Location: Crop Genetics and Breeding Research
2019 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
The primary focus of this project is to develop genetic resources and tools for breeding superior peanut cultivars with multiple-stress resistance traits.
A multi-parent advanced generation inter-cross (MAGIC) population has been developed, which can provide an increased level of recombination and mapping resolution by integrating multiple alleles from different parents for fine mapping of complex quantitative traits and for breeding selection of improved/diverse lines with novel genetic variation/traits. This MAGIC population was derived from eight founders: SunOleic 97R, NC94022, Tifrunner, GT-C20, Florida 07, GP-NC WS16 (SPT06-06), Georgia 13M, and TifNV-High O/L. InterCrosses (2-way, 4-way, 8-way) and have been made using a simple ‘funnel’ breeding scheme with the founders combined in equal proportions, followed by a single seed descent (SSD) to develop the MAGIC population. Currently, this MAGIC has been advanced to F2/F3 generation with 3575 F2:3 families. Likewise, the marker assisted recurrent selection (MARS) population with eight founders from the “S” RIL population (SunOleic 97R and NC94022) has been made at the 8-way crosses resulting in over 1000 F1s for trait stacking/pyramiding to develop superior lines of peanut using a marker assisted recurrent selection (MARS) breeding scheme.
Accomplishments
1. Fine-mapping and identification of candidate genes in chromosome A01 for resistance to tomato spotted wilt virus (TSWV) of peanut. Completion of peanut reference genomes facilitates development of peanut tools and identification of useful markers and traits/genes for improvement of peanut disease resistance and quality. TSWV causes severe yield loss in the Southeastern U.S. ARS researchers in Tifton, Georgia, have developed a recombinant inbred line population from SunOleic 97R and NC94022, and identified a major quantitative trait locus (QTL) for TSWV in chromosome A01. The QTL was mapped between the markers Ah126 and GNB842, and the nearest marker GNB555 was in the region of 20 Mb to 30 Mb of chromosome A01, which has been improved within 89.5 Kb physical interval at about 9.5 Mb using whole genome resequencing. The potential candidate gene was further narrowed at about 0.5 Mb, the distal region of chromosome A01, nucleotide-binding–leucine-rich repeat (NB-LRR)-encoding genes are of interest.
2. Whole genome re-sequencing (WGRS) facilitates identification of markers/genes linked to disease resistance. Leaf spots, including early leaf spot (ELS) and late leaf spot (LLS) are devastating diseases in peanut causing significant yield loss. ARS researchers in Tifton, Georgia constructed the first sequence-based high-density map with 8,869 SNPs assigned to 20 linkage groups, representing 20 chromosomes, for the “T” population (Tifrunner × GT-C20). The quantitative trait locus (QTL) analysis using high density genetic map and multiple season phenotyping data identified 35 main-effect QTLs. Among major effect QTLs mapped, there were two QTLs for early leaf spot on B05 with 47.42% PVE and B03 with 47.38% PVE, two QTLs for late leaf spot on A05 with 47.63% and B03 with 34.03% PVE, and one QTL for TSWV on B09 with 40.71% PVE. The identified QTL regions had disease resistance genes including R-genes and transcription factors.
Review Publications
Deng, Y., Chen, H., Zhang, C., Cai, T., Zhang, B., Zhou, S., Fountain, J., Pan, R., Guo, B., Zhuang, W. 2018. Evolution and characterization of the AhRAF4 NB-ARC gene family induced by Aspergillus flavus inoculation and abiotic stresses in peanut. Plant Biology. 20(4):641-802. https://doi.org/10.1111/plb.12726.
Bertioli, D.J., Jenkins, J., Clevenger, J., Dudchenko, O., Gao, D., Seijo, G., Leal-Bertioli, S., Ren, L., Farmer, A., Pandey, M., Samoluk, S.S., Abernathy, B., Agarwal, G., Ballen-Taborda, C., Cameron, C., Campbell, J., Chavarro, C., Chitikineni, A., Chu, Y., Dash, S., El Baidouri, M., Guo, B., Huang, W., Kim, K.D., Korani, W., Lanciano, S., Lui, C.G., Mirouze, M., Moretzsohn, M.C., Pham, M., Shin, J.H., Shirasawa, K., Sinharoy, S., Sreedasyam, A., Weeks, N.T., Zhang, X., Zheng, Z., Sun, Z., Froenicke, L., Aiden, E.L., Michelmore, R., Varshney, R.K., Holbrook Jr, C.C., Cannon, E.K., Scheffler, B.E., Grimwwood, J., Ozias-Akins, P., Cannon, S.B., Jackson, S.A., Schmutz, J. 2019. The genome sequence of segmental allotetraploid peanut Arachis hypogaea. Nature Genetics. 51:877-884. https://doi.org/10.1038/s41588-019-0405-z.
