Taxonomic classification of
1. Introduction
The world is facing a new challenge with global population predicted to plateau at nine billion people by the middle of this century (Godfray et al. 2010). Increasing food production to feed the world’s population is an even greater challenge considering that agriculture is experiencing greater competition for land, water and energy, as well as, the effects of substantial climate change and the unintended effects of crop production on the environment. Part of the solution to increasing food production on the same or less cultivated land lies in exploiting the subset of genes lost during the domestication process and subsequent targeted breeding. Currently, these valuable genes are found only in the progenitor species genepool for crop cultivars. Cultivated plants having desirable genes were utilized in intensive breeding projects focused on increasing yield for particular environments and management systems but this process has narrowed the genetic diversity (Rausher 2001). For cultivated plants, this unexploited genetic material includes both landraces and the more exotic wild relatives. Improving our understanding of this tertiary gene pool and exploiting it for crop improvement is paramount to meeting the challenges of feeding the world in this century through the integration of classical genetics and genomics-enabled research paradigms.
The loss of genetic diversity can be more problematic for self-pollinated plant species where the rate of cross pollination is below five percent, thus making it more difficult to reintroduce the lost diversity. In the case of the two major grain crops, rice (
Abiotic stress, including salinity, aluminum toxicity and acid sulfate soils, as well as, temperature and drought, complicate the difficulty of improving crop yields, especially in the face of global warming (Brooker 2006; Tilman and Lehman 2001), which makes modern cultivars even more vulnerable. Genetic sources of resistance or tolerance offer the most promising mechanism to protect plants against these unfavorable conditions. Often wild species are not included as parental lines in cultivar development because it is relatively difficult to harness desirable genes by genetic recombination and many undesirable genes are introgressed from the wild parent resulting in inferior yield, undesirable plant architecture, and/or poor grain quality (Tanksley and McCouch 1997). Recent studies, however, in rice (McCouch et al. 2007) and tomato,
The rapid advancement in molecular technologies allows for genotyping plants much more quickly and inexpensively than ever before. The availability of high resolution genotypic information creates the opportunity to further explore an expanding number of accessions in a greater depth, and harness this information to enhance the efficiency and accuracy of introgression. These developments create opportunities not previously possible, to identify molecular markers associated with desirable traits in wild species and transfer these traits into elite lines and/or varieties, as well as, to unravel multi-genic traits for crop improvement (Tanksley and McCouch 1997; McCouch et al. 2012).
Our main objective is to summarize efforts over the past 15 years to identify useful novel alleles in the
2. Phylogeny of the Oryza genus
The
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24 | AA | 420, 466 | Worldwide | |
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24 | AA | 448 | Tropical and subtropical Asia | |
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24 | AA | 439, 450 | Tropical and subtropical Asia, Tropical Australia | |
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24 | AgAg | 354 | West Africa | |
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24 | AgAg | 411 | Africa | |
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24 | AgpAgp | 464 | South and Central America | |
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24 | AlAl | 352 | Africa | |
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24 | AmAm | 435 | Tropical Australia | |
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24 48 |
BB, BBCC |
423 (BB) | Africa | |
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48 | BBCC | 1124 | Philippines, Papua New Guinea | |
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24 | CC | South Asia and East Africa | ||
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24 | CC | 653 | Tropical and subtropical Asia, Tropical Australia | |
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24 | CC | Sri Lanka | ||
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48 | CCDD | 1124 | South and Central America | |
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48 | CCDD | South and Central America | ||
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48 | CCDD | South and Central America | ||
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24 | EE | 960 | Tropical Australia | |
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24 | FF | 338 | Central Africa | |
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24 | GG | 862 | South and Southeast Asia | |
(Zoll. et (Mor. ex Steud.) Baill.) |
24 | GG | Southeast Asia | ||
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48 | HHJJ | Irian Jaya, Indonesia, Papua New Guinea | ||
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48 | HHJJ | 1283 | South Asia | |
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48 | KKLL | 771 | Asian coastal area | |
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48 | KKLL | Papua New Guinea |
Rice is the only major cereal found in the ancient lineage of the Bambusoideae and is currently placed in the subfamily Erhartoideae. Historically, the grass family, Poaceae, is thought to have evolved about 70-55 mya (million years ago) with the tribes Oryzeae and Pooideae (wheat and oats) diverging about 35 mya [reviewed by Kellogg (2009) and Vaughan et al. (2008)]. The Oryzinae and Zizaninae subtribes diverged about 20-22 mya and the
Section
The species in the
Rice,
3. Methods for developing Oryza interspecific mapping populations
Traits are classified as either qualitative or quantitative traits. Qualitative traits are controlled by one or a few genes with major effects while quantitative characters are controlled by many genes with minor effects (Poehlman and Sleper 1994). Identification of genes associated with quantitative traits is always more complicated compared to those involving qualitative traits.
Interspecific and intergenomic hybridization, hybridization between species with the same or different genomes, have been used to transfer desirable genes or QTL associated with simple or complex traits from wild species into a cultivated genetic background (Brar and Khush 1997; Dalmacio et al. 1995; Tanksley and McCouch 1997). Nevertheless hybridization success can be hindered by genomic incompatibilities and sterility barriers (Ishii et al. 1994; McCouch et al. 2007; Wang et al. 2005). The utilization of embryo rescue and other methods of producing viable and fertile hybrids combined with robust molecular markers and associated computational and statistical analyses, led to the successful generation of interspecific genetic populations that were used to link desirable traits to molecular markers and subsequent identification of the actual genes controlling the traits of interest (Ali et al. 2010; Chen et al. 2010; Ghesquière et al. 1997; Guo et al. 2013; Lexer and Fay 2005; McCouch et al. 2007). Six types of mapping populations are generated from interspecific crosses between
3.1. Recombinant Inbred Line (RIL) population
RIL populations have been the most common type of mapping population used in rice genetics and breeding when both parents are
The main advantage of the RIL method is that no backcrossing is necessary but when a wild
Commonly used softwares for creating the linkage map from the genotypic (molecular marker) data of the population for QTL analyses include MapMaker-QTL (Lander and Botstein 1989), JoinMap (Van Ooijen 2006) and MapDisto (Lorieux 2012). The possible chromosome location of the QTL for the trait being evaluated is based on the QTL having a significant LOD [logarithm (base 10) of odds] score with the LOD score detecting linkage between the molecular marker and the trait of interest. Several softwares are freely available for conducting the QTL analysis, including MapMaker-QTL (Lander and Botstein 1989), QTLCartographer (Wang et al. 2012), QGene (Joehanes and Nelson 2008), MapDisto (Lorieux 2012) and QTLNetwork (Yang et al. 2008). It is important to confirm that the software being used for QTL analysis can correctly analyze the population type since some cannot be used with BC2F2 populations based on differences in fundamental assumptions. Most recent QTL analyses with rice have been performed using either composite interval mapping (CIM) (Zeng 1994) or multiple interval mapping (MIM) (Kao and Zeng 1999) with single point analysis (SPA) (Tanksley et al. 1982), marker regression (Kearsey and Hyne 1994) and interval mapping (IM) (Haley and Knott 1992; Lander and Botstein 1989) being used in earlier analyses.
