Two Dioscorea alata populations were generated by hand pollination between contrasted diploid genitors. Population A (74F × Kabusa) was composed of 121 progenies while population B (74F × 14M) involved 193 progenies. These two populations were assessed over two consecutive years regarding important tuber quality traits. Analysis of variance showed that the genotype had the greatest influence on the phenotypic scores. Also for some traits, effect of the year_replicate was strong. The heritabilities of most traits were high. Based on these data and a reference high-density genetic map of greater yam, a total of 34 quantitative trait loci (QTLs) were detected on 8 of the 20 yam chromosomes. They corresponded to five of each of the following traits: tuber size, shape regularity, tubercular roots, skin texture, tuber flesh oxidation, six for oxidation ratio and three for flesh colour. The fraction of total phenotypic variance attributable to a single QTL ranged from 11.1 to 43.5%. We detected significant correlations between traits and QTL colocalizations that were consistent with these correlations. A majority of QTLs (62%) were found on linkage group LG16, indicating that this chromosome could play a major role in genetic control of the investigated traits. In addition, an inversion involving this chromosome was detected in the Kabusa male. Nine QTLs were validated on a diversity panel, including three for tuber size, three for shape regularity, two for skin texture and one for tubercular roots. The approximate physical localization of validated QTLs allowed the identification of various candidates genes. The validated QTLs should be useful for breeding programs using marker-assisted selection to improve yam tuber quality.

Despite the importance of yam ( Dioscorea spp.) tuber quality traits, and more precisely texture attributes, high-throughput screening methods for varietal selection are still lacking. This study sets out to define the profile of good quality pounded yam and provide screening tools based on predictive models using near infrared reflectance spectroscopy. Seventy-four out of 216 studied samples proved to be moldable, i.e. suitable for pounded yam. While samples with low dry matter (<25%), high sugar (>4%) and high protein (>6%) contents, low hardness (<5 N), high springiness (>0.5) and high cohesiveness (>0.5) grouped mostly non-moldable genotypes, the opposite was not true. This outline definition of a desirable chemotype may allow breeders to choose screening thresholds to support their choice. Moreover, traditional near infrared reflectance spectroscopy quantitative prediction models provided good prediction for chemical aspects (R2 > 0.85 for dry matter, starch, protein and sugar content), but not for texture attributes (R2 < 0.58). Conversely, convolutional neural network classification models enabled good qualitative prediction for all texture parameters but hardness (i.e. an accuracy of 80, 95, 100 and 55%, respectively, for moldability, cohesiveness, springiness and hardness). This study demonstrated the usefulness of near infrared reflectance spectroscopy as a high-throughput way of phenotyping pounded yam quality. Altogether, these results allow for an efficient screening toolbox for quality traits in yams.

No abstract