Abstract
Drought causes significant soybean [Glycine max (L.) Merr.] yield losses each year in rain-fed production systems of many regions. Genetic improvement of soybean for drought tolerance is a cost-effective approach to stabilize yield under rain-fed management. The objectives of this study were to confirm previously reported soybean loci and to identify novel loci associated with canopy wilting (CW) using a panel of 200 diverse maturity group (MG) IV accessions. These 200 accessions along with six checks were planted at six site-years using an augmented incomplete block design with three replications under irrigated and rain-fed treatments. Association mapping, using 34,680 single nucleotide polymorphisms (SNPs), identified 188 significant SNPs associated with CW that likely tagged 152 loci. This includes 87 SNPs coincident with previous studies that likely tagged 68 loci and 101 novel SNPs that likely tagged 84 loci. We also determined the ability of genomic estimated breeding values (GEBVs) from previous research studies to predict CW in different genotypes and environments. A positive relationship (P ≤ 0.05;0.37 ≤ r ≤ 0.5) was found between observed CW and GEBVs. In the vicinity of 188 significant SNPs, 183 candidate genes were identified for both coincident SNPs and novel SNPs. Among these 183 candidate genes, 57 SNPs were present within genes coding for proteins with biological functions involved in plant stress responses. These genes may be directly or indirectly associated with transpiration or water conservation. The confirmed genomic regions may be an important resource for pyramiding favorable alleles and, as candidates for genomic selection, enhancing soybean drought tolerance.
Highlights
Among the various abiotic stresses to which soybean [Glycine max (L.) Merr.] is exposed, drought causes the most severe yield losses and greatest year to year variation for rain-fed production systems throughout soybean-growing regions (Oya et al, 2004)
We evaluated the accuracy of predicting canopy wilting (CW) by correlating (PROC CORR, SAS v. 9.4, SAS Institute, 2013) observed wilting scores using three different datasets with genomic estimated breeding values (GEBVs) (Meuwissen et al, 2001) from the BayesB genomic prediction model (Pérez et al, 2010)
Soil moisture was plentiful at Research Station (RH), Pine Tree Research Station (PT), and CO for both IR and DR treatments on measurement dates in 2018 and 2019, the IR treatment received irrigation earlier in the season but the DR treatment did not, which may have resulted in differential responses
Summary
Among the various abiotic stresses to which soybean [Glycine max (L.) Merr.] is exposed, drought causes the most severe yield losses and greatest year to year variation for rain-fed production systems throughout soybean-growing regions (Oya et al, 2004). Between 1986 and 2020, the soybean production area in the United States impacted by drought ranged between 3. Mapping Canopy Wilting in Soybean and 59% (https://www.ncdc.noaa.gov/monitoring-content/ societal-impacts/cmsi/562.tot.out), and there were 11 years in which the proportion of the soybean production area impacted by drought exceeded 20%. Total estimated economic losses due to drought during this same time period (adjusted to the consumer price index) were $217 billion in the United States (https://www.ncdc.noaa.gov/billions/events/US/1980-2020). Genetic improvement of soybean for drought tolerance is a cost-effective approach to stabilize yield under rain-fed production. Past efforts to improve soybean drought tolerance through breeding have not taken full advantage of the potential genetic diversity available in germplasm collection (Frankel, 1984; Upadhyaya and Ortiz, 2001) nor have they taken direct advantage of the current understanding of physiological traits associated with drought tolerance (Sinclair et al, 2004; Sinclair and Purcell, 2005). Breeding efforts that target specific physiological traits that have agronomic advantages at the field level offer an alternative approach that draws upon previously under-utilized, diverse genetic resources (Sinclair et al, 2004; Tuberosa and Salvi, 2006)
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