3.2. Advanced Backcross (AB) population
The advanced backcross (AB)-quantitative trait locus (QTL) analysis is a powerful strategy to map desirable trait(s) discovered in the wild species (Tanksley and Nelson 1996). This method was first applied to QTL mapping in tomato, and subsequently to several other crops, including rice (Grandillo and Tanksley 2003; McCouch et al. 2007). In the process of developing the AB populations used for QTL analysis, plants or lines with unfavorable genes derived from donor parents like sterility and sometimes shattering, are often eliminated from the population after phenotypic and genotypic evaluation. Due to artificial selection in favor of lines with desirable alleles and the genetic background from the recurrent parent, the distribution can be skewed toward the recurrent parent, therefore, after the BC3 generation, the power of the statistical analysis to detect QTL decreases. Since sequential backcrossing in AB-QTL removes epistatic interactions, the chance of detecting QTLs with epistatic interactions among alleles from the donor parent decreases, while the ability to detect additive QTLs increases (Tanksley and Nelson 1996; Grandillo and Tanksley 2003).
To create an AB mapping population, one parent, usually the wild
3.3. Backcross Inbred Line (BIL) population
BIL populations are used to introgress desirable traits from the wild
The advantages of utilizing BILs are that the method is relatively straightforward and the lines are more homogeneous, having less linkage drag and fewer untargeted segments from the donor parent as compared to RILs. Furthermore, BIL populations can be used to identify major QTLs and single genes, detect QTLs with epistatic or additive effects, as well as, provide an accurate estimation of genotype x environment interactions. It takes more time to develop a BIL population than a RIL population but less time than developing CSSLs and NILs because there are fewer backcrosses to do and less emphasis on targeted segments (Fukuoka et al. 2010; Fulton et al. 1997; Jaquemin et al. 2013). Some disadvantages of this method are the genetic background of the donor parent is higher in the BILs as compared to the CSSLs and NILs, and the lines require more phenotypic evaluation but less genotypic characterization. As a result, mapping in a BIL population is more labor intensive and costly compared to RILs but less costly than NILs and CSSLs. Unfortunately, only limited success has been reported for improving quantitative traits with low heritability and identifying minor QTLs. Also, it is difficult to transfer a relatively large number of genes or QTLs associated with the desirable traits from the wild donor to an elite cultivar using lines selected from a BIL population.
3.4. Chromosome Segment Substitution Line (CSSL) library
A CSSL “library” is a set of near isogenic lines, often ranging from 26 to 80 lines, which cover the entire donor genome when the segments included in each introgression line are in the background of the recurrent parent (Fig. 1; Ali et al. 2010). The concept of CSSL libraries was initially proposed by Eshed and Zamir (1995) as introgression lines and Ghesquière et al. (1997) as contig lines. To develop CSSLs, the initial crossing follows the same scheme as described for AB and BIL populations where the wild, unadapted
A CSSL library has several advantages compared to BILs or an AB mapping population in that it can be used for fine mapping, to identify both major and minor QTLs, and validate genetic interactions. Also, due to the recurrent parent background in CSSLs, linkage drag and its negative effects on the QTL studies are significantly reduced or eliminated. This uniform genetic background enables one to make rapid progress in linkage mapping of targeted QTLs. Lastly, individual CSSLs which carry a specific trait can be used for fine mapping and gene pyramiding (Ali et al. 2010; Fukuoka et al. 2010), as illustrated by the identification of the rice stripe necrosis virus resistance introgression from
The rice universal core genetic map is a set of uniformly distributed polymorphic SSR markers that clearly differentiate
3.5. Near Isogenic Lines (NILs)
The procedure for developing a set of NILs is similar to CSSLs except the number of backcrosses is unlimited because the focus is on incorporating a single segment with the trait(s) of interest identified in the
NILs are often developed to fine map QTLs identified in primary mapping populations, like RIL or BIL, because the QTLs can be mapped precisely as single Mendelian factors (McCouch et al. 2007). Use of NILs, like CSSLs, increases the power to detect small-effect QTL and can overcome or minimize genetic incompatibility, linkage drag, cytoplasmic sterility and epistatic effects, all of which are common obstacles in wide hybridization efforts because the genetic background is more or less uniform. Although developing NILs, like CSSLs, is labor intensive, time consuming, and expensive, NILs are a valuable tool for exploring the genes underlying QTLs because the epistatic effects are removed or minimized making it easier to measure gene expression (Keurentjes et al. 2007). Finally, those NILs with valuable genes introgressed from the wild
3.6. Multi-parent Advanced Generation Inter-Cross (MAGIC) population
Recently, some efforts have turned to MAGIC populations (Cavanagh et al. 2008; Kover et al. 2009) which can serve the dual purpose of permanent mapping populations for precise QTL mapping, and for direct or indirect use in variety development, especially when the parents used to develop the population are the source of agronomically useful traits (Bandillo et al. 2013). MAGIC populations are developed by systematically crossing several F1 hybrids involving four to sixteen different parental lines to create a set of double crosses, then systematically crossing the double cross hybrids to create a set of 4-, 8-or 16-way crosses. As the final step, the lines composing the population are advanced four or more generations by single seed descent to obtain a set of advanced intercrossed lines (AILs). Bandillo et al. (2013) reported four different types of MAGIC populations being developed in rice (
