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.

Judging the Arabian Beauty: What are the Relationships Between Different Scoring Categories?

Arabian show horses are well known for their exceptional beauty and elegance. The breed type, body conformation and movement are assessed during horse shows by licensed judges. The 5 judging categories are type, head and neck, body and topline, legs and movement, which are scored on a 20 point scale. It can be hypothesized that the scores in different categories are related to each other, and that the score for the most subjective type category depends on the scores for conformation categories. We analyzed 762 sets of average scores obtained by 583 unique horses at the World Championships. Correlation analysis and general linear models were used to explore the relationships between the scores in each of the 5 categories. Despite the 20 point scale, only scores from 14.5 to 20 were observed. The correlations between the total score, and the scores for each category within the whole sample varied from r = 0.413 for legs to r = 0.907 for type. Regression analysis of the scores for type showed a strong, significant (P < .001) effect of the scores for the head and neck. The effect of scores for body and topline as well as for movement was negligible, while significant; the score for legs did not affect the score for type. The correlation values between score categories varied between sex and age classes. The estimated levels of correlations were different than expected, based on the known relationships between the phenotypic traits.

How should we manage a strong Eurasian Beaver population? A comparison of population trends in Poland and Belarus.

The Eurasian beaver is currently found in at least 32 European countries, with many of these populations being established in the 1960s. In most European countries, the beaver is under protection, however, when the population is strong, the beaver becomes a game species. In Poland, the beaver is partially protected despite the species having a strong population. In this study we aimed to compare the development trends of Eurasian beaver populations in two management regimes, in Poland (protected) and Belarus (hunted), between 2004 and 2019. We compared beaver population trends in both countries, and analyzed the factors that could impact population growth. In Poland, during this period the population increased 3.5 times, while in Belarus it was only 20%. Distinct differences in the rate of population numbers increase were also observed between regions in Poland, but a stable, slight increase similar in all regions in Belarus. Our study did not show that precipitation or the density of this species influenced the rate of population development in Poland. During this period, hunting and wolf density significantly and negatively impacted beaver population growth in Belarus, but in the long-term analysis, hunting had a lower impact on beaver population growth. We concluded that we can expect a further increase in this population in Poland. Long-term hunting at a level of 13,7% (based on the analysis of population dynamics and hunting bags for Belarus) of the annual population seems to be a safe value for the beaver population. Nevertheless more detailed analysis should be carried out in the face of the large differences between regions.

Genetic Determination of the Amount of White Spotting: A Case Study in Siberian Cats.

The current hypothesis, along with the opinion of the breeders, is that a cat with two copies of the white spotting allele (SS) has white on more than half of its body, while a cat with only one copy (Ss) has white on less than half of its body. The present study was based on the analysis of two large pedigree databases of Siberian cats (23,905 individuals in PawPeds and 21,650 individuals in Felis Polonia database). The distribution of the amount of white spotting in the offspring of cats with different amounts of white was investigated. Significant differences compared to expected distributions were observed. In many cases the amount of white in cats that were supposed to be homozygous was less than 50% of the body, while in many supposedly heterozygous cats a very large amount of white (over 50%) was observed. This phenomenon was also presented on the verified examples of the specific families excluding possible errors in determining the amount of white by the breeder. The collected evidence suggests that there are other factors involved in the inheritance of the amount of white in cats and the current hypothesis should be revised.

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.

Genetic basis of resistance to avian influenza in different commercial varieties of layer chickens.

An outbreak of H5N2 highly pathogenic avian influenza (HPAI) in 2015, resulting in mandatory euthanization of millions of chickens, was one of the most fatal in the US history. The aim of this study was to detect genes associated with survival following natural infection with HPAI during this outbreak. Blood samples were collected from 274 individuals from 3 commercial varieties of White Leghorn. Survivors and age and genetics matched non-affected controls from each variety were included in the comparison. All individuals were genotyped on the 600k SNP array. A genome-wide association study (GWAS) with the standard frequency test in PLINK was performed within each variety, whereas logistic regression with the first 3 multidimensional scaling components as covariates was used for joined analysis of all varieties. Several SNPs located within 3 regions reached the 5% Bonferroni genome-wide threshold of significance (P < 3.87E-06). The associations were identified for 2 varieties and only within genetic variety on chromosomes 11 (variety 1), 5, and 18 (variety 3). A genome-wide scan with FST was also performed for 40, 100, and 500 kb windows to support the genome-wide association analyses. The regions with highest FST values between cases and controls were located on chromosomes 1 and Z, and overlapped a number of genes with immunological function and QTL connected to health. Only a few regions were consistent between the analyses, and were significant in the FST genome-wide scan and approaching significance in GWAS. This study confirms that resistance to HPAI is a complex, polygenic trait and that mechanisms of resistance may be population specific. Further study utilizing much larger sample sizes and/or sequence data is needed to detect genes responsible for HPAI survival.

Genomic prediction of avian influenza infection outcome in layer chickens.

Avian influenza (AI) is a devastating poultry disease that currently can be controlled only by liquidation of affected flocks. In spite of typically very high mortality rates, a group of survivors was identified and genotyped on a 600K single nucleotide polymorphism (SNP) chip to identify genetic differences between survivors, and age- and genetics-matched controls from unaffected flocks. In a previous analysis of this dataset, a heritable component was identified and several regions that are associated with outcome of the infection were localized but none with a large effect. For complex traits that are determined by many genes, genomic prediction models using all SNPs across the genome simultaneously are expected to optimally exploit genomic information. In this study, we evaluated the diagnostic value of genomic estimated breeding values for predicting AI infection outcome within and across two highly pathogenic avian influenza viral strains and two genetic lines of layer chickens using receiver operating curves. We show that genomic prediction based on the 600K SNP chip has the potential to predict disease outcome especially within the same strain of virus (area under receiver operating curve above 0.7), but did not predict well across genetic varieties (area under receiver operating curve of 0.43).