Title: Breaking the curse of dimensionality to identify causal variants in Breeding 4.0
Guillaume Ramstein <firstname.lastname@example.org>
Description: Plant breeding has undergone three major transformations and is currently transitioning to a new technological phase. This phase is characterized by the development of methods for biological design of plant varieties, including transformation and gene editing techniques directed towards causal loci. The application of such technologies will require to reliably estimate the effect of loci in plant genomes by avoiding the situation where the number of loci assayed (p) surpasses the number of plant genotypes (n). In this presentation, we will consider various approaches to avoid this curse of dimensionality (n << p), which involve analyzing intermediate phenotypes such as molecular traits and component traits related to plant morphology or physiology. These approaches will call for analyses that are complementary to traditional quantitative genetic studies, being based on machine learning techniques which make efficient use of sequence and image data. Here, we will describe such analyses by concrete examples based on effects of DNA polymorphisms on gene expression across multiple grass species. Finally, we will discuss how prioritization of candidate causal loci can be leveraged in plant breeding, for prediction analyses and editing assays.
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