Characterization of Insect Resistance Loci in the USDA Soybean Germplasm Collection Using Genome-Wide Association Studies

Abstract

Management of insects that cause economic damage to yields of soybean mainly rely on insecticide applications. Sources of resistance in soybean plant introductions (PIs) to different insect pests have been reported, and some of these sources, like for the soybean aphid (SBA), have been used to develop resistant soybean cultivars. With the availability of SoySNP50K and the statistical power of genome-wide association studies, we integrated phenotypic data for beet armyworm, Mexican bean beetle (MBB), potato leafhopper (PLH), SBA, soybean looper (SBL), velvetbean caterpillar (VBC), and chewing damage caused by unspecified insects for a comprehensive understanding of insect resistance in the United States Department of Agriculture Soybean Germplasm Collection. We identified significant single nucleotide (SNP) polymorphic markers for MBB, PLH, SBL, and VBC, and we highlighted several leucine-rich repeat-containing genes and myeloblastosis transcription factors within the high linkage disequilibrium region surrounding significant SNP markers. Specifically for soybean resistance to PLH, we found the PLH locus is close but distinct to a locus for soybean pubescence density on chromosome 12. The results provide genetic support that pubescence density may not directly link to PLH resistance. This study offers a novel insight of soybean resistance to four insect pests and reviews resistance mapping studies for major soybean insects.

Genome‐Wide Association Mapping of Host‐Plant Resistance to Soybean Aphid

“…The datasets we analyzed, excluding Bhusal et al (2013) for Biotype 2 aphids (Fig. 4a), were not examined by Chang and Hartman (2017), so it is difficult to make direct comparisons between the two studies. However, Chang and Hartman (2017) used categorical phenotypic data instead of using aphid densities or rating scales reported in the literature.…”

“…4a), were not examined by Chang and Hartman (2017), so it is difficult to make direct comparisons between the two studies. However, Chang and Hartman (2017) used categorical phenotypic data instead of using aphid densities or rating scales reported in the literature. This would reduce the resolution of their phenotypic data and power to detect underlying resistance.…”

Genome-Wide Association Analyses Reveal Genomic Regions Controlling Canopy Wilting in Soybean

“…Failure to correct for population stratification in GWAS models can lead to false positives, especially if the trait of interest is correlated with the structure of the panel (Wang et al 2005). Several studies in soybean have been reported using the GWAS approach with SNP markers for many different traits, such as seed composition (Hwang et al 2014;Vaughn et al 2014;Bandillo et al 2015;Cao et al 2017), salt tolerance (Patil et al 2016;Zeng et al 2017), carbon isotope composition (Dhanapal et al 2015;Kaler et al 2017a), ureide concentration (Ray et al 2015), agronomic traits (Zhang et al 2015;Contreras-Soto et al 2017;Li et al 2017), chlorophyll traits (Dhanapal et al 2016), local adaptation (Bandillo et al 2017), insect resistance (Chang and Hartman 2017), and canopy wilting (Kaler et al 2017b). These studies have provided a useful way to identify potential genomic regions with high resolution and candidate genes or QTL for traits of interest.…”

Building a Reference Transcriptome for the Hexaploid Hard Fescue Turfgrass (Festuca brevipila) Using a Combination of PacBio Iso-Seq and Illumina Sequencing

“…For plant breeding and germplasm improvement, USDA PI accessions have proven to be a valuable resource (Pastor-Corrales, 2003;Chang and Hartman, 2017). Our previous work found that out of 127 USDA accessions of the closely related Festuca ovina, 17.3% are hexaploid, 33.1% are diploid, and 26.8% are tetraploid (Qiu et al, 2020a).…”