Open access

Unraveling the Secrets of Rice Wild Species

Written By

Ehsan Shakiba and Georgia C. Eizenga

Published: 23 April 2014

DOI: 10.5772/58393

From the Edited Volume

Rice - Germplasm, Genetics and Improvement

Edited by Wengui Yan and Jinsong Bao

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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 (Oryza sativa L.) and wheat (Triticum aestivum L.), both self-pollinated, the re-introduction genetic diversity from the wild is central to the continued success of breeding, given that viruses, fungi, and bacteria, three main causal agents of biotic stress, are constantly evolving to cause the breakdown of the host plant’s defense mechanisms (Rausher 2001).

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, Lycopersicon esculentum Mill. (Grandillo and Tanksley 2003), have shown that wild species contain genomic components that could result in genetic gains in terms of agronomic performance.

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 Oryza species that were lost during evolution and domestication, genetically dissect the traits encoded by these alleles through chromosome mapping, and incorporate these traits or alleles into an agronomically useful genetic background. To do this we will (a) briefly describe the relationships among the species in the genus Oryza, (b) describe the types of populations that have been developed for mapping desirable traits identified in the wild Oryza species to a chromosome location, and (c) summarize the quantitative trait locus (QTL) studies focused on mapping the useful traits and novel alleles to specific locations in the genomes of Oryza species.

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2. Phylogeny of the Oryza genus

The Oryza genus includes two cultivated species, Asian rice, O. sativa, which is grown throughout the tropical and temperate climates of the world, and African rice, O. glaberrima, which is found in sub-Saharan Africa along the Niger River. The 22 wild species composing the Oryza genus are characterized by eleven different genomes identified as the A-, B-, C-, D-, E-, F-, G-, H-, J-, K-and L-genomes and arranged in the following 10 genome types AA, BB, CC, BBCC, CCDD, EE, FF, GG, HHJJ and KKLL. Four of the wild Oryza species are tetraploid and the remaining 18 are diploid, as well as, the two cultivated species (Table 1).

Species No. of chromo-somes (2n) Genome Genome size (Mbp) Distribution
Section Oryza
Oryza sativa complex
O. sativa L. (ssp. japonica, ssp. indica) 24 AA 420, 466 Worldwide
O. nivara Sharma et Shastry 24 AA 448 Tropical and subtropical Asia
O. rufipogon Griff. 24 AA 439, 450 Tropical and subtropical Asia, Tropical Australia
O. glaberrima Steud. 24 AgAg 354 West Africa
O. barthii A. Chev.§ 24 AgAg 411 Africa
O. glumaepatula Steud. 24 AgpAgp 464 South and Central America
O. longistaminata A. Chev. et Roehr. 24 AlAl 352 Africa
O. meridionalis Ng 24 AmAm 435 Tropical Australia
Oryza officinalis complex
O. punctata Kotschy ex Steud. 24
48
BB,
BBCC
423 (BB) Africa
O. minuta J.S. Presl. ex C.B. Presl. 48 BBCC 1124 Philippines, Papua New Guinea
O. eichingeri A. Peter 24 CC South Asia and East Africa
O. officinalis Wall ex Watt 24 CC 653 Tropical and subtropical Asia, Tropical Australia
O. rhizomatis Vaughan 24 CC Sri Lanka
O. alta Swallen 48 CCDD 1124 South and Central America
O. grandiglumis (Doell) Prod. 48 CCDD South and Central America
O. latifolia Desv. 48 CCDD South and Central America
O. australiensis Domin. 24 EE 960 Tropical Australia
Section Brachyantha
O. brachyantha A. Chev. et Roehr. 24 FF 338 Central Africa
Section Padia
Oryza granulata complex
O. granulata Nees et Am. ex Watt 24 GG 862 South and Southeast Asia
O. meyeriana
(Zoll. et (Mor. ex Steud.) Baill.)
24 GG Southeast Asia
Oryza ridleyi complex
O. longiglumis Jansen 48 HHJJ Irian Jaya, Indonesia, Papua New Guinea
O. ridleyi Hook. F. 48 HHJJ 1283 South Asia
Oryza schlechteria complex
O. coarctata Tateoka 48 KKLL 771 Asian coastal area
O. schlerchteri Pilger 48 KKLL Papua New Guinea

Table 1.

Taxonomic classification of Oryza species including the chromosome number, genome designation, genome size and distribution for each species.

Classification for species, genome designation and distribution based on Brar and Singh (2011), Lu et al. (2014), Sanchez et al. (2013) and Vaughan (2003). The superscripts for the A-genome indicate a variation of the type of A-genome.


Genome size based on the following: O. sativa subsp. japonica (Goff et al. 2002), O. sativa subsp. indica (Yu et al. 2002) and Oryza species (Ammiraju et al. 2010).


§ O. barthii is also classified as O. breviligulata A. Chev. et Roehr.


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 Oryza and Leersia genera about 14.2 mya. The genus Oryza is divided into three sections: Padia, Brachyantha and Oryza. Padia includes the forest-dwelling Oryza, which are distributed into the O. granulata (GG), O. ridleyi (HHJJ) and O. schlechteria (KKLL) complexes. The O. granulata complex is thought to have diverged from the other Oryza species about 8 mya. O. brachyantha (FF) is the only species in the section Brachyantha. This species is widely distributed across Africa, growing in iron-pan rock pools.

Section Oryza consists of two species complexes, the O. officinalis complex with the B-, C-, D-and E-genomes and the O. sativa complex, which includes all the A-genome species. Within the O. officinalis complex, O. australiensis (EE) is the most diverged and O. eichingeri (CC) appears to be the most basal of the C-genome species.