4. Useful agronomic traits mapped in Oryza species and transferred into cultivated rice
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Days to flowering |
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IRGC100882 | IR31917-45-3-2 | IL | 10 | RFLP | Ishii et al. (1994) | ||
Days to heading |
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IR64 | BC2F3 | SPA | 2, 10 | SSR, STS | Bimpong et al. (2011) | ||
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IRGC103544 | Milyang 23 | BC3F2 | SPA | 1, 4, 7, 8 |
SSR | Suh et al. (2005) | ||
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Taichung 65 | BC4F2 | 7 | RFLP | Sanchez et al. (2003) | ||||
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IRGC101154 | Hwaseongbyeo | AB-QTL | SPA | 6 | SSR | Yoon et al. (2006) | ||
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Y73 | IR24 | RIL | CIM | 6, 7, 8, 11 |
SSR, STS | Chen et al. (2012) | ||
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IRGC100898 | Bengal | AB-QTL | MIM | 3, 6 | SSR | Eizenga et al. (2013) | ||
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IRGC100898 | Bengal | AB-QTL | MIM | 3, 4, 6, 8 |
SSR | Eizenga et al. (2013) | ||
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IRGC100195 | M-202 | AB-QTL | MIM | 3, 8 | SSR | Eizenga et al. (accepted) | ||
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IRGC105491 | Jefferson | AB-QTL | SPA, IM, CIM | 1, 2, 3, 4, 10 |
RFLP, SSR | Thomson et al. (2003) | ||
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IRGC105491 | IR64 | AB-QTL | SPA, IM, CIM | 2, 7 | RFLP, SSR | Septiningsih et al. (2003) | ||
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IRGC105491 | V20A, V20B | BC2 | ANOVA | 6, 12 | RFLP | Xiao et al. (1998) | ||
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IRGC105491 | Hwaseongbyeo | AB-QTL, NIL | SPA, IM, ANOVA | 6, 9 | SSR | Jin et al. (2009), Xie et al. (2008) |
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IRGC105491 | MR219 | AB-QTL | CIM | 3 | SSR | Wickneswari et al. (2012) | ||
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W1944 | Hwayeongbyeo | IL | SPA, IM | 1 | SPA, IM | Yuan et al.(2009) | ||
Days to maturity |
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IRGC105491 | IR64 | AB-QTL | SPA, IM, CIM | 4, 7, 8 | RFLP, SSR | Septiningsih et al. (2003) | |
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IRGC105491 | V20A, V20B | BC2 | ANOVA | 6, 12 | RFLP | Xiao et al. (1998) | ||
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IRGC105491 | MR219 | AB-QTL | CIM | 4, 6 | SSR | Wickneswari et al. (2012) | ||
Seedling height |
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W1944 | Hwayeongbyeo | RIL | SPA, CIM | 1 | SSR | Lee et al. (2005) | |
Culm length |
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IRGC103544 | Milyang 23 | BC3F2 | SPA | 2, 10 | SSR | Suh et al. (2005) | |
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IRGC101154 | Hwaseongbyeo | AB-QTL | SPA | 1, 4 | SSR | Yoon et al. (2006) | ||
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IR71033-121-15 | Junambyeo | F2:3 | SPA, IM | 6, 7, 12 | SSR, STS | Rahman et al. (2007) | ||
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W1944 | Hwayeongbyeo | RIL | SPA, CIM | 1, 6 | SSR | Lee et al. (2005) | ||
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IRGC105491 | MR219 | AB-QTL | CIM | 1, 3, 9 | SSR | Wickneswari et al. (2012) | ||
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W1944 | Hwayeongbyeo | IL | SPA, IM, | 1, 12 | SPA, IM | Yuan et al.(2009) | ||
Plant height |
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IR64 | BC2F3 | SPA | 1 | SSR, STS | Bimpong et al. (2011) | ||
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RD23 | BC7F2 | CIM | 1 | SSR | Chen et al. (2009) | |||
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IRGC100898 | Bengal | AB-QTL | MIM | 1 | SSR | Eizenga et al. (2013) | ||
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IRGC104705 | Bengal | AB-QTL | MIM | 1, 12 | SSR | Eizenga et al. (2013) | ||
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IRGC105491 | Jefferson | AB-QTL | SPA, IM, CIM | 1 | RFLP, SSR | Thomson et al. (2003) | ||
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IRGC105491 | IR64 | AB-QTL | SPA, IM, CIM | 1, 4, 10, 11 |
RFLP, SSR | Septiningsih et al. (2003) | ||
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IRGC105491 | Hwaseongbyeo | AB-QTL, NIL | SPA, IM, ANOVA | 7, 9 | SSR | Jin et al. (2009), Xie et al. (2008) |
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IRGC105491 | MR219 | AB-QTL | CIM | 1, 3, 9 | SSR | Wickneswari et al. (2012) | ||
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IC22015 | IR 58025A | AB-QTL | IM, CIM | 1 | SSR | Marri et al. (2005) | ||
Plant type (Culm habit or tiller angle) |
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IRGC100898 | Bengal | AB-QTL | MIM | 9 | SSR | Eizenga et al. (2013) | |
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IRGC104705 | Bengal | AB-QTL | MIM | 9 | SSR | Eizenga et al. (2013) | ||
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IRGC100195 | M-202 | AB-QTL | MIM | 9 | SSR | Eizenga et al. (accepted) | ||
Flag leaf length |
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IR71033-121-15 | Junambyeo | F2:3 | SPA, IM | 8, 9 | SSR, STS | Rahman et al. (2007) | |
Third node width |
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W1944 | Hwayeongbyeo | RIL | SPA, CIM | 1 | SSR | Lee et al. (2005) | |
Tiller number |
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IR64 | BC2F3 | SPA | 2, 7 | SSR, STS | Bimpong et al. (2011) | ||
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RS-16 | BG90-2 | BC2F2 | SPA, IM | 4, 5, 7, 8, 11 |
SSR, STS | Brondani et al. (2002) | ||
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RS-16 | Cica8 | BC2F2-9 | CIM | 7, 11 | SSR | Rangel et al. (2013) | ||
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IR71033-121-15 | Junambyeo | F2:3 | SPA, IM | 3 | SSR, STS | Rahman et al. (2007) | ||
