Discovery of interesting new polymorphisms in a sugar beet (elite × exotic) progeny by comparison with an elite panel

Prune Pegot-Espagnet, Olivier Guillaume, Bruno Desprez, Brigitte Devaux, Pierre Devaux, Karine Henry, Nicolas Henry, Glenda Willems, Ellen Goudemand, Brigitte Mangin

Theoretical and Applied Genetics; November 2019, Volume 132, Issue 11, pp 3063–3078

Key message

The comparison of QTL detection performed on an elite panel and an (elite ×× exotic) progeny shows that introducing exotic germplasm into breeding programs can bring new interesting allelic diversity.

Abstract

Selection of stable varieties producing the highest amount of extractable sugar per hectare (ha), resistant to diseases, and respecting environmental criteria is undoubtedly the main target for sugar beet breeding. As sodium, potassium, and αα-amino nitrogen in sugar beets are the impurities that have the biggest negative impact on white sugar extraction, it is interesting to reduce their concentration in further varieties. However, domestication history and strong selection pressures have affected the genetic diversity needed to achieve this goal. In this study, quantitative trait locus (QTL) detection was performed on two populations, an (elite ×× exotic) sugar beet progeny and an elite panel, to find potentially new interesting regions brought by the exotic accession. The three traits linked with impurities content were studied. Some QTLs were detected in both populations, the majority in the elite panel because of most statistical power. Some of the QTLs were colocated and had favorable effect in the progeny since the exotic allele was linked with a decrease in the impurity content. A few number of favorable QTLs were detected in the progeny, only. Consequently, introgressing exotic genetic material into sugar beet breeding programs can allow the incorporation of new interesting alleles.

See https://link.springer.com/article/10.1007/s00122-019-03406-0

Fig. 2 Manhattan plots in the (elite ×× exotic) progeny using the additive model of the first step of GWAS on the left, and the step selected by eBIC on the right for the mean phenotype of potassium content on the first row, for the mean phenotype of αα-amino nitrogen content on the second row and for the mean phenotype of sodium content on the third row. Note that the two steps can be the same. Stars in the step selected by eBIC represent SNPs detected and added into the model in previous steps