Separate loci underlie resistance to root infection and leaf scorch during soybean sudden death syndrome

Soybean [Glycine max (L.) Merr.] cultivars show differences in their resistance to both the leaf scorch and root rot of sudden death syndrome (SDS). The syndrome is caused by root colonization by Fusarium virguliforme (ex. F. solani f. sp. glycines). Root susceptibility combined with reduced leaf scorch resistance has been associated with resistance to Heterodera glycines HG Type 1.3.6.7 (race 14) of the soybean cyst nematode (SCN). In contrast, the rhg1 locus underlying resistance to Hg Type 0 was found clustered with three loci for resistance to SDS leaf scorch and one for root infection. The aims of this study were to compare the inheritance of resistance to leaf scorch and root infection in a population that segregated for resistance to SCN and to identify the underlying quantitative trait loci (QTL). “Hartwig”, a cultivar partially resistant to SDS leaf scorch, F. virguliforme root infection and SCN HG Type 1.3.6.7 was crossed with the partially susceptible cultivar “Flyer”. Ninety-two F5-derived recombinant inbred lines and 144 markers were used for map development. Four QTL found in earlier studies were confirmed. One contributed resistance to leaf scorch on linkage group (LG) C2 (Satt277; P = 0.004, R 2 = 15%). Two on LG G underlay root infection at R8 (Satt038; P = 0.0001 R 2 = 28.1%; Satt115; P = 0.003, R 2 = 12.9%). The marker Satt038 was linked to rhg1 underlying resistance to SCN Hg Type 0. The fourth QTL was on LG D2 underlying resistance to root infection at R6 (Satt574; P = 0.001, R 2 = 10%). That QTL was in an interval previously associated with resistance to both SDS leaf scorch and SCN Hg Type 1.3.6.7. The QTL showed repulsion linkage with resistance to SCN that may explain the relative susceptibility to SDS of some SCN resistant cultivars. One additional QTL was discovered on LG G underlying resistance to SDS leaf scorch measured by disease index (Satt130; P = 0.003, R 2 = 13%). The loci and markers will provide tagged alleles with which to improve the breeding of cultivars combining resistances to SDS leaf scorch, root infection and SCN HG Type 1.3.6.7

Genetic analysis of soybean resistance to Fusarium solani f.sp. glycines

In order to study the genetic control of soybean resistance to sudden death syndrome (SDS), a 5 x 5 diallel with the F2 generation, without the reciprocals, was carried out in a greenhouse. The following parents were used: Forrest, MG/BR-46 (Conquista), IAC-4, FT-Cristalina, and FT-Estrela. The first two cultivars are more resistant to SDS than IAC-4, which is considered to be moderately resistant to SDS, and the last two cultivars are highly susceptible. The fungus was inoculated with three colonized sorghum grains placed at the bottom of the holes with two soybean seeds. Single plants were evaluated between 14 and 37 days after emergency based on foliar severity symptoms (1-5) of SDS. The disease incidence and a disease index were also calculated for each plot (clay pots with five plants each). The analysis for severity and disease index was performed only with the data of the 37th day after emergence. Additive and dominant genetic effects were detected by Jinks-Hayman's analysis, but the dominant genetic effects were higher. The genetic parameters estimated indicated that the average degree of dominance showed the presence of overdominance; at least three loci or genic blocks that exhibited dominance were responsible for the genetic control of SDS resistance; the estimates of narrow-sense heritabilities were moderate (0.48 to 0.62), but in the broad-sense they were higher (0.90 to 0.95), thus reinforcing the presence of dominance effects; and the resistance to SDS was controlled mostly by dominant alleles. Five microsatellite markers (Satt163, Satt309, Satt354, Satt371 and Satt570), reported as linked to five QRLs of the SDS, were used to genotype the parents and showed the possibility of occurrence of multiallelism in those loci, but this evidence did not invalidate the fitting of the data to the Jinks-Hayman's model