The Chicken A and E Blood Systems Arise from Genetic Variation in and around the Regulators of Complement Activation Region.

The tightly linked A and E blood alloantigen systems are 2 of 13 blood systems identified in chickens. Reported herein are studies showing that the genes encoding A and E alloantigens map within or near to the chicken regulator of complement activation (RCA) gene cluster, a region syntenic with the human RCA. Genome-wide association studies, sequence analysis, and sequence-derived single-nucleotide polymorphism information for known A and/or E system alleles show that the most likely candidate gene for the A blood system is C4BPM gene (complement component 4 binding protein, membrane). Cosegregation of single-nucleotide polymorphism-defined C4BPM haplotypes and blood system A alleles defined by alloantisera provide a link between chicken blood system A and C4BPM. The best match for the E blood system is the avian equivalent of FCAMR (Fc fragment of IgA and IgM receptor). C4BPM is located within the chicken RCA on chicken microchromosome 26 and is separated from FCAMR by 89 kbp. The genetic variation observed at C4BPM and FCAMR could affect the chicken complement system and differentially guide immune responses to infectious diseases.

The RHCE gene encodes the chicken blood system I.

BACKGROUND: There are 13 known chicken blood systems, which were originally detected by agglutination of red blood cells by specific alloantisera. The genomic region or specific gene responsible has been identified for four of these systems (A, B, D and E). We determined the identity of the gene responsible for the chicken blood system I, using DNA from multiple birds with known chicken I blood system serology, 600K and 54K single nucleotide polymorphism (SNP) data, and lowpass sequence information. RESULTS: The gene responsible for the chicken I blood system was identified as RHCE, which is also one of the genes responsible for the highly polymorphic human Rh blood group locus, for which maternal/fetal antigenic differences can result in fetal hemolytic anemia with fetal mortality. We identified 17 unique RHCE haplotypes in the chicken, with six haplotypes corresponding to known I system serological alleles. We also detected deletions in the RHCE gene that encompass more than 6000 bp and that are predicted to remove its last seven exons. CONCLUSIONS: RHCE is the gene responsible for the chicken I blood system. This is the fifth chicken blood system for which the responsible gene and gene variants are known. With rapid DNA-based testing now available, the impact of I blood system variation on response against disease, general immune function, and animal production can be investigated in greater detail.

Candidate Genes Associated with Survival Following Highly Pathogenic Avian Influenza Infection in Chickens.

Highly pathogenic strains of avian influenza (HPAI) devastate poultry flocks and result in significant economic losses for farmers due to high mortality, reduced egg production, and mandated euthanization of infected flocks. Within recent years, HPAI outbreaks have affected egg production flocks across the world. The H5N2 outbreak in the US in 2015 resulted in over 99% mortality. Here, we analyze sequence data from chickens that survived (42 cases) along with uninfected controls (28 samples) to find genomic regions that differ between these two groups and that, therefore, may encompass prime candidates that are resistant to HPAI. Blood samples were obtained from survivors of the 2015 HPAI outbreak plus age and genetics-matched non-affected controls. A whole-genome sequence was obtained, and genetic variants were characterized and used in a genome-wide association study to identify regions showing significant association with survival. Regions associated with HPAI resistance were observed on chromosomes 1, 2, 5, 8, 10, 11, 15, 20, and 28, with a number of candidate genes identified. We did not detect a specific locus which could fully explain the difference between survivors and controls. Influenza virus replication depends on multiple components of the host cellular machinery, with many genes involved in the host response.

Genomic Regions and Candidate Genes Affecting Response to Heat Stress with Newcastle Virus Infection in Commercial Layer Chicks Using Chicken 600K Single Nucleotide Polymorphism Array.

Heat stress results in significant economic losses to the poultry industry. Genetics plays an important role in chickens adapting to the warm environment. Physiological parameters such as hematochemical parameters change in response to heat stress in chickens. To explore the genetics of heat stress resilience in chickens, a genome-wide association study (GWAS) was conducted using Hy-Line Brown layer chicks subjected to either high ambient temperature or combined high temperature and Newcastle disease virus infection. Hematochemical parameters were measured during three treatment phases: acute heat stress, chronic heat stress, and chronic heat stress combined with NDV infection. Significant changes in blood parameters were recorded for 11 parameters (sodium (Na+, potassium (K+), ionized calcium (iCa2+), glucose (Glu), pH, carbon dioxide partial pressure (PCO2), oxygen partial pressure (PO2), total carbon dioxide (TCO2), bicarbonate (HCO3), base excess (BE), and oxygen saturation (sO2)) across the three treatments. The GWAS revealed 39 significant SNPs (p < 0.05) for seven parameters, located on Gallus gallus chromosomes (GGA) 1, 3, 4, 6, 11, and 12. The significant genomic regions were further investigated to examine if the genes within the regions were associated with the corresponding traits under heat stress. A candidate gene list including genes in the identified genomic regions that were also differentially expressed in chicken tissues under heat stress was generated. Understanding the correlation between genetic variants and resilience to heat stress is an important step towards improving heat tolerance in poultry.

