Molecular tools for detecting Pdh1 can improve soybean breeding efficiency by reducing yield losses due to pod shatter
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Pod shattering is an ancestral trait that promotes seed dispersal; however, shattering can have substantial yield losses in cultivated soybean. During the improvement process, American soybean breeders virtually eliminated the shatter phenotype for released varieties, but in other countries, such as Ghana, shatter persists. The objective of our research was to find a molecular tool to implicate genetic shatter susceptibility, validate its usefulness, and apply this knowledge to identify shattering potential in parental lines. Previous research revealed the gene Pdh1 on chromosome 16 plays a crucial role in determining the shatter phenotype. A perfect molecular marker assay was developed to detect alleles of the Pdh1 gene. A genome-wide association study (GWAS) was performed using the Pdh1 allele status as a phenotype and identified a highly associated marker in the SoySNP50K array. Soybean accessions from the National Plant Germplasm System (GRIN-NPGS) with shatter score and SoySNP50K data were evaluated to determine the impact of the predicted Pdh1 alleles on early and late pod shattering. An online tool was developed to enable researchers to query the GRIN collection for the predicted Pdh1 allele status. Lines from an African soybean germplasm collection were analyzed, and it was determined that 22.5% of lines had the shatter-susceptible alleles of Pdh1; two of seven Ghanaian released soybean varieties had the shatter-susceptible alleles of Pdh1. Soybean breeding programs that access germplasm from the GRIN or the African collection can utilize these resources to eliminate the Pdh1 effects on pod shatter and thus improve yield potential.
KeywordsSoybean Breeding Domestication Molecular tools Pod shatter
This work was supported by the US Department of Agriculture-Agricultural Research Service. This research is in part funded by the USAID Feed the Future Lab for Soybean Value Chain Research. The award number is Cooperative Agreement Number AID OAA-L-14-00001.
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Conflict of interest
The authors declare that they have no conflict of interest.
- Carter TE, Nelson RL, Sneller CH, Cui Z (2004) Genetic diversity in soybean. Soybeans: Improvement, production, and uses:303–416Google Scholar
- Fang C, Ma Y, Wu S, Liu Z, Wang Z, Yang R, Hu G, Zhou Z, Yu H, Zhang M, Pan Y, Zhou G, Ren H, du W, Yan H, Wang Y, Han D, Shen Y, Liu S, Liu T, Zhang J, Qin H, Yuan J, Yuan X, Kong F, Liu B, Li J, Zhang Z, Wang G, Zhu B, Tian Z (2017) Genome-wide association studies dissect the genetic networks underlying agronomical traits in soybean. Genome Biol 18:161CrossRefGoogle Scholar
- Food and Agriculture Organization of the United Nations (2014) FAOSTAT statistics database. http://www.fao.org/faostat. Accessed 5 Feb 2019
- Goldsmith PD (2008) Economics of soybean production, marketing and utilization. Soybeans Chemistry, Production, Processing, and Utilization:117–150Google Scholar
- Joshi T, Patil K, Fitzpatrick MR, Franklin LD, Yao Q, Cook JR, Wang Z, Libault M, Brechenmacher L, Valliyodan B, Wu X, Cheng J, Stacey G, Nguyen HT, Xu D (2012) Soybean Knowledge Base (SoyKB): a web resource for soybean translational genomics. BMC Genomics 13(Suppl 1):S15. https://doi.org/10.1186/1471-2164-13-S1-S15 CrossRefPubMedPubMedCentralGoogle Scholar
- Joshi T, Wang J, Zhang H, Chen S, Zeng S, Xu B, Xu D (2017) The evolution of soybean Knowledge Base (SoyKB). In: Plant Genomics Databases. Springer, pp 149–159Google Scholar
- Kong F, Liu B, Xia Z, Sato S, Kim BM, Watanabe S, Yamada T, Tabata S, Kanazawa A, Harada K, Abe J (2010) Two coordinately regulated homologs of FLOWERING LOCUS T are involved in the control of photoperiodic flowering in soybean. Plant Physiol 154:1220–1231. https://doi.org/10.1104/pp.110.160796 CrossRefPubMedPubMedCentralGoogle Scholar
- Liu Y, Khan SM, Wang J, Rynge M, Zhang Y, Zeng S et al (2016) PGen: large-scale genomic variations analysis workflow and browser in SoyKB. BMC Bioinformatics 17(Suppl 13):337Google Scholar
- Masuda T, Goldsmith PD (2009) World soybean production: area harvested, yield, and long-term projections. Int Food Agribusiness Manag Rev 12:143–162Google Scholar
- Nguyen H, et al. (2018) Large scale soybean genome re-sequencing consortium, pre-publication. https://soybase.org/projects/SoyBase.B2014.02/resequencing%20data%20usage%20policy.htm. Accessed May 2018
- Saxe L, Clark C, Lin S, Lumpkin T (1996) Mapping the pod-shattering trait in soybean. Soybean Genetics Newsletter (USA)Google Scholar
- Zhou Z, Jiang Y, Wang Z, Gou Z, Lyu J, Li W, Yu Y, Shu L, Zhao Y, Ma Y, Fang C, Shen Y, Liu T, Li C, Li Q, Wu M, Wang M, Wu Y, Dong Y, Wan W, Wang X, Ding Z, Gao Y, Xiang H, Zhu B, Lee SH, Wang W, Tian Z (2015) Resequencing 302 wild and cultivated accessions identifies genes related to domestication and improvement in soybean. Nat Biotechnol 33:408–414CrossRefGoogle Scholar