Biology subjects

Szyda, J.

Publications and source records attributed to Szyda, J..

16 recordsLinked to original sources

bioRxiv2025

Modeling missing parents in single-step test-day SNP-BLUP evaluation of dairy cattle

In many countries, single-step genomic models have replaced multiple step models for routine evaluation. These models use all available information on animals phenotypes, genotypes, and pedigrees, yet missing parental information in pedigrees remains a challenge that affects genomic breeding value (GEBV) predictions. Therefore, the choice of method for handling missing parents can affect the prediction of breeding values. Here, we compared three approaches to model missing parental information for three levels of missing pedigree data: P_Real - pedigree from routine evaluation, P_2010 - at least 20 percent of dams and 10 percent of sires born before 2019 were set to missing, and P_4020 - at least 40 percent of dams and 20 percent of sires born before 2019 were set to missing. Missing parents information was expressed through missing codes in the raw pedigree (RP) by defining genetic groups (GG) that represent missing parents grouped based on year of birth, sex, and country of origin, or by defining metafounders (MF), which represent missing parents grouped by average genetic relationships estimated from the genomic information of their descendants. The genomic breeding values for fat yield were estimated using the single-step test-day SNP-BLUP model implemented with MiXBLUP software. For the considered scenarios, the results were presented separately for sires and dams, as well as for genotyped and ungenotyped individuals. We observed differences in the prediction quality between genotyped and ungenotyped animals. While GEBV predictions for the former were generally stable across scenarios, the predictions for the ungenotyped individuals varied. In particular, the removal of parental information led to less stable results when missing parental information was expressed by MF, where insufficient pedigree completeness resulted in an overestimation of the genetic trend. In conclusion, for informative pedigrees with a small percentage of missing parents, the incorporation of GG and MF results in very similar GEBV predictions, however GG appear to be a more robust approach for ungenotyped individuals in highly incomplete pedigrees.

animal behavior and cognition↗

bioRxiv2025

Supplementation with effective microorganisms in earthen ponds affects common carp growth but not overall microbial communities

Probiotics are increasingly explored in aquaculture to enhance fish health and growth without leaving harmful residues. However, their efficacy in real-world pond environments remains poorly understood. Here, we conducted a 103-day field experiment to assess the effects of two effective microorganisms commercial products supplementations on microbial communities and growth performance of common carp (Cyprinus carpio). Effective microorganisms were added both to feed and directly to pond water. Microbial diversity was analysed using 16S rRNA and whole-genome shotgun sequencing across three environments - water (three time points), sediment (two time points) and fish intestine (one time point) - from 25 experimental ponds. Bioinformatics processing involved QIIME2 and MG-TK pipeline with taxonomic classification based on the SILVA database. The results showed that although supplemented bacterial families did not establish significantly in pond environments, fish exposed to specific effective microorganisms treatments exhibited improved growth metrics. These findings suggest that effective microorganisms can enhance carp growth in aquaculture without significantly altering resident microbial communities, offering a promising residue-free alternative to traditional additives.

microbiology↗

bioRxiv2024

Unravelling traits with complex inheritance mode: a gene panel perspective

Although gene panels for complex phenotypes target genes contributing significantly to phenotypic variation, identifying causal mutations remains challenging. We aimed to define the process of analysing gene panel data to uncover genomic insights for traits with complex inheritance. The implemented procedure included the exploration of (i) the association between phenotypic variability and the variability of SNPs within genes, (ii) the association attributed to specific variants, (iii) causality potential by analysing the genomic context in the vicinity of the significantly associated SNPs (iii_a) either through clustering - to identify variants with genotypic patterns similar to the significant SNPs, or (iii_b) through smoothing the set of original P values to incorporate the information on the impact of the particular mutations, as expressed by the SNP-specific PhRED-CADD scores. This procedure was applied to the data from the obesity gene panel. No significant gene-level associations were found. However, the SNP-level results were promising in narrowing down the initially available set of variants to determine the true causal mutations. We introduced a procedure for analysing gene panel data to identify potentially causal mutations for phenotypes with complex inheritance patterns. The procedure allows for narrowing down the originally available set of variants offered by a gene panel, consisting of genes important for the phenotype under study, to SNPs that have a statistically and genomically validated potential to be causal in modifying protein structure.

