Research Article Precision Phenotyping Reveals Novel Loci for Quantitative Resistance to Septoria Tritici Blotch Steven Yates , 1 Alexey Mikaberidze, 2 Simon G. Krattinger, 3 Michael Abrouk , 3 Andreas Hund , 4 Kang Yu , 4 Bruno Studer, 1 Simone Fouche, 2 Lukas Meile, 2 Danilo Pereira , 2 Petteri Karisto , 2 and Bruce A. McDonald 2 1 Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland 2 Plant Pathology, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland 3 Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia 4 Crop Science, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland Correspondence should be addressed to Bruce A. McDonald; bruce.mcdonald@usys.ethz.ch Received 6 July 2019; Accepted 2 September 2019; Published 29 September 2019 Copyright © 2019 Steven Yates et al. Exclusive Licensee Science and Technology Review Publishing House. Distributed under a Creative Commons Attribution License (CC BY 4.0). Accurate, high-throughput phenotyping for quantitative traits is a limiting factor for progress in plant breeding. We developed an automated image analysis to measure quantitative resistance to septoria tritici blotch (STB), a globally important wheat disease, enabling identification of small chromosome intervals containing plausible candidate genes for STB resistance. 335 winter wheat cultivars were included in a replicated field experiment that experienced natural epidemic development by a highly diverse but fungicide-resistant pathogen population. More than 5.4 million automatically generated phenotypes were associated with 13,648 SNP markers to perform the GWAS. We identified 26 chromosome intervals explaining 1.9-10.6% of the variance associated with four independent resistance traits. Sixteen of the intervals overlapped with known STB resistance intervals, suggesting that our phenotyping approach can identify simultaneously (i.e., in a single experiment) many previously defined STB resistance intervals. Seventeen of the intervals were less than 5 Mbp in size and encoded only 173 genes, including many genes associated with disease resistance. Five intervals contained four or fewer genes, providing high priority targets for functional validation. Ten chromosome intervals were not previously associated with STB resistance, perhaps representing resistance to pathogen strains that had not been tested in earlier experiments. The SNP markers associated with these chromosome intervals can be used to recombine different forms of quantitative STB resistance that are likely to be more durable than pyramids of major resistance genes. Our experiment illustrates how high-throughput automated phenotyping can accelerate breeding for quantitative disease resistance. 1. Introduction Genome-wide association studies (GWAS) provide a powerful approach to identify genetic markers associated with important quantitative traits in crops (e.g., [1, 2]). The single nucleotide polymorphism (SNP) markers sig- nificantly associated with a trait in the GWAS can be directly used in breeding programs for marker-assisted selection or genomic selection and also as tools to enable map-based cloning of the corresponding genes underlying quantitative traits. An abundant supply of SNP genetic markers is now avail- able for most crops as a result of rapid advances in sequenc- ing technologies. Because phenotyping technologies have not developed as quickly as genotyping technologies, the ability to generate accurate and reproducible phenotypes for quanti- tative traits is now the primary limitation to progress in breeding for favorable traits [3, 4], including resistance to pests and pathogens [5]. Many research groups are working to develop automated/semiautomated and high-throughput phenotyping of important traits under field conditions, with some reports of success [5–7], but we remain far from the AAAS Plant Phenomics Volume 2019, Article ID 3285904, 11 pages https://doi.org/10.34133/2019/3285904