Biology subjects

Voss-Fels, K. P.

Publications and source records attributed to Voss-Fels, K. P..

9 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

High-throughput sequencing of unmethylated DNA (UMR-seq) reveals extensive epigenetic variation in the hypomethylome of diverse plant species

The unmethylated regions (UMRs) in a genome can reveal the location of genes and gene regulatory loci. However, discovering UMR variation across and between entire genomes and disentangling the contribution of genetics and epigenetics to traits are challenging because they require separate and costly whole-DNA methylome and whole-genome genetic analyses. In many species this is prohibitively expensive for even a small number of samples, curtailing analysis on a population or breeding scale. Here we introduce UMR-seq, a method for the specific capture of UMRs from a genome and simultaneous sequencing of genetic variants in a single economical and scalable assay. UMR-seq charts the hypomethylome, revealing that it is highly enriched with functionally important regulatory loci. While UMRs are highly stable between vegetative tissues in plants, we find they are dynamic in the endosperm and vary substantially between accessions. Adaptable to diverse species, using this method we uncover a positive correlation between gene number in a species and UMR content, reflecting gene regulatory space in a genome. At 1/50th of the conventional cost of methylome sequencing in wheat, UMR-seq enables simultaneous population scale analysis of genetic and epigenetic variation in the functional genomic space. We reveal that in a panel of 187 elite wheat cultivars, profiling UMR variation improved genomic prediction across population groups, and UMR-based association mapping identified trait associations that were largely distinct from those detected with SNPs, providing new opportunities to understand trait biology and to enhance predictive breeding.

genomics↗

bioRxiv2026

A dual genomic-epigenomic map of clonal evolution in grapevine

Grapevine is one of the worlds oldest and most economically important perennial crops and has been vegetatively propagated for centuries to millennia. This long history of clonal propagation has generated substantial intra-varietal diversity, but its molecular basis has remained difficult to resolve with short-read sequencing, which misses large structural variation and DNA base modifications. Here, we generated a high-quality, phased diploid reference genome for the elite cultivar Pinot noir and integrate it with Oxford Nanopore sequencing of 23 distinct clones to build an integrated, genome-wide map of clonal genetic and epigenetic variation in Vitis vinifera. The genome assembly reveals a deep history of ancient inbreeding, with approximately 12% of the genome occurring in extended runs of homozygosity. Across molecular layers, we observe contrasting evolutionary dynamics. Somatic genetic variation (67,277 SNPs and 4,037 SVs) is dominated by rare variants and strongly depleted from coding regions, consistent with strong purifying selection. A substantial fraction of the structural variation among clones is mechanistically linked to instability in repetitive DNA, particularly centromeric repeats. In contrast, CG context epigenetic variation is abundant (250,382 differentially methylated cytosines) and strongly enriched within gene bodies, highlighting it as a major source of clonal divergence. Most importantly, CG methylation patterns alone reconstruct clonal phylogenetic relationships with high fidelity, closely mirroring the SNP-based lineages. This congruence, absent in transient non-CG methylation, indicates that stable CG epialleles are mitotically inherited and preserve a persistent record. Together, our results show that clonal identity in grapevine is jointly encoded by genome and methylome, with stable CG methylation capturing propagation history at near-genetic resolution.

plant biology↗

bioRxiv2025

Local genomic estimates provide a powerful framework for haplotype discovery

Quantitative trait loci (QTL) discovery studies on diversity panels or breeding populations typically use genome-wide association studies (GWAS) to estimate marker effects. For plant and animal breeding applications, researchers increasingly recognize the potential benefits of identifying superior haplotypes (markers in linkage disequilibrium; LD) rather than relying on single markers, as traditional approaches inefficiently account for cumulative signals from incomplete LD with QTL or split effects when multiple markers are in high LD with QTL. Using the genomic prediction framework, the local genomic estimated breeding values (localGEBV) method was developed in animal breeding and has been adopted in crop haplotype mapping studies; however, no study has thoroughly quantified the utility of this method or systematically compared outcomes to traditional GWAS approaches. Here, we characterized a strategy to group markers in chromosomal segments based on LD (haplotype blocks or haploblocks), computed localGEBV as a linear contrast of marker effects within each haploblock, and utilised the variance of localGEBV to enhance QTL discovery compared to traditional GWAS. Marker effects for localGEBV were estimated with ridge-regression best linear unbiased prediction (rrBLUP) and BayesR, with results compared to two common GWAS approaches. Using the barley row-type trait, we demonstrated that localGEBV improved QTL discovery and phenotypic prediction compared to single markers. Furthermore, localGEBV results were robust to the choice of prior marker assumptions and blocking parameters, enabling flexibility in fine or broad-scale QTL mapping. Overall, our findings establish localGEBV as a haplotype-based strategy capable of leveraging localized genomic effects to improve QTL discovery and, potentially, genomic selection.