bioinformatics↗

bioRxiv2024

The impact of probiotic supplementation with effective microorganisms on water and gut microbiome of the Common carp

The efficacy of probiotic products in aquaculture covers multiple aspects that act directly on fish, such as improving resistance to fungal, bacterial, and viral infections, reproduction, feed efficiency, and overall growth performance, or indirectly through improved water quality. In our experiment, the application of two commercial water and two commercial food EM supplements was tested for their effect on water and the intestinal microbiome of common carp (Cyprinus carpio), as well as on carp growth performance. Probiotic supplementation did not result in a clear pattern of changes in overall microbial diversity or in the modification of the abundance of single genera. However, its effect was clearly manifested by an increase in the final weight of the fish. The most consistent alteration was the significant increase in Lactococcus abundance in the intestinal microbiome community compared to the control observed in both experimental setups. We conclude that the positive effect of Lactococcus on growth performance can be achieved by feed supplementation, which allows the increased abundance of Lactococcus in the fish intestine, which has a positive impact on glucose absorption and metabolism already demonstrated in the literature.

microbiology↗

bioRxiv2024

Approaches to dimensionality reduction for ultra-high dimensional models

The rapid advancement of high-throughput sequencing technologies has revolutionised genomic research by providing access to large amounts of genomic data. However, the most important disadvantage of using Whole Genome Sequencing (WGS) data is its statistical nature, the so-called p>>n problem. This study aimed to compare three approaches of feature selection allowing for circumventing the p>>n problem, among which one is a novel modification of Supervised Rank Aggregation (SRA). The use of the three methods was demonstrated in the classification of 1,825 individuals representing the 1000 Bull Genomes Project to 5 breeds, based on 11,915,233 SNP genotypes from WGS. In the first step, we applied three feature (i.e. SNP) selection methods: the mechanistic approach (SNP tagging) and two approaches considering biological and statistical contexts by fitting a multiclass logistic regression model followed by either 1-dimensional clustering (1D-SRA) or multi-dimensional feature clustering (MD-SRA) that was originally proposed in this study. Next, we perform the classification based on a Deep Learning architecture composed of Convolutional Neural Networks. The classification quality of the test data set was expressed by macro F1-Score. The SNPs selected by SNP tagging yielded the least satisfactory results (86.87%). Still, this approach offered rapid computing times by focussing only on pairwise LD between SNPs and disregarding the effects of SNP on classification. 1D-SRA was less suitable for ultra-high-dimensional applications due to computational, memory and storage limitations, however, the SNP set selected by this approach provided the best classification quality (96.81%). MD-SRA provided a very good balance between classification quality (95.12%) and computational efficiency (17x lower analysis time and 14x lower data storage), outperforming other methods. Moreover, unlike SNP tagging, both SRA-based approaches are universal and not limited to feature selection for genomic data. Our work addresses the urgent need for computational techniques that are both effective and efficient in the analysis and interpretation of large-scale genomic datasets. We offer a model suitable for the classification of ultra-high-dimensional data that implements fusing feature selection and deep learning techniques.

bioinformatics↗

bioRxiv2024

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 WGS data is variant calling. Despite its importance, variant calling is prone to errors. Our study investigated the association between incorrect SNP and variant quality metrics and nucleotide context. In our study, incorrect SNPs were defined in twenty Holstein-Friesian cows by comparing their SNPs genotypes identified by whole genome sequencing with the IlluminaNovaSeq6000 and the EuroGMD50K genotyping microarray. The data set was divided into the correct set of SNPs (666,333 SNPs) and the incorrect set of SNPs (4,557 SNPs). The training data set consisted of only the correct SNPs, while the test data set contained a balanced mix of all the incorrectly and correctly called SNPs. An autoencoder was constructed to identify systematically incorrect SNPs that were marked as outliers by a one-class support vector machine and isolation forest algorithms. The results showed that 59.53% ({+/-}0.39%) of the incorrect SNPs had systematic patterns, with the remainder being random errors. The frequent occurrence of the CGC trimer was due to mislabeling a call for C. Incorrect T instead A call was associated with the presence of T in the neighboring downstream position. These errors may arise due to the fluorescence patterns of nucleotide labelling.

bioinformatics↗

bioRxiv2024

Exploring the distribution of single nucleotide polymorphisms across human exons and introns

