Zed Chen
Postdoctoral Research Associate in Quantitative Genomics
Address
- Street
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The Royal (Dick) School of Veterinary Studies
Easter Bush - City
- Midlothian
- Post code
- EH25 9RG
Background
Zed Chen is a Postdoctoral Research Associate in quantitative genomics at the Roslin Institute, University of Edinburgh, working within the Highlander Lab under the supervision of Hannes Becher and Gregor Gorjanc. His research focuses on advancing the application of Ancestral Recombination Graphs (ARGs) in predictive plant breeding and genome editing, as part of the OptiME project.
Zed holds a PhD in Infectious Diseases and Immunology from the University of Edinburgh, where he developed reproducible NGS pipelines to analyse complex genomic and metagenomic datasets of microbial pathogens, contributing to diagnostic assay development and understanding of drug resistance. He also holds an MSc in Bioinformatics (with Merit) from the University of Edinburgh and a BA in Biology and Mathematics (Magna Cum Laude) from Bowdoin College.
Prior to his current role, Zed worked as a Bioinformatician at the Royal Botanic Garden Edinburgh, where he led data analysis on a Biodiversity Genomics Europe project, designed genome-wide barcode screening pipelines, and delivered over 30 hands-on workshops in bioinformatics to research staff and students. His earlier research included leading computational analyses for international collaborations in pathogen genomics, with contributions to five high-impact publications and presentations at five international conferences.
Zed is passionate about translating complex genomic questions into actionable insights and bridging the gap between method development and practical application in plant breeding. He is committed to reproducible research, open science, and training the next generation of researchers in quantitative genomics and bioinformatics.
CV
159417.pdf
Qualifications
PhD in Infectious Diseases & Immunology | The University of Edinburgh
- Thesis involved the development of reproducible NGS pipelines to analyse the complex genomic/metagenomic datasets of micro-pathogens to develop diagnostic assay and understand drug resistance, leading to 5 peer-reviewd publications so far (3 first-author publications in progress) and successful grant applications.
MSc in Bioinformatics (Merit) | The University of Edinburgh
- Relevant course work: Dynamic Modelling, Biostatistics
- Collaborated with veterinary teams and applied statistical analysis in R (PCA, PLS-DA, volcano plots, Bioconductor), leading to a peer-reviewed publication.
BA in Biology & Mathematics (Magna Cum Laude) | Bowdoin College
Research summary
- Ancestral Recombination Graphs (ARGs) in Plant Breeding – Applying ARG-based methods to improve genomic prediction accuracy, identify causal variants, and estimate haplotype and mutation effects in crop breeding programmes.
- Digital Twin Simulations for Breeding – Developing and implementing simulation frameworks (AlphaSimR, msprime, SLiM) to model plant breeding programmes and evaluate novel quantitative genetic methods.
- Integration of Evolutionary and Quantitative Genomics – Bridging population genomic inference (demographic modelling, ARG estimation) with predictive breeding and genome editing applications.
- Crop Genomics and Trait Prediction – Using genomic and phenotypic data from diverse crops (rice, maize, sorghum, cassava, cowpea) to improve predictions for complex traits such as yield, quality, and disease resistance.
- Reproducible and Scalable Computational Workflows – Building robust, documented pipelines for large-scale genomic data analysis and simulation.
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Pathogen Genomics and Evolution – Analysing genomic and metagenomic datasets of trypanosomatid parasites to understand drug resistance, diagnostic marker development, and mitochondrial genome dynamics.
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Mitochondrial Genome Biology – Investigating the evolution, maintenance, and fitness consequences of mitochondrial genomes in clonal and sexually reproducing parasites.
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RNA Editing in Kinetoplastids – Studying the functional organization, regulation, and evolutionary history of RNA editing complexes in trypanosomes.
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Metagenomics and Microbiome Analysis – Applying statistical modelling and machine learning to characterise gastrointestinal microbiomes in equine disease.
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Molecular Diagnostics – Developing novel molecular assays for pathogen detection and species identification using whole-genome sequencing data.
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Bioinformatics Training and Capacity Building – Designing and delivering workshops in NGS data analysis, pipeline development, and machine learning for diverse research audiences
Current research interests
ARG, Digital Twin, Quantative GenomicsPast research interests
RNA editing, Pathogen Genomics, Kinetoplast, Trypanosoma, Metagenomics, Plant Science, Lichen Diversity, Molecular Diagnostics, Biodiversity and ConservationProject activity
The current work involves developing digital twin simulations of plant breeding programmes using AlphaSimR, inferring ARGs from real crop data using tsinfer and the wider tskit ecosystem, and evaluating ARG-based methods for genomic prediction, GWAS, and estimation of haplotype and mutation effects. He works closely with international collaborators across the University of Oxford, the University of Michigan, and the Allele Mining projects in maize, sorghum, rice, cassava, and cowpea, with a focus on establishing rice as the focal crop for this research.