Genetic components and major QTL confer resistance to bean pyralid (Lamprosema indicata Fabricius) under multiple environments in four RIL populations of soybean

Abstract

Bean pyralid (BP; Lamprosema indicata Fabricius) is one of the major leaf-feeding insects that affect soybean crops in central and southern China. Four recombinant inbred line populations (KY, WT, XG and SX) were tested during 2004-2006 in Nanjing, China, to identify quantitative trait loci (QTL) for resistance to BP on the basis of data for rolled leaflet percentage under field infestation conditions. The mapping was performed using QTL Network V2.0 and checked with Windows QTL Cartographer V2.5 and IciMapping V2.2. The results showed that 81-92 % of the phenotypic variation was accounted for by additive QTL (27-43 %), epistatic QTL pairs (5-13 %), and collective unmapped minor QTL (38-58 %). In total, 17 QTL were detected on 11 linkage groups, of which two had additive effects, six had both additive and epistatic effects, and nine had only epistatic effects. Eight epistatic QTL pairs were observed, of which three pairs involved two QTL with additive effects, one involved one QTL with additive effect, and four involved no QTL with additive effects. Different genetic structures for BP resistance were found among the populations. Eight QTL (five additive and three epistatic pairs) were detected in KY, ten QTL (four additive and five epistatic pairs) were detected in WT, and only one additive QTL was detected in both the XG and the SX populations. BP12-1 and BP1-1 are major QTL, with the former accounting for 15, 31, and 50 % of the total genetic variation (including epistasis) in KY, WT, and XG, respectively, and the latter accounting for 13 and 32 % of the total genetic variation in KY and SX, respectively. The additive × year and epistasis × year interaction effects were negligible, indicating that the QTL were stable over the years. Because 41-68 % of the total genetic variation could not be accounted for by these QTL, the use of both identified QTL and unmapped minor QTL in breeding for BP resistance should be considered.

Identification of major QTLs and epistatic interactions for seed protein concentration in soybean under multiple environments based on a high-density map

“…The general MCIM model incorporated the fixed terms of additive effects (A) and additive 9 additive epistatic effects (AA) as well as random terms for the environment, additive 9 environment interactions, and epistasis 9 environment interaction effects; in addition, the critical F value of the MCIM model was calculated with 10,000 permutation tests. The QTL effects were estimated using the Markov chain Monte Carlo method with 20,000 Gibbs sampler iterations and candidate interval selection and putative QTL detection, and the QTL effects were calculated with an experiment-wise type I error under a = 0.001 (Wang et al 1994;Yang et al 2007;Xing et al 2012). In this study, we analyzed the protein concentration data from all 20 environments.…”

“…The identification of genes/QTLs and the development of higher-density molecular genetic maps have facilitated the study of complex quantitative traits, and the analysis of genes has resolved the related traits into individual Mendelian factors (Xing et al 2012). Highdensity or integrated genetic linkage maps of soybean have been constructed by Cregan et al (1999), Song et al (2004), Choi et al (2007), Hyten et al (2010a, b) and Qi et al (2014a, b).…”

QTL Study to Reveal Soybean Response on Abiotic and Biotic Stresses

“…QTLs associated with complex abiotic and biotic stresses tolerance traits have been targeted and elucidated their relations with other traits by several approaches (www.phytozome.net/soybean; http://soybase.org). A number of QTLs for abiotic and biotic stresses as well as flowering/maturity have been identified using several populations across environments (Abdel-Haleem et al, 2012;Cheng et al, 2011;Hamwieh et al, 2011;Liu et al, 2011;Xing et al, 2012).…”

“…Hundreds of QTLs for fungal disease resistance have been detected, such as sudden death syndrome, phomopsis seed decay, Sclerotinia stem rot, soybean rust, and Phytopthora root rot. A number of QTLs related to insect and cyst nematode resistances have been identified using various soybean populations under multiple environments (Terry et al, 2000;Xing et al, 2012). Studies on QTL and MAS have also been focused on mitigating negative effects of abiotic stress.…”

Proteomic analysis by iTRAQ-MRM of soybean resistance to Lamprosema Indicate

“…In the event of serious pest damaged years, only veins and petioles have been left on the blades, thereby causing serious yield losses [10]. Relevant research has been reported in regard to the resource excavations of soybeans’ resistance to Lamprosema indicata [10, 11], resistance identification [12, 13], inheritance of the resistance [14, 15] and related gene QTL locations [16, 17]. However, the results of proteomics research which has focused on soybeans’ resistance to Lamprosema indicata has not yet been made available.…”