Breaking the curse of dimensionality to identify causal variants in Breeding 4

• Published in: Theoretical and applied genetics Vol. 132; no. 3; pp. 559 - 567
• Main Authors: Ramstein, Guillaume P., Jensen, Sarah E., Buckler, Edward S.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2019; Springer; Springer Nature B.V; Springer Nature
• Subjects: Agriculture; Analysis; artificial intelligence; Base Sequence; BASIC BIOLOGICAL SCIENCES; Biochemistry; Biomedical and Life Sciences; Biotechnology; cultivars; Data processing; Dimensional analysis; DNA; DNA sequencing; gene editing; Genes; Genetic research; Genetic transformation; Genetic Variation; Genetics & Heredity; genome; Genome editing; Genomes; Genomics; genotype; Genotypes; Learning algorithms; Life Sciences; loci; Machine Learning; New technologies for plant breeding; Novels; Nucleotide sequence; nucleotide sequences; Perspective Article; phenotype; Phenotypes; Phenotyping; Plant Biochemistry; Plant breeding; Plant Breeding - methods; Plant Breeding/Biotechnology; Plant genetics; Plant Genetics and Genomics; plant morphology; Plant Sciences; Quantitative genetics; Quantitative Trait, Heritable; Statistics as Topic; Technology
• ISSN: 0040-5752; 1432-2242; 1432-2242
• DOI: 10.1007/s00122-018-3267-3

In the past, plant breeding has undergone three major transformations and is currently transitioning to a new technological phase, Breeding 4. This phase is characterized by the development of methods for biological design of plant varieties, including transformation and gene editing techniques directed toward 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 ). Here, we discuss approaches to avoid this curse of dimensionality ( n  ≪  p ), which will involve analyzing intermediate phenotypes such as molecular traits and component traits related to plant morphology or physiology. Because these approaches will rely on novel data types such as DNA sequences and high-throughput phenotyping images, Breeding 4 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. In this article, we will present some of these techniques and their application for prioritizing causal loci and developing improved varieties in Breeding 4.