Application of Bayesian genomic prediction methods to genome-wide association analyses.

BACKGROUND: Bayesian genomic prediction methods were developed to simultaneously fit all genotyped markers to a set of available phenotypes for prediction of breeding values for quantitative traits, allowing for differences in the genetic architecture (distribution of marker effects) of traits. These methods also provide a flexible and reliable framework for genome-wide association (GWA) studies. The objective here was to review developments in Bayesian hierarchical and variable selection models for GWA analyses. RESULTS: By fitting all genotyped markers simultaneously, Bayesian GWA methods implicitly account for population structure and the multiple-testing problem of classical single-marker GWA. Implemented using Markov chain Monte Carlo methods, Bayesian GWA methods allow for control of error rates using probabilities obtained from posterior distributions. Power of GWA studies using Bayesian methods can be enhanced by using informative priors based on previous association studies, gene expression analyses, or functional annotation information. Applied to multiple traits, Bayesian GWA analyses can give insight into pleiotropic effects by multi-trait, structural equation, or graphical models. Bayesian methods can also be used to combine genomic, transcriptomic, proteomic, and other -omics data to infer causal genotype to phenotype relationships and to suggest external interventions that can improve performance. CONCLUSIONS: Bayesian hierarchical and variable selection methods provide a unified and powerful framework for genomic prediction, GWA, integration of prior information, and integration of information from other -omics platforms to identify causal mutations for complex quantitative traits.

Genetic analysis of disease resilience of wean-to-finish pigs under a natural disease challenge model using reaction norms.

BACKGROUND: Disease resilience is the ability to maintain performance across environments with different disease challenge loads (CL). A reaction norm describes the phenotypes that a genotype can produce across a range of environments and can be implemented using random regression models. The objectives of this study were to: (1) develop measures of CL using growth rate and clinical disease data recorded under a natural polymicrobial disease challenge model; and (2) quantify genetic variation in disease resilience using reaction norm models. METHODS: Different CL were derived from contemporary group effect estimates for average daily gain (ADG) and clinical disease phenotypes, including medical treatment rate (TRT), mortality rate, and subjective health scores. Resulting CL were then used as environmental covariates in reaction norm analyses of ADG and TRT in the challenge nursery and finisher, and compared using model loglikelihoods and estimates of genetic variance associated with CL. Linear and cubic spline reaction norm models were compared based on goodness-of-fit and with multi-variate analyses, for which phenotypes were separated into three traits based on low, medium, or high CL. RESULTS: Based on model likelihoods and estimates of genetic variance explained by the reaction norm, the best CL for ADG in the nursery was based on early ADG in the finisher, while the CL derived from clinical disease traits across the nursery and finisher was best for ADG in the finisher and for TRT in the nursery and across the nursery and finisher. With increasing CL, estimates of heritability for nursery and finisher ADG initially decreased, then increased, while estimates for TRT generally increased with CL. Genetic correlations for ADG and TRT were low between high versus low CL, but high for close CL. Linear reaction norm models fitted the data significantly better than the standard genetic model without genetic slopes, while the cubic spline model fitted the data significantly better than the linear reaction norm model for most traits. Reaction norm models also fitted the data better than multi-variate models. CONCLUSIONS: Reaction norm models identified genotype-by-environment interactions related to disease CL. Results can be used to select more resilient animals across different levels of CL, high-performance animals at a given CL, or a combination of these.

Genome-wide association studies for egg quality traits in White Leghorn layers using low-pass sequencing and SNP chip data.

Low-pass sequencing data have been proposed as an alternative to single nucleotide polymorphism (SNP) chips in genome-wide association studies (GWAS) of several species. However, it has not been used in layer chickens yet. This study aims at comparing the GWAS results of White Leghorn chickens using low-pass sequencing data (1×) and 54 k SNP chip data. Ten commercially relevant egg quality traits including albumen height, shell strength, shell colour, egg weight and yolk weight collected from up to 1,420 White Leghorn chickens were analysed. The results showed that the genomic heritability estimates based on low-pass sequencing data were higher than those based on SNP chip data. Although two GWAS analyses showed similar overall landscape for most traits, low-pass sequencing captured some significant SNPs that were not on the SNP chip. In GWAS analysis using 54 k SNP chip data, after including more individuals (up to 5,700), additional significant SNPs not detected by low-pass sequencing data were found. In conclusion, GWAS using low-pass sequencing data showed similar results to those with SNP chip data and may require much larger sample sizes to show measurable advantages.

