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Societal Impact StatementAgricultural practices have had a negative impact on the physical, chemical, and biological components of soil. Perennial cropping systems that facilitate positive soil microbial interactions could not only rebuild soils but also sustain productivity through expected variations in environmental conditions. Here, we show the presence of arbuscular mycorrhizal (AM) fungi, soil symbionts that can improve host performance and soil health, increased the growth of intermediate wheatgrass, a novel perennial grain crop, in populations that have been increasingly bred for desirable agricultural characteristics. The right pairing of intermediate wheatgrass and a beneficial AM fungal community could lead to more sustainable agroecosystems. SummaryIntermediate wheatgrass (IWG) is a novel perennial grain that can provide many soil health benefits in agroecosystems; however, little is known about how selection for agronomic traits has impacted interactions with soil biota. Here, we assess how the selection for agronomic traits in IWG has impacted its relationship with arbuscular mycorrhizal (AM) fungi.First, growth response to AM fungi was compared across five generations of IWG with varying degrees of selection. Second, variation in AM fungal responsiveness was compared among genets of IWG individuals within a more advanced generation. Finally, a meta‐analysis was performed on all published studies exploring AM fungal inocula effects on IWG performance to increase understanding of selection effects.AM fungal responsiveness increased with selection for agronomic traits, responsiveness varied among genets in the advanced generation, and a majority of genets performed better in the presence of AM fungi. The meta‐analysis supported the findings that AM fungal responsiveness has increased with selection in IWG.Further studies are needed to realize the combined potential soil health and sustainability benefits of IWG and AM fungi, including assessment of symbiotic benefits beyond biomass production, identification of IWG traits correlated with responsiveness, and characterization of AM fungal community response to IWG. 

Abstract The development of next-generation sequencing (NGS) enabled a shift from array-based genotyping to directly sequencing genomic libraries for high-throughput genotyping. Even though whole-genome sequencing was initially too costly for routine analysis in large populations such as breeding or genetic studies, continued advancements in genome sequencing and bioinformatics have provided the opportunity to capitalize on whole-genome information. As new sequencing platforms can routinely provide high-quality sequencing data for sufficient genome coverage to genotype various breeding populations, a limitation comes in the time and cost of library construction when multiplexing a large number of samples. Here we describe a high-throughput whole-genome skim-sequencing (skim-seq) approach that can be utilized for a broad range of genotyping and genomic characterization. Using optimized low-volume Illumina Nextera chemistry, we developed a skim-seq method and combined up to 960 samples in one multiplex library using dual index barcoding. With the dual-index barcoding, the number of samples for multiplexing can be adjusted depending on the amount of data required, and could be extended to 3,072 samples or more. Panels of doubled haploid wheat lines (Triticum aestivum, CDC Stanley x CDC Landmark), wheat-barley (T.aestivumxHordeum vulgare) and wheat-wheatgrass (Triticum durum x Thinopyrum intermedium) introgression lines as well as known monosomic wheat stocks were genotyped using the skim-seq approach. Bioinformatics pipelines were developed for various applications where sequencing coverage ranged from 1 × down to 0.01 × per sample. Using reference genomes, we detected chromosome dosage, identified aneuploidy, and karyotyped introgression lines from the skim-seq data. Leveraging the recent advancements in genome sequencing, skim-seq provides an effective and low-cost tool for routine genotyping and genetic analysis, which can track and identify introgressions and genomic regions of interest in genetics research and applied breeding programs.