Research Project: Improvement of Genetic Resistance to Multiple Biotic and Abiotic Stresses in Peanut

Location: Crop Genetics and Breeding Research

2022 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
As DNA sequencing costs decrease and bioinformatics advances, it is increasingly feasible for genetic and genomic mapping studies to be based on whole genome sequencing data, even for large populations. The resolution of genetic mapping is often insufficient to pinpoint causal genes in bi-parental and smaller-sized populations. Recently, we developed a multiparent advanced generation intercross (MAGIC) population with eight parental founders to conduct high-resolution mapping of quantitative traits, including peanut early leaf spot (ELS) and late leaf spot (LLS). This population comprises 2775 F7 recombinant inbred lines (RILs). A subset of 310 RILs were randomly selected to evaluate the suitability of the population for genetic and genomic studies and to map the causal quantitative trait loci (QTLs) or genes precisely. The genotyping was conducted by whole genome re-sequencing at low coverage, and single nucleotide polymorphism (SNPs) were called using a new sequence analysis pipeline KHUFU. The phenotypic data collected in the first year include disease rating for leaf spots, tomato spotted wilt virus (TSWV), total seed oil chemistry, pod constriction and reticulation, 100 pod-weight, 100 seed-weight, and shelling percentage. These phenotypic data showed significant variation within this MAGIC population and demonstrated normal distribution for all traits, indicating the potential utility of this MAGIC as a new genetic resource for dissection of complex traits and for breeding selection. The controlled inoculation in the greenhouse and laboratory will be carried out for parental lines to confirm the resistance and susceptibility to ELS and LLS pathogens. This peanut MAGIC could serve as an important resource used for fine mapping of disease resistance, yield, and quality.

Accomplishments
1. Development of a pangenome for Aspergillus flavus, a carcinogenic aflatoxin producing fungal pathogen. Pangenome is a framework for analyzing the genomic diversity of entire genes in one species such as Aspergillus flavus, a fungal pathogen producing carcinogenic aflatoxin which is a threat to human health, global food safety and security. A pangenome of Aspergillus flavus can be used as a reference genome to discover novel aflatoxin regulator genes and will be used to identify the origin of a new variant in certain isolates of Aspergillus flavus, which produce much higher aflatoxin in contaminated crops. ARS researchers at Tifton, Georgia, sequenced a total of 264 isolates, including 100 isolates of corn from Mississippi State and 164 isolates from Georgia soils of different cropping systems. Whole genome sequencing data were used for genome assemblies for each isolate and the sequences of 121 isolates from public database were also used in this study. There was a total of 1.2 million variations called single nucleotide polymorphisms (SNPs) identified among these Aspergillus flavus isolates. Population structure analysis identified four sub-populations, using these SNP variations, indicating that there were significant genomic diversities among these isolates. This study discovered 7,315 core genes presenting in all isolates and 10,540 accessory genes which present only in some isolates. Interestingly, this study identified 5,994 new genes which have not been reported in Aspergillus flavus genome. Furthermore, this pangenome will be used for genome-wide association (GWAS) study to test the utility for identification of genes associated with aflatoxin and secondary metabolite productions.

Review Publications
Zhang, X., Pandey, M.K., Wang, J., Zhao, K., Ma, X., Li, Z., Zhao, K., Gong, F., Guo, B., Varshney, R.K., Yin, D. 2021. Chromatin spatial organization of wild type and mutant peanuts reveals high-resolution genomic architectures and interaction alterations. Genome Biology. 22:315. https://doi.org/10.1186/s13059-021-02520-x.
Korani, W., O'Connor, D., Chu, Y., Chavarro, C., Ballen, C., Guo, B., Ozias-Akins, P., Wright, G., Clevenger, J. 2021. De novo QTL-seq identifies loci linked to blanchability in peanut (Arachis hypogaea) and refines previously identified QTL with low coverage sequence. Agronomy. 11(11):2201. https://doi.org/10.3390/agronomy11112201.
Parmar, S., Sharma, V., Deekshitha, B., Soni, P., Joshi, P., Gangurde, S.S., Wang, J., Bera, S.K., Bhat, R.S., Desmae, H., Shirasawa, K., Guo, B., Varshney, R.K., Pandey, M.K. 2022. Recent advances in genetics, genomics, and breeding for nutritional quality in groundnut. In Gosal, S.S., Wani, S.H., editors. Accelerated Plant Breeding. Volume 4. New York, NY: Springer Nature. p. 111-137. https://doi.org/10.1007/978-3-030-81107-5_4.
Bomireddy, D., Gangurde, S.S., Variath, M.T., Janila, P., Manohar, S.S., Sharma, V., Parmar, S., Deshmukh, D., Mangala, R., Mohan Reddy, D., Sudhakar, P., Bhaskara Reddy, B., Varshney, R.K., Guo, B., Pandey, M.K. 2022. Discovery of major quantitative trait loci and candidate genes for fresh seed dormancy in groundnut. Agronomy. 12(2):404. https://doi.org/10.3390/agronomy12020404.