Location: Vegetable Research
Title: Multipartite interactions involving sweetpotato and its leaf microbiomeAuthor
ADAMS, ALISON - University Of Tennessee | |
RICKMAN, TARA - University Of Tennessee | |
Wadl, Phillip | |
YENCHO, CRAIG - North Carolina State University | |
OLUKOLU, BODE - University Of Tennessee |
Submitted to: Meeting Abstract
Publication Type: Abstract Only Publication Acceptance Date: 12/15/2019 Publication Date: N/A Citation: N/A Interpretive Summary: Technical Abstract: Sweetpotato, Ipomoea batatas, is an important crop for food security with global production of over 100 million metric tons annually. However, up to 50% of Sweetpotato yields are lost to plant viruses. In addition to host genetic control of disease progression, host-associated biotic factors might explain a significant proportion of variation in viral disease severity and symptoms. The complexity of these factors tend to reduce utility of breeding models for crop improvement. To develop a robust and predictive model to dissect the impacts of sweetpotato and microbial genetics on phenotypic expression, we present a study that integrates high-density SNP data and meta-data derived from the leaf microbiomes of 767 sweetpotato accessions. The strain-level profile of the microbiomes was analyzed using Qmatey. Six sweetpotato viruses were identified, along with several pathogenic and beneficial organisms. GWASPoly was used to identify 80,000 dosage-based associated single nucleotide polymorphism (SNPs) underlying molecular interactions between sweetpotato and individuals within the microbiome. These associations were modeled based on additive and various dominance effect models that account for allele dosage. Plausible functions of candidate genes co-localized with significantly associated SNPs are described based on functional annotations of gene orthologs. An initial inquiry revealed associations between viral presence and enrichment of host genes that function in intracellular signaling, transport, and disease resistance. The comprehensive insight into multi-way interactions within the microbiome holds great promise for precision breeding by providing more accurate models that account for biotic factors within the environment. |