Integration of Major QTLs of Important Agronomic Traits in Soybean

Abstract

Soybean [Glycine max (L.) Merr.] is one of the most important crops in the world, accounting for 48% of the world market in oil crops, and is used mostly for animal feed and for oil production. In soybean, most agronomic traits, such as abiotic stress, biological stress, protein concentration, oil content and yield, are quantitative traits controlled by multiple genes. Moreover, genotypic expression of the phenotype is environmentally dependent, which affected QTLs detection greatly. QTLs detection was also affected by marker sets, experimental design, mapping populations, and statistical methods, which result in the location of QTLs mapped by the same marker differently. From the first publication of quantitative trait locus (QTL) of soybean using molecular markers (Paterson et al. 1988), numerous QTLs underlying different traits of soybean have been identified in different genetic backgrounds and environments. However, few identical QTLs were identified in same experimental population or same environment by different researchers or in different years (Orf 1999; Mansur 1993, 1996). All of these interfered in QTLs to improve oil content of soybean by marker-assisted selection (MAS). Integrated QTL analysis can facilitate the identification of “real” QTL and has attracted a great deal of attention. Recently, the International Maize and Wheat Improvement Center put forward a proposal to find universal QTLs by building a consensus map. This proposal offers a basis for QTL analysis and marker-assisted selection (MAS). Meta-analysis method led to about twofold increase in precision in the estimates of QTL position compared to the most precise initial QTL position within the corresponding region. In this chapter, QTL meta-analysis for major agronomic traits in soybean was performed for the first time. Published QTLs were collected, a consensus map of published maps with a reference map was created, consensus QTLs were acquired by the meta-analysis approach, genes were mined using bioinformatics tools, and markers of consensus QTLs with high effects and small CIs were provided for MAS.