Skip to main content
ARS Home » Midwest Area » Madison, Wisconsin » U.S. Dairy Forage Research Center » Dairy Forage Research » Research » Publications at this Location » Publication #336894
Title: Use of natural variation to identify loci associated with relevant agronomic phenotypic traits

Author
item BUELL, ROBIN - Michigan State University
item Casler, Michael
item KAEPPLER, SHAWN - University Of Wisconsin
item DE LEON, NATALIA - University Of Wisconsin

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 1/13/2017
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: Analysis of natural allelic variation is a useful discovery tool to identify novel alleles in genes and pathways that are consistent with agronomic productivity and environmental stability. Switchgrass, a native perennial North American prairie grass and emerging biofuel feedstock species, is divided into two ecotypes (lowland and upland) based on habitat, genome ploidy, and phenotype. Extensive phenotypic variation is present in switchgrass; using an exome capture sequencing approach, we identified approximately 1.9 million bi-allelic single nucleotide polymorphic loci from three diversity panels of switchgrass that represent a total of 1169 individuals across 140 populations. With this large variant dataset, we have catalogued genetic diversity across North American switchgrass, developed genome selection models, and associated genetic markers with important biofuel feedstock traits including biomass, lignin composition, and flowering time. Maize, a homozygous inbred diploid, provides a powerful genetic model system to accelerate research in related grass species such as switchgrass that are polyploid, heterozygous, and perennial. Use of a transcriptome dataset derived from a set of 503 diverse maize inbreds revealed sequence variants and transcripts associated with phenotypic variation of biofuel feedstock traits, as well as thousands of transcripts not present in the reference genotype B73. This suggested that presence/absence of variants in the overall maize pan-genome/pan-transcriptome may contribute to phenotypic diversity. We have increased our genome/transcriptome diversity datasets by expanding the Wisconsin Diversity Panel to 959 total inbred lines and generated de novo sequences for two key inbred lines relevant to bioenergy production. When coupled with a broad range of phenotype datasets collected in the field, we are poised to associate loci/alleles, copy number variants, presence/absence variants, and transcriptional variants with phenotypic traits in maize, and leverage these to related species such as switchgrass.