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IRGC105491 | MR219 | AB-QTL | CIM | 2, 5, 8 | SSR | Wickneswari et al. (2012) | ||
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Panicle exsertion |
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W1944 | Hwayeongbyeo | RIL | SPA, CIM | 1 | SSR | Lee et al. (2005) | |
Panicle density |
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IRGC 105491 | Hwaseongbyeo | NIL | ANOVA | 9 | SSR | Xie et al. (2008) | |
Panicle number |
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IRGC103544 | Milyang 23 | BC3F2 | SPA | 4 | SSR | Suh et al. (2005) | |
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RS-16 | BG90-2 | BC2F2 | SPA, IM | 5, 8, 11 | SSR, STS | Brondani et al. (2002) | ||
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RS-16 | Cica8 | BC2F2-9 | CIM | 7, 11 | SSR | Rangel et al. (2013) | ||
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IR71033-121-15 | Junambyeo | F2:3 | SPA, IM | 4 | SSR, STS | Rahman et al. (2007) | ||
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IRGC100195 | M-202 | AB-QTL | MIM | 7 | SSR | Eizenga et al. (accepted) | ||
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IRGC105491 | Jefferson | AB-QTL | SPA, IM, CIM | 3, 7 | RFLP, SSR | Thomson et al. (2003) | ||
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IRGC105491 | IR64 | AB-QTL | SPA, IM, CIM | 2 | RFLP, SSR | Septiningsih et al. (2003) | ||
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IRGC105491 | V20A, V20B | BC2 | ANOVA | 1, 2 | RFLP | Xiao et al. (1998) | ||
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IRGC105491 | MR219 | AB-QTL | CIM | 2, 8 | SSR | Wickneswari et al. (2012) | ||
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IC22015 | IR58025A | AB-QTL | IM, CIM | 2 | SSR | Marri et al. (2005) | ||
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W1944 | Hwayeongbyeo | RIL, IL | SPA, IM, CIM | 1, 7, 12 | SSR | Lee et al. (2004), Yuan et al. (2009) |
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YJCW | 93-11 | AB-QTL | SPA, IM, CIM | 1, 2, 7, 8, 11 | SSR | Fu et al. (2010) | ||
Panicle length |
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IRGC103544 | Milyang 23 | BC3F2 | SPA | 2, 5, 6, 10, 12 | SSR | Suh et al. (2005) | |
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Y73 | IR24 | RIL | CIM | 1, 2 | SSR, STS | Chen et al. (2012) | ||
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IR71033-121-15 | Junambyeo | F2:3 | SPA, IM | 6, 7, 8 | SSR, STS | Rahman et al. (2007) | ||
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IRGC105491 | Hwaseongbyeo | NIL | SPA, IM, ANOVA | 9 | SSR | Xie et al. (2008) | ||
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IRGC105491 | IR64 | AB-QTL | SPA, IM, CIM | 1, 9, 10 | RFLP, SSR | Septiningsih et al. (2003) | ||
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IRGC105491 | Jefferson | AB-QTL | SPA, IM, CIM | 1, 2, 4, 9, 12 |
RFLP, SSR | Thomson et al. (2003) | ||
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IRGC105491 | V20A, V20B | BC2 | ANOVA | 1, 2, 4, 8, 9, 12 |
RFLP | Xiao et al. (1998) | ||
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IC22015 | IR 58025A | AB-QTL | IM, CIM | 2, 5, 9 | SSR | Marri et al. (2005) | ||
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W1944 | Hwayeongbyeo | RIL | SPA, CIM | 1 | SSR | Lee et al. (2005) | ||
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W1944 | Hwayeongbyeo | IL | SPA, IM, | 1, 2 | SPA, IM | Yuan et al.(2009) | ||
Primary branches per panicle |
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IRGC101144 | Hwaseongbyeo | NIL | 7 | SSR | Balkunde et al. (2013) | ||
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W1944 | Hwayeongbyeo | RIL | SPA, CIM | 1 | SSR | Lee et al. (2005) | ||
Secondary branches per panicle |
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IRGC105491 | Hwaseongbyeo | AB-QTL | SPA, IM | 6, 8 | SSR | Jin et al. (2009) | |
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W1944 | Hwayeongbyeo | RIL, IL | SPA, IM, CIM | 1, 2, 9 | SSR | Lee et al. (2005), Yuan et al. (2009) |
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Pollen (male) sterility |
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IRGC105688 | Taichung 65 | BC4F2 | 2, 7 | RFLP | Sobrizal et al. (2000a, 2000b) | ||
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RD23 | BC7F2 | CIM | 6 | SSR | Win et al. (2009; 2011) | |||
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IRGC105444 | Taichung 65 | IL-BC4F1 | 4, 8, 12 | RFLP, SSR, SNP | Chen et al. (2009) | |||
Hybrid breakdown locus |
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IRGC105444 | Koshihikari | BC4F3 | 2 | SSR, SNP | Miura et al. (2008) | ||
Panicle fertility |
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IR64 | BC2F3 | SPA | 2, 10 | SSR, STS | Bimpong et al. (2011) | ||
Productive panicle number |
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G52-9 | Yuexiangzhan | AB-QTL | CIM | 2, 3, 7 | SSR | Jing et al. (2010) | |
Spikelets per plant |
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IRGC101144 | Hwaseongbyeo | NIL | 7 | SSR | Balkunde et al. (2013) | ||
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G52-9 | Yuexiangzhan | AB-QTL | CIM | 2 | SSR | Jing et al. (2010) | ||
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IC 22015 | IR 58025A | AB-QTL | IM, CIM | 2, 5 | SSR | Marri et al. (2005) | ||
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IRGC105491 | V20A, V20B | BC2 | ANOVA | 1 | RFLP | Xiao et al. (1998) | ||
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IRGC105491 | MR219 | AB-QTL | CIM | 1 | SSR | Wickneswari et al. (2012) | ||
Spikelets per panicle |
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IRGC103544 | Milyang 23 | BC3F2 | SPA | 3 | SSR | Suh et al. (2005) | |
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IRGC101144 | Hwaseongbyeo | AB-QTL | SPA | 2, 3, 4, 11 |
SSR | Yoon et al. (2006) | ||
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IR71033-121-15 | Junambyeo | F2:3 | SPA, IM | 6 | SSR, STS | Rahman et al. (2007) | ||
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IRGC105491 | Hwaseongbyeo | NIL, AB-QTL |
