Dr. Patrick Thorwarth

Academic Director and Head of LSA

As Academic Director of the State Plant Breeding Institute (LSA), I head a research institute that can look back on 120 years of breeding research and that is dedicated to applied plant breeding research. My aim is to combine scientific excellence with practical questions in order to develop sustainable solutions for agriculture. We focus on the topics of sustainable genetic resistance, improved product quality, high nutrient efficiency and tolerance to abiotic stress factors.

My scientific work includes the analysis of multi-omics and phenotypic data using state-of-the-art machine learning methods and classical methods such as GWAS, QTL mapping and genomic prediction. I develop strategies to optimize field trials considering genotype-environment interactions and envirotyping. I also research genetic diversity in crop plants and use digital tools to harness new insights for plant breeding.

In addition to research, I support the training of the next generation of plant breeders as a university lecturer. My teaching concept is based on constructive alignment to provide students not only with theoretical knowledge but also with practical skills. I am also active in the development of new digital tools for genetic data analysis, especially in the area of open source platforms.

The LSA works closely with scientific and industrial partners to transfer the latest research findings into practice. The successful acquisition of third-party funding and the strategic orientation of our research program are crucial to driving innovation in plant breeding.

With a deep-rooted interest in biodiversity, sustainable agriculture and digitalization in plant research, I am committed to future-oriented and responsible plant breeding and support colleagues across all our working groups.

Research interest

Plant breeding & genetics
- Genetic diversity
- Genotype-Environment Interactions (GEI)
- GWAS (genome-wide association studies)
- QTL mapping
- Genomic selection
- Marker-assisted selection
Data analysis & modeling
- Multivariate statistics
- Machine learning for plant breeding
- Phenotypic and genomic predictions
- Digitalization in plant research
Applied plant breeding & field trials
- Optimization of field test designs
- Sparse testing & prediction models
- Protein utilization efficiency & grain quality
- Resistance breeding against diseases and pests
- Adaptation to climate change
Specific cultures & utilization concepts
- Breeding of wheat, pulses & special crops
- Hemp breeding for fiber use
- Biodiversity and conservation of plant genetic resources
- Alternative protein sources in plant production
Technology & IT infrastructure
- Development of web-based tools for in-house data analysis
- R-based data analysis for plant breeding
- High-performance computing for genetic analyses
- Linux system administration in research

Curriculum Vitae

2023 - today	Academic Director and Head of the State Seed Breeding Institute	University of Hohenheim, Stuttgart, Germany
2022 - 2023	Academic Senior Councillor and Head of the State Seed Breeding Institute	University of Hohenheim, Stuttgart, Germany
2021 - 2022	Group leader for quantitative genetics and biostatistics	KWS Saat SE, Einbeck, Germany
2020 - 2021	Senior Research Lead Biostatistics & Data Science at KWS Saat SE	KWS Saat SE, Einbeck, Germany
2019 - 2020	Head of Research for Biostatistics and Data Science at KWS Saat SE	KWS Saat SE, Einbeck, Germany
2016 - 2019	Research assistant at the University of Hohenheim, State Seed Breeding Institute, Wheat Working Group	University of Hohenheim, Stuttgart, Germany
2012 - 2016	PhD student at the University of Hohenheim, Department of Crop Biodiversity and Breeding Informatics	University of Hohenheim, Stuttgart, Germany

Teaching and Training activities/Research Projects

Teaching and training activities

Summer semester:

3502-470	Plant Genetic Ressources

Winter semester:

3502-441	Methods of Scientific Working
3502-450	Population and Quantitative Genetics
3501-450	Breeding Methodology

Research projects

Current:

bwHemp2.0 – Establishment of a hemp breeding program to strengthen the regional value chain
PoHaBI – Evaluation of hemp fiber quality for breeding use
SENSOJA – Sensor-assisted breeding of high-performance soybean varieties with increased tolerance to abiotic stress
PhenoDiDur – Establishment of phenomic selection in spelt and durum

Completed:

bwHemp: Validation of the cultivation potential of industrial hemp in Baden-Württenberg
ZUCHTWERT – Breeding methodology for the utilization of heterosis in wheat
BARSELECT: Genomic selection in barley breeding