Michalina Jakimowicz

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 Fisher’s 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. Interpretive summary: Genetic studies on dairy cattle health and calving traits often show limited replication of individual variants across independent populations. This study addresses this limitation by moving beyond conventional single-marker meta-analysis and integrating genome-wide association data at higher levels of biological organization: genes, proteins, pathways, and quantitative trait loci. By mapping variants from 20 independent studies to these higher-order functional units, we revealed a shared biological architecture - such as immune and calcium signaling - linking calving difficulties with mastitis susceptibility. These findings provide biological priors that can enhance genomic prediction models and breeding programs for dairy cattle functional traits.

Background: Effective microorganisms are increasingly explored in aquaculture to improve 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 supplementation with two effective commercial microorganism products on the microbial communities and growth performance of common carp ( Cyprinus carpio ). Background: Effective microorganisms were added to the feed and directly to the pond water. Microbial diversity was analysed via 16 S 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 was performed using the QIIME2 and MG-TK pipelines 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 microorganism treatments showed improved growth metrics. Conclusions: These findings suggest that effective microorganisms can increase carp growth in aquaculture without significantly altering the resident microbial communities, suggesting a promising residue-free alternative to traditional additives in aquaculture.

The integration of phenotypic, genomic, and pedigree data into a single-step model for predicting genomically enhanced estimated breeding values (GEBVs) has become crucial for the accurate genetic evaluation of dairy cattle. This study compared two widely used software implementations, MiXBLUP and BLUPF90IOD3, for the prediction of breeding values using the single-step G-BLUP model based on data from the Polish national evaluation for stature. Four core animal sets were tested, which differed in the selection of bulls and cows. The GEBVs were predicted and validated using different subsets of the population. Both software packages resulted in high correlations (0.89 and 0.97) between full and truncated dataset predictions and similar validation performance, with MiXBLUP exhibiting slightly greater consistency across different sets of core animals. The ranking of the top 50 bulls remained stable across the implementations. This study concludes that both software implementations provide comparable GEBV predictions, suggesting that software choice should consider computational efficiency, cost, and modeling flexibility, with MiXBLUP offering additional options for GEBV estimation.

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.

We considered the spatial (dis)similarities in the number of SARS-CoV-2 infected cases across Europe, through the first 300 days of the pandemic. The data represents the SARS-CoV-2 Repository provided by the Center for Systems Science and Engineering at Johns Hopkins University. From this data set, we extracted daily numbers of infected cases per million inhabitants for 33 European countries between February and November 2020. Spatial clustering was applied to define groups of countries with similar infection patterns. Two clusters of countries comprised: 10 low-incidence countries and 23 ‘remaining’ countries. Moran indices estimated for all countries, as well as separately within each cluster, every 10 days, did not significantly deviate from zero. Reasons for the lack of spatial correlations comprise differences in the regulation of social contacts, demographic patterns, public health facilities, and varying accuracy of data recording. The major limitation of the analysis is data quality, which hampers epidemiological inferences.

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. We estimated the SNP effects (based on 46,216 polymorphisms from Illumina BovineSNP50 BeadChip Version 2) using a multi-SNP mixed model. The SNP positions were mapped to genes and the GO terms/KEGG pathways of the corresponding genes were assigned. The estimation of the GO term/KEGG pathway effects was based on a mixed model using the SNP effects as dependent variables. The number of significant SNPs comprised 59 for DCE, 25 for DSB and MSP, 17 for MCE and MSB, and 7 for TEM. Significant KEGG pathways were found for MSB (2), TEM (2), and MSP (1) and 11 GO terms were significant for MSP, 10 for DCE, 8 for DSB and TEM, 5 for MCE, and 3 for MSB. From the perspective of a better understanding of the genomic background of the phenotypes, traits with low heritabilities suggest that the focus should be moved from single genes to the metabolic pathways or gene ontologies significant for the phenotype.

(1) Background: The goal of our study was to identify SNPs, metabolic pathways (KEGG), and gene ontology (GO) terms significantly associated with calving and workability; (2) Methods: Based on the EuroGenomics reference data set, we analyzed direct (DCE) and maternal (MCE) calving ease, direct (DSB), and maternal (MSB) stillbirth, milking speed (MSP), and temperament (TEM). We estimated SNP effects using a multi-SNP mixed-model. Further, SNP positions were mapped to genes, and GO terms/KEGG pathways of the corresponding genes were assigned. The estimation of GO term/KEGG pathway effects was based on a mixed-model using SNP effects as dependent variables; (3) Results: The number of significant SNPs comprised 59 for DCE, 25 for DSB and MSP, 17 for MCE and MSB, and 7 for TEM. Significant KEGG pathways were found for MSB (2), TEM (2), and MSP (1), while 11 GO terms were significant for MSP, 10 for DCE, 8 for DSB and TEM, 5 for MCE, and 3 for MSB.; (4) Conclusions: From the perspective of a better understanding of the genomic background of the phenotypes, traits with low heritabilities suggest that the focus should be moved from single genes to metabolic pathways or gene ontologies significant for the phenotype.

The experiments described in this research article were designed to test the effect of rare variants into genomic prediction in dairy cattle. Common polymorphisms are able to explain only a small proportion of the underlying genetic variation of complex phenotypes. Variants representing functional mutations with large effects on complex phenotypes are expected to be rare due to natural (humans) or artificial (livestock) selection pressure. Therefore, it is important to check whether the use of rare variants could increase the accuracy of ranking of animals by providing the tool for more precise differentiation among the bulls with high additive genetic merit. The goal of our study was to verify whether including rare variants in a genomic selection model allows for a more accurate description of the additive genetic background of traits under selection in dairy cattle. We used the linear mixed model for comparison SNP estimates for Holstein-Friesian cattle of the two data sets – a set containing only single nucleotide polymorphisms defined by minor allele frequency ≥ 0.01, which is routinely used in the Polish genomic evaluation system (46,216 SNPs), and a set containing SNPs selected based only on the call rate (54,378 SNPs). Based on the SNP estimates we also calculated DGV and GEBV and compared them between both data sets. In all the analyses we used production, fertility, conformation and udder health traits. We also assessed the time required for the two most computationally demanding components of genomic selection: preparing genotype data, and estimation of SNP effects between those two data sets. The results of our study indicated that the analysis including rare variants resulted in changes in the individual ranking of the top 100 male and female candidates, but had no effect on the outcome of the quality of EBV prediction as expressed by the Interbull validation test.