Extended Data Fig. 6: Genomic prediction results using the 150 individually genotyped samples (Dataset B) as both training and testing set, showing little difference between GWAS SNPs and random SNPs in correlations between GEBVs and health statuses.
From: Genomic basis of European ash tree resistance to ash dieback fungus
(A) GEBV-health status correlation using GWAS candidate SNPs with all data filters applied (mapping quality, indel and repeat removal); (B) GEBV-health status correlation using GWAS candidate SNPs only filtering by mapping quality and indel removal; (C) GEBV-health status correlation using random selection of SNPs and all data filters (mean and standard error shown for N=10 runs, each of 500 iterations); (D) GP allocation accuracy using GWAS candidate SNPs with all data filters applied. The scale on the left hand vertical axis is for correlation, and the scale on the right hand vertical axis is for accuracy. 100 to 5 million SNPs used to train and test the rrBLUP model.
