Genetic-basis analysis of heterotic loci in Dongxiang common wild rice (Oryza rufipogon Griff.)

Summary

Heterosis is widely used in genetic crop improvement; however, the genetic basis of heterosis is incompletely understood. The use of whole-genome segregating populations poses a problem for establishing the genetic basis of heterosis, in that interactions often mask the effects of individual loci. However, introgression line (IL) populations permit the partitioning of heterosis into defined genomic regions, eliminating a major part of the genome-wide epistasis. In our previous study, based on mid-parental heterosis (H_MP) value with single-point analysis, 42 heterotic loci (HLs) associated with six yield-related traits were detected in wild and cultivated rice using a set of 265 ILs of Dongxiang common wild rice (Oryza rufipogon Griff.). In this study, the genetic effects of HLs were determined as the combined effects of both additive and dominant gene actions, estimated from the performance values of testcross F₁s and the dominance effects estimated from the H_MP values of testcross F₁s. We characterized the gene action type at each HL. Thirty-eight of the 42 HLs were over-dominant, and in the absence of epistasis, four HLs were dominant. Therefore, we favour that over-dominance is a major genetic basis of ‘wild-cultivar’ crosses at the single functional Mendelian locus level.

1. Introduction

Heterosis, or hybrid vigour, has been widely used in genetic crop improvement, and the genetic basis of heterosis has also been investigated. Dominance (Davenport, 1908) and over-dominance (Shull, 1908) are two hypotheses that were proposed a century ago to explain the genetic basis of heterosis. Recent advances in genome research involving a number of molecular-marker techniques and the availability of high-density molecular linkage maps, together with developments in analytical methods (Zeng, 1994), facilitated the analysis of the genetic basis of quantitative traits. Many quantitative trait locus (QTL) mapping studies have provided insight into the genetic basis of heterosis (Xiao et al., 1995; Li et al., 2001, 2008; Hua et al., 2003; Melchinger et al., 2008; Luo et al., 2009a), but reached different conclusions. One known problem in establishing the genetic basis of heterosis is the use of whole-genome segregating populations, where interactions often mask the effects of individual loci (Semel et al., 2006).

Introgression lines (ILs) are the results of using marker-assisted selection (MAS) to introgress small chromosomal segments from the donor into the recurrent parent by consecutive backcrossing and selfing (Eshed & Zamir, 1994). Any phenotypic difference between such an IL and its recurrent parent should be due to QTLs located on the introgressed segments from the donor. Consequently, ILs are a more precise estimate of the genetic effects of introgression under a relatively uniform and elite lineage background (Tanksley & Nelson, 1996). They are, therefore, well suited for use in the genetic analysis of heterosis.

Recently, we reported QTL analysis of panicle-related traits and identified heterotic loci (HLs) associated with six yield-related traits in a set of 265 ILs (Luo et al., 2009b). The lines were generated from a cross between Guichao 2, a high-yield commercial Indica cultivar (Oryza sativa L.), as the recurrent parent, and a common wild rice accession collected from Dongxiang County, Jiangxi Province, China, as the donor parent. In our previous study, the mid-parental heterosis (H _MP) values were calculated as H _MP=F₁–(IL+Guichao 2)/2. The H _MP values were used to identify loci affecting heterosis in the six yield-related traits. An HL is determined when a locus demonstrates a significant difference between the heterozygote and the mean of the two corresponding homozygotes; that is, the HL is a QTL for heterosis (Luo et al., 2011).

In this study, based on the set of 265 ILs and 265 testcross F₁s (derived from the ILs and the recurrent parent, Guichao 2), QTLs and HLs associated with yield-related traits in testcross F₁s were analysed. The genetic effects and main features of the HLs were discussed.

2. Materials and methods

(i) Experimental population and field trials

The IL population comprised 265 lines carrying variant introgressed segments of Dongxiang common wild rice (Oryza rufipogon) collected from Dongxiang county, Jiangxi Province, in the background of an Indica (O. sativa L. ssp. indica) cultivar, Guichao 2. The F₁ population comprised 265 testcrosses derived from crosses between the 265 ILs and the recurrent parent Guichao 2. These 265 lines represented 81·5% of the O. rufipogon genome. The detailed characteristics of the ILs were presented in Luo et al. (2009b, 2011). The F₁ testcross individuals and the corresponding parental ILs were evaluated in the summer of 2004 at the Experiment Station of the China Agricultural University (ESCAU), Beijing (39°N, 116°E).

