence of blood and meat inclusions is an internal egg quality defect. Mass candling reveals most of the spots, but because brown eggshell hampers selection in brown chicken lines it has not been possible to eliminate the defect by selection. Estimated frequency of blood and meat inclusions in brown layers is about 18% whereas it is 0.5% in white egg layers. Several factors are known to increase the incidence of this fault: genetic background, low level of vitamin A and/or D, stress or infections, for instance. To study the genetic background of the defect, a mapping population of 1599 F2 hens from a cross of White Rock and Rhode Island Red lines was set up. RESULTS: Our histopathological analyses show that blood spots consist of mainly erythrocytes and that meat spots are accumulations of necrotic material. Linkage analysis of 27 chromosomes with 162 microsatellite markers revealed one significant quantitative trait locus (QTL) affecting blood spot and meat spot frequency. We sequenced a fragment of a candidate gene within the region, ZO-2, coding for a tight junction protein. Nine polymorphisms were detected and two of them were included in fine-mapping and association analysis. Fine-mapping defined the QTL result. To further verify the QTL, association analyses were carried out in two independent commercial breeding lines with the marker MCW241 and surrounding SNPs. Association was found mainly in a 0.8 Mb-wide chromosomal area on GGAZ. CONCLUSIONS: There was good agreement between the location of the QTL region on chromosome Z and the association results in the commercial breeds analyzed. Variations found in tight junction protein ZO-2 and microRNA gga-mir-1556 may predispose egg layers to blood and meat spot defects. This paper describes the first results of detailed QTL analyses of the blood and meat spots trait(s) in chickens.