Biology subjects

Brunner, S.

Publications and source records attributed to Brunner, S..

7 recordsLinked to original sources

bioRxiv2026

Genomic and Epigenomic Relationships Capture Different Patterns of Genotype-by-Environment Interaction in Grapevine Clones

Genotype-by-environment interaction (GEI) challenges selection in clonally propagated perennial crops by reducing the consistency of clone performance across years. In perennial crops, both genetic and epigenetic variation are likely to contribute to complex trait variation among clones. However, the extent to which each molecular source contribute to GEI remains poorly understood. Here, we used UMR-seq, an assay that recovers SNPs and unmethylated-region (UMR; DNA methylation) profiles from a single library, to generate paired genomic and epigenomic profiles for 206 Riesling (Vitis vinifera L.) clones phenotyped for yield and juice quality traits across nine growing seasons. We then partitioned the contribution of each omic layer to clonal variation and GEI, and examined the genomic distribution of their effects. In models without explicit GEI, environmental and residual effects were the largest source of phenotypic variance and the clonal main effect was modest. Genomic relationships accounted for most of the clonal variance in four of five traits, and adding an epigenomic relationship matrix (ERM) to the genomic relationship matrix (GRM) had little effect. In contrast, when GEI effects were modelled using factor-analytic covariance structures, the epigenomic layer captured a substantial proportion of GEI variance and exceeded the genomic contribution in four of five traits. The two omic layers differed not only in the amount of GEI captured, but also in the pattern of clone re-ranking across years. When back-solved effects were aggregated into genomic windows, the largest genomic and epigenomic effects tended to fall in different regions, with overlap no greater than expected by chance. Together, these results show that genomic and epigenomic relationships capture complementary components of year-to-year variation in clone performance, with the epigenomic contribution expressed primarily through GEI. These findings provide a basis for investigating the molecular drivers of GEI and for testing whether genomic and epigenomic information can inform selection for stable clonal performance across variable environments.

genetics↗

bioRxiv2026

Leveraging synthetic environmental covariates to explore crop genotype by environment interactions

Environments are unpredictable. Genotype by environment interactions (GEI) further complicate any assessment of genotype performance or stability across diverse environments posing a challenge for plant breeders. Several approaches exist to address this challenge. However existing approaches present a trade-off between scale and interpretability often acting in a 'black box' manner. Here we extend the Finlay-Wilkinson regression on synthetic environmental covariates of Piepho and Blancon (2023) to dissect GEI at the level of individual developmental windows, resolving which growth stages drive GEI and how genotype stability varies across them. Yield data for 961 genotypes from a barley breeding program, tested across 12 environments, is used as a case study to illustrate how this approach can provide valuable insight into the GEI present. Exploring the dynamic impact of environmental variables across growth stages revealed that environmental factors during early grain fill contributed the most to yield GEI variance. Stability was found to be dynamic across development windows, presenting growth-stage-specific yield stability as a new potential breeding target. This approach yields new, growth-stage-resolved measures of yield stability and a new means of dissecting GEI in complex traits, while also opening a route to environmental clustering based on stage-specific GEI patterns.

genetics↗

bioRxiv2026

Identifying water stress response haplotypes in barley using latent environmental covariates

PurposeGenotype-by-environment (G x E) interactions represent a major obstacle to increasing genetic gain in crop breeding, with the underlying physiological drivers often remaining obscured within conventional statistical models. This case study presents a novel framework that transforms the latent factors from Factor Analytic (FA) multi-environment trial (MET) models into heritable quantitative traits, enabling the genetic dissection of adaptive response patterns. MethodsA Factor Analytical Linear Mixed Model (FA-LMM) was fit to plot-level yield data for 1,036 barley genotypes across eight Australian trials. ResultsCorrelation of the factor loadings with APSIM-simulated environmental covariates demonstrated that the second latent factor FA2 was strongly correlated with the Water Stress Index (r = -0.83) during the critical flowering period, establishing water availability as the main biological axis of crossover Gx E. Genotypic scores for the derived traits, Overall Performance (OP) and Water Stress Response (WSR), were subjected to high-resolution haplotype-based mapping using local Genomic Estimated Breeding Values (GEBV). ConclusionThis analysis successfully identified major genomic regions that accounted for a substantial proportion of the additive genetic variance. Gene Ontology enrichment of candidate genes within the top haploblocks implicated fundamental pathways related to energy homeostasis, root development, and stress response, with notable candidates including FTsH11, BPS1, and TDP1. The distribution of favourable Haplotypes of Interest (HOI) in elite cultivars suggested a historical signature of inadvertent selection for these adaptive mechanisms. This framework provides an explicit bridge between statistical modelling and functional genomics, offering breeders actionable genetic targets for accelerated development of climate-resilient cereals.

