Image: Malin Grönborg
Research group Many traits that determine plant performance — including stress tolerance, growth, flowering time, reproduction, and yield stability — are complex traits controlled by many genes whose effects depend on the environment. Our research seeks to understand how genetic variation and environmental conditions interact to shape these traits, and how this knowledge can be used to predict plant performance, improve breeding, and conserve adaptive genetic diversity.
How does natural genetic variation shape plant responses to a changing climate, and can we predict those responses from the genomic variation?
We study how plants respond to drought, heat, and other climate-related challenges by linking genomic variation with phenotypic plasticity across environments. By combining genomics, quantitative genetics, and computational modelling, we investigate why different genotypes respond differently to the same environment, how adaptive responses evolve, and how environmentally dependent genetic effects can be predicted.
Our long-term goal is to turn large-scale genomic, phenotypic, and environmental data into predictive understanding. We aim to use this knowledge to support climate-resilient plant breeding and to preserve the genetic diversity that enables future adaptation in natural, agricultural, and forestry systems.
1. Genetic and genomic basis of climate-responsive trait plasticity
A central focus of our research is to understand how genetic variation shapes phenotypic plasticity — the ability of the same genotype to produce different phenotypes in different environments. We are particularly interested in complex traits whose expression changes strongly with environmental conditions, including flowering time, reproductive success, growth, stress tolerance, and yield-related traits.
Using Arabidopsis thaliana as a major model system, together with crop and forest species, we investigate how plants differ in their responses to drought, heat, and other climate-related stresses. We use natural genetic variation to dissect genotype-by-environment interactions, identify the genetic architecture of plastic responses, and uncover mechanisms connecting genomic variation with differences in plant performance across environments.
Through this work, we seek to understand why genotypes differ in environmental sensitivity, how adaptive plasticity evolves, and how complex traits respond to heterogeneous and changing climates.
2. Genome diversity, genome structure, and adaptive variation
Plant genomes contain substantially more diversity than can be represented by a single reference genome. We develop and use genomic resources to characterize this diversity across natural populations, breeding materials, species, and evolutionary timescales.
Using systems including Arabidopsis thaliana, Nicotiana tabacum, populus and conifers, we study structural variation, pan-genome diversity, genome organization, and other forms of genomic variation that are often missed by conventional reference-based approaches.
We investigate how genome structure differs among individuals, populations, lineages, and breeding materials, and how these differences contribute to phenotypic diversity, environmental adaptation, and long-term resilience. By revealing previously hidden genomic variation, we aim to better understand the evolutionary raw material on which natural and artificial selection act.
3. Predictive models for climate-resilient breeding and deployment
A major goal of our group is to translate biological understanding into better prediction. If we can understand how genetic variation influences plant performance across environments, we can make more informed decisions about which genotypes to select, breed, conserve, and deploy under future climate conditions.
We develop quantitative and computational models that integrate genomic, phenotypic, and environmental information. Our work includes multi-environment genomic prediction, modelling of genotype-by-environment interactions and phenotypic plasticity, and approaches for improving trial design, phenotyping strategies, and breeding populations.
In agricultural and forest systems, these methods can improve the efficiency and robustness of selection across variable environments. In natural populations, similar approaches can help identify adaptive genetic variation and populations that may be particularly important for future resilience.
We aim to build a mechanistic and predictive understanding of how plants respond and adapt to environmental change by linking genomic variation to phenotypic plasticity and plant performance.
By integrating genomics, quantitative genetics, evolutionary biology, and computational modelling, we seek to uncover the mechanisms underlying plant adaptation and translate this knowledge into better prediction for breeding, forestry, and biodiversity conservation.
Ultimately, we want to understand not only which plants perform well in particular environments, but why they do so, how their responses vary across environments, and whether future performance can be predicted from their genomes.