SPA, IM, ANOVA | 8, 9 | SSR | Xie et al. (2008), Jin et al. (2009) |
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IRGC105491 | Jefferson | AB-QTL | SPA, IM, CIM | 2, 3, 9 | RFLP, SSR | Thomson et al. (2003) | ||
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IRGC105491 | V20A, V20B | BC2 | ANOVA | 1, 9 | RFLP | Xiao et al. (1998) | ||
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W1944 | Hwayeongbyeo | IL, RIL | SPA, IM, CIM | 1 | SSR | Yuan et al. (2009), Lee et al. (2005) |
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YJCW | 93-11 | AB-QTL | SPA, IM, CIM | 3 | SSR | Fu et al. (2010) | ||
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Milyang 23 | BC2F5 | 2 | SSR | Kang et al. (2008) | ||||
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IRGC105491 | MR219 | AB-QTL | CIM | 3 | SSR | Wickneswari et al. (2012) | ||
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IRGC101144 | Hwaseongbyeo | NIL | 7 | SSR | Balkunde et al. (2013) | |||
Spikelet fertility |
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IRGC103544 | Milyang 23 | BC3F2 | SPA | 2, 4, 8 | SSR | Suh et al. (2005) | |
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RD23 | BC7F2 | CIM | 6 | SSR | Chen et al. (2009) | |||
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IR71033-121-15 | Junambyeo | F2:3 | SPA, IM | 6 | SSR, STS | Rahman et al. (2007) | ||
Shattering |
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IRGC105491 | Jefferson | AB-QTL | SPA, IM, CIM | 8 | RFLP, SSR | Thomson et al. (2003) | |
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W1944 | Hwayeongbyeo | RIL | SPA, CIM | 1, 3, 6 | SSR | Lee et al. (2005) | ||
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W1944 | Hwayeongbyeo | IL, RIL | SPA, IM, CIM | 1, 4, 5 | SSR | Yuan et al. (2009), Lee et al. (2005) |
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Grains per panicle |
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IRGC101144 | Hwaseongbyeo | NIL | 7 | SSR | Balkunde et al. (2013) | ||
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IRGC105491 | Jefferson | AB-QTL | SPA, IM, CIM | 2, 3, 8, 9 |
RFLP, SSR | Thomson et al. (2003) | ||
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IRGC105491 | V20A, V20B | BC2 | ANOVA | 1, 8, 12 | RFLP | Xiao et al. (1998) | ||
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IRGC105491 | Hwaseongbyeo | AB-QTL, NIL | SPA, IM, ANOVA | 8, 9 | SSR | Jin et al. (2009), Xie et al. (2008) |
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IC22015 | IR 58025A | AB-QTL | IM, CIM | 2, 5 | SSR | Marri et al. (2005) | ||
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G52-9 | Yuexiangzhan | AB-QTL | CIM | 4, 10, 11 | SSR | Jing et al. (2010) | ||
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YJCW | 93-11 | AB-QTL | SPA, IM, CIM | 1, 3 | SSR | Fu et al. (2010) | ||
Percent seed set |
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Y73 | IR24 | RIL | CIM | 8 | SSR, STS | Chen et al. (2012) | |
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IRGC105491 | MR219 | AB-QTL | CIM | 3 | SSR | Wickneswari et al. (2012) | ||
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IRGC105491 | V20A, V20B | BC2 | ANOVA | 2, 4 | RFLP | Xiao et al. (1998) | ||
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W1944 | Hwayeongbyeo | IL, RIL | SPA, IM, CIM | 10 | SSR | Lee et al. (2005) | ||
Awn length |
|
IRGC101144 | Hwayeongbyeo | AB-QTL | SPA, CIM | 6, 9 | SSR | Linh et al. (2004) | |
|
IR71033-121-15 | Junambyeo | F2:3 | SPA, IM | 5, 9 | SSR, STS | Rahman et al. (2007) | ||
|
IRGC105491 | Hwaseongbyeo | AB-QTL | SPA, IM | 8 | SSR | Jin et al. (2009) | ||
|
W1944 | Hwayeongbyeo | RIL | SPA, CIM | 8, 11 | SSR | Lee et al. (2005) | ||
|
W1944 | Hwayeongbyeo | IL | SPA, IM, | 1, 8, 11, 12 |
SPA, IM | Yuan et al.(2009) | ||
|
|||||||||
Grain (kernel) length |
|
IRGC101154 | Hwaseongbyeo | AB-QTL | SPA | 11 | SSR | Yoon et al. (2006) | |
|
IR71033-121-15 | Junambyeo | F2:3 | SPA, IM | 3, 5, 6, 7, 9 |
SSR, STS | Rahman et al. (2007) | ||
|
IRGC100195 | M-202 | AB-QTL | MIM | 1 | SSR | Eizenga et al. (accepted) | ||
|
IRGC100898, IRGC104705 | Bengal | AB-QTL | MIM | 1, 9 | SSR | Eizenga et al. (2013) | ||
|
IRGC105491 | Hwaseongbyeo | NIL | SPA, IM, ANOVA | 8 | SSR | Xie et al. (2006) | ||
|
W1944 | Hwayeongbyeo | RIL | SPA, CIM | 1, 2, 3, 5, 6 | SSR | Lee et al. (2005) | ||
Grain (kernel) width |
|
IRGC103544 | V20A | AB-QTL | SPA, IM, CIM | 10, 11 | RFLP, SSR | Li et al. (2004) | |
|
IRGC81848 | Swarna | BC2F2 | IM, CIM | 3, 6 | SSR | Swamy et al. (2012) | ||
|
IRGC105491 | Hwaseongbyeo | NIL | SPA, IM, ANOVA | 8 | SSR | Xie et al. (2006) | ||
Grain (kernel) length to width ratio |
|
IRGC103544 | Caiapó | BC3F1 | IM, CIM | 1 | SSR | Aluko et al. (2004) | |
|
IRGC103544 | V20A | AB-QTL | SPA, IM, CIM | 12 | RFLP, SSR | Li et al. (2004) | ||
|
IRGC101154 | Hwaseongbyeo | AB-QTL | SPA | 2, 11 | SSR | Yoon et al. (2006) | ||
|
W1944 | Hwayeongbyeo | RIL | SPA, CIM | 1, 2, 5 | SSR | Lee et al. (2005) | ||
|
IR71033-121-15 | Junambyeo | F2:3 | SPA, IM | 2, 3, 5 | SSR, STS | Rahman et al. (2007) | ||
|
IRGC100195 | M-202 | AB-QTL | MIM | 1, 5 | SSR | Eizenga et al. (accepted) | ||
|
IRGC81848 | Swarna | BC2F2 | IM, CIM | 12 | SSR | Swamy et al. (2012) | ||
Grain thickness |
|
IRGC101154 | Hwaseongbyeo | AB-QTL | SPA | 6, 11 | SSR | Yoon et al. (2006) | |
|
IRGC105491 | Hwaseongbyeo | NIL | SPA, IM, ANOVA | 8 | SSR | Xie et al. (2006) | ||
|
W1944 | Hwayeongbyeo | RIL | SPA, CIM | 1, 12 | SSR | Lee et al. (2005) | ||
Pericarp color |
|
IRGC105491 | Ce64, Caiapó, Hwacheong, Jefferson, IR64 | AB-QTL | IM, CIM | 7 | SSR | McCouch et al. (2007) | |
|
W1944 | Hwayeongbyeo | IL | SPA, IM, | 1, 7 | SPA, IM | Yuan et al.(2009) | ||
|
|||||||||
Grain weight |
|
IRGC103544 | Milyang 23 | BC3F2 | SPA | 2, 3 | SSR | Suh et al. (2005) | |
|
IRGC101154 | Hwaseongbyeo | AB-QTL | SPA | 3, 6, 8, 11 |
SSR | Yoon et al. (2006) | ||
|
Y73 | IR24 | RIL | CIM | 3, 9, 11 | SSR, STS | Chen et al. (2012) | ||
|
IR71033-121-15 | Junambyeo | F2:3 | SPA, IM | 3, 7, 11 | SSR, STS | Rahman et al. (2007) | ||
|
