- 🔭 I am a early-career plant breeding researcher trained in quantitative genetics, field phenotyping, and -omics analysis.
- 🌱 My research interests lie in understanding how genetic and biological variation shapes complex crop performance, and how this knowledge can be translated into crop improvement.
- 🚀 I am open to job and PhD opportunities related to plant breeding, quantitative genetics, crop phenotyping, genomics, multi-omics integration, and sustainable crop improvement.
- 📫 How to reach me: manze.yu@outlook.com
- Quantitative genetics and breeding for intercropping 🧬
- Plant metabolomics and pathway reconstruction 🧪
- Applied breeding and novel crops🌾
My research story started from quantitative genetics and field phenotyping. During my MSc thesis, I worked on maize–faba bean strip intercropping, where I estimated direct and indirect genetic effects of maize under monoculture and intercropping systems. Through this project, I learned how complex crop performance can be shaped not only by the genotype itself, but also by its interaction with the environment and neighboring plants. This experience gave me strong training in mixed models, genetic effects, genotype-by-environment and genotype-by-genotype interactions, and the statistical analysis of complex field traits.
Motivated by the question of which genetic loci may underlie these complex phenotypic patterns, I further explored GWAS as an approach to connect phenotypic variation with genomic variation. This helped me think beyond estimating genetic effects and move toward identifying genomic regions associated with complex crop performance. It also strengthened my interest in using quantitative and genomic approaches to understand traits that are relevant for breeding.
At the same time, these experiences also made me realize that quantitative models and GWAS can help us identify what varies, which genotypes perform differently, and which genomic regions may be associated with the traits of interest, but they do not always fully explain why these differences occur biologically. This motivated me to move further into metabolomics and pathway reconstruction during my minor thesis, where I studied glucosinolate biosynthetic pathways in Brassicaceae using molecular networking and computational annotation. This experience helped me think about crop traits from a more mechanistic perspective, linking metabolite diversity, pathway variation, and evolutionary differences across species.
More recently, my work in aardaker breeding has brought me closer to the practical side of crop improvement. Working with a novel nitrogen-fixing tuber crop has made me realize how important it is to connect quantitative analysis, genomic information, and biological understanding with real breeding decisions, especially for underdeveloped and sustainable crop systems.
Therefore, my current research identity lies at the interface of quantitative genetics, crop phenotyping, genomics, and multi-omics. I am particularly interested in understanding how genetic and biological variation shapes complex crop performance, and how this knowledge can be translated into breeding strategies for sustainable crop improvement.
- Statistical Modelling: Linear mixed models, Generalized linear (mixed) models; Model comparison
- Phenotypic Analysis: Genotype × Environment x Management analysis
- Genomic Analysis: QTL mapping, GWAS; Multi sequence alignment; Comparative & phylogenomic analysis
- Metabolomics Analysis: Computational annotation; Molecular networking; Metabolomics profiling
R programming (lme4, AsRemlR, etc.)
DNA/RNA isolation; PCR and qPCR; gel electrophoresis; light microscopy; plant tissue culture; molecular cloning; basic experience with Agrobacterium-mediated transformation workflow