Biology subjects

Neuditschko, M.

Publications and source records attributed to Neuditschko, M..

4 recordsLinked to original sources

bioRxiv2026

Temporal, spatial, and parasitic drivers of microbial variation in European honey bees

Although the roles of host-associated microbiomes in animal health are increasingly recognised, the factors influencing their variation remain understudied. The relatively simple microbiome of honey bees is a relevant system to address this gap. In particular, the relationship between variations in microbiome composition and the ectoparasite Varroa destructor, the main threat to honey bee health worldwide, is poorly established. In this study, we used metagenomic and statistical analyses of 1442 European honey bee colonies to investigate the relationships between the honey bee microbiome, temporality, location, V. destructor load, and behavioural response to its infestation by the host. While season, year, and location were identified as the main drivers of microbiome variation, V. destructor load emerged as a significant factor associated with microbiome variation. Notably, we identify several pathogens and opportunists that correlated positively with V. destructor load, while the core symbiont Bombilactobacillus correlated negatively. This is compatible with a shift in the microbiome toward dysbiosis, which may be driven by or promote V. destructor parasitism. By contrast, we found only limited evidence of an association between the microbiome and resistance behaviours of the host against this parasite. While the study cannot establish causal relationships, we present the largest metagenomic analysis of honey bee microbiomes to date, providing robust, generalisable evidence about the factors driving variation in the microbiome composition of this ecologically and economically important pollinator. These findings may serve as additional markers in selective breeding programs targeting V. destructor resistance, which could ultimately improve honey bee health.

microbiology↗

bioRxiv2025

An increased number of heterozygous calls in the AxiomTM Equine Genotyping Array

Single nucleotide polymorphism (SNP) arrays are commonly used in livestock genetics to investigate complex traits including genome-wide analysis and fine mapping, genomic prediction, genetic diversity and selection signature analyses. In the context of a European Equine diversity study, we analysed the Axiom Equine 670K SNP genotype data from 2,768 equids representing 20 horse breeds and one donkey breed. While assessing genome-wide runs of homozygosity (ROH) patterns, we observed an increased number of heterozygous calls in 167 purebred horses, which exhibited fewer ROH segments compared to F1 crosses, with 24 of them completely lacking any ROH segments. To further investigate this, we conducted a 4-fold genotype concordance analysis of replicate pairs on the same Axiom batch, between two different Axiom batches, between Illumina EquineSNP50 BeadChip(R) and between Illumina paired-end HiSeq 2000 whole genome sequencing data. Additionally, we used SNPolisher classification on data from the Axiom Equine 670K SNP array to evaluate the genotype performance of the 670,806 genome-wide SNPs. When comparing the overlapping SNPs between the different genotyping platforms, replicated pairs within the same Axiom batch showed the highest average genotype concordance (98.81%), followed by Illumina 50K (97.88%) and whole genome sequencing (96.84%). In contrast, re-genotyped horses with few ROH segments (i.e., replicates on different batches) showed the lowest concordance (93.52%). A lower pass rate was observed in one batch, suggesting a processing performance issue that contributed to the reduced concordance between batches. According to SNPolisher classification a total of 120,838 genome-wide SNPs were not recommended for reproducibility. After calling genotypes of the two different batches together in accordance with Axiom Best Practice (e.g. removing failing samples before the final genotyping) and excluding non-recommended SNPs, genotype concordance improved in all comparisons: same Axiom batch (99.84%), Illumina 50K (98.33%), whole genome sequencing (97.81%), and different Axiom batches (98.59%). Based on these findings, we recommend excluding horses exhibiting an unusually high number of heterozygous calls, using only SNPs with validated genotype performance, and accounting for batch effects when analyzing Axiom Equine 670K SNP genotype data from different batches. Article SummaryIn a European Equine diversity study including over 2,000 equids from 20 horse breeds and one donkey breed, we detected an unusually high number of heterozygous calls using the Axiom Equine 670K SNP array. Genotype concordance testing across different platforms showed that horses with fewer runs of homozygosity (ROH) had notably lower genotype concordance rates. By applying Axiom Best Practices and excluding SNPs with poor reproducibility, genotype concordance significantly improved. These findings highlight the critical need to validate SNP quality to ensure reliable genotypes for equine genomics projects and to account for batch effects when analyzing SNP data from different Axiom batches.

genomics↗

bioRxiv2024

Evaluation of genomic and phenomic prediction for application in apple breeding

Apple breeding schemes can be improved by using genomic prediction models to forecast the performance of breeding material. These models predictive ability depends on factors like trait genetic architecture, training set size, relatedness of the selected material to the training set, and the validation method used. Alternative genotyping methods such as RADseq and complementary data from near-infrared spectroscopy could help improve the cost-effectiveness of genomic selection. However, the impact of these factors and alternative approaches on predictive ability beyond experimental populations still need to be investigated. In this study, we evaluated 137 prediction scenarios varying the described factors and alternative approaches, offering recommendations for implementing genomic selection in apple breeding. Our results show that extending the training set with germplasm related to the predicted breeding material can improve average predictive ability across eleven studied traits by up to 0.08. The study emphasizes the usefulness of leave-one-family-out validation, reflecting the application of genomic selection to a new family, although it reduced average predictive ability across traits by up to 0.24 compared to cross-validation. Similar average predictive abilities across traits indicate that imputed RADseq data could be a suitable genotyping alternative to SNP array datasets. The best-performing scenario using near-infrared spectroscopy data for phenomic prediction showed a 0.35 decrease in average predictive ability across traits compared to conventional genomic prediction, suggesting that the tested phenomic selection approach is impractical. These findings offer valuable guidance for applying genomic selection in apple breeding, ultimately leading to the development of breeding material with improved quality.

genomics↗

bioRxiv2024

Sequence-based genome-wide association studies reveal the polygenic architecture of Varroa destructor resistance in Western honey bees Apis mellifera

Honey bees, Apis mellifera, have experienced the full impacts of globalisation, including the recent invasion by the parasitic mite Varroa destructor which has become one of the main causes of colony losses worldwide. Despite its lethal effects, some colonies have developed defence strategies conferring colony resistance and, assuming non-null heritability, selective breeding of naturally resistant bees could be a sustainable way to fight infestations. Here we report on the largest genome-wide association study performed on honey bees to understand the genetic basis of multiple phenotypes linked to varroa resistance. This study was performed on whole genome sequencing of more than 1,500 colonies belonging to different ancestries and combined in a meta-analysis. Results show that varroa resistance is polygenic. A total of 60 genetic markers were identified as having a significant impact in at least one of the tested populations pinpointing several regions of the honey bee genome. Our results also support strategies for genomic selection in honey bee breeding.

genetics↗