genetics↗

bioRxiv2023

Maintaining long-term genetic gain using a segment-stacking algorithm

Loss of genetic diversity in elite crop breeding pools can severely limit long-term genetic gains, and limit ability to make gains in new traits, like heat tolerance, that are becoming important as the climate changes. Here we investigate and propose potential breeding program applications of optimal haplotype selection (OHS), a selection method which retains useful diversity in the population. OHS selects sets of candidates containing, between them, haplotype segments with very high segment breeding values for the target trait. We compared the performance of OHS, the similar method optimal population value (OPV), truncation selection on genomic estimated breeding values (GEBVs), and optimal cross selection (OCS) in stochastic simulations of recurrent selection on founder wheat genotypes. After 100 generations of inter-crossing and selection, OCS and truncation selection had exhausted the genetic diversity, while considerable diversity remained in the OHS population. Gain under OHS in these simulations ultimately exceeded that from truncation selection or OCS. OHS achieved faster gains when the population size was small, with many progeny per cross. A promising hybrid strategy, involving a single cycle of OHS selection in the first generation followed by recurrent truncation selection, substantially improved long term gain compared with truncation selection, and performed similarly to OCS. The results of this study provide initial insights into where OHS could be incorporated into breeding programs. Core IdeasO_LIWe investigate potential uses for a haplotype-stacking strategy, optimal haplotype selection (OHS) C_LIO_LISeveral selection strategies were compared in stochastic simulations of recurrent selection in wheat C_LIO_LIOHS maintained more useful diversity than optimal cross selection or truncation-based genomic selection C_LIO_LIRates of gain from OHS are competitive in small populations C_LIO_LIOne generation of OHS in a truncation selection program can increase short and long term genetic gain C_LI Plain language summaryBreeders use selection strategies based on genetic and phenotypic information to choose parents that will improve agriculturally-relevant traits (eg. grain yield) in their progeny. Generally, this involves estimating breeding values (scores) for each candidate parent. This study investigated an alternative haplotype stacking approach called Optimal Haplotype Selection (OHS), which instead estimates breeding values for each unique genome segment in the population, then selects a group of parents who, between them, carry the haplotypes with the highest estimated breeding value at each chromosomal segment. In simulations, OHS gives improvements close to existing methods when populations are small, and outperforms them in the long term (100+ generations). Using just one generation of OHS boosts the performance of other methods in the short and long term. Breeders might consider adopting haplotype stacking in their programs, once techniques to do so are established.

genetics↗

bioRxiv2022

Investigations into the emergent properties of gene-to-phenotype networks across cycles of selection: A case study of shoot branching in plants

Predictive breeding is now widely practised in crop improvement programs and has accelerated selection response (i.e., the amount of genetic gain between breeding cycles) for complex traits. However, world food production needs to increase further to meet the demands of the growing human population. The prediction of complex traits with current methods can be inconsistent across different genetic, environmental, and agronomic management contexts because the complex relationships between genomic and phenotypic variation are not well accounted for. Therefore, developing gene-to-phenotype network models for traits that integrate the knowledge of networks from systems biology, plant and crop physiology with population genomics has been proposed to close this gap in predictive modelling. Here, we develop a gene-to-phenotype network for shoot branching, a critical developmental pathway underpinning harvestable yield for many crop species, as a case study to explore the value of developing gene-to-phenotype networks to enhance understanding of selection responses. We observed that genetic canalization is an emergent property of the complex interactions among shoot branching gene-to-phenotype network components, leading to the accumulation of cryptic genetic variation, reduced selection responses, and large variation in selection trajectories across populations. As genetic canalization is expected to be pervasive in traits, such as grain yield, that result from interactions among multiple genes, traits, environments, and agronomic management practices, the need to model traits in crop improvement programs as outcomes of gene-to-phenotype networks is highlighted as an emerging opportunity to advance our understanding of selection response and the efficiency of developing resilient crops for future climates.