The species in the O. sativa complex prefer full sun, and grow near lakes, rivers and seasonal pools of water. Molecular data suggests that O. meridionalis diverged from the other A-genome species about 2 mya. Also, the perennial African species, O. longistaminata, diverged from the Asian A-genome species about the same time period, 2-3 mya. The second divergence between the Asian and African A-genome species, O. barthii and O. glaberrima, occurred 0.6 to 0.7 mya. More recently, possibly about 0.4 mya (or more than 0.2 mya), the O. rufipogon clade(s) that eventually diverged into the O. sativa subspecies (subsp.) Japonica and Indica. Later, the Indica subspecies differentiated into the indica and aus subpopulations and the Japonica subspecies into the aromatic (Group V), tropical japonica and temperate japonica subpopulations (Garris et al. 2005, Zhao et al. 2011, Huang et al. 2012). Archaeobotanical evidence from spikelet bases and changes in grain size document this domestication process (Fuller et al. 2010). Recently, based on genome sequences of 446 geographically diverse O. rufipogon accessions, Huang et al. (2012) further subdivided O. rufipogon accessions into three major O. rufipogon clades: one closely aligned with O. sativa subsp. japonica, one aligned with O. sativa subsp. indica, and the third clade was independent of O. sativa. Furthermore, as part of this study, a neighbor-joining tree constructed from sequence differences of 15 representative A-genome accessions suggested within Indica, different O. rufipogon clades were associated with the aus and indica subpopulations, whereas the three Japonica subpopulations arose from a single O. rufipogon clade. This phylogenetic tree also supported the aforementioned genetic distance between O. meridionalis, O. longistaminata, O. barthii and O. glaberrima.

Rice, O. sativa, the first monocot plant with a reference genome, is the central comparative genomics model for all grasses, and has been compared to all major cereals. To lay the foundation for interrogating the rice wild relatives, 18 bacterial artificial chromosome (BAC) libraries for 16 different Oryza species spanning all 10 Oryza genome types including the AA-genome species (O. nivara, O. rufipogon, O. glaberrima, O. barthii, O. glumaepatula, O. longistaminata, O. meridionalis), O. punctata (BB), O. officinalis (CC), O. minuta (BBCC), O. alta (CCDD), O. australiensis (EE), O. brachyantha (FF), O. granulata (GG), O. ridleyi (HHJJ) and O. coarctata (HHKK), were generated through the Oryza Map Alignment Project (OMAP) as summarized by Ammiraju et al. (2010). Subsequently, the International OMAP consortium was formed in 2007 to (a) generate reference sequences and transcriptome data sets of the eight A-genome species and representative species of the other genome types, (b) generate advanced mapping populations for the A-genome species, and (c) identify naturally occurring populations of the wild Oryza species for diversity and evolutionary analyses, as well as, conservation (Jacquemin et al. 2013; Sanchez et al. 2013). The species included in the sequencing effort were A-genome species (O. nivara, O. rufipogon, O. barthii, O. glaberrima, O. glumaepatula, O. longistaminata, O. meridionalis and both O. sativa subsp. indica and subsp. japonica), O. punctata (BB), C-genome species (O. officinalis, O. eichingeri, O. rhizomatis), O. australiensis (EE), O. brachyantha (FF), O. granulata (GG), and the outgroup, Leersia perrieri. To date, the sequencing of nine genomes and L. perrieri has been completed, in addition to the established reference sequences for O. sativa subsp. japonica and subsp. indica genomes (Wing 2013). Currently, two additional O. sativa subsp. indica cultivars are being sequenced representing the aus (DJ123) and indica (IR64) subpopulations (McCombie 2013).

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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 Oryza species and O. sativa including (a) recombinant inbred line (RIL), (b) advanced backcross (AB), (c) backcross inbred line (BIL), (d) chromosome segment substitution line (CSSL), (e) near isogenic line (NIL) and (f) multi-parent advanced generation inter-cross (MAGIC). A discussion of each of these populations follows and examples are included in the third section describing agronomically important traits attributed to the Oryza species donor.

Figure 1.

A comparison of the methods for creating primary and advanced bi-parental mapping populations, including recombinant inbred lines (RILs), backcross inbred lines (BILs), chromosome segment substitution lines (CSSLs) and near isogenic lines (NILs) as summarized by Fukuoka et al. (2010). Also shown are the number of backcrosses (BC) required and the genotypes of the lines obtained by each method. Karyotypes of the three CSSLs illustrate how chromosome 1 of the donor can be introgressed into the recurrent parent. The three NIL genotypes are based on the JeffersonNILs, each with a different O. rufipogon (IRGC105491) introgression selected for a different yield QTL (Imai et al. 2013).

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 O. sativa but a limited number of interspecific populations have been reported. To develop a RIL population, two contrasting cultivars or accessions for the trait(s) to be mapped are crossed together to create an F1 hybrid. By successive self-pollination starting from the F1 generation, subsequent generations of segregants are produced (up to F3), representing multiple rounds of recombination and eventually fixation to homozygosity towards either of the parental alleles (Fig. 1). This derived population is advanced for several generations by the single seed descent (SSD) method, where a single F3 seed from each F2 plant is planted to produce the F4 generation, subsequently a single F4 seed is selected from each line to produce the F5 generation with the SSD method usually continuing until F8 seed are produced. At the F7, the RILs exhibit genetic homogeneity, such that the genomic contribution of each parent is fixed, and together these RILs compose a mapping population. If selections are being made for improved lines with a particular trait(s), this selection often begins in the F5-F6 if individual plants can be selected for the trait; otherwise, the selection is postponed to later generations (F7-Fn) (Nguyen et al. 2003, Poehlman and Sleper 1995). The procedure continues until the superior lines with desirable traits are produced.