Field trials of the 265 ILs, the 265 testcross F₁s and the recurrent parent Guichao 2 were conducted at Beijing–ESCAU in summer 2004. The detailed field trials were presented in Luo et al. (2011) . Ten plants from each line were harvested at maturity in both the IL and F₁ populations, and the following traits were scored: the number of spikelets per panicle (SP), the number of filled grains per panicle (GP), per cent seed set (SSP), 1000-grain weight (GW), the number of panicles per plant (PP) and grain yield per plant (YP).

(ii) Data analysis

The simple sequence repeat (SSR) markers analysed in this study were taken from previous publications (McCouch et al., 2002). The detailed characteristics of the SSR markers in the ILs were presented in Luo et al. (2009b) . A total of 160 polymorphic SSR markers were used to genotype the 265 ILs and the recurrent parent Guichao 2, following Tian et al. (2006) . The F₁ testcross genotypes were deduced based on the genotype of their corresponding parental ILs. The direct trait measurement values from the six yield-related traits obtained from the ILs were used to identify the associated QTLs. The testcross F₁ trait measurements were used to identify loci affecting testcross F₁ performance.

Based on IL structure, QTLs can be mapped on introgressed chromosome segments. One representative marker for each specific introgressed segment was defined as a QTL (Luo et al., 2011). The association between the phenotype and 160 SSR marker data was investigated by single-point analysis using the software package Map Manager QTXb17 (Manly et al., 2001). The statistical a priori threshold for main effect loci was P<0·01 (the probability that loci had no effect on the trait).

The genetic effects in the testcross F₁s were defined as follows: d=H _MP=[F₁–(IL+Guichao 2)/2] (Luo et al., 2011); the trait mean values in the testcross F₁s were , where a is the additive effects from the performance values of testcross F₁; IL is the mean value for the same measured trait in the corresponding IL parent, and the homozygous IL genotype value was 2a; here Guichao 2 genotype value was assumed as zero for a simple case; subsequently HL effects were inferred by comparisons between the genetic effects on F₁ performance and H _MP. HLs with the ratio between dominant and additive effects d/a⩽1 were considered complete or partial dominant loci, and expected to generate an estimate of F₁ performance (a+d) equal to or higher than twice the H _MP (d). HLs with d/a>1, that is, 2d (2×H _MP)>a+d (F₁), or only detectable for H _MP, were determined as over-dominant loci (Melchinger et al., 1998).

3. Results

(i) The relationships among the mean trait values of ILs, H_MP, and F₁ performance

The correlation coefficients between testcross F₁ mean values, H _MP values and parental IL mean values for yield-related traits are shown in Table 1. In general, a positive but lower correlation between IL trait values and the F₁s was observed; the average R² (determination coefficients) was 0·235, which suggested that additive gene action made a small contribution to F₁ performance. A general negative correlation trend was evident between IL and H _MP trait values, clearly suggesting that additive and dominant gene action operated independently in the testcross population. In the 265 testcross F₁s, a highly positive correlation between F₁ performance and H _MP values was observed for all traits, with an average R² =0·580 (range of 0·428 for GW to 0·775 for YP), indicating that H _MP largely influenced F₁ performance.

Table 1. Phenotypic correlation (R) and determination coefficients (R²) for six yield-related traits between IL and testcross F₁ performance values and H _MP values

^a Trait abbreviations: the number of spikelets per panicle (SP), the number of filled grains per panicle (GP), per cent seed set (SSP), 1000-grain weight (GW), the number of panicles per plant (PP) and grain yield per plant (YP).

* Significance levels P<0·01.

(ii) Genetic effects of QTLs in F₁ testcross population

IL phenotypic data from six yield-related traits were used to identify the associated QTLs. Fifty-four QTLs were detected (partial QTLs detected in ILs are indicated in underlined text in Table 2). Trait phenotypic values from F₁ testcrosses were used to infer the QTLs contributing to F₁ testcross performance. Fifty-one QTLs influencing F₁ testcross performance were detected for the six yield-related traits (partial QTLs detected in the F₁ testcrosses are shown in Table 2). In our previous study, the H _MP values in F₁ testcross were used to infer which QTLs contributed to heterosis. A total of 42 H _MP QTLs (or HLs) associated with H _MP values were detected for the six yield-related traits (Luo et al., 2011, all H _MP QTLs are shown in Table 2).

Table 2. The genetic effects of HLs on six yield-related traits in the testcross F₁s (the HLs were mapped by Luo et al., 2011)

^a See Table 1 for abbreviations.

