Exploring the impact of sequence context on errors in SNP genotype calling with whole genome sequencing data using AI-based autoencoder approach.

A critical step in the analysis of whole genome sequencing data is variant calling. Despite its importance, variant calling is prone to errors. Our study investigated the association between incorrect single nucleotide polymorphism (SNP) calls and variant quality metrics and nucleotide context.

An Explainable Deep Learning Classifier of Bovine Mastitis Based on Whole-Genome Sequence Data-Circumventing the p >> n Problem.

The serious drawback underlying the biological annotation of whole-genome sequence data is the p >> n problem, which means that the number of polymorphic variants (p) is much larger than the number of available phenotypic records (n). We propose a way to circumvent the problem by combining a LASSO logistic regression with deep learning to classify cows as susceptible or resistant to mastitis, based on single nucleotide polymorphism (SNP) genotypes. Among several architectures, the one with 204,642 SNPs was selected as the best.

Nextflow vs. plain bash: different approaches to the parallelization of SNP calling from the whole genome sequence data.

This study compared computational approaches to parallelization of an SNP calling workflow. The data comprised DNA from five Holstein-Friesian cows sequenced with the Illumina platform. The pipeline consisted of quality control, alignment to the reference genome, post-alignment, and SNP calling.

Long non-coding RNA variability in porcine skeletal muscle.

Recently, numerous studies including various tissues have been carried out on long non-coding RNAs (lncRNAs), but still, its variability has not yet been fully understood. In this study, we characterised the inter-individual variability of lncRNAs in pigs, in the context of number, length and expression. Transcriptomes collected from muscle tissue belonging to six Polish Landrace boars (PL1-PL6), including half-brothers (PL1-PL3), were investigated using bioinformatics (lncRNA identification and functional analysis) and statistical (lncRNA variability) methods.

Genetic predisposition to differentiated thyroid cancer in the Polish population.

Introduction: Genome sequencing technologies reveal molecular mechanisms of differentiated thyroid cancer (DTC). Unlike somatic mutation analysis from thyroidectomy samples, germline mutations showing genetic susceptibility to DTC are less understood.

Objectives: The study aimed to assess the prevalence of germline mutations predisposing to DTC in a cohort of Polish individuals based on their whole genome sequencing data.

Heterogeneity in convergence behaviour of the single-step SNP-BLUP model across different effects and animal groups.

Background: The single-step model is becoming increasingly popular for national genetic evaluations of dairy cattle due to the benefits that it offers such as joint breeding value estimation for genotyped and ungenotyped animals. However, the complexity of the model due to a large number of correlated effects can lead to significant computational challenges, especially in terms of accuracy and efficiency of the preconditioned conjugate gradient method used for the estimation. The aim of this study was to investigate the effect of pedigree depth on the model's overall convergence rate as well as on the convergence of different components of the model, in the context of the single-step single nucleotide polymorphism best linear unbiased prediction (SNP-BLUP) model.

Genetics, Genomics and Emerging Molecular Therapies of Pancreatic Cancer.

The number of cases of pancreatic cancers in 2019 in Poland was 3852 (approx. 2% of all cancers). The course of the disease is very fast, and the average survival time from the diagnosis is 6 months.

Bioinformatic modelling of SARS-CoV-2 pandemic with a focus on country-specific dynamics.

Background: One of the seminal events since 2019 has been the outbreak of the SARS-CoV-2 pandemic. Countries have adopted various policies to deal with it, but they also differ in their socio-geographical characteristics and public health care facilities. Our study aimed to investigate differences between epidemiological parameters across countries.

Better safe than sorry-Whole-genome sequencing indicates that missense variants are significant in susceptibility to COVID-19.

Undoubtedly, genetic factors play an important role in susceptibility and resistance to COVID-19. In this study, we conducted the GWAS analysis. Out of 15,489,173 SNPs, we identified 18,191 significant SNPs for severe and 11,799 SNPs for resistant phenotype, showing that a great number of loci were significant in different COVID-19 representations.

An effect of large-scale deletions and duplications on transcript expression.

Since copy number variants (CNVs) have been recognized as an important source of genetic and transcriptomic variation, we aimed to characterize the impact of CNVs located within coding, intergenic, upstream, and downstream gene regions on the expression of transcripts. Regions in which deletions occurred most often were introns, while duplications in coding regions. The transcript expression was lower for deleted coding (P = 0.

Application of mixed linear models for the estimation of functional effects on bovine stature based on SNP summary statistics from a whole-genome association study.

Genome-wide association studies (GWAS) help identify polymorphic sites or genes linked to phenotypic variance, but a few identified genes and/or single nucleotide polymorphisms (SNPs) are unlikely to explain a large part of the phenotypic variability of complex traits. In this study, the focus was moved from single loci to functional units, expressed by the metabolic pathways as defined in the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database. Consequently, the aim of this study was to estimate KEGG effects on stature in three Nordic dairy cattle breeds using SNP effects from GWAS as the dependent variable.

Host transcriptome and microbiome interactions in Holstein cattle under heat stress condition.

Climate change affects animal physiology. In particular, rising ambient temperatures reduce animal vitality due to heat stress and this can be observed at various levels which included genome, transcriptome, and microbiome. In a previous study, microbiota highly associated with changes in cattle physiology, which included rectal temperature, drooling score and respiratory score, were identified under heat stress conditions.

Gene Variants Related to Cardiovascular and Pulmonary Diseases May Correlate with Severe Outcome of COVID-19.