IRGC100195 | M-202 | AB-QTL | MIM | 10 | SSR | Eizenga et al. (accepted) | ||
|
IRGC105491 | Hwaseongbyeo | NIL | SPA, IM, ANOVA | 8 | SSR | Xie et al. (2006) | ||
|
IRGC105491 | Jefferson | AB-QTL | SPA, IM, CIM | 1, 5 | RFLP, SSR | Thomson et al. (2003) | ||
|
IRGC105491 | IR64 | AB-QTL | SPA, IM, CIM | 1, 3 | RFLP, SSR | Septiningsih et al. (2003) | ||
|
IRGC105491 | V20A, V20B | BC2 | ANOVA | 4, 8, 9, 11, 12 | RFLP | Xiao et al. (1998) | ||
|
IRGC105491 | MR219 | AB-QTL | CIM | 6 | SSR | Wickneswari et al. (2012) | ||
|
IRGC105491 | Hwaseongbyeo | NIL | ANOVA | 9 | SSR | Xie et al. (2008) | ||
|
IRGC105491 | Hwaseongbyeo | AB-QTL | SPA, IM | 8 | SSR | Jin et al. (2009) | ||
|
IRGC105491 | Ce64&V20A, Caiapó, Hwacheong, Jefferson, IR64 | AB-QTL | IM, CIM | 3 | SSR | McCouch et al. (2007) | ||
|
IC22015 | IR 58025A | AB-QTL | IM, CIM | 2, 9 | SSR | Marri et al. (2005) | ||
|
W1944 | Hwayeongbyeo | IL | SPA, IM | 1 | SPA, IM | Yuan et al.(2009) | ||
|
YJCW | 93-11 | AB-QTL | SPA, IM, CIM | 1 | SSR | Fu et al. (2010) | ||
Brown rice yield |
|
IRGC103544 | V20A | AB-QTL | SPA, IM, CIM | 12 | RFLP, SSR | Li et al. (2004) | |
Grain yield per plant |
|
IR64 | BC2F3 | SPA | 2, 6, 8, 9 |
SSR, STS | Bimpong et al. (2011) | ||
|
IRGC103544 | V20A | AB-QTL | SPA, IM, CIM | 12 | RFLP, SSR | Li et al. (2004) | ||
|
MR219 | AB-QTL | 1 | SSR | Bhuiyan et al. (2011) | ||||
|
G52-9 | Yuexiangzhan | AB-QTL | CIM | 1, 2, 3 | Jing et al. (2010) | |||
|
IC22015 | IR 58025A | AB-QTL | IM, CIM | 2, 9 | SSR | Marri et al. (2005) | ||
|
IRGC105491 | IR64 | AB-QTL | SPA, IM, CIM | 1 | RFLP, SSR | Septiningsih et al. (2003) | ||
|
IRGC105491 | Hwaseongbyeo | NIL | ANOVA | 9 | SSR | Xie et al. (2008) | ||
|
IRGC 105491 | V20A, V20B | BC2 | ANOVA | 1, 2, 8 | RFLP | Xiao et al. (1998) | ||
Yield |
|
IRGC103544 | Milyang 23 | BC3F2 | SPA | 2, 3, 4, 6, 8 |
SSR | Suh et al. (2005) | |
|
IRGC103544 | Milyang 23 | BC2F5 | 2 | SSR | Kang et al. (2008) | |||
|
IRGC101154 | Hwaseongbyeo | AB-QTL | SPA | 2 | SSR | Yoon et al. (2006) | ||
|
IRGC101144 | Hwaseongbyeo | NIL | 7 | SSR | Balkunde et al. (2013) | |||
|
Y73 | IR24 | RIL | CIM | 6 | SSR, STS | Chen et al. (2012) | ||
|
MR219 | AB-QTL | 4 | SSR | Bhuiyan et al. (2011) | ||||
|
IC22015 | IR58025A | AB-QTL | IM, CIM | 1, 2, 8 | SSR | Marri et al. (2005) | ||
|
IRGC105491 | Jefferson | AB-QTL | SPA, IM, CIM | 2, 3, 6, 9 |
RFLP, SSR | Thomson et al. (2003) | ||
|
IRGC105491 | IR64 | AB-QTL | SPA, IM, CIM | 1 | RFLP, SSR | Septiningsih et al. (2003) | ||
|
IRGC105491 | V20A, V20B | BC2 | ANOVA | 1, 2, 8, 12 |
RFLP | Xiao et al. (1998) | ||
|
IRGC105491 | Hwaseongbyeo | AB-QTL | SPA, IM | 8 | SSR | Jin et al. (2009) | ||
|
YJCW | 93-11 | AB-QTL | SPA, IM, CIM | 1 | SSR | Fu et al. (2010) | ||
Harvest index |
|
IR64 | BC2F3 | SPA | 2, 7 | SSR, STS | Bimpong et al. (2011) | ||
|
IC22015 | IR58025A | AB-QTL | IM, CIM | 2 | SSR | Marri et al. (2005) |
4.1. Yield enhancing QTL from exotic Oryza genomes
Several plant traits directly or indirectly affect rice grain yield including days to heading and maturity; plant height; panicle length; number of panicles per plant, spikelets per panicle and grains per panicle; seed set; grain weight; grain size and shape; and shattering (Table 2). The most important yield components in rice are the number of panicles per plant, number of grains per panicle, and grain weight (Chen et al. 2012; Lee et al. 2004; Septiningsih et al. 2003; Thomson et al. 2003). Yield improvement can be achieved as a result of the vast allelic diversity for these traits found in interspecific populations, especially number of grains per panicle which has proven to have the greatest relevance for rice breeding programs (Li et al. 1998; Liu et al. 2008; Tian et al. 2006).
Modern rice varieties are developed after an extensive selection process to improve a few targeted traits related to cultivation and end-use quality but primarily those associated with yield components, such as resistance to shattering, compact growth habit and improved seed germination (Tanksley and McCouch 1997). This prolonged breeding procedure can lead to a reduction in the genetic variability found in modern cultivated rice (Rangel et al. 2008), thus identifying genetic sources for agronomically important traits from wild
Other AB-QTL populations developed using the same
Tian et al. (2006) selected an introgression line, SIL040, from the BC4F4 lines of
Two AB-QTL populations were developed using the
Similarly, a CSSL library composed of 133 lines selected from an AB-QTL population with an
To identify the genetic potential of
BILs in the BC5F5:6 were derived from
To evaluate the effect of
Awns are an important trait in wild rice species because it protects the seeds from birds and other animals. By contrast, the majority of modern rice cultivars have short awns so that it is easier to harvest the seed. This trait is reported to be controlled by several genes located in different chromosomes, including
A doubled haploid (DH) population was developed from Caiapó (
These studies give several examples of QTL or genes for yield and yield components being attributed to the wild donor parent not only the ancestral A-genome species,
4.2. Genes for sterility
Reproductive barriers, such as crossability, hybrid seed inviability, hybrid sterility and hybrid breakdown, have significantly limited the success of interspecific hybridization between
The phenomenon of transmission ratio distortion (TRD) where one allele is transmitted more frequently than the opposite allele in interspecific and intraspecific hybrids has been discovered in a broad range of organisms and is often a reproductive barrier (Koide et al. 2012). Recently, Koide et al. (2012) identified a unique sex-independent TRD (
4.3. Grain quality traits
Acceptable rice grain quality is a major goal of rice breeding programs worldwide because it determines the acceptability of cooked rice to the consumer. Grain quality is a combination of several components including milling efficiency, physical appearance, cooking and eating characteristics, and nutritional quality (Aluko et al. 2004; Li et al. 2004). A few interspecific populations were evaluated for grain quality traits (Table 3). These studies showed the
