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. 2020 Sep 2;133(12):3345–3363. doi: 10.1007/s00122-020-03673-2

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© The Author(s) 2020

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 5 — Model comparison in potato. Box plots comparing predictive ability of additive-effects-only model (Add_4x_GBSCornell); additive and non-additive effects (Add + Non_4x_GBSCornell); and pseudo-diploidized dosage data (Pseudo_2x_GBSCornell); using minimum allele frequency (MAF) ≥ 30% (A; 411 SNPs) and MAF ≥ 40% (B; 178 SNPs). LB2014_O = late blight in Oxapampa (Peru) in 2014, LB2016_Y = late blight in Yunnan (China) in 2016, PVY_L = potato virus Y in Lima (Peru), AYP_K = average yield per plant in Kunming (China), WMT_K = weight of marketable tubers in Kunming, TTW16_Ica = total tuber weight in Ica (Peru) in 2016 across three drought treatments, TTW16_HLJ = total tuber weight in Heilongjiang (China) in 2016, single treatment, in potato