Severe outcomes of COVID-19 account for up to 15% of all cases. The study aims to check if any gene variants related to cardiovascular (CVD) and pulmonary diseases (PD) are correlated with a severe outcome of COVID-19 in a Polish cohort of COVID-19 patients. In this study, a subset of 747 samples from unrelated individuals collected across Poland in 2020 and 2021 was used and whole-genome sequencing was performed.

Fecal microbiota and their association with heat stress in Bos taurus.

Background: Humans have been influencing climate changes by burning fossil fuels, farming livestock, and cutting down rainforests, which has led to global temperature rise. This problem of global warming affects animals by causing heat stress, which negatively affects their health, biological functions, and reproduction. On the molecular level, it has been proved that heat stress changes the expression level of genes and therefore causes changes in proteome and metabolome.

Beyond GWAS-Could Genetic Differentiation within the Allograft Rejection Pathway Shape Natural Immunity to COVID-19?

COVID-19 infections pose a serious global health concern so it is crucial to identify the biomarkers for the susceptibility to and resistance against this disease that could help in a rapid risk assessment and reliable decisions being made on patients' treatment and their potential hospitalisation. Several studies investigated the factors associated with severe COVID-19 outcomes that can be either environmental, population based, or genetic. It was demonstrated that the genetics of the host plays an important role in the various immune responses and, therefore, there are different clinical presentations of COVID-19 infection.

DNA sequence features underlying large-scale duplications and deletions in human.

Copy number variants (CNVs) may cover up to 12% of the whole genome and have substantial impact on phenotypes. We used 5867 duplications and 33,181 deletions available from the 1000 Genomes Project to characterise genomic regions vulnerable to CNV formation and to identify sequence features characteristic for those regions. The GC content for deletions was lower and for duplications was higher than for randomly selected regions.

Genome-Wide Genomic and Functional Association Study for Workability and Calving Traits in Holstein Cattle.

The goal of our study was to identify the SNPs, metabolic pathways (KEGG), and gene ontology (GO) terms significantly associated with calving and workability traits in dairy cattle. We analysed direct (DCE) and maternal (MCE) calving ease, direct (DSB) and maternal (MSB) stillbirth, milking speed (MSP), and temperament (TEM) based on a Holstein-Friesian dairy cattle population consisting of 35,203 individuals. The number of animals, depending on the trait, ranged from 22,301 bulls for TEM to 30,603 for DCE.

The Thousand Polish Genomes-A Database of Polish Variant Allele Frequencies.

Although Slavic populations account for over 4.5% of world inhabitants, no centralised, open-source reference database of genetic variation of any Slavic population exists to date. Such data are crucial for clinical genetics, biomedical research, as well as archeological and historical studies.

Identification of functional features underlying heat stress response in Sprague-Dawley rats using mixed linear models.

Since global temperature is expected to rise by 2 °C in 2050 heat stress may become the most severe environmental factor. In the study, we illustrate the application of mixed linear models for the analysis of whole transcriptome expression in livers and adrenal tissues of Sprague-Dawley rats obtained by a heat stress experiment. By applying those models, we considered four sources of variation in transcript expression, comprising transcripts (1), genes (2), Gene Ontology terms (3), and Reactome pathways (4) and focussed on accounting for the similarity within each source, which was expressed as a covariance matrix.

SNP prioritization in targeted sequencing data associated with humoral immune responses in chicken.

Our study aimed to identify single nucleotide polymorphisms (SNPs) with a significant impact on the innate immunity represented by antibody response against lipopolysaccharide (LPS) and lipoteichoid acid (LTA) and the adaptive immune response represented toward keyhole limpet hemocyanin (KLH) using the SNP prioritization method. Data set consisted of 288 F2 experimental individuals, created by crossing Green-legged Partridgelike and White Leghorn. The analyzed SNPs were located within 24 short genomic regions of GGA1, GGA2, GGA3, GGA4, GGA9, GGA10, GGA14, GGA18, and GGZ, pre-targeted based on literature references and database information.

Exploring the Potential Genetic Heterogeneity in the Incidence of Hoof Disorders in Austrian Fleckvieh and Braunvieh Cattle.

Genetic heterogeneity denotes the situation when different genetic architectures underlying diverse populations result in the same phenotype. In this study, we explore the genetic background underlying differences in the incidence of hoof disorders between Braunvieh and Fleckvieh cattle in the context of genetic heterogeneity between the breeds. Despite potentially higher power of testing due to twice as large sample size, none of the SNPs was significantly associated with the total number of hoof disorders in Fleckvieh, while 15 SNPs were significant in Braunvieh.

Correction to: The application of deep learning for the classification of correct and incorrect SNP genotypes from whole-genome DNA sequencing pipelines.

The original version on this paper contained an error. Figure 5 was published with the same image of Fig. 4.

The application of deep learning for the classification of correct and incorrect SNP genotypes from whole-genome DNA sequencing pipelines.

A downside of next-generation sequencing technology is the high technical error rate. We built a tool, which uses array-based genotype information to classify next-generation sequencing-based SNPs into the correct and the incorrect calls. The deep learning algorithms were implemented via Keras.

Patterns of DNA variation between the autosomes, the X chromosome and the Y chromosome in Bos taurus genome.

The new ARS-UCD1.2 assembly of the bovine genome has considerable improvements over the previous assembly and thus more accurate identification of patterns of genetic variation can be achieved with it. We explored differences in genetic variation between autosomes, the X chromosome, and the Y chromosome.