Biology subjects

Suchocki, T.

Publications and source records attributed to Suchocki, T..

6 recordsLinked to original sources

bioRxiv2026

From variants to networks: resolving low SNP overlap in dairy cattle meta-analysis through multi-level functional mapping

The objective of this study was to perform a systematic review and multi-level meta-analysis of genome-wide association studies (GWAS) for functional traits in dairy cattle, focusing on calving performance and udder health. We hypothesized that despite the limited replication of individual SNPs across independent studies, functionally related variants would converge on common genes, biological pathways, and genomic regions. Following a systematic literature search and a two-stage filtering process, we extracted data from 20 independent articles covering seven traits: direct and maternal calving ease, direct and maternal stillbirth, clinical mastitis, somatic cell count, and somatic cell score. We first conducted a conventional SNP-level meta-analysis, which demonstrated minimal variant overlap across studies, preventing the detection of significant pooled effects. To resolve this low overlap, we mapped the associated variants to higher organizational levels, including genes, proteins, KEGG and Reactome pathways, and quantitative trait loci (QTL), and combined the statistical evidence using Fishers method. This multi-level functional mapping revealed significant biological convergence. At the gene level, we identified shared genes such as CCDC170 linking maternal calving ease and maternal stillbirth, while DCK, TMPRSS11F, and SLC4A4 were significant for udder health traits. Pathway-level meta-analysis highlighted major functional networks, demonstrating that mastitis-related traits were strongly associated with immune response, signal transduction, and cellular stress pathways, whereas maternal calving traits were dominated by endocrine and calcium signaling. Furthermore, our cross-trait overlap analysis identified 35 shared pathways and 30 shared QTL, reflecting a common biological architecture connecting calving difficulties and mastitis susceptibility. Notably, the innate immune system and signal transduction pathways consistently overlapped across multiple functional traits. In conclusion, conventional SNP-level meta-analysis has limited utility for integrating dairy cattle GWAS due to insufficient cross-study marker overlap. Moving from individual variants toward higher-order functional units effectively reveals reproducible genetic signals and functional convergence. The identified genes, biological pathways, and genomic regions offer valuable insights that can be leveraged as biological priors to improve genomic prediction models for dairy cattle health and reproduction.

genetics↗

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↗

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↗

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

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↗