Among all types of mutations, single nucleotide polymorphisms are the most common type of genomic variation. In our study, we explore the counts of single nucleotide polymorphisms in particular exons and introns of the human genome based on the data set of 1,222 individuals of Polish origin that comprises 41,836,187 polymorphisms. In particular, chromosomes 1 and 22 were considered as representatives of two markedly different DNA molecules, since HSA01 represents the longest and HSA22 is one of the shortest chromosomes. The results demonstrate that outer (first, last) exons as well as the first introns harbour significantly more SNPs than other genic regions. The observed differences in counts reflect the distinct functional roles of those genomic units.

bioinformatics↗

bioRxiv2024

Nextflow vs. plain Bash: Different Approaches to the Parallelisation of SNP Calling from the Whole Genome Sequence Data

This study compared computational approaches to parallelisation of an SNP calling workflow. 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. Three approaches to parallelisation were compared: (i) a plain Bash script in which a pipeline for each cow was executed as separate processes invoked at the same time, (ii) a Bash script wrapped in a single Nextflow process, and (iii) a Nextflow script with each component of the pipeline defined as a separate process. The results demonstrated that on average, the multi-process Nextflow script performed 15% to 27% faster depending on the number of assigned threads, with the biggest execution time advantage over the plain Bash approach observed with 10 threads. In terms of RAM usage, the most substantial variation was observed for the multi-process Nextflow, for which it increased with the number of assigned threads, while RAM consumption of the other setups did not depend much on the numbers of threads assigned for computations. Due to intermediate and log files generated, disk usage was markedly higher for the multi-process Nextflow than for the plain Bash and for the single-process Nextflow.

bioinformatics↗

bioRxiv2023

Genome-wide association study of heat stress response in Bos taurus

Heat stress is a major challenge in cattle production, affecting animal welfare, productivity, and the economic viability of the industry. In this study, we conducted a genome-wide association study to identify genetic markers associated with heat stress tolerance in Chinese Holstein cattle. We genotyped 68 cows using Illumina 150K Bovine BeadChip microarray and analyzed 112,081 single nucleotide polymorphisms using a linear model-based GWAS approach. We identified 17 SNPs distributed over three chromosomes that showed statistically significant associations with heat stress tolerance in Chinese Holstein cattle. Five of them were located in introns of two genes, PDZRN4 and PRKG1. PDZRN4 is involved in protein degradation pathways, while PRKG1 encodes a protein kinase involved in smooth muscle relaxation and blood vessel dilation. Our findings highlight the potential importance of PDZRN4 and PRKG1 in heat stress tolerance in cattle and provide valuable genetic markers for further research and breeding programs aimed at improving heat stress tolerance in Holstein cattle. However, further studies are needed to elucidate the exact mechanisms by which these SNPs contribute to heat stress tolerance and their potential implications for practical cattle breeding strategies.

bioinformatics↗

bioRxiv2023

An explainable deep learning classifier of bovine mastitis based on whole genome sequence data - circumventing the p>>>n problem

The most serious drawback underlying the biological annotation of Whole Genome Sequence data is the p>>n problem, meaning that the number of polymorphic variants (p) is much larger than the number of available phenotypic records (n). Therefore, the major aim of the study was to propose a way to circumvent the problem by combining a LASSO logistic regression model with Deep Learning (DL). That was illustrated by a practical biological problem of classification of cows into mastitis-susceptible or mastitis-resistant, based on genotypes of Single Nucleotide Polymorphisms (SNPs) identified in their WGS. Among several DL architectures proposed via optimisation of DL hyperparameters using the Optuna software, imposed on different SNP sub-sets defined by LASSO logistic regressions with different penalty values, the architecture with 204,642 SNPs was selected as the best one. This architecture was composed of 2 layers with respectively 7 and 46 units per layer as well as respective drop-out rates of 0.210 and 0.358. The classification of the test data set resulted in the AUC=0.750, accuracy=0.650, sensitivity=0.600, and specificity=0.700 was selected as the best model and thus proceeded to genomic and functional annotations. Significant SNPs were selected based on the SHapley Additive exPlanation values transformed to Z-scores to assess the underlying type I-error. These SNPs were annotated to genes. As a final result, a single GO term related to the biological process and thirteen GO terms related to the molecular function were significantly enriched in the gene set that corresponded to the significant SNPs. Author SummaryOur objective is to distinguish between cows that are susceptible and resistant to bovine mastitis by analysing their genomic data. However, we face a significant challenge due to the large number of single nucleotide polymorphisms (SNPs) and limited sample size. To address this challenge, we utilize two methods: feature selection algorithms and deep learning. We experiment with various ways of implementing these techniques and evaluate their performance on a validation set. Our findings reveal that the optimal approach can accurately predict a cows susceptibility or resistance status around 65% of the time. Additionally, we employ a technique to identify the most crucial SNPs and their biological functions. Our results indicate that some of these SNPs are related to immune response or protein synthesis pathways, implying that they may affect the cows health and productivity.

