Bayesian mapping of quantitative trait loci (QTL) controlling soybean cyst nematode resistant

Abstract

The soybean cyst nematode (SCN) is one of the most economically important pathogens of soybean. Molecular mapping of quantitative trait loci (QTL) for resistance to SCN is a proven useful strategy in order to assist in the development of resistant soybean cultivars. In the present study, a Bayesian modeling approach was performed to map QTL controlling genetic resistance to SCN races 3 and 14. For this purpose, a population of recombinant inbred lines derived from the cross between line Y23 (susceptible) and cv. Hartwig (resistant) was used. A total of 144 microsatellites markers (Simple Sequence Repeats) were selected and synthesized for mapping purpose. Posterior marginal parameter distributions were computed using the Reversible Jump Markov Chain Monte Carlo (RJ-MCMC) algorithm. It was determined the existence of four QTLs on three linkage groups (LG); that is LG A2 for race 3, LG C2 for race 14, and LG G for both races. The estimates of posterior modes of the heritability were 0.038 and 0.53 for the LGs A2 and G respectively (race 3). For the race 14 the posterior modes of the heritability were 0.044 and 0.05 for the LGs C2 and G. The identified QTLs explained about 57 and 9 % of the total phenotypic variance, for the races 3 and 14, respectively. These results confirm the effectiveness of the Bayesian method to map QTL controlling resistance to SCN in soybean. Accordingly, integrating QTL mapping with Bayesian methods will enable response to selection for quantitative traits of interest in soybean to be improved.