^b Markers indicated in underlined text are QTLs identified in ILs.

^c H _MP is the mid-parental heterosis of testcross F₁.

^d PV, the phenotypic variance explained by the locus; P, the probability that the marker genotype had no effect on the trait; a+d, the additive and dominance effects from the performance values of testcross F₁; d, the dominance effect from the H _MP values.

^e d/a, the ratio between dominant and additive effects.

Table 2 indicates the genetic overlap of H _MP and QTLs detected in the F₁ testcrosses. Of 42 H _MP QTLs, 21 loci were only associated with H _MP, and showed over-dominant expression. The other 21 loci simultaneously influenced H _MP and F₁ performance. A comparison of the genetic effects of loci detected in both H _MP and F₁ testcross performance indicated a d/a⩽1 in hsp2, hgp9a, hssp5 and hgw8, suggesting dominant loci, and over-dominant effects in the remaining 17 loci (d/a>1). In 42 of the HLs, 38 (90·5%) were over-dominant and four appeared dominant. These results indicated that at the single locus level, HLs were predominantly over-dominant. In IL QTL analysis, nine (21·4%) of the above 42 HLs were resolved at the same statistical threshold, and showed less genetic overlap with the six yield-related trait QTLs. These results are consistent with a lower correlation between IL trait values and the corresponding F₁s.

4. Discussion

The complex nature of heterosis makes it difficult to partition it into individual components, particularly in F₂, backcrossed, and recombinant inbred populations; the epistatic interactions among the many segregating loci throughout the genome makes it difficult to define specific heterotic phenotypes and the individual genomic loci that control them (Li et al., 2001; Semel et al., 2006; Xin et al., 2011). To overcome these limitations, we developed a set of ILs carrying a few chromosome segments from the wild rice species O. rufipogon (Luo et al., 2009b). This IL population allowed us to partition heterosis into defined genomic regions, eliminating a major part of the genome-wide epistasis. The heterotic effects were determined as the combined effects of both additive and dominant gene actions, estimated from the performance values of testcross F₁s and the dominance effects estimated from the H _MP values of testcross F₁s. Based on this strategy, we characterized the gene action type at each HL. Forty-two HLs for six yield-related traits revealed two different genetic effects: dominance or over-dominance. These HL data indicated that over-dominance was the major underlying factor of heterosis. Thirty-eight (90·5%) HLs exhibited over-dominant effects and only four HLs showed dominant effects. Notably, Semel et al. (2006) carried out quantitative genetic and phenotypic analyses on an IL population of tomato (Solanum lycopersicum) carrying a single chromosomal segment from the distantly related wild species Solanum pennellii. That study generated results congruent with the present study; in the absence of epistasis, at a single locus level, over-dominant loci had greater effects on tomato yield and fitness.

The exploitation of favourable genes from wild rice might further improve tolerance to biotic and abiotic stress, yield and other important agronomic traits for rice variety. Luo et al. (2011) investigated the HLs derived from wild rice, thought that favourable HLs capable of improving agronomic traits are available, and indicated that the identification of HLs between wild rice and cultivated rice could lead to a new strategy for the application of heterosis in rice breeding. It is generally known that the over-dominant HLs is more advantageous than dominant HLs in heterosis utilization. In this study, 28 (66·7%) of 42 HLs showed significantly positive over-dominant effects (P<0·01) on yield-related traits, suggesting that these markers are viable candidates for marker-aided improvement of rice yield potential.

Previous study (Li et al., 2001) revealed that backcross F₁ performance was largely determined by dominant gene action. Mei et al. (2005) analysed the correlation between RILs and backcrossed populations for agricultural traits, and considered backcross F₁ performance was mainly determined by non-additive gene action. Our study employed a similar experimental design and found a highly positive correlation between testcross F₁ and H _MP F₁ testcross performance values, and a lower positive correlation between IL performance values and F₁ testcross performance values (Table 1). These results indicated that dominance gene action rather than additive gene action was a substantial contributor to F₁ testcross performance. Furthermore, the testcross described in this study corresponds to previous backcross studies (Li et al., 2001; Mei et al., 2005). The negative correlation between IL performance values and H _MP values of the F₁ testcross population clearly indicated that additive and dominant gene action acted independently in the testcross population. QTL and HL analyses demonstrated that nine of the 42 HLs were also detected in the QTL analysis (Table 2), and exhibited less genetic overlap with QTLs, consistent with results reported by Hua et al. (2003) . Therefore, heterosis and trait performance may be conditioned by different sets of loci.