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).