Chicken nucleated red blood cells have thirteen alloantigen systems (A, B, C, D, E, H, I, J, K, L, N, P, R) that have been identified using specific alloantisera and molecular analyses. Until recently, the only alloantigen with its genetic region identified was the B system which is the chicken MHC. This study sought to identify the chromosomal location and the gene encoding chicken L alloantigen. Multiple unrelated genetic resources for which both DNA and serologically defined L alloantigen information were utilized including segregating pedigreed samples, elite commercially utilized egg production lines with known L system serological information, and genome sequence from selected research lines with known L system alleles. Genomic information was obtained from 600 K or 54 K SNP arrays, or low pass sequence information and utilized in GWAS to identify a candidate region. A strong GWAS peak was identified on chromosome 4 between 29.8 and 32 Mbp from multiple sample sets. The candidate gene identified was ABCE1, whose mammalian gene product is non-membrane bound. The specific mechanism by which the ABCE1 gene product in chickens impacts a serological determinant detected by hemagglutination remains unknown.

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.

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.

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 five 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 five 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<0.001) 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.

Proper management and genetic monitoring of the modern European bison (Bison bonasus) population is one of the most important responsibilities for this species’ conservation. Up-to-date, complex genetic analysis performed using a consistent molecular method is needed for population management as a tool to further validate and maintain the genetic diversity of the species. The identification of the genetic line when pedigree data are missing, as well as the identification of parentage and individuals, are crucial for this purpose. The aim of our research was to create a small but informative panel of SNP (single-nucleotide polymorphism) markers that can be used for routine genotyping of the European bison at low cost. In our study, we used a custom-designed microarray to genotype a large number of European bison, totaling 455 samples from two genetic lines. The results of this analysis allowed us to select highly informative markers. In this paper, we present an effective single nucleotide polymorphism set, divided into separate panels to perform genetic analyses of European bison, which is needed for population monitoring and management. We proposed a total of 20 SNPs to detect hybridization with Bos taurus and Bison bison, a panel of 50 SNPs for individuals and parentage identification, as well as a panel of 30 SNPs for assessing membership of the genetic line. These panels can be used together or independently depending on the research goal and can be applied using various methods.

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.

Horse racing is an important test for selecting breeding stock. However, some elite racehorses are born by mares with unsatisfying racing performance, while the performance of the best racing mares’ progeny can be poor. Therefore, the relationship between racing performance of dams and their offspring is not necessarily linear. The aim of this study is to assess the influence of various dam-related factors on racing performance of their offspring in two breeds; Thoroughbreds and Purebred Arabians. We used logistic and mixed linear regression models to analyze the relationship between racing career results of dams and their offspring. In this analysis, 1099 Thoroughbreds out of 416 dams, and 1022 Purebred Arabians out of 357 dams were included. Each observation was described by 47 variables in total. The results show that the relationship between racing performance of mares and their offspring, is in general, positive. The length and intensity of a mare’s racing career negatively influences the career of her offspring. It lowers the chances of entering the race by offspring in Thoroughbreds and the percentage of races won in both breeds. Similarly to previous studies, our results show the significant influence of a dam’s age on the offspring’s racing performance. The most successful Purebred Arabian racehorses are out of 7- and 8-years old mares. The results suggest that long racing career of a mare may have a negative impact on her future breeding performance, especially in Thoroughbreds.

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.

