The ‘PI 438489B’ by ‘Hamilton’ SNP-Based Genetic Linkage Map of Soybean [Glycine max (L.) Merr.] Identified Quantitative Trait Loci that Underlie Seedling SDS Resistance

Abstract

Soybeans [Glycine max (L.) Merr.] are susceptible to many diseases including fungal diseases such as soybean sudden death syndrome (SDS). Several studies reported SDS resistance quantitative trait loci (QTL) on the soybean genome using different recombinant inbred line (RIL) populations and low density genetic linkage maps. High density exclusively single nucleotide polymorphisms-based (SNP-based) maps were not yet reported in soybean. The objectives of this study were (1) to construct a high density SNP-based genetic linkage map of soybean using the 'PI438489B' by 'Hamilton' (PIxH, n=50) recombinant inbred line population, and (2) to map QTL for SDS resistance using this high-density reliable genetic SNP-based map. The PI438489B by Hamilton high-density SNP-based genetic map was a high density map composed of 31 LGs, 648 SNPs, and covered 1,524.7 cM with an average of 2.37 cM between two adjacent SNP markers. Fourteen significant QTL were identified for SDS resistance using interval mapping (IM) and composite interval mapping (CIM) with LOD scores that ranged between 2.6 and 5.0. Twelve QTL were identified for foliar disease severity (FDS) and three QTL for root rot severity (RRS) of which one QTL underlain both FDS and RRS. The fourteen QTL were mapped onto ten separate chromosomes of the soybean genome. Seven of the intervals encompassing the QTL had been identified previously (on LGs C1, C2, D1b, G, L, N and O) associated with resistance to SDS but seven were novel (LGs A2 (2), B1, C2, D1a, D1b and O). We constructed the first PI438489B by Hamilton exclusively SNP-Based map and identified fourteen QTL that underlie SDS resistance including both resistances to foliar and root rot symptoms caused by Fusarium virguliforme infection. The QTL discovered here for SDS resistance could be useful to include in breeding programs in developing soybean cultivars resistant to SDS.

Additional Quantitative Trait Loci and Candidate Genes for Seed Isoflavone Content in Soybean

“…The identification of genes within the QTL regions was performed as described earlier (Barakat, Staton, Cheng, Park, Yassin, Ficklin, Yeh, Hebard, & Sederoff, 2012;Kassem et al, 2012). Briefly, each QTL was bounded by two single nucleotide polymorphism markers (SNPs).…”

Genetic Analysis of Yield Components in the PI 438489B by ‘Hamilton’ Recombinant Inbred Line (RIL) Population of Soybean [Glycine max (L.) Merr.]

“…The 'PI 438489B' By 'Hamilton' RIL population (PIxH, n=50) used in this study was developed at the University of Missouri Agronomy Research Center (Yue, Arelli, & Sleper, 2001) and was extensively studied to map QTL for SDS resistance (Kassem et al, 2012), SCN resistance (Yue et al, 2001). …”

“…A particularly important advantage of the Illumina-based SNP allele detection over the SSR marker allele detection is the elimination of the tedious gel-based marker allele visualization required for the latter. The SNP-based genetic linkage map (Kassem et al, 2012) was used for genetic analysis of yield components of a soybean RIL population in this study.…”

Quantitative Trait Loci Associated with Moisture, Protein, and Oil Content in Soybean [Glycine max (L.) Merr.]

“…On chromosome 1 (LG D1a) 'qOIL001' was identified to be associated with the oil trait by the marker interval ss107915391-ss107917465. On the same chromosome, QTL for soybean sudden death syndrome (SDS) (Kassem et al, 2012) and soybean cyst nematode (SCN) resistance were previously identified using the same soybean population. QTL for several other agronomic traits such as Japanese root-knot nematode resistance, protein concentration, oil content, and leaf length, were also identified in the same chromosome 1 (LG D1a;SoyBase, 2012 (Orf et al, 1999;Mansur, Lark, Kross, & Oliveira, 1993;Mansur et al, 1996).…”

“…All of these interfered in QTLs to improve protein and oil content of soybean by marker-assisted selection (MAS). On the other hand, several studies reported SNP markers associated with QTL for several traits in soybean (Vuong, Wu, Pathan, Valliodan, & Nguyen, 2007), but there are only two studies reported an exclusively SNP-based genetic map (Kassem, 2012;Hyten et al, 2010a). The genetic relationship between moisture, protein, oil and other traits could be explored by comparing QTL mapping results when available and fine mapping of each trait would provide accurate genetic information.…”