Gangurde, S.S., Kumar, R., Pandey, A.K., Burow, M., Laza, H.E., Nyak, S.N., Guo, B., Liao, B., Bhat, R.S., Madhuri, N., Hemalatha, S., Sudini, H.K., Janila, P., Latha, P., Khan, H., Motagi, B.N., Radhakrishnan, T., Puppala, N., Varshney, R.K., Pandey, M.K. 2019. Climate-smart groundnuts for achieving high productivity and improved quality: current status, challenges and opportunities. Book Chapter. p. 133-172. https://doi.org/10.1007/978-3-319-93536-2_3.
Zhuang, W., Chen, H., Yang, M., Wang, J., Pandey, M.K., Zhang, C., Chang, W., Zhang, L., Zhang, X., Tang, R., Garg, V., Wang, X., Tang, H., Chow, C., Wang, J., Deng, Y., Wang, D., Khan, A.W., Yang, Q., Cai, T., Bajaj, P., Wu, K., Guo, B., Zhang, X., Li, J., Liang, F., Hu, J., Liao, B., Liu, S., Chitikineni, A., Yan, H., Zheng, Y., Shan, S., Liu, Q., Xie, D., Wang, Z., Khan, S.A., Ali, N., Zhao, C., Li, X., Luo, Z., Zhang, S., Zhuang, R., Peng, Z., Wang, S., Mamadou, G., Zhuang, Y., Zhao, Z., Yu, W., Xiong, F., Quan, W., Yuan, M., Li, Y., Zou, H., Xia, H., Zha, L., Fan, J., Yu, J., Xie, W., Yuan, J., Chen, K., Zhao, S., Chu, W., Chen, Y., Sun, P., Meng, F., Zhuo, T., Zhao, Y., Li, C., He, G., Zhao, Y, Wang, C., Kavikishor, P.B., Pan, R., Paterson, A.H., Wang, X., Ming, R., Varshney, R.K. 2019. The genome of cultivated peanut provides insight into legume karyotypes, polyploid evolution and crop domestication. Nature Genetics. 51:865-876. https://doi.org/10.1038/s41588-019-0402-2.
Zhao, Y., Ma, J., Li, M., Deng, L., Li, G., Xia, H., Zhao, S., Hou, L., Li, P., Ma, C., Yuan, M., Ren, L., Gu, J., Guo, B., Zhao, C., Wang, X. 2019. Whole-genome resequencing-based QTL-seq identified AhTc1 gene encoding a R2R3-MYB transcription factor controlling peanut purple testa colour. Plant Biotechnology Journal. https://doi.org/10.1111/pbi.13175.
Pandey, M.K., Kumar, R., Pandey, A.K., Soni, P., Gangurde, S.S., Sudini, H.K., Fountain, J.C., Liao, B., Desmae, H., Okori, P., Chen, X., Jiang, H., Mendu, V., Falalou, H., Njoroge, S., Mwololo, J., Guo, B., Zhuang, W., Wang, X., Liang, X., Varshney, R.K. 2019. Mitigating aflatoxin contamination in groundnut through a combination of genetic resistance and post-harvest management practices. Toxins. 11:315. https://doi.org/10.3390/toxins11060315.
Fountain, J.C., Abbas, H.K., Ni, X., Scully, B.T., Lee, R.D., Kemerait, R.C., Guo, B. 2018. Evaluation of maize inbred lines and topcross progeny for resistance to pre-harvest aflatoxin contamination in the field. The Crop Journal. 7:118-125. https://doi.org/10.1016/j.cj.2018.10.001.
Khera, P., Pandey, M.K., Mallikarjuna, N., Sriswathi, M., Roorkiwal, M., Janila, P., Sharma, S., Shilpa, K., Sudini, H., Guo, B., Varshney, R.K. 2018. Advanced backcross QTL analysis for disease resistance, oil quality and yield component traits revealed genetic imprints of domestication in groundnut (Arachis hypogaea L.). Molecular Genetics and Genomics. 294:365-378.