Two mutations affecting the ovulation rate and litter size are segregating in Olkuska sheep population, FecX degrees in the BMP15 gene, and the G7 site mutation in GDF9 gene. Homozygous carriers of both mutations are hyperprolific, contrary to the sterility observed in homozygous carriers of most other BMP15 and GDF9 mutations. The objective of this study was to assess frequency and phenotypic effects of both mutations. Blood samples were obtained from 740 individuals, 111 rams and 629 ewes, out of which 91 rams and 561 ewes were successfully genotyped for the BMP15 and GDF9 loci. The reproductive performance included a number of lambs born/born alive and a number of lambs reared until 60 days of age, and for a subset of ewe ovulation rates. The study proved a high frequency of the FecX degrees mutation in two flocks that have been selected for many years for increased litter size (0.7-1.0 in breeding ewes and rams respectively), and a moderate frequency in another 19 private flocks (0.4-0.5). The frequency of the GDF9 mutation was low, with only 50 sheep out of 312 genotyped being carriers of the GDF9/G7 mutation, including three homozygous carriers. The FecX degrees mutation in the BMP15 had a significant effect on both litter size and the ovulation rate. The single copy in heterozygous carriers increased litter size by 0.255 (0.063), while the effect of two copies in homozygous genotypes was +0.874 (0.081) lambs born. Due to the loss frequency of the GDF9 mutation, it can only be preliminarily concluded that litter size has been increased in double carriers of both the BM15 and GDF9 mutation, which may suggest their additive interaction. The positive effect of both mutations supports their direct use in selection programmes.

DNA samples collected from survivors of recent outbreaks of highly pathogenic avian influenza (HPAI) in Mexico and the USA have provided a rare opportunity to study the genetic mechanisms underpinning susceptibility of chickens to this devastating and economically impactful disease which normally exhibits 70-100% mortality in the chicken host. Whole Genome Sequence (WGS) data has been used to perform Genome-Wide Association Studies (GWAS), which have highlighted single nucleotide polymorphisms (SNPs) segregating significantly between survivors and controls, with a pedigreed experimental group exhibiting a highly significant signal on chicken chromosome 2 in the region of a biologically relevant gene. Candidate SNPs for resilience are currently being validated using in vitro gene editing methodologiesthat modulate candidate gene expression to investigate the effect on viral replication and cellular response to HPAI infection. A detailed understanding of the genomic resilience to HPAI from this study will have implications for both the poultry industry and for public health.

Several genomic methods were applied for predicting shell quality traits recorded at 4 different hen ages in a White Leghorn line. The accuracies of genomic prediction of single-step GBLUP and single-trait Bayes B were compared with predictions of breeding values based on pedigree-BLUP under single-trait or multitrait models. Breaking strength (BS) and dynamic stiffness (Kdyn) measurements were collected on 18,524 birds from 3 consecutive generations, of which 4,164 animals also had genotypes from an Affymetrix 50K panel containing 49,591 SNPs after quality control edits. All traits had low to moderate heritability, ranging from 0.17 for BS to 0.34 for Kdyn. The highest accuracies of prediction were obtained for the multitrait single-step model. The use of marker information resulted in higher prediction accuracies than pedigree-based models for almost all traits. A genome-wide association study based on a Bayes B model was conducted to detect regions explaining the largest proportion of genetic variance. Across all 8 shell quality traits analyzed, 7 regions each explaining over 2% of genetic variance and 54 regions each explaining over 1% of genetic variance were identified. The windows explaining a large proportion of genetic variance overlapped with several potential candidate genes with biological functions linked to shell formation. A multitrait repeatability model using a single-step method is recommended for genomic evaluation of shell quality in layer chickens.

Highly pathogenic avian influenza (HPAI) poses a huge threat to poultry production and also introduces an epidemiological risk in the human population. Thus far, HPAI has been controlled mainly through widespread implementation of biosecurity, and in the case of an outbreak, liquidation of flocks and establishment of protection zones. Alternative strategies for combating HPAI include the use of vaccines, genetic modification, and genetic selection for increased general and specific immunity in birds. These kinds of strategies often require identification of the genes involved in the immune response to the pathogen. Many genes have been identified as potentially associated with differences in the response to HPAI between poultry species and between individuals. Thus far, the most attention has been focused on genes taking part in regulating the innate immune response, which is responsible for preventing infection and limiting the replication and spread of the virus. The most commonly mentioned candidates for layer chickens include interferon-stimulated genes (ISGs) and RIG-I-like receptors. Proteins encoded by genes of the BTLN family, defensins, and proteins involved in apoptosis have also been associated with differences in the response to HPAI. Recent years have seen an increasing number of studies on the genetic determinants of individual differences in the response to HPAI in chickens. Data from HPAI outbreaks in the US in the spring of 2015 and Mexico in the years 2012-2016 have enabled a more precise analysis of this problem. A number of genes have been identified as associated with the immune response, but their specific role in determining the survival of birds requires further study. Preliminary results indicate that genetic determinants of resistance to HPAI are highly complex and can vary depending on the virus strain and the genetic line of birds.