The main advantage of the RIL method is that no backcrossing is necessary but when a wild Oryza species is a parent, often undesirable traits associated with the wild parent, especially shattering and sterility are problematic, thus it is often necessary to backcross. RIL populations are suitable for identifying major gene(s) or QTL(s), and to detect genetic interactions such as epistasis (Fukuoka et al. 2010). Other advantages are, the individual RIL may contain more than one introgressed segment in their chromosomes, representing different recombination events and a higher recombination frequency. As a result, fewer progeny lines are required to cover the complete donor genome as compared to other types of bi-parental mapping populations that include a backcross generation. Moreover, epistatic effects can be detected in RILs due to the presence of several introgressed segments in each line (Keurentjes et al. 2007). Because several segments of each parent are present in each individual line composing the population, there is less homogeneity in RIL populations as compared to most other types of populations. This heterogeneity is easy to observe and provides an excellent opportunity for phenotypic evaluation. In summary, the RIL method has proven to be useful when both parents are O. sativa but with interspecific and intergenomic crosses, backcrossing is often necessary (Fukuoka et al. 2010).

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 Oryza species, identified as the donor parent, is crossed with the recurrent parent, usually an elite cultivar, which will be crossed with the hybrid parent in subsequent crosses (illustrated in Ali et al. 2010). Often the donor parent is used as a male and the recurrent parent as the female to avoid the cytoplasmic male sterility and because it is usually easier to emasculate the cultivated parent. The F1 plant(s) is one parent in the second generation and it is crossed with the recurrent parent, which is defined as backcrossing. The resulting first backcross generation (BC1) may be backcrossed again with the recurrent parent to generate a BC2 population. If the BC2 progeny are sterile, it is best to advance the population to the BC3 generation by crossing the BC2 plants to the recurrent parent a third time. After the progeny lines are advanced to the BC2 (or BC3) generation and allowed to self pollinate, these BC2F2 (or BC3F2) progeny plants are grown to collect phenotypic and genotypic data for the QTL analysis. After the AB-QTL mapping, the AB population can be advanced by (a) allowing all the progeny lines to self-pollinate and be advanced by SSD for three to four additional generations, thus developing a BIL population or (b) backcrossing the progeny lines additional generations to develop a library of CSSLs or NILs for targeted traits (Fig. 1).

3.3. Backcross Inbred Line (BIL) population

BIL populations are used to introgress desirable traits from the wild Oryza species donor into rice with the potential of improving the agronomic performance of elite cultivars and develop mapping populations (Fig. 1). After backcrossing, as described in the aforementioned AB population development, the individual lines, BC1, BC2 or BC3 generation, are self-pollinated for about four generations to the BC2F5, as described in the RIL population development. If a specific trait is being selected, the BILs will be screened for that trait and backcrossed as described in the NIL section (Blanco et al. 2003; Fukuoka et al. 2010; Fulton et al. 1997; Bernacchi et al. 1998; Talamè et al. 2004).

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 Oryza species is the donor parent and the recurrent parent is usually an elite cultivar. To confirm the entire donor genome is included in the CSSL library, a set of polymorphic markers is often used to assist in selecting lines for each generation, beginning with the BC1F1 generation. To develop a CSSL library usually requires backcrossing to the recurrent parent for three to four additional generations (BC4F1 or BC5F1). The set of polymorphic markers can be used each generation to confirm the targeted segment is present in each line composing the CSSL library as illustrated in Ali et al. (2010). Alternatively, several hundred lines can be backcrossed for 4 to 5 generations and a CSSL library can be selected after genotyping in the BC4 or BC5 generation. Once the desired BC4:5F1 lines are selected, the lines are self-pollinated to achieve homozygosity and the lines homozygous for the individual targeted segment are selected from the BC4:5F2 progeny lines. The BC4:5F3 seed is used to establish the CSSL library composed of a set near isogenic lines covering the entire donor genome (Ali et al. 2010; Fukuoka et al. 2010).

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 O. glaberrima (Gutiérrez et al. 2010).

The rice universal core genetic map is a set of uniformly distributed polymorphic SSR markers that clearly differentiate O. sativa cultivars and wild Oryza species accessions, especially within the AA genome (Orjuela et al. 2010). If polymorphic SSR (simple sequence repeat) markers for several different CSSL libraries or other mapping populations are selected from the core map, such that the markers are in approximately the same location, comparisons can be made across several different CSSL libraries or mapping populations. More recently, SNP (single nucleotide polymorphism) markers have been used to genotype the putative lines being selected for the CSSL libraries. For this purpose, several different 384-SNP assays have been used to identify the target donor segment and recurrent parent background (Ali et al. 2010; Tung et al. 2010; McCouch et al. 2010) and most recently a single 6,000 SNP assay is being employed (Zhou et al. 2013; SR McCouch, Cornell University, personal communication).

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 Oryza species donor into the background of the recurrent parent (Fig. 1). With NILs, the focus is on a particular set of lines for the trait(s) of interest, not covering the entire donor genome as with a CSSL library. As with CSSLs, once the targeted segment is introgressed into the recurrent parent background, the pre-NIL lines are allowed to self-pollinate, so that the NILs will be homozygous for the targeted segment. Molecular markers, such as SSRs and SNPs, are used to select for the targeted segment and determine the number of chromosomal segments from the donor parent remaining in the background (Fukuoka et al. 2010).

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 Oryza species donor, can be used as parental lines in breeding programs.

