BackgroundCopy 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.ResultsThe 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.ConclusionsCommonly 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.Electronic supplementary materialThe online version of this article (10.1186/s12711-018-0428-4) contains supplementary material, which is available to authorized users.

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.

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.

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.