A low-coverage skim-sequencing and imputation pipeline for genomic selection

Genomic selection (GS) can accelerate plant breeding gains by reducing breeding cycle times, reducing phenotyping costs, or improving selection accuracy. GS is especially promising for perennial crops such as intermediate wheatgrass (IWG, Thinopyrum intermedium) that may require multiple years of evaluation under phenotypic recurrent selection. A major obstacle in implementing GS is the need for an affordable, high-density, genetic marker system that is scalable to thousands or tens-of-thousands of samples in breeding programs, especially in emerging or minor crop species. As sequencing costs continue to decrease, low-coverage whole genome skim-sequencing (skim-seq) has become an attractive method for GS. Using commercial laboratory products and open-source software, we implemented whole genome prediction at breeding program scale using ultra-low coverage (0.01x– 0.05x, 100–125 million reads per sample) whole genome skim-seq. Using STITCH (Sequencing to Imputation Through Constructing Haplotypes) imputation software, we evaluated optimization of imputation parameters including sequence coverage and number of assumed ancestral haplotypes. Finally, we evaluated whole genome prediction cross-validation accuracies using reduced representation genotyping-by-sequencing (GBS) versus skim-seq data for IWG, an outcrossing, heterozygous, large genome (12.7 Gb), polyploid perennial species. Our results indicate correlations between cross-validation accuracies across five traits in IWG using skim-seq data (r = 0.29–0.61) can be used as effectively as GBS (r = 0.29–0.55) while generating low-coverage archival sequence data that will be robust to technological advances. These methods will be applicable to a wide range of crops and scale to breeding program size, allowing for more tractable implementation of GS within breeding programs.