The effect of marker types and density on genomic prediction and GWAS of key performance traits in tetraploid potato - Aalborg, Trine; Sverrisdóttir, Elsa; Kristensen, Heidi Thorgaard; Nielsen, Kåre Lehmann (Frontiers in Plant Science, 2024)
Genomic prediction and genome-wide association studies are becoming widely employed in potato key performance trait QTL identifications and to support potato breeding using genomic selection. Elite cultivars are tetraploid and highly heterozygous but also share many common ancestors and generation-spanning inbreeding events, resulting from clonal propagation of potatoes through seed potatoes. Consequentially, many SNP markers are not in 1:1 relationship with a single allele variant but shared over several alleles that might exert varying effects on a given trait. The impact of such redundant 'diluted' predictors on the statistical models underpinning GWAS and genomic prediction have scarcely been evaluated, despite the potential impact on model accuracy and performance. We evaluated the impact of marker location, -type, and -density on genomic prediction and GWAS of five key performance traits in tetraploid potato (chipping quality, dry matter content, length/width ratio, senescence, and yield). A 762 offspring panel of a diallel cross of 18 elite cultivars was genotyped-by-sequencing, and markers were annotated according to a reference genome. Genomic prediction models (GBLUP) were trained on four marker subsets (non-synonymous [29,553 SNPs], synonymous [31,229], non-coding [32,388], and a combination) and robustness to marker reduction was investigated. Single-marker regression GWAS was performed for each trait and marker subset. Best cross-validated prediction correlation coefficients of 0.54, 0.75, 0.49, 0.35, and 0.28 were obtained for chipping quality, dry matter content, length/width ratio, senescence, and yield, respectively. Trait prediction abilities were similar across all marker types, only non-synonymous variants improving yield predictive ability by 16 %. Marker reduction response did not dependent on marker type but rather on trait. Traits with high predictive abilities, e.g. dry matter content, reached a plateau using fewer markers than for traits with intermediate-low correlations, such as yield. Predictions were unbiased across all traits, marker types, and all marker densities > 100 SNPs. Our results suggest using non-synonymous variants does not enhance performance of genomic prediction of most traits. Major known QTLs were identified by GWAS and were reproducible across exonic and whole-genome variant sets for dry matter content, length/width ratio, and senescence. In contrast, minor QTLs detection was marker type dependent.