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 (O. sativa) at the International Rice Research Institute (IRRI) which are described as (1) Indica MAGIC composed of 1,831 S8 AILs; (2) MAGIC Plus with 2,214 S6 AILs; (3) Japonica MAGIC with approximately 400 S6 AILs; and (4) MAGIC Global with 1,402 AILs in the S5 generation. Currently, a Wild MAGIC population is being developed by a team at IRRI (K. Jena, H. Leung, K. McNally) in collaboration with J. Hibberd (University of Cambridge, U.K.), and I. Mackay (NIAB, Cambridge, U.K.) using multiple accessions of all eight A-genome species (McNally, personnel communication). In most cases, for this population, the initial crosses had O. sativa as the female parent, and the goal is to produce 16-way crosses with highly mixed genomes.

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4. Useful agronomic traits mapped in Oryza species and transferred into cultivated rice

Trait Donor species Donor accession Recurrent parent(s) Type of mapping popu-lation QTL mapping analysis Chromo-some location§ Type of mark-er Reference
Vegetative Growth Stages
Days to flowering O. australiensis IRGC100882 IR31917-45-3-2 IL 10 RFLP Ishii et al. (1994)
Days to heading O. glaberrima IR64 BC2F3 SPA 2, 10 SSR, STS Bimpong et al. (2011)
O. glaberrima IRGC103544 Milyang 23 BC3F2 SPA 1, 4,
7, 8
SSR Suh et al. (2005)
O. glumaepatula Taichung 65 BC4F2 7 RFLP Sanchez et al. (2003)
O. grandiglumis IRGC101154 Hwaseongbyeo AB-QTL SPA 6 SSR Yoon et al. (2006)
O. meyeriana Y73 IR24 RIL CIM 6, 7,
8, 11
SSR, STS Chen et al. (2012)
O. nivara IRGC100898 Bengal AB-QTL MIM 3, 6 SSR Eizenga et al. (2013)
O. nivara IRGC100898 Bengal AB-QTL MIM 3, 4,
6, 8
SSR Eizenga et al. (2013)
O. nivara IRGC100195 M-202 AB-QTL MIM 3, 8 SSR Eizenga et al. (accepted)
O. rufipogon IRGC105491 Jefferson AB-QTL SPA, IM, CIM 1, 2, 3,
4, 10
RFLP, SSR Thomson et al. (2003)
O. rufipogon IRGC105491 IR64 AB-QTL SPA, IM, CIM 2, 7 RFLP, SSR Septiningsih et al. (2003)
O. rufipogon IRGC105491 V20A, V20B BC2 ANOVA 6, 12 RFLP Xiao et al. (1998)
O. rufipogon IRGC105491 Hwaseongbyeo AB-QTL, NIL SPA, IM, ANOVA 6, 9 SSR Jin et al. (2009),
Xie et al. (2008)
O. rufipogon IRGC105491 MR219 AB-QTL CIM 3 SSR Wickneswari et al. (2012)
O. rufipogon W1944 Hwayeongbyeo IL SPA, IM 1 SPA, IM Yuan et al.(2009)
Days to maturity O. rufipogon IRGC105491 IR64 AB-QTL SPA, IM, CIM 4, 7, 8 RFLP, SSR Septiningsih et al. (2003)
O. rufipogon IRGC105491 V20A, V20B BC2 ANOVA 6, 12 RFLP Xiao et al. (1998)
O. rufipogon IRGC105491 MR219 AB-QTL CIM 4, 6 SSR Wickneswari et al. (2012)
Seedling height O. rufipogon W1944 Hwayeongbyeo RIL SPA, CIM 1 SSR Lee et al. (2005)
Culm
length
O. glaberrima IRGC103544 Milyang 23 BC3F2 SPA 2, 10 SSR Suh et al. (2005)
O. grandiglumis IRGC101154 Hwaseongbyeo AB-QTL SPA 1, 4 SSR Yoon et al. (2006)
O. minuta IR71033-121-15 Junambyeo F2:3 SPA, IM 6, 7, 12 SSR, STS Rahman et al. (2007)
O. rufipogon W1944 Hwayeongbyeo RIL SPA, CIM 1, 6 SSR Lee et al. (2005)
O. rufipogon IRGC105491 MR219 AB-QTL CIM 1, 3, 9 SSR Wickneswari et al. (2012)
O. rufipogon W1944 Hwayeongbyeo IL SPA, IM, 1, 12 SPA, IM Yuan et al.(2009)
Plant
height
O. glaberrima IR64 BC2F3 SPA 1 SSR, STS Bimpong et al. (2011)
O. longistaminata RD23 BC7F2 CIM 1 SSR Chen et al. (2009)
O. nivara IRGC100898 Bengal AB-QTL MIM 1 SSR Eizenga et al. (2013)
O. nivara IRGC104705 Bengal AB-QTL MIM 1, 12 SSR Eizenga et al. (2013)
O. rufipogon IRGC105491 Jefferson AB-QTL SPA, IM, CIM 1 RFLP, SSR Thomson et al. (2003)
O. rufipogon IRGC105491 IR64 AB-QTL SPA, IM, CIM 1, 4,
10, 11
RFLP, SSR Septiningsih et al. (2003)
O. rufipogon IRGC105491 Hwaseongbyeo AB-QTL, NIL SPA, IM, ANOVA 7, 9 SSR Jin et al. (2009),
Xie et al. (2008)
O. rufipogon IRGC105491 MR219 AB-QTL CIM 1, 3, 9 SSR Wickneswari et al. (2012)
O. rufipogon IC22015 IR 58025A AB-QTL IM, CIM 1 SSR Marri et al. (2005)
