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

Plant breeding has been revolutionized by advances in genotyping technology. With the ability to genetically profile large populations, plant breeders can more effectively select plants that have greater agronomic performance based completely on their genetic makeup. However, few plant breeding programs have adopted these technological advances due to the challenges and cost associated with genotyping breeding scale (thousands) of plants. We investigated low-cost sequencing techniques and computational methods to infer each plants genetic makeup. Our results indicate that these methods can be used effectively, providing a road map for low resourced species and breeding programs to exploit the genomics revolution through open-source, non-proprietary methods. Furthermore, the data generated can be used for additional genetic studies.