Most interesting was the BC3F1 progeny of the Caiapó x
|
|
|
|
|
|
|
|||
|
|||||||||
Apparent amylose content |
|
IRGC103544 | V20A | AB-QTL | SPA, IM, CIM | 6, 12 | RFLP, SSR | Li et al. (2004) | |
|
IRGC101154 | Hwaseong- byeo |
AB-QTL | SPA | 3, 5, 7 | SSR | Yoon et al. (2006) | ||
|
IRGC81848 | Swarna | BC2F2 | IM, CIM | 2 | SSR | Swamy et al. (2012) | ||
|
IRGC100195 | M-202 | AB-QTL | MIM | 6 | SSR | Eizenga et al (accepted) | ||
|
IRGC105491 | IR64 | AB-QTL | IM, CIM | 6 | RFLP, SSR | Septiningsih et al. (2003b) | ||
Alkali spreading value (ASV) or gel consistency |
|
IRGC103544 | Caiapó | BC3F1 | IM, CIM | 6 | SSR | Aluko et al. (2004) | |
|
IRGC103544 | V20A | AB-QTL | SPA, IM, CIM | 12 | RFLP, SSR | Li et al. (2004) | ||
|
IRGC100195 | M-202 | AB-QTL | MIM | 6 | SSR | Eizenga et al. (accepted) | ||
|
IRGC105491 | IR64 | AB-QTL | IM, CIM | 6 | RFLP, SSR | Septiningsih et al. (2003b) | ||
Kernel elongation |
|
IRGC 103544 | V20A | AB-QTL | SPA, IM, CIM | 3 | RFLP, SSR | Li et al. (2004) | |
Protein |
|
IRGC103544 | Caiapó | BC3F1 | IM, CIM | 2, 6 | SSR | Aluko et al. (2004) | |
|
IRGC103544 | V20A | AB-QTL | SPA, IM, CIM | 8 | RFLP, SSR | Li et al. (2004) | ||
Percent rice bran |
|
IRGC103544 | Caiapó | BC3F1 | IM, CIM | 4, 7 | SSR | Aluko et al. (2004) | |
Percent milled rice |
|
IRGC103544 | Caiapó | BC3F1 | IM, CIM | 5 | SSR | Aluko et al. (2004) | |
|
IRGC81848 | Swarna | BC2F2 | IM, CIM | 1 | SSR | Swamy et al. (2012) | ||
|
|||||||||
Bacterial blight |
|
IR31917-45-3-2 | MAAL | 12 | Multani et al. (1994) | ||||
O. brachyantha O. longistaminata O. officinalis |
AIL | 5, 6, 8, 11 |
SSR | Hechanova et al. (2008) | |||||
|
IRGC100914 | IR31917-45-3-2 | AIL | ANOVA | 12, others | SSR, SNP, STS, InDel | Angeles-Shim et al. (accepted) | ||
|
WLO2 | BS125 | NIL | 11 | RFLP, RAPD | Ronald et al. (1992) | |||
|
IR24 | 11 | Khush et al. (1990) | ||||||
|
Y73 | IR24 | RIL | CIM | 1, 3, 5, 10, 11 | SSR, STS | Chen et al. (2012) | ||
|
78-1-5 | IR24 | F2- BC1 | 6 | RAPD, AFLP | Gu et al. (2004) | |||
|
IRGC81825 | PR114 | RIL, BIL, IL | SMA-IM | 4 | SSR, STS | Cheema et al. (2008) | ||
Blast disease |
|
IRGC100882 | Lijiangxintuan-heigu | 6 | CAPS, SSR, STS | Jeung et al. (2007) | |||
|
IRGC101141 | IR31917 | F2 | 6 | RAPD | Liu et al. (2002) | |||
|
IRGC100898 | Bengal | AB-QTL | MIM | 8 | SSR | Eizenga et al. (2013) | ||
|
IRGC104705 | Bengal | AB-QTL | MIM | 8, 12 | SSR | Eizenga et al. (2013) | ||
|
IRGC104812 | Koshihikari | IL | 3, 11 | Hirabayashi et al. (2010); Sobrizal et al. (1999) | ||||
|
IRGC104814 | Koshihikari | IL | 3, 5, 6 | Hirabayashi et al. (2010) | ||||
Sheath blight disease |
|
IRGC100898 | Bengal | AB-QTL | MIM | 6 | SSR | Eizenga et al. (2013) | |
|
IRGC104705 | Bengal | AB-QTL | MIM | 3, 6 | SSR | Eizenga et al. (2013) | ||
Stem rot |
|
IRGC100912 (87-Y-550) |
L-201 (long grain-breeding lines) | F2 | SPA | 2, 3 | AFLP | Ni et al. (2001) | |
Grassy stunt virus |
|
IRGC101508 | IR8, IR20, IR22, IR24, IR773A-1-3 |
F2 | Nuque et al. (1982) | ||||
Rice stripe necrosis resistance |
|
IRGC103544 | Caiapó | CSSL | IM, CIM | 11 | SSR | Gutiérrez et al. (2010) | |
|
|||||||||
Brown planthopper |
|
IRGC100882 | IR31917-45-3-2 | IL | 12 | RFLP | Ishii et al. (1994) | ||
|
IR65482-7-216-1-2 | Jinbubyeo | F2, BC2F2 | ANOVA | 12 | SSR, STS | Jena et al. (2006) | ||
|
IR31917-45-3-2 | MAAL | 12 | Multani et al. (1994) | |||||
|
IRGC105159 | 2428 | F2, BC1F1 | 2 | RFLP, SSR | Guoqing et al. (2001) | |||
|
B14 | Taichung | RIL | 4 | SSR, RFLP | Yang et al. (2002) | |||
|
101141 | Junambyeo | F3 | 4, 12 | SSR, STS | Rahman et al. (2009) | |||
|
IRGC100878, IRGC100896, IRGC101150, IRGC101412, IRGC102385 | IR31917-45-3, IR25587-109-3 | BC2F8 | 4, 10, 12 | RFLP | Jena et al. (1992) | |||
|
IR54745-2-21-12-17-6 | IR50 | 3 | RAPD, STS | Renganayaki et al. (2002) | ||||
|
B5 | 1826, 93-11 | 3, 4 | SSR | Li et al. (2006) | ||||
|
IRGC100896 | IR31917-45-3-2 | F2 | 11 | RAPD | Jena et al. (2002) | |||
|
3 | RFLP | Hirabayshi et al. (1998) | ||||||
|
IR54745-2-21-12-17-6 | IR50 | RIL | 3 | RAPD | Renganayaki et al. (2002) | |||
|
B5 | Minghui 63 | F2 | 3 | RFLP | Huang et al. (2001) | |||
|
B5 | RIL | 4 | AFLP, RFLP, SSR | Yang et al. (2004) | ||||
|
WBO1 | Minghui 63 | F2 | 4, 8 | SSR | Hou et al. (2011) | |||
Green rice leafhopper |
|
IRGC104038 | Taichung 65 | NIL | IM, CIM | 3, 7, 9, 10 |
SSR | Fujita et al. (2010) | |
|
W1962 | Taichung 65 | NIL, BC4F2 | 8 | SSR | Fujita et al. (2006) | |||
White-backed planthopper |
|
B5 | Minghui 63 | RIL | 3, 4 | SSR | Tan et al. (2006b) | ||
|
BILs-DWR/Dingxiang | XieqingzaoB | BIL | CIM, MIM | 2, 5, 9 | SSR | Chen et al. (2010) | ||
|
|||||||||
Aluminum tolerance |
|
IRGC106424 | IR64 | RIL | IM | 1, 3, 9 (2, 7, 8) |
RFLP | Nguyen et al. (2003) | |
Drought tolerance |
|
Guichao 2 | IL | SMR | 2, 12 | SSR | Zhang et al. (2006) | ||
|
W630 | Nipponbare | BIL | IM | 1, 5 | SSR | Thanh et al. (2011) | ||
Heat tolerance |
|
YJCWR | TeQing | IL | CIM | 1 | SSR | Lei et al. (2013) | |
Low temperature tolerance |
|
IRGC100195 | M-202 | AB-QTL | MIM | 5 | SSR | Eizenga et al. (accepted) | |
|
W1943 | Guang-lu-ai 4 | BC4F2 | IM, CIM | 3, 11 | SNP | Koseki et al. (2010) | ||
|
Dongxiang | Nanjing11 | BC2F1 | CIM | 10 | SSR | Xia et al. (2010) | ||
|
W1944 | Hwayeong- byeo |
RIL | SPA, CIM | 2, 5 | SSR | Lee et al. (2005) | ||
Salt tolerance |
|
TeQing | IL | SPA | 1, 2, 3, 6, 7, 10 |
SSR | Tian et al. (2011) | ||
Submergence stress |
|
9 | Li et al. (2011) | ||||||
|
|
IR64 | BC2F3 | SPA | 1, 2, 3, 6, 10 |
SSR, STS | Bimpong et al. (2011) |
4.4. Resistance to biotic stress
4.4.1. Disease resistance
Pathogenic microorganisms, such as fungi, oomycetes, viruses and bacteria, and pests, such as insect herbivores, significantly reduce rice seed yield and quality. The most destructive rice diseases include bacterial blight caused by
Seed yield losses due to bacterial blight were reported to be up to 75% in India, Indonesia, and the Philippines, and 20 to 30% in Japan (Mew et al. 1993; Nino-Liu et al. 2006). Thus far 31 bacterial blight resistance genes have been reported and six of these were identified in species other than
