Location: Crop Genetics and Breeding Research
2021 Annual Report
Objectives
1. Elucidate the interactions of responses in peanut to multiple biotic and abiotic stress factors, such as drought, tomato spotted wilt virus, leaf spots, white mold, and root-knot nematode; determine overlapping response pathways; discover selection targets (genes or networks); and work with breeders to use the information in developing peanut varieties with broad spectrum stress resistance/tolerance.
1A. Develop next-generation fine-mapping population segregating multiple traits of interest, such as Multi-parent Advanced Generation Inter-Cross (MAGIC), and conduct phenotypes in the field.
1B. Construct high resolution genetic and trait maps using single nucleotide polymorphism (SNP) markers for fine-mapping of QTLs/markers linked to the traits of interest.
1C. Apply molecular markers in breeding and trait stacking/pyramiding to develop superior lines of peanut using Marker Assisted Recurrent Selection (MARS) breeding scheme.
Approach
1. Identifying natural allelic variation that underlies quantitative trait variation remains a challenge in genetic studies. Development and phenotypic evaluation of a multi-parental MAGIC mapping population, along with high density genotyping tools available, such as newly developed peanut 58K SNP array and/or whole genome re-sequencing (WGRS), will be essential for quantitative trait loci/marker and trait mapping analyses. The primary aim of this objective is to develop the first next-generation fine-mapping population of peanut that can be used by the peanut research community, and to conduct high-resolution phenotyping of this population. Because of the size of the population, as large as 2,000 to 3,000, the entire population will be genotyped. A core subset (or different core subsets) of the entire population will be developed (divided) based on the genetic similarity or based on unique marker scores for different trait (disease resistance). Therefore, the subset of individuals could be manageable in a replicated test in the field or greenhouse for testing a specific trait of disease resistance such as nematode resistance. Drought stress study will include irrigation and non-irrigation.
2. We will use the WGRS approach for the parental lines “SunOleic 97R and NC94022”, “Tifrunner and GT-C20”, and the derived RILs (referred to as the “S” and the “T” populations) to identify the SNPs and genotype the populations. In order to improve the map density and fine-map the QTLs for MAS, we plan to use WGRS approach to genotype this population to improve the genetic map density and to identify genomic regions/candidate genes controlling the resistant traits. SNP marker validation will be conducted through KASP assay. The KASP genotyping assay is a fluorescence based assay for identification of biallelic SNPs. KASP marker data will be analyzed using SNPviewer software (LGC Genomics) (http://www.lgcgroup.com) to generate genotype calls for each RIL and parental line, and were correlated with observed disease ratings (phenotypes) in the field for selection.
3. Recurrent selection is defined as re-selection generation after generation, with inter-mating of selected lines, such as RILs, to produce the population for the next cycle of selection. There are two methods using MAS in breeding selection for breeders. Recurrent selection is an efficient breeding method for increasing the frequency of superior genes for various economic characters. One RIL population described in Sub-objective 1B is the “S” population, and QTL mapping has been completed for targeted traits: total oil content, oil quality, disease resistance to early leaf spot (ELS), late leaf spot (LLS), and TSWV. Therefore, we propose to select eight RIL lines (founders) with known markers/QTL associated with specific traits for inter-crossing in order to stack/pyramid all favorable alleles in peanut breeding for superior cultivars with multiple traits. All traits of interest will be considered concurrently. The goal is to develop superior peanut lines, which have either high oil content (50% or above) or low oil content (40% or less) with high oleic acid and resistance to ELS, LLS, and TSWV.
Progress Report
ARS scientists at Tifton, Georgia, suggest identification of genetic markers linked to resistance to late leaf spot (LLS) in peanut has been a focus of molecular breeding for several years. Efforts have been hindered by limited mapping resolution due to low levels of genetic recombination and marker density available in traditional biparental mapping populations. To address this, multi-parental mapping populations including Nested Association Mapping (NAM) populations have been developed by ARS scientists at Tifton, Georgia, along with high-throughput genotyping single nucleotide polymorphism (SNP) arrays. ARS scientists at Tifton, Georgia, utilized a subset of two NAM populations, NAM_Tifrunner and NAM_Florida-07, which were genotyped with the Axiom_Arachis 58K SNP array and phenotyped for three years for LLS severity. These data were used by ARS scientists at Tifton, Georgia, for quantitative trait locus (QTL) and genome-wide association study (GWAS) analyses. These markers identified were associated with several putative resistance genes and pointed to a prominent role for Recognition of Peronospora Parasitica 13 (RPP13)-like NBS-LRR R-genes in LLS resistance, here named the peanut RPP13 (pRPP13) gene. This gene still requires functional validation and characterization. This approach clearly demonstrates the power of NAM populations for marker-trait association in peanut.
