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

Authors: SAJAL, STHAPIT - The Land Institute; CRAIN, JARED - Kansas State University; Larson, Steven; ANDERSON, JAMES - University Of Minnesota; Bajgain, Prabin; DEHAAN, LEE - The Land Institute; POLAND, JESSE - King Abdullah University Of Science And Technology

Submitted to: The Plant Genome
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/3/2025
Publication Date: 10/23/2025
Citation: Sajal, S.R., Crain, J., Larson, S.R., Anderson, J.A., Bajgain, P., Dehaan, L.R., Poland, J. 2025. A low-coverage skim-sequencing and imputation pipeline for genomic selection. The Plant Genome. https://doi.org/10.1002/tpg2.70139.
DOI: https://doi.org/10.1002/tpg2.70139

Interpretive Summary: Few plant breeding programs have adopted modern technological advances in genetic fingerprinting of thousands of plants because of cost and resource challenges associated with these methods. This is particularly true in the case of perennial crops as they impose additional hurdles because of the time, money, and labor needed to complete their multi-year long breeding cycles. In this study, we used a large and diverse population of the perennial grass intermediate wheatgrass to investigate low coverage whole genome sequencing techniques and computational methods to construct the genetic makeup of each single plant. Our results indicate that these methods can be used effectively in 1) improving intermediate wheatgrass breeding populations, and 2) providing a road map for low-resourced species and breeding programs to exploit the genomics revolution through open-source, non-proprietary methods. Additionally, the data generated can be used for additional genetic studies within this perennial grass crop.

Technical Abstract: Genomic selection (GS) can accelerate plant breeding gains by reducing breeding cycle times, reducing phenotyping costs, or improving selection accuracy. Genomic selection is especially promising for perennial crops that may require multiple years of evaluation before selection 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 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 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 genotyping-by-sequencing (GBS) and skim-seq data for intermediate wheatgrass (Thinopyrum intermedium) an outcrossing, heterozygous, large genome (12.7 GB) perennial species. Our results indicate skim-seq (r = 0.962–.985) can be used as effectively as GBS (r = 0.962-.977) 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.