Impact

Our goal is not simply to catalogue diversity but to understand its effects well enough to deliver outcomes in breeding and impacts on sustainable agrifood. Our research links fundamental questions in genome evolution to strategies that accelerate genetic gain and support sustainable global food production.

From genome evolution to crop improvement

A central aim of the lab is to translate knowledge of hybridisation, polyploidisation, introgression, structural variation, and copy number variation into approaches and resources that improve the efficiency of crop improvement. Because hybridisation is widely exploited for crop improvement, including trait introgression and hybrid breeding, understanding its mechanistic manifestations and evolutionary consequences provides a direct pathway to impact.

This translational perspective runs through the whole programme. We work on biologically challenging but agriculturally important crop systems, including bananas, rice, common bean, white clover, celery, and tropical forage grasses. Across these systems, we ask how complex genomic variation shapes agronomic traits and how that knowledge can be used in breeding.

Breeder-ready resources

One of the clearest forms of impact in our work is the generation of breeder-ready resources.

With Germinal Seeds, we are characterising a diverse panel of white clover and testing associations with yield across fertilisation regimes and temperature treatments using GWAS, alongside landscape-genomic analyses of climatic adaptation, to identify suitable breeding materials. These analyses also allow us to test whether GWAS using pangenome graphs outperforms single-reference approaches.

With Tozer Seeds, we are characterising a celery diversity panel and conducting multi-trial GWAS to deliver markers for marker-assisted selection in breeding populations. This work has also served as an example of transferability to new crops and commercial settings, highlighting that domestication history and population structure can be more decisive than phylogenetic distance for robust association mapping.

In common bean, we mapped QTL for domestication-associated traits, notably photoperiod insensitivity and determinacy, and later identified variation in drought-response strategies together with markers associated with these strategies that support marker-assisted selection in breeding populations.

Better decisions in breeding

Over the past two years, we have expanded into phenotype prediction in breeding. This work provides a longer-term route to impact by helping genomics support decision-making in breeding and reducing the burden of trials through better predictions.

In the Legume Generation consortium, we support marker-assisted and genomic prediction in legumes. My group curates and manages raw trial data under FAIR principles, establishes standards and ontologies, and performs multi-site, multi-season analyses to generate breeder-ready phenotypes. We also collaborate with breeders to evaluate imputation accuracy and benchmark genomic prediction models, including mixed models and machine-learning approaches, for sparse testing across environments.

This work matters because breeding programmes increasingly depend on combining genomic, phenotypic, and environmental data under real constraints. By improving how these data are organised, analysed, and translated into usable predictions, we help make breeding more efficient and better adapted to climate and production challenges.

Disease tolerance and crop resilience

Another major pathway to impact is through crop resilience and disease tolerance.

In banana, we mapped introgression and linked it to fruit-related traits using genome-wide association analyses in a panel spanning variable ploidy. We also identified clonal groups corresponding to named varieties shaped by long-term farmer selection and maintained through clonal propagation. This previous work enabled a rational choice of representative genotypes for costly downstream assays.

More recently, in collaboration with Tropic, we established macropropagation and infection-assay protocols and evaluated Fusarium tolerance using representative accessions from those clonal groups. We constructed a banana pan-NLRome and combined NLR presence/absence variation with disease response to prioritise candidate disease-response loci for editing.

Because most edible banana cultivars are clonally propagated, banana is also a tractable system for relating somatic genomic change to phenotypic effects. In the long term, this integrated workflow will allow us to test whether specific structural variants, copy number variants, and methylation shifts predict Fusarium tolerance and trait stability, and to provide early warnings of non-true-to-type deleterious variation in precision breeding production.

Genomic resources and technical capacity

Our work also delivers impact by creating genomic resources and analytical approaches that fill technical gaps in crop genomics.

Long-read sequencing has allowed us to place greater emphasis on polyploids, where many community tools remain primarily optimised for diploid organisms. We have completed and published haplotype-resolved polyploid genomes and are developing end-to-end long-read workflows to analyse structural and copy number variation in complex crop genomes.

These resources matter because many agriculturally important crops are hybrids, polyploids, or clonally propagated lineages in which important variation is poorly captured by SNP-centred methods alone. By developing tools and workflows that can resolve dosage effects, homoeologous exchange, introgression, and other complex forms of variation, we contribute resources that are useful beyond a single crop or project.

Sustainable agrifood

The broader impact of this work is in sustainable agrifood. Our research is developed in response to stakeholder needs and existing collaborations, ensuring that the science leads to socioeconomic benefits as well as biological insight. We focus on crop systems that matter for climate adaptation, disease resilience, sustainable livestock production, and the long-term improvement of agricultural diversity.

By connecting genome evolution to breeding outcomes, we aim to support crop improvement strategies that are more predictive, more biologically informed, and more effective under real agricultural conditions.