Plant type (Culm habit or tiller angle) O. nivara IRGC100898 Bengal AB-QTL MIM 9 SSR Eizenga et al. (2013)
O. nivara IRGC104705 Bengal AB-QTL MIM 9 SSR Eizenga et al. (2013)
O. nivara IRGC100195 M-202 AB-QTL MIM 9 SSR Eizenga et al. (accepted)
Flag leaf length O. minuta IR71033-121-15 Junambyeo F2:3 SPA, IM 8, 9 SSR, STS Rahman et al. (2007)
Third node width O. rufipogon W1944 Hwayeongbyeo RIL SPA, CIM 1 SSR Lee et al. (2005)
Tiller number O. glaberrima IR64 BC2F3 SPA 2, 7 SSR, STS Bimpong et al. (2011)
O. glumaepatula RS-16 BG90-2 BC2F2 SPA, IM 4, 5, 7,
8, 11
SSR, STS Brondani et al. (2002)
O. glumaepatula RS-16 Cica8 BC2F2-9 CIM 7, 11 SSR Rangel et al. (2013)
O. minuta IR71033-121-15 Junambyeo F2:3 SPA, IM 3 SSR, STS Rahman et al. (2007)
O. rufipogon IRGC105491 MR219 AB-QTL CIM 2, 5, 8 SSR Wickneswari et al. (2012)
Panicle Development
Panicle exsertion O. rufipogon W1944 Hwayeongbyeo RIL SPA, CIM 1 SSR Lee et al. (2005)
Panicle density O. rufipogon IRGC 105491 Hwaseongbyeo NIL ANOVA 9 SSR Xie et al. (2008)
Panicle number O. glaberrima IRGC103544 Milyang 23 BC3F2 SPA 4 SSR Suh et al. (2005)
O. glumaepatula RS-16 BG90-2 BC2F2 SPA, IM 5, 8, 11 SSR, STS Brondani et al. (2002)
O. glumaepatula RS-16 Cica8 BC2F2-9 CIM 7, 11 SSR Rangel et al. (2013)
O. minuta IR71033-121-15 Junambyeo F2:3 SPA, IM 4 SSR, STS Rahman et al. (2007)
O. nivara IRGC100195 M-202 AB-QTL MIM 7 SSR Eizenga et al. (accepted)
O. rufipogon IRGC105491 Jefferson AB-QTL SPA, IM, CIM 3, 7 RFLP, SSR Thomson et al. (2003)
O. rufipogon IRGC105491 IR64 AB-QTL SPA, IM, CIM 2 RFLP, SSR Septiningsih et al. (2003)
O. rufipogon IRGC105491 V20A, V20B BC2 ANOVA 1, 2 RFLP Xiao et al. (1998)
O. rufipogon IRGC105491 MR219 AB-QTL CIM 2, 8 SSR Wickneswari et al. (2012)
O. rufipogon IC22015 IR58025A AB-QTL IM, CIM 2 SSR Marri et al. (2005)
O. rufipogon W1944 Hwayeongbyeo RIL, IL SPA, IM, CIM 1, 7, 12 SSR Lee et al. (2004),
Yuan et al. (2009)
O. rufipogon YJCW 93-11 AB-QTL SPA, IM, CIM 1, 2, 7, 8, 11 SSR Fu et al. (2010)
Panicle length O. glaberrima IRGC103544 Milyang 23 BC3F2 SPA 2, 5, 6, 10, 12 SSR Suh et al. (2005)
O. meyeriana Y73 IR24 RIL CIM 1, 2 SSR, STS Chen et al. (2012)
O. minuta IR71033-121-15 Junambyeo F2:3 SPA, IM 6, 7, 8 SSR, STS Rahman et al. (2007)
O. rufipogon IRGC105491 Hwaseongbyeo NIL SPA, IM, ANOVA 9 SSR Xie et al. (2008)
O. rufipogon IRGC105491 IR64 AB-QTL SPA, IM, CIM 1, 9, 10 RFLP, SSR Septiningsih et al. (2003)
O. rufipogon IRGC105491 Jefferson AB-QTL SPA, IM, CIM 1, 2, 4,
9, 12
RFLP, SSR Thomson et al. (2003)
O. rufipogon IRGC105491 V20A, V20B BC2 ANOVA 1, 2, 4,
8, 9, 12
RFLP Xiao et al. (1998)
O. rufipogon IC22015 IR 58025A AB-QTL IM, CIM 2, 5, 9 SSR Marri et al. (2005)
O. rufipogon W1944 Hwayeongbyeo RIL SPA, CIM 1 SSR Lee et al. (2005)
O. rufipogon W1944 Hwayeongbyeo IL SPA, IM, 1, 2 SPA, IM Yuan et al.(2009)
Primary branches per panicle O. minuta IRGC101144 Hwaseongbyeo NIL 7 SSR Balkunde et al. (2013)
O. rufipogon W1944 Hwayeongbyeo RIL SPA, CIM 1 SSR Lee et al. (2005)
Secondary branches per panicle O. rufipogon IRGC105491 Hwaseongbyeo AB-QTL SPA, IM 6, 8 SSR Jin et al. (2009)
O. rufipogon W1944 Hwayeongbyeo RIL, IL SPA, IM, CIM 1, 2, 9 SSR Lee et al. (2005),
Yuan et al. (2009)
Reproductive Growth Stages
Pollen (male) sterility O. glumaepatula IRGC105688 Taichung 65 BC4F2 2, 7 RFLP Sobrizal et al. (2000a, 2000b)
O. longistaminata RD23 BC7F2 CIM 6 SSR Win et al. (2009; 2011)
O. nivara IRGC105444 Taichung 65 IL-BC4F1 4, 8, 12 RFLP, SSR, SNP Chen et al. (2009)
Hybrid breakdown locus O. nivara IRGC105444 Koshihikari BC4F3 2 SSR, SNP Miura et al. (2008)
Panicle fertility O. glaberrima IR64 BC2F3 SPA 2, 10 SSR, STS Bimpong et al. (2011)
Productive panicle number O. rufipogon G52-9 Yuexiangzhan AB-QTL CIM 2, 3, 7 SSR Jing et al. (2010)
Spikelets per plant O. minuta IRGC101144 Hwaseongbyeo NIL 7 SSR Balkunde et al. (2013)
O. rufipogon G52-9 Yuexiangzhan AB-QTL CIM 2 SSR Jing et al. (2010)