bioinformatics↗

bioRxiv2022

Host transcriptome and microbiomeinteractions in Holstein cattle under heatstress condition

Climate changes 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 were identified under heat stress conditions which included rectal temperature, drooling score, and respiratory score. In the present study, genes differentially expressed between individuals were selected representing different additive genetic effects towards the heat stress response in cattle in their production condition. Moreover, a correlation network analysis was performed to identify interactions between the transcriptome and microbiome for 71 Chinese Holstein cows sequenced for mRNA from blood samples and for 16S rRNA genes from fecal samples. Bioinformatics analysis was performed comprising: i) clustering and classification of 16S rRNA sequence reads, ii) mapping cows transcripts to the reference genome and their expression quantification, and iii) statistical analysis of both data types - including differential gene expression analysis and gene set enrichment analysis. A weighted co-expression network analysis was carried out to assess changes in the association between gene expression and microbiota abundance as well as to find hub genes/microbiota responsible for the regulation of gene expression under heat stress. Results showed 1,851 differentially expressed genes were found that were shared by three heat stress phenotypes. These genes were predominantly associated with the cytokine-cytokine receptor interaction pathway. The interaction analysis revealed three modules of genes and microbiota associated with rectal temperature with which two hubs of those modules were bacterial species, demonstrating the importance of the microbiome in the regulation of gene expression during heat stress. Genes and microbiota from the significant modules can be used as biomarkers of heat stress in cattle.

bioinformatics↗

bioRxiv2022

The 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 / Single Nucleotide Polymorphisms 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: Kyoto Encyclopaedia of Genes and Genomes (KEGG). Consequently, this study aimed to estimate KEGG effects on stature in three Nordic dairy cattle breeds using SNPs effects from GWAS as the dependent variable. The SNPs were annotated to genes, then the genes to KEGG pathways. The effects of KEGG were estimated separately for each breed using a mixed linear model incorporating the similarity between pathways expressed by common genes. The KEGG pathway D-amino acid metabolism (map00473) was estimated as significant on stature in two of the analysed breeds and revealed a borderline significance in the third breed. Interestingly, biological evidence exists that described the importance of D-amino acids for growth in experimental organisms as well as in cattle.

bioinformatics↗

bioRxiv2021

Fecal microbiota and their association with heat stress in Bos taurus

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. The importance of a microbiome in many studies showed that it is considered as individuals "second genome". Physiological changes caused by heat stress may impact the microbiome composition. In this study, we identified fecal microbiota associated with heat stress that was quantified by three metrics - rectal temperature, drooling, and respiratory scores and represented by their Estimated Breeding Values. For this purpose, the 16S rRNA sequencing technique was used. We analyzed the microbiota from 136 fecal samples of Chinese Holstein cows through a 16S rRNA gene sequencing approach. Sequence data were processed using a pipeline involving QIIME2 software together with SILVA database. Statistical modeling was performed using a negative binomial regression. The analysis revealed the total number of 24 genera and 12 phyla associated with heat stress metrics. Rhizobium and Pseudobutyrivibrio turned out to be the most significant genera, while Acidobacteria and Gemmatimonadetes were the most significant phyla. Phylogenetic analysis revealed that that three heat stress indicators quantify different metabolic ways of animals reaction to heat stress. Other studies already identified that those genera had significantly increased abundance in mice exposed to stressor-induced changes. Moreover, identified microbiota significantly associated with heat stress measures were mostly mesophilic, so their association seems to be due to heat stress-induced secondary, metabolic changes, and not directly by temperature. Moreover, high fold changes of many genera suggest that they may be used as biomarkers for monitoring the level of heat stress in cattle.

bioinformatics↗

bioRxiv2021

The Thousand Polish Genomes Project- a national database of Polish variant allele frequencies