genetics↗

bioRxiv2021

genomicSimulation: fast R functions for stochastic simulation of breeding programs

Simulation tools are key to designing and optimising breeding programs that are multi-year, high-effort endeavours. Tools that operate on real genotypes and integrate easily with other analysis software are needed to guide users to crossing decisions that best balance genetic gains and diversity to maintain gains in the future. This paper presents genomicSimulation, a fast and flexible tool for the stochastic simulation of crossing and selection on real genotypes. It is fully written in C for high execution speeds, has minimal dependencies, and is available as an R package for integration with Rs broad range of analysis and visualisation tools. Comparisons of a simulated recreation of a breeding program to the real data shows that the tools simulated offspring correctly show key population features, such as linkage disequilibrium patterns and genomic relationships. Both versions of genomicSimulation are freely available on GitHub: The R package version at https://github.com/vllrs/genomicSimulation/ and the C library version at https://github.com/vllrs/genomicSimulationC/

genetics↗

bioRxiv2021

Physiological and genetic drivers underpinning canopy development are associated with durum wheat yield in rainfed environments

New durum wheat (Triticum turgidum L. ssp. Durum) cultivars with improved adaptation to variable rainfall environments are required to sustain productivity in the face of climate change. Physiological traits related to canopy development underpin the production of biomass and yield, as they interact with solar radiation and affect the timing of water use throughout the growing season. This study explored the temporal canopy dynamics of durum wheat using a nested-association mapping population evaluated for longitudinal normalized difference vegetation index (NDVI) measurements. Association mapping was performed to identify quantitative trait loci (QTL) for time-point NDVI and spline-smoothed NDVI trajectory traits. Yield effects associated with QTL for canopy development were investigated using data from four rainfed field trials. Four QTL associated with slower canopy closure, improved yield in specific environments, and notably, were not associated with a yield penalty in any environment. This was likely due to optimised timing of water-use and pleiotropic effects on yield component traits, including spike number and spike length. Overall, this study suggests that slower canopy closure is beneficial for durum wheat production in rainfed environments. Selection for traits or loci associated with canopy development may improve yield stability of durum wheat in water limited environments.

genetics↗

bioRxiv2020

Modelling selection response in plant breeding programs using crop models as mechanistic gene-to-phenotype (CGM-G2P) multi-trait link functions

Plant breeding programs are designed and operated over multiple cycles to systematically change the genetic makeup of plants to achieve improved trait performance for a Target Population of Environments (TPE). Within each cycle, selection applied to the standing genetic variation within a structured reference population of genotypes (RPG) is the primary mechanism by which breeding programs make the desired genetic changes. Selection operates to change the frequencies of the alleles of the genes controlling trait variation within the RPG. The structure of the RPG and the TPE has important implications for the design of optimal breeding strategies. The breeders equation, together with the quantitative genetic theory behind the equation, informs many of the principles for design of breeding programs. The breeders equation can take many forms depending on the details of the breeding strategy. Through the genetic changes achieved by selection, the cultivated varieties of crops (cultivars) are improved for use in agriculture. From a breeding perspective, selection for specific trait combinations requires a quantitative link between the effects of the alleles of the genes impacted by selection and the trait phenotypes of plants and their breeding value. This gene-to-phenotype link function provides the G2P map for one to many traits. For complex traits controlled by many genes, the infinitesimal model for trait genetic variation is the dominant G2P model of quantitative genetics. Here we consider motivations and potential benefits of using the hierarchical structure of crop models as CGM-G2P trait link functions in combination with the infinitesimal model for the design and optimisation of selection in breeding programs.

genetics↗