Switchgrass (Panicum virgatum L.) is a warm-season perennial grass with promising potential as a bioenergy crop in the United States. However, the lack of genomic resources has slowed the development of plant lines with optimal characteristics for sustainable feedstock production. We generated high-density single nucleotide polymorphism (SNP) linkage maps using a reduced-representation sequencing approach by genotyping 231 F₁ progeny of a cross between two parents of lowland ecotype from the cultivars Kanlow and Alamo. Over 350 million reads were generated and aligned, which enabled identification and ordering of 4611 high-quality SNPs. The total lengths of the resulting framework maps were 1770 cM for the Kanlow parent and 2059 cM for the Alamo parent. These maps show collinearity with maps generated with polymerase chain reaction (PCR)-based simple-sequence repeat (SSR) markers, and new SNP markers were identified in previously unpopulated regions of the genome. Transmission segregation distortion affected all linkage groups (LGs) to differing degrees, and ordering of distorted markers highlighted several regions of unequal inheritance. Framework maps were adversely affected by the addition of distorted markers with varying severity, but distorted maps were of higher marker density and provided additional information for analysis. Alignment of these linkage maps with a draft version of the switchgrass genome assembly demonstrated high levels of collinearity and provides greater confidence in the validity of both resources. This methodology has proven to be a rapid and cost-effective way to generate high-quality linkage maps of an outcrossing species.

To improve the efficiency of high-density genotype data storage and imputation in bread wheat (Triticum aestivum L.), we applied the Practical Haplotype Graph (PHG) tool. The Wheat PHG database was built using whole-exome capture sequencing data from a diverse set of 65 wheat accessions. Population haplotypes were inferred for the reference genome intervals defined by the boundaries of the high-quality gene models. Missing genotypes in the inference panels, composed of wheat cultivars or recombinant inbred lines genotyped by exome capture, genotyping-by-sequencing (GBS), or whole-genome skim-seq sequencing approaches, were imputed using the Wheat PHG database. Though imputation accuracy varied depending on the method of sequencing and coverage depth, we found 92% imputation accuracy with 0.01× sequence coverage, which was slightly lower than the accuracy obtained using the 0.5× sequence coverage (96.6%). Compared to Beagle, on average, PHG imputation was ∼3.5% (P-value < 2 × 10-14) more accurate, and showed 27% higher accuracy at imputing a rare haplotype introgressed from a wild relative into wheat. We found reduced accuracy of imputation with independent 2× GBS data (88.6%), which increases to 89.2% with the inclusion of parental haplotypes in the database. The accuracy reduction with GBS is likely associated with the small overlap between GBS markers and the exome capture dataset, which was used for constructing PHG. The highest imputation accuracy was obtained with exome capture for the wheat D genome, which also showed the highest levels of linkage disequilibrium and proportion of identity-by-descent regions among accessions in the PHG database. We demonstrate that genetic mapping based on genotypes imputed using PHG identifies SNPs with a broader range of effect sizes that together explain a higher proportion of genetic variance for heading date and meiotic crossover rate compared to previous studies.