Susan Thomson

Over the past decade there has been increasing interest in the use of microhaplotype markers in autopolyploid taxa. This has been driven by theoretical and observed improvements in signals of allelic dosage, linkage, and heritability. Yet, to date there has been little investigation into the suitability of microhaplotype markers for estimating kinship. Here, we develop the theory of kinship estimation from microhaplotypes, introduce the MCHap microhaplotype caller for autopolyploid populations, and apply these methods to a highly diverse germplasm population of mixed-ploidy Actinidia (kiwifruit and relatives). We find that microhaplotype-based kinship estimates are generally superior to equivalent single nucleotide variant based estimates. This is because microhaplotypes minimize the coalescent signal among alleles which may bias estimates within the context of a recent reference population. Hence, kinship estimates from microhaplotypes more accurately capture the recent demographic history of a population. These findings are supported by both coalescent simulations and the analysis of real data. Our findings are relevant to organisms of any ploidy, but most actionable in highly heterozygous taxa such as Actinidia .

Transposable elements (TEs) are major contributors to genome structure and evolution. However, our ability to study them is limited by the difficulty of annotating and curating them, especially in non-model organisms. This challenge is compounded by a lack of ground-truth datasets for benchmarking, which are nearly impossible to generate through manual curation alone. To overcome this critical limitation, we developed TEgenomeSimulator, a flexible framework for generating synthetic genomes with configurable TE landscapes. TEgenomeSimulator supports both randomly generated and biologically derived backbone sequences, enabling the modeling of TE insertions under diverse structural and evolutionary contexts. Benchmarking against existing simulators demonstrated that TEgenomeSimulator reproduces realistic TE composition, sequence divergence, and integrity distributions while offering greater flexibility in modeling chromosome-level structure. Its modular design offers a tuneable continuum between biological fidelity and experimental control, facilitating systematic benchmarking, algorithm development, and evolutionary modeling of TE dynamics in silico , filling a major gap in the field. The source code of TEgenomeSimulator and the scripts for this paper are available at https://github.com/Plant-Food-Research-Open/TEgenomeSimulator .

Motivation: In the past decades, many statistical methods for integrating multi-omics data have been developed. They have been implemented into software tools, which differ widely in their programming choices, such as the format required for data input, or the format of the generated integration results. This lack of standards renders cumbersome and time-intensive the application and comparison of different integration tools to the same multi-omics dataset. Results: We have developed the moiraine R package for constructing reproducible multi-omics integration pipelines, which enables users to apply one or more statistical methods for multi-omics integration to their own multi-omics dataset. moiraine facilitates the preprocessing of the omics datasets and automates their formatting for the integration step. It simplifies the interpretation and evaluation of the integration results through the construction of visualizations in which metadata about samples and features can easily be included. Crucially, it enables the comparison of results obtained with different integration tools, allowing users to assess the robustness of their results. Availability and implementation: The moiraine R package is publicly available at https://github.com/Plant-Food-Research-Open/moiraine; an archival snapshot of the package is available on Zenodo at https://doi.org/10.5281/zenodo.17172718. A detailed tutorial is available at https://plant-food-research-open.github.io/moiraine-manual/.

Detecting protein-coding genes in nucleotide sequences is a significant challenge for understanding genome and transcriptome function, yet the reliability of bioinformatic tools for this task remains largely unverified. This is despite some tools being available for several decades and widely used for genome and transcriptome annotation. We perform an assessment of nucleotide sequence and alignment-based de novo protein-coding detection tools. The controls we use exclude any previous training data set and include coding exons as a positive set and length-matched intergenic and shuffled sequences as negative sets. Our work demonstrates that several widely used tools are neither accurate nor computationally efficient for the protein-coding sequence detection problem. In fact, just three of nine tools significantly outperformed a naive scoring scheme. Furthermore, we note a high discrepancy between self-reported accuracies and the accuracy achieved in our study. Our results show that the extra dimension from conserved and variable nucleotides in alignments has a significant advantage over single-sequence approaches. These results highlight significant limitations in existing protein-coding annotation tools that are widely used for lncRNA annotation. This shows a need for more robust and efficient approaches to training and assessing the performance of tools for identifying protein-coding sequences. Our study paves the way for future advancements in comparative genomic approaches, and we hope will popularize more robust approaches to genome and transcriptome annotation.

Genome expansion and contraction are reportedly driven by transposable element (TE) activity, but the underlying dynamics remain enigmatic due to a lack of historical records tracing these changes. Here, we present PrinTE for versatile, forward-time simulation of whole-genome sequences with highly customizable transposon dynamics. Through simulations, we confirm that the distribution of TE sequence divergence reflects their historical insertion and deletion dynamics, which can be used to infer TE dynamic parameters through PrinTE simulations. We analyzed the pangenome of Pucciniomycotina , a subdivision of fungi containing myrtle rust ( Austropuccinia psidii ), which drastically expanded its genome size to 1018 Mb. Our analyses reveal that the best strategy for controlling genome size is to avoid the invasion of LTR retrotransposons (LTR-RTs). While illegitimate recombination (IR) is considered the most effective counteraction of LTR-RT invasions leaving only solo LTR remnants, we observed a strong positive correlation between solo:intact LTR ratio (strength of LTR-RT removal) and genome size ( r = 0.65), and a near-linear correlation between solo LTR count and genome size ( r = 0.98). This result suggests that IR alone may not effectively prevent genome obesity. Through simulation of Pucciniomycotina genomes, we proposed that A. psidii might experience a prolonged period of genome expansion followed by a short, potent, and likely ongoing period of contraction. PrinTE is freely available at https://github.com/cwb14/PrinTE.git .