SNP chips can be used to provide genomic information on copy number variations (CNV) in addition to information on the SNPs (single nucleotide polymorphism) for which they were originally designed. Although some CNVs may be missed compared to methods specifically designed for CVN detection, CNVs derived from SNP chips can contribute valuable information on additional genetic variation without additional cost.

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).

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.

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 F-ST was also performed for 40, 100, and 500 kb windows to support the genome-wide association analyses. The regions with highest F-ST 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 F-ST 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.

The development of SNP chips has enabled rapid genotyping of hundreds of thousands of loci at a relatively low cost. In addition to providing SNP genotype information, copy number variation (CNV) can also be inferred from intensity data generated by the same chips. The aim of this study was to detect and describe CNVs in five lines of layer chickens using different SNP chips. A total of 18,719 individuals from four pure lines and one commercial cross were genotyped using four different SNP chips (Illumina 42K, Affymetrix 600K, and two customized Affymetrix 50K chips). Analysis software Axiom® CNV Summary Tools and PennCNV were used to identify CNVs from Affymetrix chips and cnvPartition in Genome Studio was used to identify CNV’s from the Illumina chip. The CNV regions (CNVR) within lines were defined using the BedTools software, through merging CNVs overlapping by at least 1 bp. CNVRs identified across all panels were selected with BedTools intersect to choose regions with highest confidence. Gene enrichment analysis was performed for genes that overlap identified CNVs to detect overrepresented biological processes and pathways. The mean number of detected CNVs per individual varied depending on the population and size of the SNP set, from 0.50 with the 50K chip in one of the white layer lines up to 4.87 with the 600K chip in one of the brown layer lines. There were considerable differences in the number of detected CNVs between lines, probably due to breeding history. The length of detected CNVs ranged from 1.16 kb to 3.16 Mb. The mean length of detected CNVs was higher for the 42K and 50K chips than for the 600K chip, which is most likely a result of low detectability of shorter CNVs due to larger distances between markers in comparison to the 600K chip. The low frequency of CNVs detected on the 50K panels could also result from their custom design, which intentionally eliminated poorly clustering SNPs. Most of the detected CNVs had low population frequencies. In total CNVs were merged into 2687 CNVRs and overlapped 495.73 Mb of the genome. Intersecting CNVRs across all lines and panels yielded 4131 CNVRs from which 2139 were observed in at least two individuals. In conclusion, commonly used SNP chip platforms and analysis using relevant software can be used to identify CNVs in commercially relevant chicken layer lines. The number of detected CNVs and their length depends on the population and density of SNP on the chip.

Two highly pathogenic avian influenza (HPAI) outbreaks have affected commercial egg production flocks in the American continent in recent years; a H7N3 outbreak in Mexico in 2012 that caused 70% to 85% mortality and a H5N2 outbreak in the United States in 2015 with over 99% mortality. Blood samples were obtained from survivors of each outbreak and from age and genetics matched non-affected controls. A total of 485 individuals (survivors and controls) were genotyped with a 600 k single nucleotide polymorphism (SNP) array to detect genomic regions that influenced the outcome of highly pathogenic influenza infection in the two outbreaks. A total of 420458 high quality, segregating SNPs were identified across all samples. Genetic differences between survivors and controls were analyzed using a logistic model, mixed models and a Bayesian variable selection approach. Several genomic regions potentially associated with resistance to HPAI were identified, after performing multidimensional scaling and adjustment for multiple testing. Analysis conducted within each outbreak identified different genomic regions for resistance to the two virus strains. The strongest signals for the Iowa H5N2 survivor samples were detected on chromosomes 1, 7, 9 and 15. Positional candidate genes were mainly coding for plasma membrane proteins with receptor activity and were also involved in immune response. Three regions with the strongest signal for the Mexico H7N3 samples were located on chromosomes 1 and 5. Neuronal cell surface, signal transduction and immune response proteins coding genes were located in the close proximity of these regions.