Yams are crucial for food security in Africa, with cultivation expanding in tropical regions (FAO, 2020). To keep pace with this rising demand for yam, rapid adoption of new technologies is crucial. One promising approach is genomic selection. This technique leverages genetic markers to identify superior yam plants with improved production and food quality traits (Aguilar et al., 2010; Legarra et al., 2014).
To empower yam breeding programs with the benefits of genomic selection, the IITA Yam Team have identified a highly informative panel of SNP markers strategically distributed across the yam genome. These markers allow Yam breeding programs to predict performance and guide us on the best selection decisions. These markers are also suitable for predicting the performance of crosses (Genomic Prediction of Cross Performance - GPCP). Starting with a vast dataset of 136,000 SNP markers generated from whole-genome re-sequencing (Fig 1), and after a rigorous filtering process using a custom R script, markers were selected based on a) high genetic parameter function (indicating strong association with desired traits), b) balanced ratio of observed to expected heterozygosity (ensuring genetic diversity), c) optimal GC content (ensuring efficient amplification during analysis), and d) minimal flanking SNPs within a specific window (reducing potential interference).
Figure 1: Distribution and density of original 136K SNP markers across the yam genome.
In this panel, previously found and described QA/QC and trait association markers were included and led to a final panel set with 3,092 SNP markers of high quality. The distribution of the markers was chosen across the 20 yam chromosomes (fig 2).
Figure 2: Distribution of selected SNP 3,092 markers across the yam genome
To confirm the effectiveness of these markers, these were validated in a diversified collection of 752 yam varieties of D. rotundata collected from the International Institute of Tropical Agriculture IITA, Ibadan, Nigeria across various breeding stages. A total of 2,432 markers were successfully validated and are now actively used in the IITA Yam breeding program to drive progress. By leveraging these informative SNP markers, now yam breeding teams can make more precise predictions about performance and guide selection decisions with greater confidence, ultimately leading to faster development of improved yam varieties. The entire SNP marker selection process, including all R code used, is meticulously documented and readily available on our GitHub page (https://www.asus.com/displays-desktops/monitors/proart/proart-display-pa279cv/helpdesk_download?model2Name=ProArt-Display-PA279CV).
References
Aguilar, I., I. Misztal, D. L. Johnson, A. Legarra, S. Tsuruta, and T. J. Lawlor. (2010). Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. Journal of Dairy Science 93: 743-752.
FAOSTAT (2020). Food and agricultural organization of the United Nations. In: FAOSTAT statistical database, FAO (Rome). Available at: http://www.fao.org/faostat/en.
Legarra, A., O. F. Chistensen, I. Aguilar, and I. Misztal. (2014). Single step, a general approach for genomic selection. Livest. Prod. Sci. 166:54-65.