O. rufipogon IC 22015 IR 58025A AB-QTL IM, CIM 2, 5 SSR Marri et al. (2005)
O. rufipogon IRGC105491 V20A, V20B BC2 ANOVA 1 RFLP Xiao et al. (1998)
O. rufipogon IRGC105491 MR219 AB-QTL CIM 1 SSR Wickneswari et al. (2012)
Spikelets per panicle O. glaberrima IRGC103544 Milyang 23 BC3F2 SPA 3 SSR Suh et al. (2005)
O. grandiglumis IRGC101144 Hwaseongbyeo AB-QTL SPA 2, 3,
4, 11
SSR Yoon et al. (2006)
O. minuta IR71033-121-15 Junambyeo F2:3 SPA, IM 6 SSR, STS Rahman et al. (2007)
O. rufipogon IRGC105491 Hwaseongbyeo NIL,
AB-QTL
SPA, IM, ANOVA 8, 9 SSR Xie et al. (2008),
Jin et al. (2009)
O. rufipogon IRGC105491 Jefferson AB-QTL SPA, IM, CIM 2, 3, 9 RFLP, SSR Thomson et al. (2003)
O. rufipogon IRGC105491 V20A, V20B BC2 ANOVA 1, 9 RFLP Xiao et al. (1998)
O. rufipogon W1944 Hwayeongbyeo IL, RIL SPA, IM, CIM 1 SSR Yuan et al. (2009),
Lee et al. (2005)
O. rufipogon YJCW 93-11 AB-QTL SPA, IM, CIM 3 SSR Fu et al. (2010)
O. glaberrima Milyang 23 BC2F5 2 SSR Kang et al. (2008)
O. rufipogon IRGC105491 MR219 AB-QTL CIM 3 SSR Wickneswari et al. (2012)
O. minuta IRGC101144 Hwaseongbyeo NIL 7 SSR Balkunde et al. (2013)
Spikelet fertility O. glaberrima IRGC103544 Milyang 23 BC3F2 SPA 2, 4, 8 SSR Suh et al. (2005)
O. longistaminata RD23 BC7F2 CIM 6 SSR Chen et al. (2009)
O. minuta IR71033-121-15 Junambyeo F2:3 SPA, IM 6 SSR, STS Rahman et al. (2007)
Shattering O. rufipogon IRGC105491 Jefferson AB-QTL SPA, IM, CIM 8 RFLP, SSR Thomson et al. (2003)
O. rufipogon W1944 Hwayeongbyeo RIL SPA, CIM 1, 3, 6 SSR Lee et al. (2005)
O. rufipogon W1944 Hwayeongbyeo IL, RIL SPA, IM, CIM 1, 4, 5 SSR Yuan et al. (2009),
Lee et al. (2005)
Grains per panicle O. minuta IRGC101144 Hwaseongbyeo NIL 7 SSR Balkunde et al. (2013)
O. rufipogon IRGC105491 Jefferson AB-QTL SPA, IM, CIM 2, 3,
8, 9
RFLP, SSR Thomson et al. (2003)
O. rufipogon IRGC105491 V20A, V20B BC2 ANOVA 1, 8, 12 RFLP Xiao et al. (1998)
O. rufipogon IRGC105491 Hwaseongbyeo AB-QTL, NIL SPA, IM, ANOVA 8, 9 SSR Jin et al. (2009),
Xie et al. (2008)
O. rufipogon IC22015 IR 58025A AB-QTL IM, CIM 2, 5 SSR Marri et al. (2005)
O. rufipogon G52-9 Yuexiangzhan AB-QTL CIM 4, 10, 11 SSR Jing et al. (2010)
O. rufipogon YJCW 93-11 AB-QTL SPA, IM, CIM 1, 3 SSR Fu et al. (2010)
Percent seed set O. meyeriana Y73 IR24 RIL CIM 8 SSR, STS Chen et al. (2012)
O. rufipogon IRGC105491 MR219 AB-QTL CIM 3 SSR Wickneswari et al. (2012)
O. rufipogon IRGC105491 V20A, V20B BC2 ANOVA 2, 4 RFLP Xiao et al. (1998)
O. rufipogon W1944 Hwayeongbyeo IL, RIL SPA, IM, CIM 10 SSR Lee et al. (2005)
Awn length O. minuta IRGC101144 Hwayeongbyeo AB-QTL SPA, CIM 6, 9 SSR Linh et al. (2004)
O. minuta IR71033-121-15 Junambyeo F2:3 SPA, IM 5, 9 SSR, STS Rahman et al. (2007)
O. rufipogon IRGC105491 Hwaseongbyeo AB-QTL SPA, IM 8 SSR Jin et al. (2009)
O. rufipogon W1944 Hwayeongbyeo RIL SPA, CIM 8, 11 SSR Lee et al. (2005)
O. rufipogon W1944 Hwayeongbyeo IL SPA, IM, 1, 8,
11, 12
SPA, IM Yuan et al.(2009)
Grain (kernel) Traits
Grain (kernel) length O. grandiglumis IRGC101154 Hwaseongbyeo AB-QTL SPA 11 SSR Yoon et al. (2006)
O. minuta IR71033-121-15 Junambyeo F2:3 SPA, IM 3, 5, 6,
7, 9
SSR, STS Rahman et al. (2007)
O. nivara IRGC100195 M-202 AB-QTL MIM 1 SSR Eizenga et al. (accepted)
O. nivara IRGC100898, IRGC104705 Bengal AB-QTL MIM 1, 9 SSR Eizenga et al. (2013)
O. rufipogon IRGC105491 Hwaseongbyeo NIL SPA, IM, ANOVA 8 SSR Xie et al. (2006)
O. rufipogon W1944 Hwayeongbyeo RIL SPA, CIM 1, 2, 3, 5, 6 SSR Lee et al. (2005)
Grain (kernel) width O. glaberrima IRGC103544 V20A AB-QTL SPA, IM, CIM 10, 11 RFLP, SSR Li et al. (2004)
O. nivara IRGC81848 Swarna BC2F2 IM, CIM 3, 6 SSR Swamy et al. (2012)
O. rufipogon IRGC105491 Hwaseongbyeo NIL SPA, IM, ANOVA 8 SSR Xie et al. (2006)
Grain (kernel) length to width ratio O. glaberrima IRGC103544 Caiapó BC3F1 IM, CIM 1 SSR Aluko et al. (2004)
O. glaberrima IRGC103544 V20A AB-QTL SPA, IM, CIM 12 RFLP, SSR Li et al. (2004)
