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- Title of proposed research project:
Genetic analysis of yield components under drought conditions and QTL mapping for drought tolerance in soybean [Glycine max (L.) Merr.]
- Summary of research
An estimated 40% of soybean [Glycine max (L.) Merr.] production worldwide is lost each year due to drought and such losses may further increase as droughts become more frequent and severe because of climate change. That is why the development of soybean drought-tolerant varieties becomes increasingly more important. We will perform a phenotypic characterization of soybean population derived from a cross between drought-tolerant genotype and drought-sensitive genotype under both well-watered (WW) and water-stressed (WS) conditions. Joint linkage quantitative trait loci (QTL) mapping will be performed to detect the genomic regions that control soybean drought tolerance under different water regimes. Finally, Genome-Wide Association Studies (GWAS) will be performed to identify the candidate genes associated with yield components under drought conditions. This research aims to present drought tolerance QTLs and candidate genes for soybean scientific community and provide a significant contribution to the breeding effort for soybean drought tolerance.
Keywords: Soybean [Glycine max (L.) Merr.], Yield components, Drought tolerance, Quantitative trait loci
Drought is one of the major constraints to soybean [Glycine max (L.) Merr.] production in China and worldwide. Up to 40% of soybean productivity is lost because of drought  and such losses may further increase as droughts become more frequent and severe because of climate change . Irrigation is not an option for large numbers of farmers and there is limited potential for any expansion of irrigation in developing countries .
The use of genetics to improve drought tolerance and provide yield stability is an important part of the solution to stabilizing global production. That is why the development of soybean varieties with enhanced tolerance to drought stress and higher water use efficiency (WUE) has become a high priority goal for major breeding programs, both in the private and public sectors. The breeding programs improve drought tolerance via diverse strategies such as recurrent selection and evaluation of segregating population under managed and multi-location drought-stress environment, use of secondary traits for selection under drought condition, genomic-based approach and transgenic technology.
Great breeding efforts have made for drought tolerance in soybean using different yield components, secondary traits and molecular markers for selection. Mian et al. identified four QTL associated with WUE using 120 F4-derived soybean population from a cross Young × PI416937 . In another study, they developed a population of 116 F2-derived line from a cross of S100 × Tokyo and reported an additional QTL for WUE in soybean . More recently, 40 QTLs including 17 for leaf water status traits under drought stress and 23 for seed yield under well-watered and drought-stressed conditions were identified using soybean recombinant inbred population with 184 F2:7:11 lines developed from a cross between Kefeng1 (drought tolerant) and Nannong1138-2 (drought sensitive) . Using the same population, Du et al. have also reported 19 QTLs associated with seed yield per plant and 10 QTLs associated with drought susceptibility index . In a different study, Zhang et al. constructed a set of BC2F3 lines with Hongfeng 11 as recurrent parent and Harosoy as donor parent and detected 18 QTLs of drought tolerance at germination stage in soybean . Recently, Khan et al. used restricted two-stage multi-locus genome-wide association studies with a nested association mapping population with 403 lines comprising two recombinant inbred line populations: M8206 × TongShan and ZhengYang × M8206 to identify 73 and 38 QTLs for the drought tolerance traits relative root length and relative shoot length, respectively .
Although several studies have reported QTLs for drought tolerance in soybean, the QTL mapping results need to be validated before application to cultivar development. That is why a new study is necessary in order to provide more information and significantly contribute to the finding universal QTLs.
This study aims to:
- Perform a phenotypic characterization of soybean population derived from a cross between drought-tolerant genotype and drought susceptible genotype under both well-watered (WW) and water-stressed (WS) conditions;
- Perform a Joint linkage QTL mapping to detect the genomic regions that control soybean drought tolerance under different water regimes;
- Perform a Genome-Wide Association Studies (GWAS) to identify the candidate genes associated yield components under drought conditions.
- Material and Methods
Plant materials and field trials
An F2 population derived from a cross between drought-tolerant genotype and drought-susceptible genotype will be used to construct the genetic linkage map. The two parents and the F2 plants will be evaluated both under field and greenhouse conditions with two water regimes: well-watered (control) and drought stress. The experiment will be arranged in a random complete block design with three replicates.
