@article{fraher_kinczyk_siqueira gesteira_heim_williamson_olukolu_silva pereira_mollinari_hamilton_buell_et al._2026, title={Discovery of a major QTL for resistance to <i>Fusarium</i> wilt ( <i>Fusarium oxysporum</i> f. sp. <i>batatas</i> ) in the hexaploid Covington sweetpotato}, volume={1}, DOI={10.1002/csc2.70239}, abstractNote={Abstract Fusarium oxysporum f. sp. batatas , the causal agent of Fusarium wilt disease, was once the most damaging pathogen of sweetpotato in the United States. Breeding for cultivar resistance has largely addressed this issue, however, little is known about the genetic basis for resistance. Historically, sweetpotato breeders have relied on the high heritability of Fusarium wilt resistance, so identification of a region controlling resistance would be a major first step in implementing marker‐assisted selection for this trait. We assayed a biparental mapping population, NCDM04‐0001 × ‘Covington’ (DC), consisting of a susceptible by resistant cross composed of 454 progenies, for resistance to Fusarium wilt disease using visual assessments and an ordinal disease severity rating scale. Parental and check lines performed as expected, and the DC population exhibited segregation for resistance across trials over 3 years and in a joint analysis. We next performed quantitative trait locus (QTL) analyses using a linkage map based on the Ipomoea trifida diploid reference genome. Across multiple trials, we repeatedly detected a major QTL on chromosome 10, herein named qIbFo‐10.1. This QTL had a heritability of 33.8%, suggesting that a single locus explains a large amount of variation for resistance to this critically important trait. A basic local alignment search tool revealed several candidate genes: itf10g19820 (transcriptional factor B3 family protein/auxin‐responsive factor AUX/IAA‐related), four LRR‐kinases (leucine‐rich repeat receptor kinase) (itf10g21910, itf10g19200, itf10g19260, and itf10g20000), and two toll‐interleukin‐resistance genes (itf10g20200 and itf10g20220). Future efforts should develop molecular tools for Fusarium wilt resistance breeding, resulting in shorter breeding cycles and faster variety releases.}, journal={Crop Science}, author={Fraher, Simon and Kinczyk, Jonathan and Siqueira Gesteira, Gabriel and Heim, Chris and Williamson, Sharon and Olukolu, Bode A. and Silva Pereira, Guilherme and Mollinari, Marcelo and Hamilton, John P. and Buell, C. Robin and et al.}, year={2026}, month={Jan} }
 @article{fraher_watson_nguyen_moore_lewis_kudenov_yencho_gorny_2024, title={A Comparison of Three Automated Root-Knot Nematode Egg Counting Approaches Using Machine Learning, Image Analysis, and a Hybrid Model}, volume={108}, ISSN={0191-2917 1943-7692}, url={http://dx.doi.org/10.1094/PDIS-01-24-0217-SR}, DOI={10.1094/PDIS-01-24-0217-SR}, abstractNote={Meloidogyne spp. (root-knot nematodes [RKNs]) are a major threat to a wide range of agricultural crops worldwide. Breeding crops for RKN resistance is an effective management strategy, yet assaying large numbers of breeding lines requires laborious bioassays that are time-consuming and require experienced researchers. In these bioassays, quantifying nematode eggs through manual counting is considered the current standard for quantifying establishing resistance in plant genotypes. Counting RKN eggs is highly laborious, and even experienced researchers are subject to fatigue or misclassification, leading to potential errors in phenotyping. Here, we present three automated egg counting models that rely on machine learning and image analysis to quantify RKN eggs extracted from tobacco and sweet potato plants. The first method relied on convolutional neural networks trained using annotated images to identify eggs (M. enterolobii R 2 = 0.899, M. incognita R 2 = 0.927, M. javanica R 2 = 0.886), whereas a second contour-based approach used image analysis to identify eggs from their morphological characteristics and did not rely on neural networks (M. enterolobii R 2 = 0.977, M. incognita R 2 = 0.990, M. javanica R 2 = 0.924). A third hybrid model combined these approaches and was able to detect and count eggs nearly as well as human raters (M. enterolobii R 2 = 0.985, M. incognita R 2 = 0.992, M. javanica R 2 = 0.983). These automated counting protocols have the potential to provide significant time and resource savings annually for breeders and nematologists and may be broadly applicable to other nematode species.}, number={9}, journal={Plant Disease}, publisher={Scientific Societies}, author={Fraher, Simon P. and Watson, Mark and Nguyen, Hoang and Moore, Savannah and Lewis, Ramsey S. and Kudenov, Michael and Yencho, G. Craig and Gorny, Adrienne M.}, year={2024}, month={Sep}, pages={2625–2629} }
 @article{fraher_schwarz_heim_gesteira_mollinari_pereira_zeng_brown-guedira_gorny_yencho_2024, title={Discovery of a major QTL for resistance to the guava root-knot nematode (Meloidogyne enterolobii) in ‘Tanzania’, an African landrace sweetpotato (Ipomoea batatas)}, volume={137}, DOI={10.1007/s00122-024-04739-1}, abstractNote={Sweetpotato, Ipomoea batatas (L.) Lam. (2n = 6x = 90), is among the world's most important food crops and is North Carolina's most important vegetable crop. The recent introduction of Meloidogyne enterolobii poses a significant economic threat to North Carolina's sweetpotato industry and breeding resistance into new varieties has become a high priority for the US sweetpotato industry. Previous studies have shown that 'Tanzania', a released African landrace, is resistant to M. enterolobii. We screened the biparental sweetpotato mapping population, 'Tanzania' x 'Beauregard', for resistance to M. enterolobii by inoculating 246 full-sibs with 10,000 eggs each under greenhouse conditions. 'Tanzania', the female parent, was highly resistant, while 'Beauregard' was highly susceptible. Our bioassays exhibited strong skewing toward resistance for three measures of resistance: reproductive factor, eggs per gram of root tissue, and root gall severity ratings. A 1:1 segregation for resistance suggested a major gene conferred M. enterolobii resistance. Using a random-effect multiple interval mapping model, we identified a single major QTL, herein designated as qIbMe-4.1, on linkage group 4 that explained 70% of variation in resistance to M. enterolobii. This study provides a new understanding of the genetic basis of M. enterolobii resistance in sweetpotato and represents a major step towards the identification of selectable markers for nematode resistance breeding.}, number={10}, journal={Theoretical and Applied Genetics}, author={Fraher, Simon and Schwarz, Tanner and Heim, Chris and Gesteira, Gabriel De Siqueira and Mollinari, Marcelo and Pereira, Guilherme Da Silva and Zeng, Zhao-Bang and Brown-Guedira, Gina and Gorny, Adrienne and Yencho, G. Craig}, year={2024}, month={Sep} }