Although Slavic populations account for over 3.5% of world inhabitants, no centralized, open source reference database of genetic variation of any Slavic population exists to date. Such data are crucial for either biomedical research and genetic counseling and are essential for archeological and historical studies. Polish population, homogenous and sedentary in its nature but influenced by many migrations of the past, is unique and could serve as a good genetic reference for middle European Slavic nations. The aim of the present study was to describe first results of analyses of a newly created national database of Polish genomic variant allele frequencies. Never before has any study on the whole genomes of Polish population been conducted on such a large number of individuals (1,079). A wide spectrum of genomic variation was identified and genotyped, such as small and structural variants, runs of homozygosity, mitochondrial haplogroups and Mendelian inconsistencies. The allele frequencies were calculated for 943 unrelated individuals and released publicly as The Thousand Polish Genomes database. A precise detection and characterisation of rare variants enriched in the Polish population allowed to confirm the allele frequencies for known pathogenic variants in diseases, such as Smith-Lemli-Opitz syndrome (SLOS) or Nijmegen breakage syndrome (NBS). Additionally, the analysis of OMIM AR genes led to the identification of 22 genes with significantly different cumulative allele frequencies in the Polish (POL) vs European NFE population. We hope that The Thousand Polish Genomes database will contribute to the worldwide genomic data resources for researchers and clinicians.

genomics↗

bioRxiv2020

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. That might be assumed that a 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. In particular, densities of variants, annotation, lengths (only for InDels), nucleotide divergence, and Tajimas D statistic between chromosomes. Whole-genome DNA sequences of 217 individuals representing different cattle breeds were examined. The analysis included the alignment to the new reference genome and variant calling. 23,655,295 SNPs and 3,758,781 InDels were detected. In contrast to autosomes, both sex chromosomes had negative values of Tajimas D and lower nucleotide divergence. That implies a correlation between nucleotide diversity and recombination rate, which is obviously reduced for sex chromosomes. Moreover, accumulation of nonsynonymous mutations on the Y chromosome could be associated with loss of recombination. Also, the relatively lower effective population size for sex chromosomes leads to a lower expected density of variants.

bioinformatics↗

bioRxiv2020

Exploring the potential genetic heterogeneity in the incidence of hoof and leg disorders in Austrian Fleckvieh and Braunvieh cattle

BackgroundGenetic heterogeneity denotes the situation when different genetic architectures underlying diverse populations result in the same phenotype. In this study, we explore the nature of differences in the incidence of the number of hoof and leg disorders between Braunvieh and Fleckvieh cattle in the context of genetic heterogeneity between the breeds. ResultsDespite potentially higher power of testing due to twice as large sample size, none of the SNPs was significantly associated with the number of hoof and leg disorders in Fleckvieh, while 16 SNPs were significant in Braunvieh. The most promising candidate genes in Braunvieh are: CBLB on BTA01, which causes arthritis in rats; CAV2 on BTA04, which in effects mouse skeletal muscles; PTHLH on BTA05, which causes disease phenotypes related to the skeleton in humans, mice and zebrafish; SORCS2 on BTA06, which causes decreased susceptibility to injury in the mouse. Some of the significant SNPs (BTA01, BTA04, BTA05, BTA13, BTA16) reveal allelic heterogeneity - i.e. differences due to different allele frequencies between Fleckvieh and Braunvieh. Some of the significant regions (BTA01, BTA05, BTA13, BTA16) correlate to inter-breed differences in LD structure and may thus represent false-positive heterogeneity. However, positions on BTA06 (SORCS2), BTA14 and BTA24 mark Braunvieh-specific regions. ConclusionsWe hypothesise that the observed genetic heterogeneity of hoof and leg disorders is a by-product of multigenerational differential selection of the breeds - towards dairy production in the case of Braunvieh and towards beef production in the case of Fleckvieh. Based on the current data set it is no possibly to unequivocally confirm/exclude the hypothesis of genetic heterogeneity in the susceptibility to leg disorders between Fleckvieh and Braunvieh because only explore it through associations and not the causal mutations. Rationales against genetic heterogeneity comprise a limited power of detection of true associations as well as differences in the length of LD blocks and in linkage phase between breeds. On the other hand, multigenerational differential selection of the breeds and no systematic differences in LD structure between the breeds favour the heterogeneity hypothesis at some of the significant sites.

zoology↗