Both bacterial blight and blast resistance were identified in the tetraploid CCDD genome species,
Lastly, the line Y73 was selected for a high level of bacterial blight resistance from the hybrid progeny of an asymmetric somatic hybridization between a resistant
Blast is considered the most destructive fungal disease in irrigated rice. The symptoms include lesions on leaves, stems, peduncles, panicles, seeds and roots (Savary and Willocquet 2000; Khush et al. 2009). To date, 41 blast resistance genes have been reported; however, there are only two genes,
Rice sheath blight,
Stem rot, a fungal disease caused by
African cultivated rice,
4.4.2. Insect resistance
Genetic resistance is an effective method of protecting rice from insect pests in Asia and the Americas (Kiritani 1979; Way 1990) and avoids the possible environmental contamination associated with chemical control (Zhang 2007). The brown planthopper,
Early efforts to evaluate the
The white-backed planthopper,
Guo et al. (2013) analyzed 131 BC4F2 ILs resulting from a cross between
Rice monosomic alien addition lines (MAALs) contain the complete
Green leafhopper [
The wild
4.5. Genes for abiotic stress
Abiotic stresses, including drought and flood, high and low temperatures, high salinity, high aluminum and acid sulfate soils, have a negative impact on rice productivity worldwide. Rice, like other plant species, has evolved to adapt to different environmental stresses using different mechanisms and strategies with multiple sensors. When the sensors identify a stress, a signal transduction pathway is invoked, which activates genes conferring the initial response for short term or long term tolerance to the stress (Grennan 2006; Lexer and Fay 2005). Recent studies identified many genes involved in plant tolerance to abiotic stress, which are classified into two groups based on their products. The first group includes genes that protect the cells via synthesis of chaperones, a group of proteins that help non-covalent folding and unfolding of other proteins in the cell under stress conditions, and enzymes for protecting metabolites and proteins. The second group includes those genes that regulate stress responses acting as transcriptional factors to control stress genes or by producing hormones (Grennan 2006).
4.5.1. Tolerance to drought and heat
Drought reduces grain yield and affects yield stability in many rainfed regions by decreasing the number of tillers per plant, plant height, number of leaves and leaf width; and delaying anthesis and maturity as shown by Ndjiondjop et al. (2010) using 202 BILs derived from WAB56-104 (
Bocco et al. (2012) evaluated the morphological and agronomical traits of 60 genotypes including 54 BC3F6 introgression lines from IR64 (recurrent parent, elite
Several accessions of
4.5.2. Tolerance to low temperatures
Low temperatures during the rice growing season causes poor germination, slow growth, withering and anther injury (Andaya et al. 2007; Hu et al. 2008). To cope with cold stress, many plant species including rice have developed several physiological and biochemical pathways for surviving and adapting to stress conditions (Ingram and Bartels 1996; Pastori and Foyer 2002; Hu et al. 2008). Rice is predominately grown in tropical and sub-tropical regions; therefore, many cultivars are sensitive to cold temperature especially during the seedling stage. The optimum temperature range for germination and early seedling growth is 25-30oC, and temperatures below 15-17oC during this period delay plant establishment, reduce plant competitive ability against weeds, delay plant maturity, and decrease grain yield. Improving cold tolerance at this stage is one of the most effective ways to achieve yield stability and genetic tolerance is the most promising strategy (Andaya and Mackill 2003; Fujino et al. 2004; Koseki et al. 2009). Overall, the
Wild rice species, such as
Koseki et al. (2010) analyzed 184 F2 introgression lines from crosses of Guang-lu-ai 4 (cold sensitive,
Seedling cold tolerance was measured in the M-202 (medium grain, U.S.
4.5.3. Tolerance to aluminum and acid soils
Aluminum toxicity is another abiotic stress that causes grain yield reduction especially when rice is grown in an acidic soil (IRRI 1978). If the soil pH falls below 5.5, aluminum will more likely separate from the soil colloids and come into a solution phase. Aluminum at toxic levels slows root development, reduces the plant’s ability to take up water and nutrients, and decreases plant growth, consequently reducing grain yield and grain quality (Foy 1992). Application of lime to the soil, reduces soil acidity and improves soil fertility but the results have showed limited success in overcoming the effects of aluminum toxicity. Aluminum tolerance is a quantitative trait and varies among rice species. Both additive and dominance effects contribute to the genetic heritability of aluminum tolerance as documented by the importance of both general combining ability and specific combining ability (Howeler and Cadavid 1976; Wu et al. 1997).
In the past decade, one
Even though traits associated with abiotic stress are more difficult to evaluate because of environmental effects and interactions between genes, the development of the AS996 variety is an exciting success story. The release of Arizona-1 and Arizona-2 could make significant contributions to improving rice yields in areas where high temperatures routinely lower yield. With the improved molecular techniques for dissecting these traits and the gene functions related to abiotic stress, more significant advances should be made in the near future, especially as the scientific community provides the tools for rice producers to deal with global climate change.
5. Conclusions
The repositories of
Acknowledgments
The support of National Science Foundation-Plant Genome Project: “The Genetic Basis of Transgressive Variation in Rice” (Award no. 1026555) to Ehsan Shakiba is gratefully acknowledged. Dr. Paul L. Sanchez and Dr. Benildo G. de los Reyes are acknowledged for their critical reading of this manuscript.
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