1 3 Theor Appl Genet DOI 10.1007/s00122-015-2566-1 ORIGINAL ARTICLE Confirmation of delayed canopy wilting QTLs from multiple soybean mapping populations Sadal Hwang 1 · C. Andy King 1 · Jeffery D. Ray 2 · Perry B. Cregan 3 · Pengyin Chen 1 · Thomas E. Carter Jr. 4 · Zenglu Li 5 · Hussein Abdel-Haleem 6 · Kevin W. Matson 7 · William Schapaugh Jr. 8 · Larry C. Purcell 1 Received: 27 February 2015 / Accepted: 16 June 2015 © Springer-Verlag Berlin Heidelberg 2015 on Gm02, Gm05, Gm11, Gm14, Gm17, and Gm19 identi- fied from at least two different populations, but a simula- tion study indicated that the QTLs on Gm14 could be false positives. A QTL on Gm08 in the 93705 KS4895 × Jack- son population co-segregated with a QTL for wilting pub- lished previously in a Kefeng1 × Nannong 1138-2 popula- tion, indicating that this may be an additional QTL cluster. Excluding the QTL cluster on Gm14, results of the simu- lation study indicated that the eight remaining QTL clus- ters and the QTL on Gm08 appeared to be authentic QTLs. QTL × year interactions indicated that QTLs were stable over years except for major QTLs on Gm11 and Gm19. The stability of QTLs located on seven clusters indicates that they are possible candidates for use in marker-assisted selection. Introduction In North America over the last 60 years, soybean breed- ing has produced over 500 cultivars and increased yield by more than 25 % (Fox et al. 2013; Specht et al. 1999). Abstract Key message QTLs for delayed canopy wilting from five soybean populations were projected onto the con- sensus map to identify eight QTL clusters that had QTLs from at least two independent populations. Abstract Quantitative trait loci (QTLs) for canopy wilting were identified in five recombinant inbred line (RIL) popu- lations, 93705 KS4895 × Jackson, 08705 KS4895 × Jack- son, KS4895 × PI 424140, A5959 × PI 416937, and Ben- ning × PI 416937 in a total of 15 site-years. For most environments, heritability of canopy wilting ranged from 0.65 to 0.85 but was somewhat lower when averaged over environments. Putative QTLs were identified with com- posite interval mapping and/or multiple interval mapping methods in each population and positioned on the consen- sus map along with their 95 % confidence intervals (CIs). We initially found nine QTL clusters with overlapping CIs Communicated by I. Rajcan. Mention of a trademark or proprietary product does not constitute a guarantee or warranty of the product by the U.S. Department of Agriculture and does not imply approval or the exclusion of other products that may also be suitable. * Larry C. Purcell lpurcell@uark.edu 1 Department of Crop, Soil, and Environmental Sciences, University of Arkansas, 1366 Altheimer Drive, Fayetteville, AR 72704, USA 2 Crop Genetics and Production Research Unit, USDA-ARS, Stoneville, MS 38776, USA 3 Soybean Genomics and Improvement Laboratory, USDA- ARR, BARC-West, Beltsville, MD 20705-2350, USA 4 Department of Crop Science, North Carolina State University, USDA-ARS, Raleigh, NC 27695, USA 5 Department of Crop and Soil Sciences and Center for Applied Genetic Technologies, The University of Georgia, 111 Riverbend Rd., Athens, GA 30602-6810, USA 6 US Arid-Land Agricultural Research Center, USDA-ARS, 21881 North Cardon Lane, Maricopa, AZ 85138, USA 7 Global Soybean Breeding, Monsanto Company, St. Louis, MO 63167, USA 8 Department of Agronomy, Kansas State University, 2004C Throckmorton Hall, Manhattan, KS 6506-5501, USA