In breeding programmes, accurate estimation of breeding values is crucial for selecting superior genotypes. Traditional methods rely on phenotypic observations and pedigree information to estimate variance components and heritability. However, pedigree errors can significantly affect the accuracy of these estimates, especially in long-lived perennial vines. This study evaluates the effect of pedigree errors on breeding value predictions in kiwiberry breeding and explores the benefits of using genomic selection. We applied Best Linear Unbiased Prediction (BLUP) to estimate breeding values for each genotype for a given trait. Four scenarios with varying degrees of alteration in pedigree-based relationship matrices were used to represent inaccurate relationships between genotypes. Pedigree-based breeding values were compared with genomic estimated breeding values for one vine-related and four fruit-related quantitative traits. The results showed that as the degree of altered population structure increased, the prediction accuracy of pedigree-based breeding values decreased. In contrast, genomic selection, which uses marker inheritance, maintained realised relationships between genotypes, making it a more robust method for predicting genetic merit. In kiwiberries, as in all species of the genus Actinidia, only female vines bear fruit. The genotypic merit of fruit-related traits in male genotypes can only be estimated indirectly. Marker-based predictions outperformed pedigree-based predictions, especially for genotypes without phenotypic observations, such as male siblings. This study reviewed the induced population structures and introduced genomic selection into the kiwiberry breeding programme. We demonstrated that genomic selection provides more accurate breeding values by capturing true genetic relationships and reducing the effects of misidentified relationships between individuals.

Inter-specific hybridisation between natural populations within the genus Actinidia is a common phenomenon and has been used in breeding programmes. Hybridisation between species increases the diversity of breeding populations, incorporating new desirable traits into potential cultivars. We explored genomic prediction in Actinidia breeding, focusing on the closely related species Actinidia arguta and Actinidia melanandra. We investigated the potential of genomic selection by analysing four quantitative traits across intra-specific A. arguta crosses and inter-specific crosses between A. arguta and A. melanandra. The continuous distributions of the studied traits in both intra-specific and inter-specific crosses indicated a polygenic background. A linear mixed model approach was used, incorporating the factor of year of season and a marker-based relationship matrix instead of pedigree as a random effect. After evaluation, the best model was applied to assess variance components and heritability for each quantitative trait. Expanding beyond intra-specific crosses, predictive ability was calculated to investigate inter-specific cross effect. Considering predictive ability, this study explored the impacts of sample size and population structure. A reduction in sample size correlated with decreased predictive ability, while the influence of population structure was particularly pronounced in inter-specific crosses. Finally, the prediction accuracy of genomic estimated breeding values, for parental genotypes, revealed an inter-species effect on prediction confidence. Considering the imbalance in genotype numbers between intra- and inter-specific cross populations, this research highlights the difficulty of genomic prediction in hybrid populations. Understanding prediction accuracy in inter-species crossing designs provides valuable insights for optimising genomic selection.

Pseudomonas syringae pv. actinidiae biovar 3 (Psa3) has decimated kiwifruit orchards growing susceptible kiwifruit Actinidia chinensis varieties. Effector loss has occurred recently in Psa3 isolates from resistant kiwifruit germplasm, resulting in strains capable of partially overcoming resistance present in kiwiberry vines (Actinidia arguta, Actinidia polygama, and Actinidia melanandra). Diploid male A. melanandra recognises several effectors, sharing recognition of at least one avirulence effector (HopAW1a) with previously studied tetraploid kiwiberry vines. Sequencing and assembly of the A. melanandra genome enabled the characterisation of the transcriptomic response of this non‐host to wild‐type and genetic mutants of Psa3. A. melanandra appears to mount a classic effector‐triggered immunity (ETI) response to wildtype Psa3 V‐13, as expected. Surprisingly, the type III secretion (T3SS) system‐lacking Psa3 V‐13 ∆hrcC strain did not appear to trigger pattern‐triggered immunity (PTI) despite lacking the ability to deliver immunity‐suppressing effectors. Contrasting the A. melanandra responses to an effectorless Psa3 V‐13 ∆33E strain and to Psa3 V‐13 ∆hrcC suggested that PTI triggered by Psa3 V‐13 was based on the recognition of the T3SS itself. The characterisation of both ETI and PTI branches of innate immunity responses within A. melanandra further enables breeding for durable resistance in future kiwifruit cultivars.

Background/Objectives: We present hidecan, an R package for generating visualisations that summarise the results of one or more genome-wide association studies (GWAS) and differential expression analyses, as well as manually curated candidate genes, e.g., extracted from the literature. This tool is applicable to all ploidy levels; we notably provide functionalities to facilitate the visualisation of GWAS results obtained for autotetraploid organisms with the GWASpoly package. Results: We illustrate the capabilities of hidecan with examples from two autotetraploid potato datasets. Conclusions: The hidecan package is implemented in R and is publicly available on the CRAN repository and on GitHub. A description of the package, as well as a detailed tutorial, is made available alongside the package. It is also part of the VIEWpoly tool for the visualisation and exploration of results from polyploids computational tools.