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

Ramstein, Guillaume P.; Jensen, Sarah E.; Buckler, Edward S.

doi:10.1007/s00122-018-3267-3

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

Journal Article · 13 December 2018 · Theoretical and Applied Genetics

DOI: https://doi.org/10.1007/s00122-018-3267-3 · OSTI ID:1623594

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Cornell Univ., Ithaca, NY (United States). Inst. of Biotechnology, Inst. for Genomic Diversity
Cornell Univ., Ithaca, NY (United States). Section of Plant Breeding and Genetics
Cornell Univ., Ithaca, NY (United States). Inst. of Biotechnology, Inst. for Genomic Diversity and Section of Plant Breeding and Genetics; US Dept. of Agriculture (USDA)., Ithaca, NY(United States). Agricultural Research Service (ARS)

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 ($$n\ll p$$), 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.

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Research Organization:: UHV Technologies, Inc., Fort Worth, TX (United States); Cornell Univ., Ithaca, NY (United States)

Sponsoring Organization:: USDA; Agricultural Research Service (ARS); National Science Foundation (NSF); USDOE Advanced Research Projects Agency - Energy (ARPA-E); US Agency for International Development (USAID)

Grant/Contract Number:: AR0000422

OSTI ID:: 1623594

Journal Information:: Theoretical and Applied Genetics, Vol. 132, Issue 3; ISSN 0040-5752

Publisher:: Springer NatureCopyright Statement

Country of Publication:: United States

Language:: English

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