Relative water content and excised leaf water losing rate and leaf wilting will be measured in order to find out the water status in plants under drought stress. Seed yield per plant will be measured in both drought-stressed and well-watered control plots. The following yield components will be considered: number of nodes per plant, number of pods per node, number of seeds per pod and seed size.
Using SAS software (SAS Institute, lnc.), we will perform a descriptive statistics analysis on the phenotypic data in order to compare the traits of the parents with the F2 plants.
We will harvest leaf samples from the parent genotypes and each F2 plant for DNA extraction using CTAB procedure. For Single Nucleotide Polymorphism (SNPs) genotyping, the restriction-site-association DNA sequencing (RAD-seq) will be used. The genotypic data will be imported into PowerMarker V3.25  for calculation of descriptive statistics. We will evaluate the population structure using the software STRUCTURE 2.2 .
Pearson’s correlation coefficients will be calculated to determine relationships between the various traits under study. QTL analysis will be performed using composite interval mapping in WinQTL cartographer, version 2.5 (http://statgen.ncsu.edu).
- Anticipated outcome and value of the research
This study will detect QTLs under well-watered and water-stressed conditions by joint linkage mapping in soybean F2 population. The genome-wide association analysis will identify SNPs associated with yield components under drought condition and locate the SNPs in candidate genes. This research aims to present drought tolerance QTLs and candidate genes for soybean scientific community and provide a significant contribution to the breeding effort for soybean drought tolerance.
7. Literature cited
 D. T. Le et al., “Differential Gene Expression in Soybean Leaf Tissues at Late Developmental Stages under Drought Stress Revealed by Genome-Wide Transcriptome Analysis,” PLoS One, vol. 7, no. 11, p. e49522, Nov. 2012.
 FAOSTAT, “Statistical Database of the Food and Agriculture of the United Nations,” Rome, 2010.
 World Bank, “Agriculture Investment Sourcebook. Agriculture and Rural Development,” Washington DC, 2005.
 M. A. R. Mian et al., “Molecular Markers Associated with Water Use Efficiency and Leaf Ash in Soybean,” Crop Sci., vol. 36, no. 5, p. 1252, 1996.
 M. A. R. Mian, D. A. Ashley, and H. R. Boerma, “An Additional QTL for Water Use Efficiency in Soybean,” Crop Sci., vol. 38, no. 2, p. 390, 1998.
 W. Du, D. Yu, and S. Fu, “Detection of Quantitative Trait Loci for Yield and Drought Tolerance Traits in Soybean Using a Recombinant Inbred Line Population,” J. Integr. Plant Biol., vol. 51, no. 9, pp. 868–878, Sep. 2009.
 W. Du, M. Wang, S. Fu, and D. Yu, “Mapping QTLs for seed yield and drought susceptibility index in soybean (Glycine max L.) across different environments,” J. Genet. Genomics, vol. 36, no. 12, pp. 721–731, Dec. 2009.
 W. B. Zhang et al., “Dissection of genetic overlap of drought and low-temperature tolerance QTLs at the germination stage using backcross introgression lines in soybean,” Mol. Biol. Rep., vol. 39, no. 5, pp. 6087–6094, May 2012.
 M. A. Khan, F. Tong, W. Wang, J. He, T. Zhao, and J. Gai, “Analysis of QTL–allele system conferring drought tolerance at seedling stage in a nested association mapping population of soybean [Glycine max (L.) Merr.] using a novel GWAS procedure,” Planta, vol. 248, no. 4, pp. 947–962, Oct. 2018.
 K. Liu and S. V. Muse, “PowerMarker: an integrated analysis environment for genetic marker analysis,” Bioinformatics, vol. 21, no. 9, pp. 2128–2129, May 2005.
 J. K. Pritchard, M. Stephens, and P. Donnelly, “Inference of Population Structure Using Multilocus Genotype Data,” Genetics, vol. 155, no. 2, pp. 945–959, 2000.
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