Our company boasts extensive expertise in genotyping and data analysis, and can serve as a valuable partner to breeders seeking to accelerate the development of new plant varieties that are better adapted to the needs of farmers and consumers. By collaborating with breeders to develop genotyping strategies that prioritize the identification of genetic markers associated with specific traits of interest, we can help them make more informed decisions about which plants to breed and how to design their breeding programs.

Once genotyping has been performed, our team can then analyze the data to identify the most promising plant varieties based on their genetic profiles. We utilize raw genotyping outputs from next generation sequencing and convert them into an understandable genomic output, ready to be used in further analysis. In addition, we can help breeders to design and implement breeding programs that maximize the genetic diversity of the resulting plant varieties. By calculating kinship relationships between individuals and using this information to guide breeding decisions, we can help breeders to produce more genetically diverse offspring. Overall, our company’s expertise in genotyping and data analysis can help breeders to streamline the breeding process, resulting in more efficient breeding programs and better crops. By providing customized genotyping and data analysis plans tailored to the specific needs of each breeding program, we can help breeders achieve their goals and bring new, high-performing plant varieties to market more quickly and effectively.

Selective phenotyping is an approach in genomic selection that involves the use of a subset of phenotypic data for training genomic prediction models. The primary goal of selective phenotyping is to reduce the cost and time required for data collection while maintaining the accuracy of the genomic predictions. Breicon Genomics develop mathematical approaches to

to identify most informative traits, prioritize phenotyping for individuals with the most informative genotypes, as well as, a wide experience in training optimization algorithms to minimize breeding resources and enhance gain.

The introduction of environmental data in genomic selection involves incorporating information about environmental factors that can affect the expression of phenotypic traits into the genomic prediction models. This can improve the accuracy of genomic predictions by accounting for the effects of environmental factors that are not captured by genetic markers alone. Genotype by environment interaction can reduce prediction accuracy, increase complexity, and produce unstable genetic effects across different environments.

Breicon Genomics uses unique multi-environmental models and machine learning approaches that integrates multiple data sources, from weather data to soil conditions. We use novel algorithms to identify complex interactions between genetic and environmental factors that can affect phenotypic traits. In addition, we work on sparse testing algorithms to reduce breeding resources requirements, and optimizing genotypes entries and locations within breeding programs.

Our optimization algorithms are designed to identify the minimum amount of data needed to make optimal decisions and facilitate the decision-making process. The result is a more efficient breeding program that produces higher profits, faster returns to market and reduced R&D costs. We use a data collection strategy that prioritizes accuracy over quantity, avoiding the risk of data overload and ensuring optimal decision making during the replication process. We work with a wide variety of organizations, from those with a rich data set to those starting from scratch, leveraging their expertise in using data to make the most of existing information. We strongly believe in approaching breeding as an art, where creativity and precision are crucial to producing new varieties that meet specific product profiles and consumer preferences.