Accomplishments
1. New reference genomes and comparative genomics analyses in Aspergillus flavus. ARS scientists at Tifton, Georgia, believe available genomes of Aspergillus flavus have been invaluable for and have enabled genomics-assisted experiments including characterization of genes involved in a number of primary and secondary metabolic pathways. An understanding of A. flavus phenotypic diversity has been hindered by the lack of suitable and diverse references. To address these concerns, ARS researchers at Tifton, Georgia, led a team of collaborators in constructing two reference genomes for the A. flavus isolates AF13 and NRRL3357. These isolates were chosen based on two biologically-driven questions: (1) what are the causes of variation in A. flavus isolates’ aflatoxin production and (2) why do these isolates exhibit contrasting responses to reactive oxygen species (ROS), reactive compounds associated with drought stress which exacerbate aflatoxin production. Comparative analyses between these genomes revealed a large 310kb insertion on Chromosome 1 unique to AF13 containing 60 genes including a novel bZIP transcription factor gene, atfC, which may be involved in oxidative stress tolerance and aflatoxin production. Using this information, 264 genomes of Aspergillus isolates from field soils in Georgia and corn plants in Mississippi have been sequenced. These data will be used for genome wide association studies (GWAS) for identifying novel aflatoxin regulators, shedding light on the origin and evolution of the 310kb insertion, and as valuable tools for the aflatoxin and crop research communities for continuing study in this system.
Review Publications
Sharma, V., Gupta, P., Priscilla, K., Kumar, S., Ashree, B., Veershetty, A., Ramrao, D.P., Kambar, R., Naik, G.R., Kumar, A., Guo, B., Zhuang, W., Varshney, R.K., Pandey, M.K., Kumar, R. 2021. Metabolomics intervention towards better understanding plant traits. Cells. 10(2):346. https://doi.org/10.3390/cells10020346.
Gangurde, S.S., Nayak, S.N., Joshi, P., Shilp, P., Sudini, H.K., Chitikineni, A., Hong, Y., Guo, B., Chen, X., Pandey, M., Varshney, R.K. 2021. Comparative transcriptome analysis identified candidate genes for late leaf spot resistance and cause of defoliation in groundnut. International Journal of Molecular Sciences. 22, 4491:1-23.
Ali, M., Gunn, M., Stackhouse, T., Waliullah, S., Guo, B., Culbreath, A., Brenneman, T. 2021. Sensitivity of Aspergillus flavus isolates from peanut seeds in Georgia to Azoxystrobin, a Quinone outside Inhibitor (QoI) fungicide. Toxins. 7, 284:1-11. https://doi.org/10.3390/jof7040284.
Fountain, J.C., Clevenger, J.P., Nadon, B.D., Youngblood, R.C., Chang, P., Starr, D., Wang, H., Wiggins, R., Kemerait, R.C., Bhatnagar, D., Ozias-Akins, P., Varshney, R.K., Scheffler, B.E., Vaughn, J.N., Guo, B. 2020. Two new Aspergillus flavus reference genomes reveal a large insertion potentially contributing to isolate stress tolerance and aflatoxin production. Genes, Genomes, and Genomics. 10(9). https://doi.org/10.1534/g3.120.401405.
Fountain, J.C., Clevenger, J.P., Nadon, B.D., Wang, H., Abbas, H.K., Kemerait, R.C., Scully, B.T., Vaughn, J.N., Guo, B. 2020. Draft genome sequences of one Aspergillus parasiticus idolate and nine Aspergillus flavus isolates with varying stress tolerance and aflatoxin production. Microbiology Resource Announcements. 9e00478-20. https://doi.org/10.1128/MRA.00478-20.
Soni, P., Pandey, A.K., Nayak, S.N., Pandey, M.K., Tolani, P., Pandey, S., Sudini, H.K., Bajaj, P., Fountain, J.C., Singam, P., Guo, B., Varshney, R.K. 2021. Global transcriptome profiling identified transcription factors, biological process, and associated pathways for pre-harvest aflatoxin contamination in peanut. The Journal of Fungi. 7:1-18. https://doi.org/10.3390/jof7060413.
Soni, P., Nayak, S.N., Kumar, R., Pandey, M.K., Singh, N., Sudini, H.K., Bajaj, P., Fountain, J.C., Singam, P., Hong, Y., Chen, X., Zhuang, W., Liao, B., Guo, B., Varshney, R.K. 2020. Transcriptome analysis identified coordinated control of key pathways regulating cellular physiology and metabolism upon Aspergillus flavus infection resulting in reduced aflatoxin production in groundnut. The Journal of Fungi. 6(4):370. https://doi.org/10.3390/jof6040370.
