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TraitSeq is an AI-based
Technology Platform
for Complex Trait Development

TraitSeq applies cutting-edge AI to accurately predict complex agricultural traits, for the development of high-yielding and climate-resilient crop varieties, animal breeds, agrochemical and biological inputs, and gene edits that are critical to feeding an increasing global population. TraitSeq, an AI-based pipeline accelerates product development to enrich breeding strategies.

TraitSeq is a low-cost, high throughput, platform technology that uses bespoke machine learning methods to identify biomarkers that are utilised for training predictive models for complex traits in agriculture.

TraitSeq’s trait prediction models achieve beyond state-of-the-art levels of accuracy and can incorporate environmental variation – enabling highly accurate trait performance predictions in the field.

Global food security

TraitSeq will benefit the agricultural sector in the areas of breeding, gene editing, and crop protection to increase the efficiency of crop development and increase food production.


Ability to directly predict complex traits

TraitSeq enables breeders to directly predict complex traits across their livestock or germplasm to determine which are the best performing lines for important complex traits in a much faster and cost-effective approach.

Crop resilience to climate change

Using TraitSeq to identify biomarkers for traits associated with climate change such as water use efficiency, the development of crops can be accelerated making them more robust to environmental variation and the environmental challenges of the future.


Gene editing

Early evaluation of gene edits could be possible through TraitSeq’s complex trait prediction models, significantly improving the efficiency and speed of validating gene edits whilst also reducing the cost. In addition to providing early evaluation of gene edits, TraitSeq’s generated biomarkers could serve as novel targets for editing.



TraitSeq’s machine learning models are demonstrably more accurate compared to existing models.


TraitSeq’s pipeline enables the submission of new samples or can recycle existing datasets.


Early, rapid, and cheaper evaluation of gene edits for complex traits.

Time saving

Biomarker-based models that are significantly faster compared to field trials.

Complex Traits

Time-consuming and expensive traits to measure such as nitrogen use efficiency and disease resistance can be incorporated in breeding programmes using TraitSeq’s predictive models.

Range of Applications

TraitSeq’s scalable & high throughput assessment has a variety of application such as crop breeding, gene editing, livestock breeding, and gene discovery.

TraitSeq Team

Dr Josh Colmer

Dr Josh Colmer

Chief Executive Officer (CEO)

Joshua Colmer, CEO, and co-founder of TraitSeq, is an Agritech entrepreneur who combines his bioinformatics and commercial experience to deliver the company’s AI-based technology. Josh’s technical and commercial skills are invaluable.

Prof Anthony Hall

Prof Anthony Hall

Chief Scientific Officer (CSO)

Prof. Anthony Hall (CSO), renowned in the field of plant genomics, leads the discipline at the Earlham Institute. Leveraging his extensive network within crop breeding, he played a crucial role in securing key partnerships for TraitSeq.

John Bloomer

John Bloomer

Director & Co-founder

As director of successful Agri-tech advisory company JMB Consulting (Pleshey) Ltd, John has over 30 years of commercial expertise in global research-based agribusiness. He plays an indispensable role in shaping TraitSeq’s commercial strategy.

Dr Felicity Knowles

Dr Felicity Knowles

Project Manager

Felicity is an experienced commercialisation professional with almost a decade of experience in supporting biotechnology projects in both industry and academia. Felicity is fortunate to have supported TraitSeq prior to their spin out from the Earlham Institute.

Contact Us


If you would like to learn more about TraitSeq, please complete our contact form to get in touch with our team.