"We combine experimental and computational approaches to elucidate epigenetic mechanisms of genome regulation and inheritance in plants."

Current research projects include

Epigenetic basis of complex traits
We have a long-standing interest in exploring how epigenetic variation contributes to the heritability of complex traits. Currently, we are investigating the epigenetic basis of heterosis. It is well known that hybrid genomes undergo substantial functional remodeling at the transcriptional and epigenetic level. Our goal is to predict this remodeling from the epigenetic profiles of the parents, and to assess if/how it relates to phenotypic heterosis. 

Key paper:  
Cortijo et al. (2014) Mapping the epigenetic basis of complex traits. Science [Abstract]

Spontaneous epimutations
DNA methylation maintenance mistakes lead to 'somatic epimutations' during plant development. We want to understand how the accumulation of these epimutations generates somatic epigenetic heterogeity in plants, including in trees. In plants, a subset of somatic epimutations are inherited across sexual and clonal generations. We are interested in quantifying the stability / dynamics of these transgenerationally heritable epimutations, and document how they contribute to epigenetic diversity in natural populations.

Key paper: 
van der Graaf et al. (2015) Rate, spectrum and evolutionary dynamics of spontaneous epimutations. PNAS [Abstract]

Epigenetic clocks
We have begun to use DNA methylation data for phylogenetic inference in plants. The goal is to accurately reconstruct and date shallow phylogenies of the recent past, where too few genetic mutations may have accumulated to achieve this. Analogous to work in animal systems, we are also developing DNA methylation-based aging clocks in trees. The goal is to replace invasive age-estimates based on tree ring counts. 

Key paper: 
Yao et al. (2021) Epimutations define a fast-ticking molecular clock in plants. Trends in Genetics [Abstract]

Machine learning of 3D chromatin interactions
Gene regulation in eukaryotes is profoundly shaped by the 3D organization of chromatin. We are using deep learning approaches to predict long-range chromatin interactions from DNA sequence features. 

Key paper: 
Piecyk et al. (2022) Predicting 3D chromatin interactions from DNA sequence using Deep Learning.
Computational and Structural Biotechnology Journal [Abstract]