plant biology↗

bioRxiv2025

The potential to breed for genetic legacy effects in sustainable farming systems

Legume crops provide protein-rich food, critical disease breaks in cereal rotations, and contribute to soil fertility through symbiotic nitrogen fixation. However, crop improvement programs typically focus on within-crop performance rather than system-level benefits. We hypothesise that legacy effects (the influence of one crops genotype on subsequent crop performance) are under genetic control and could be leveraged in breeding programs. To test this, we evaluated how 309 genetically diverse mungbean genotypes influence subsequent wheat performance. The mungbean panel was grown, followed by a single wheat cultivar sown in the same plot locations. Remarkably, wheat yield varied by nearly 1 t ha{square}{superscript 1} (2.52-3.49 t ha{square}{superscript 1}) depending solely on the preceding mungbean genotype, with legacy traits displaying moderate heritability (H{superscript 2}: 0.43-0.65), demonstrating untapped genetic potential for breeding. Analyses of mungbean traits, soil properties, and volatile organic compounds implicated root architecture, symbiotic nitrogen fixation and soil microbiome as potential biological drivers of legacy effects. Haplotype mapping identified genomic regions in mungbean associated with wheat yield and protein, revealing trade-offs between within-crop and legacy performance. Genetic simulations using empirically derived marker effects compared genomic selection strategies targeting mungbean yield, wheat yield, or both simultaneously. Balanced selection (50:50 weighting) achieved simultaneous gains in both crops (19.5% and 7.6%), highlighting the opportunity to breed for system-level productivity with reduced input requirements.

plant biology↗

bioRxiv2025

Somatic TYK2 activating mutations in tumor-infiltrating T cells promote anti-cancer immunity

Cancer cells evolve to increase fitness and evade the immune system, but it is not clear if tumor infiltrating lymphocytes (TILs) undergo selection for somatic mutations that augment anti-cancer immunity. Using single molecule whole exome sequencing in TILs, we identified somatic mutations in Tyrosine-protein kinase 2 (TYK2), several of which increased TYK2 phosphorylation, JAK-STAT signaling, and interferon gamma signaling. Among these mutations, TYK2D810V was recurrently observed in patients with diverse cancer types. In vitro, TYK2D810V enhanced T cells effector functions and cytokine production. We generated mice with a germline Tyk2D807V mutation that recapitulated the human TYK2D810Vmutation. These mice were healthy and did not develop autoimmune disorders. However, they possessed enhanced anti-tumor effects in the context of syngeneic and autochthonous cancer models. Adoptive therapy with Tyk2D807V CD8 T cells also decreased cancer growth. Thus, naturally occurring mutations in non-malignant lymphocytes can antagonize cancer and inspire new immunotherapies strategies.

cancer biology↗

bioRxiv2023

Aftereffects of Delay Adaptation in Visual and Tactile Duration Reproduction

Brief actions involve multiple temporal cues that may not always synchronize, and in basic action-effect relationships, the effect is often delayed. How the brain incorporates delays across modalities in a sensorimotor duration reproduction remains unclear. To investigate this, we conducted two experiments on duration reproduction with delayed sensory feedback. Participants reproduced durations in either visual (Experiment 1) or tactile (Experiment 2) modalities. In the adaptation phase, a contingent sensory feedback, either visual or tactile, was delayed by 150 ms in one session and synchronized in the control session, with accuracy feedback provided after each trial. In the testing phase, random action-effect delays (0-150 ms) were introduced and accuracy feedback was removed. The findings revealed that accuracy feedback effectively recalibrated motor time but did not eliminate the delay. Without accuracy feedback, tactile reproduction relied more on the tactile feedback than on motor time, resulting in greater lengthening of motor reproduction compared to the visual feedback. These findings suggest that temporal delay adaptation is influenced by accuracy feedback and sensorimotor integration, with sensorimotor reliability assigning a higher weight to the tactile than the visual modality.

neuroscience↗

bioRxiv2022

Pan-cancer whole genome comparison of primary and metastatic solid tumors

Metastatic cancer remains almost inevitably a lethal disease. A better understanding of disease progression and response to therapies therefore remains of utmost importance. Here, we characterize the genomic differences between early-stage untreated primary tumors and late-stage treated metastatic tumors using a harmonized pan-cancer (re-)analysis of 7,152 whole-genome-sequenced tumors. In general, our analysis shows that metastatic tumors have a low intra-tumor heterogeneity, high genomic instability and increased frequency of structural variants with comparatively a modest increase in the number of small genetic variants. However, these differences are cancer type specific and are heavily impacted by the exposure to cancer therapies. Five cancer types, namely breast, prostate, thyroid, kidney clear carcinoma and pancreatic neuroendocrine, are a clear exception to the rule, displaying an extensive transformation of their genomic landscape in advanced stages. These changes were supported by increased genomic instability and involved substantial differences in tumor mutation burden, clock-based molecular signatures and the landscape of driver alterations as well as a pervasive increase in structural variant burden. The majority of cancer types had either moderate genomic differences (e.g., cervical and colorectal cancers) or highly consistent genomic portraits (e.g., ovarian cancer and skin melanoma) when comparing early- and late-stage disease. Exposure to treatment further scars the tumor genome and introduces an evolutionary bottleneck that selects for known therapy-resistant drivers in approximately half of treated patients. Our data showcases the potential of whole-genome analysis to understand tumor evolution and provides a valuable resource to further investigate the biological basis of cancer and resistance to cancer therapies.

genomics↗