In the world of plant breeding and seed production, nothing is more valuable than confidence. Confidence that what you’ve developed is what’s being grown, harvested, and sold. This is usually achieved by labor-intensive phenotypic observations at an advanced developmental stage. Genetic testing at the seedling stage can provide a much more precise and efficient solution for that confidence.

Even with rigorous protocols in place, unintended genetic variation can find its way into a population. Whether through cross-contamination, mislabeling, or genetic drift, a group assumed to be uniform may include off-types, unrecognized hybrids, or subgroups with distinct genetic origins.

That’s why many breeders and seed producers turn to genomic validation—not just to catch errors, but to deeply understand the structure of their populations. With the right tools, it’s possible to uncover hidden structures within populations, distinguishing natural diversity from unintentional mixtures. This approach can ultimately save significant time and cost compared to phenotypic off-typing.

The process begins with advanced clustering methods, such as Principal Component Analysis (PCA) and hierarchical clustering, to visualize genetic relationships and detect outliers. But visual inspection alone isn’t enough. Our tools compute a probability score for each individual sample, assessing how likely it is to belong to the target group. This is done by examining each SNP in relation to allele frequencies within the population, helping flag individuals that deviate from the group profile.

^{Figure 1+2:
A True-to-Type (TTT) analysis illustrates a group of samples that clearly forms two distinct genetic subpopulations. The agreement score histogram, with samples color-coded by the clusters, discovered in the hierarchical pairwise clustering heatmap both highlight this structure. The red and yellow clusters represent one genetic subgroup, while the purple and blue clusters form a second, genetically distinct group. The green cluster predominantly consists of off-type samples.}

In cases where anomalies are detected, further analysis can compare these samples to a broader diversity population to infer their genetic origin. When dealing with hybrids, it’s also possible to assess whether two individuals from a given diversity population could have served as parental sources, an especially valuable insight for tracing the origin of off-types or validating hybrid populations.

This kind of in-depth quality control is critical for downstream breeding applications and improvement of breeding practices. Clean populations ensure that conclusions about traits, performance, or stability are grounded in reliable data. And sometimes, the process reveals more than just off-types, it can uncover previously unrecognized genetic subgroups or clarify lineage questions that improve long-term breeding strategies.

At NRGene, we empower researchers and breeders to make more informed decisions, combining data-driven pattern detection methods like PCA with advanced genomic models. These tools help ensure that genetic data isn’t just accurate, it’s actionable.

Because whether it’s tomatoes, carrots, or artichokes, protecting your variety means knowing exactly what’s in your population – and what isn’t.

Case Study: Uncovering a Hidden Genetic Mixture

The following three plots illustrate a real-world use case in which a customer suspected that one of their field plots was genetically mixed, but the source of the contamination was unknown.
Using True-to-Type (TTT) genetic analysis, we identified that the group was indeed composed of two genetically distinct subpopulations. This was clearly reflected in the hierarchical clustering results. We then separated the samples into two genetic groups and conducted TTT analysis on each cluster individually.

The agreement score histograms—run separately for each cluster and plotted alongside samples from a known diversity population—reveal the following:

• A clear separation between each cluster and the diversity population.
  
• The identification of the most likely genetic source for each cluster, shown by a red-labeled diversity sample that crosses the TTT threshold.
  
• The detection of off-type individuals within each cluster, represented by blue-labeled samples falling below the TTT threshold.
  

This analysis provided the customer with a detailed genetic breakdown of the mixture, enabling informed decisions on seed use, breeding direction, and contamination prevention.