Novel QTL and Meta-QTL Mapping for Major Quality Traits in Soybean

Soybean (Glycine max L. Merr.), a leguminous plant that originated from China, is one of the most important crops globally, for it is rich in isoflavones, protein, and oil

Soy isoflavone, a kind of plant secondary metabolite, belongs to a group of 3-phenyl derivatives synthesized by cinnamyl-CoA. Soy isoflavone is generally classified into four main categories: aglycones (AGL), glycosides, acetylglycosides, and malonylglycosides. Each category can be further divided into three kinds as well. The AGL include daidzein (DAE), glycitein (GLE), and genistein (GEE). The glucosides (GLU) include daidzin (DA), glycitin (GL), and genistin (GE). The acetylglucosides include acetyldaidzin, acetylglycitin, and acetylgenistin, while the malonylglucosides include malonyldaidzin, malonylglycitin, and malonylgenistin. All aglycones are derived from the phenylalanine pathway and can be synthesized into glycosides by the reaction with UDP-Glucose, while glycosides can be synthesized into other two main categories by the increase of the acetyl or malonyl group. Isoflavones are referred to as phytoestrogen, for their structure is similar to estrogen. Nowadays, this secondary metabolite has been widely applied to the clinic, for it could reduce blood pressure, prevent hormone-dependent cancers, alleviate menopausal symptoms, and has numerous other features as well. Isoflavones also play an irreplaceable role in plant disease-resistance, insect resistance, and many other types of stresses, for they are the precursors of major phytoalexins in the system of plant defense responses.

Soy protein, generally referred to as the crude protein of soybean, is divided into globulins and albumins based on the solubility patterns. The globulins, categorized as 7S vicilin-type and 11S legumain-type, are the most abundant protein in soybean. Soy protein plays a vital role in human health, for it provides a good balance of essential amino acids for the human diet, and is strongly correlated with lower cholesterol levels and a reduced risk of cardiovascular diseases. Although breeding soybean cultivars with high protein content has been a major target for decades, the intricate and indistinct genetic background of protein accumulation in plants has hampered this process.

Soybean oil, an important quality trait-like isoflavone and protein, contains five types of fatty acids: saturated palmitic acids, stearic acids, unsaturated oleic acids, linoleic acids, and linolenic acids. In general, soybean seed is abundant in linoleic acids and linolenic acids, which are essential fatty acids (EFA) for humans. The low saturated fatty acid levels in soybean oil may reduce the risk of coronary diseases and cancer if the ratio of soybean oil in the human diet is increased. Thus, similar to isoflavone and soy protein, soybean oil could also play a key role in human health.

Due to the crucial importance of these quality traits in plant growth and human health, many studies investigated the accumulation of isoflavones, protein, or oil in soybean seed with final aims to account for the genetic background of these traits, as well as to breed cultivars with high isoflavone, protein, or oil contents. Previous researches suggest that all these quality traits are typically quantitative characters and depend on both environmental and genetic factors. The discrepancies of these traits between RILs with high, intermediate, and low contents have been presented to be relatively consistent in different environment conditions. Up to now, as the Soybase database shows that there are 261 quantitative trait locus (QTL) associated with protein content and 315 QTL associated with oil content in soybean. In addition, among these 576 QTL, 5 QTL are associated with seed oil plus protein. Two-hundred ninety-seven QTL related to isoflavones have been detected in soybean including 61 QTL related to seed daidzein, 68 QTL related to seed genistein, 71 QTL related to seed glycitein, and 11 QTL related to seed total isoflavone.

Identifying the number of stable QTL for these quality traits in soybean is essential to understand the genetic factors, unfortunately, there were two inadequacies in previous researches: the low-density genetic map and lack of QTL integration.

Most of the QTL related to soybean isoflavone, protein, or oil contents in previous studies were based on the low-density genetic maps with low throughput molecular markers, such as restriction fragment length polymorphism (RFLP), the variable number of tandem repeats (VNTR), and simple sequence repeat (SSR), which resulted in the low efficiency and accuracy of QTL mapping. Nowadays, with the development of sciences and DNA sequencing technologies, the QTL mapping can be accomplished based on the high throughput molecular markers and high-density genetic maps. High-throughput sequencing, a next-generation sequencing (NGS) technology, is a powerful technique to construct a high-density genetic map based on a large-scale identification of single nucleotide polymorphisms (SNP) markers, and is an efficient tool to map QTL.

Since the QTL is mostly derived from diverse populations and environments in different studies, the integration of QTL is an efficient way to obtain the stable QTL through comparing and combining the QTL from varied researches, as well as of crucial importance in understanding the complicated quantitative characters. The meta-analysis, a method to integrate data from different sources into a single study, has been used mainly by researchers in medical, social, and behavioral sciences. Goffinet and Gerber (2000) reported a technique to combine QTL mapped from multiple independent experiments, and provided a modified Akaike criterion that could be applied to determine which QTL was actually represented by the QTL acquired from different studies. Meta-QTL, the QTL integrated from multiple experiments using meta-analysis and consisting of shorter confidence intervals relative to the original QTL, could be more representative and accurate.

The aims of this research were: (1) to detect more stable QTL associated with isoflavone contents (both individual and total isoflavone), protein, and oil based on a high-density genetic map; (2) to integrate the QTL obtained from the previous researches according to meta-analysis method; (3) to predict candidate genes which may influence the accumulation of these quality traits using gene functional annotation. The results could facilitate elucidating the molecular mechanism of isoflavone, protein, and oil biosynthesis and regulation, as well as constructing molecular modular of great quality traits in soybean.