O. grandiglumis IRGC101154 Hwaseongbyeo AB-QTL SPA 2, 11 SSR Yoon et al. (2006)
O. rufipogon W1944 Hwayeongbyeo RIL SPA, CIM 1, 2, 5 SSR Lee et al. (2005)
O. minuta IR71033-121-15 Junambyeo F2:3 SPA, IM 2, 3, 5 SSR, STS Rahman et al. (2007)
O. nivara IRGC100195 M-202 AB-QTL MIM 1, 5 SSR Eizenga et al. (accepted)
O. nivara IRGC81848 Swarna BC2F2 IM, CIM 12 SSR Swamy et al. (2012)
Grain thickness O. grandiglumis IRGC101154 Hwaseongbyeo AB-QTL SPA 6, 11 SSR Yoon et al. (2006)
O. rufipogon IRGC105491 Hwaseongbyeo NIL SPA, IM, ANOVA 8 SSR Xie et al. (2006)
O. rufipogon W1944 Hwayeongbyeo RIL SPA, CIM 1, 12 SSR Lee et al. (2005)
Pericarp color O. rufipogon IRGC105491 Ce64, Caiapó, Hwacheong, Jefferson, IR64 AB-QTL IM, CIM 7 SSR McCouch et al. (2007)
O. rufipogon W1944 Hwayeongbyeo IL SPA, IM, 1, 7 SPA, IM Yuan et al.(2009)
Yield Traits
Grain weight O. glaberrima IRGC103544 Milyang 23 BC3F2 SPA 2, 3 SSR Suh et al. (2005)
O. grandiglumis IRGC101154 Hwaseongbyeo AB-QTL SPA 3, 6,
8, 11
SSR Yoon et al. (2006)
O. meyeriana Y73 IR24 RIL CIM 3, 9, 11 SSR, STS Chen et al. (2012)
O. minuta IR71033-121-15 Junambyeo F2:3 SPA, IM 3, 7, 11 SSR, STS Rahman et al. (2007)
O. nivara IRGC100195 M-202 AB-QTL MIM 10 SSR Eizenga et al. (accepted)
O. rufipogon IRGC105491 Hwaseongbyeo NIL SPA, IM, ANOVA 8 SSR Xie et al. (2006)
O. rufipogon IRGC105491 Jefferson AB-QTL SPA, IM, CIM 1, 5 RFLP, SSR Thomson et al. (2003)
O. rufipogon IRGC105491 IR64 AB-QTL SPA, IM, CIM 1, 3 RFLP, SSR Septiningsih et al. (2003)
O. rufipogon IRGC105491 V20A, V20B BC2 ANOVA 4, 8, 9, 11, 12 RFLP Xiao et al. (1998)
O. rufipogon IRGC105491 MR219 AB-QTL CIM 6 SSR Wickneswari et al. (2012)
O. rufipogon IRGC105491 Hwaseongbyeo NIL ANOVA 9 SSR Xie et al. (2008)
O. rufipogon IRGC105491 Hwaseongbyeo AB-QTL SPA, IM 8 SSR Jin et al. (2009)
O. rufipogon IRGC105491 Ce64&V20A, Caiapó, Hwacheong, Jefferson, IR64 AB-QTL IM, CIM 3 SSR McCouch et al. (2007)
O. rufipogon IC22015 IR 58025A AB-QTL IM, CIM 2, 9 SSR Marri et al. (2005)
O. rufipogon W1944 Hwayeongbyeo IL SPA, IM 1 SPA, IM Yuan et al.(2009)
O. rufipogon YJCW 93-11 AB-QTL SPA, IM, CIM 1 SSR Fu et al. (2010)
Brown rice yield O. glaberrima IRGC103544 V20A AB-QTL SPA, IM, CIM 12 RFLP, SSR Li et al. (2004)
Grain yield per plant O. glaberrima IR64 BC2F3 SPA 2, 6,
8, 9
SSR, STS Bimpong et al. (2011)
O. glaberrima IRGC103544 V20A AB-QTL SPA, IM, CIM 12 RFLP, SSR Li et al. (2004)
O. rufipogon MR219 AB-QTL 1 SSR Bhuiyan et al. (2011)
O. rufipogon G52-9 Yuexiangzhan AB-QTL CIM 1, 2, 3 Jing et al. (2010)
O. rufipogon IC22015 IR 58025A AB-QTL IM, CIM 2, 9 SSR Marri et al. (2005)
O. rufipogon IRGC105491 IR64 AB-QTL SPA, IM, CIM 1 RFLP, SSR Septiningsih et al. (2003)
O. rufipogon IRGC105491 Hwaseongbyeo NIL ANOVA 9 SSR Xie et al. (2008)
O. rufipogon IRGC 105491 V20A, V20B BC2 ANOVA 1, 2, 8 RFLP Xiao et al. (1998)
Yield O. glaberrima IRGC103544 Milyang 23 BC3F2 SPA 2, 3, 4,
6, 8
SSR Suh et al. (2005)
O. glaberrima IRGC103544 Milyang 23 BC2F5 2 SSR Kang et al. (2008)
O. grandiglumis IRGC101154 Hwaseongbyeo AB-QTL SPA 2 SSR Yoon et al. (2006)
O. minuta IRGC101144 Hwaseongbyeo NIL 7 SSR Balkunde et al. (2013)
O. meyeriana Y73 IR24 RIL CIM 6 SSR, STS Chen et al. (2012)
O. rufipogon MR219 AB-QTL 4 SSR Bhuiyan et al. (2011)
O. rufipogon IC22015 IR58025A AB-QTL IM, CIM 1, 2, 8 SSR Marri et al. (2005)
O. rufipogon IRGC105491 Jefferson AB-QTL SPA, IM, CIM 2, 3,
6, 9
RFLP, SSR Thomson et al. (2003)
O. rufipogon IRGC105491 IR64 AB-QTL SPA, IM, CIM 1 RFLP, SSR Septiningsih et al. (2003)
O. rufipogon IRGC105491 V20A, V20B BC2 ANOVA 1, 2,
8, 12
RFLP Xiao et al. (1998)
O. rufipogon IRGC105491 Hwaseongbyeo AB-QTL SPA, IM 8 SSR Jin et al. (2009)
O. rufipogon YJCW 93-11 AB-QTL SPA, IM, CIM 1 SSR Fu et al. (2010)
Harvest index O. glaberrima IR64 BC2F3 SPA 2, 7 SSR, STS Bimpong et al. (2011)
O. rufipogon IC22015 IR58025A AB-QTL IM, CIM 2 SSR Marri et al. (2005)