Heritability of perching behavior and its genetic relationship with incidence of floor eggs in Rhode Island Red chickens.

BACKGROUND: As cage-free production systems become increasingly popular, behavioral traits such as nesting behavior and temperament have become more important. The objective of this study was to estimate heritabilities for frequency of perching and proportion of floor eggs and their genetic correlation in two Rhode Island Red lines. RESULTS: The percent of hens observed perching tended to increase and the proportion of eggs laid on the floor tended to decrease as the test progressed. This suggests the ability of hens to learn to use nests and perches. Under the bivariate repeatability model, estimates of heritability in the two lines were 0.22 ± 0.04 and 0.07 ± 0.05 for the percent of hens perching, and 0.52 ± 0.05 and 0.45 ± 0.05 for the percent of floor eggs. Estimates of the genetic correlation between perching and floor eggs were - 0.26 ± 0.14 and - 0.19 ± 0.27 for the two lines, suggesting that, genetically, there was some tendency for hens that better use perches to also use nests; but the phenotypic correlation was close to zero. Random regression models indicated the presence of a genetic component for learning ability. CONCLUSIONS: In conclusion, perching and tendency to lay floor eggs were shown to be a learned behavior, which stresses the importance of proper management and training of pullets and young hens. A significant genetic component was found, confirming the possibility to improve nesting behavior for cage-free systems through genetic selection.

The concomitant use of lapatinib and paracetamol - the risk of interaction.

Lapatinib is a tyrosine kinase inhibitor used for the treatment of breast cancer. Paracetamol is an analgesic commonly applied to patients with mild or moderate pain and fever. Cancer patients are polymedicated, which involves high risk of drug interactions during therapy. The aim of the study was to assess the interaction between lapatinib and paracetamol in rats. The rats were divided into three groups of eight animals in each. One group received lapatinib + paracetamol (IL + PA), another group received lapatinib (IIL), whereas the last group received paracetamol (IIIPA). A single dose of lapatinib (100 mg/kg b.w.) and paracetamol (100 mg/kg b.w.) was administered orally. Plasma concentrations of lapatinib, paracetamol and its metabolites - glucuronide and sulphate, were measured with the validated HPLC-MS/MS method and HPLC-UV method, respectively. The pharmacokinetic parameters of both drugs were calculated using non-compartmental methods. The co-administration of lapatinib and paracetamol increased the area under the plasma concentration-time curve (AUC) and the maximum concentration (Cmax) of lapatinib by 239.6% (p = 0.0030) and 184% (p = 0.0011), respectively. Lapatinib decreased the paracetamol AUC0-∞ by 48.8% and Cmax by 55.7%. In the IL + PA group the Cmax of paracetamol glucuronide was reduced, whereas the Cmax of paracetamol sulphate was higher than in the IIIPA group. Paracetamol significantly affected the enhanced plasma exposure of lapatinib. Additionally, lapatinib reduced the concentrations of paracetamol. The co-administration of lapatinib decreased the paracetamol glucuronidation but increased the sulphation. The findings of this study may be of clinical relevance to patients requiring analgesic therapy.

Detection of copy number variations in brown and white layers based on genotyping panels with different densities.

BACKGROUND: Copy number variations (CNV) are an important source of genetic variation that has gained increasing attention over the last couple of years. In this study, we performed CNV detection and functional analysis for 18,719 individuals from four pure lines and one commercial cross of layer chickens. Samples were genotyped on four single nucleotide polymorphism (SNP) genotyping platforms, i.e. the Illumina 42K, Affymetrix 600K, and two different customized Affymetrix 50K chips. CNV recovered from the Affymetrix chips were identified by using the Axiom® CNV Summary Tools and PennCNV software and those from the Illumina chip were identified by using the cnvPartition in the Genome Studio software. RESULTS: The mean number of CNV per individual varied from 0.50 to 4.87 according to line or cross and size of the SNP genotyping set. The length of the detected CNV across all datasets ranged from 1.2 kb to 3.2 Mb. The number of duplications exceeded the number of deletions for most lines. Between the lines, there were considerable differences in the number of detected CNV and their distribution. Most of the detected CNV had a low frequency, but 19 CNV were identified with a frequency higher than 5% in birds that were genotyped on the 600K panel, with the most common CNV being detected in 734 birds from three lines. CONCLUSIONS: Commonly used SNP genotyping platforms can be used to detect segregating CNV in chicken layer lines. The sample sizes for this study enabled a detailed characterization of the CNV landscape within commercially relevant lines. The size of the SNP panel used affected detection efficiency, with more CNV detected per individual on the higher density 600K panel. In spite of the high level of inter-individual diversity and a large number of CNV observed within individuals, we were able to detect 19 frequent CNV, of which, 57.9% overlapped with annotated genes and 89% overlapped with known quantitative trait loci.