Table 2.

Summary of QTLs for improved yield and yield components attributed to the non-O. sativa parent.

Abbreviations for mapping population types are: AB-QTL, advanced backcross-quantitative trait locus; IL, inbred line; NIL, near isogenic line; RIL, recombinant inbred line.


Abbreviations for QTL analysis method are: ANOVA, analysis of variance; CIM, composite interval mapping; IM, interval mapping; MIM, multiple interval mapping; SPA, single point analysis.


§ Only the chromosomes where the QTL increase is attributed to the wild parent are listed.


Abbreviations for types of markers are: RAPD, random amplified polymorphic DNA; RFLP, restriction fragment length polymorphism; SNP, single nucleotide polymorphism; SSR, simple sequence repeat; STS, sequence-tagged site.


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 Oryza species and introgressing them into cultivated rice is desirable and necessary. Although wild Oryza species are inferior in grain yield, especially when compared to cultivated rice, transgressive segregation resulting from a cross between cultivated rice and a wild Oryza species, especially the ancestral species, O. rufipogon and O. nivara, revealed the presence of favorable alleles from the wild parent that can increase yield in the genetic background of cultivated rice (Brar and Singh 2011). Xiao et al. (1996) developed a genetic population by initially crossing the cytoplasmic male sterile parent, V20A, with O. rufipogon (IRGC105491), the donor, as male parent. Subsequently, F1 plants and later BC1 plants selected for vigor, plant type, maturity and fertility, were backcrossed to V20B (maintainer line of V20A). A selected subset of 300 BC2F1 lines was crossed with Ce64 to create the genotype of the Chinese hybrid rice variety V/64, developed from V20A x Ce64. Xiao et al. (1996) reported that 15% of the testcross families outperformed the recurrent parent, 14% of the yield improvement was related to grains per plant and 56% was related to 1000-grain weight. Subsequently, Xiao et al. (1998) identified 35 QTLs associated with yield improvement, 19 of which, including yld1.1 and yld 2.1, were located on chromosomes 1 and 2, respectively. They also observed no undesirable alleles causing negative effects on yield components, thus the presence of alleles in wild O. rufipogon can improve rice yields.

Other AB-QTL populations developed using the same O. rufipogon (IRGC105491) donor parent with recurrent parents representing various O. sativa subpopulations including indica with IR64 (Septiningsih et al. (2003), upland tropical japonica with Caiapó (Moncada et al. 2001), irrigated tropical japonica with Jefferson (Thomas et al. 2003), and temperate japonica with Hwaseongbyeo (Xie et al. 2006 & 2008), revealed enhanced yield and yield components attributed to the O. rufipogon donor parent. Selected progeny lines were advanced to BILs or NILs and this yield enhancement was confirmed in field studies with the recurrent parents IR64 (Cheema et al. 2008a), Jefferson (Imai et al. 2013), and Hwaseongbyeo (Jin et al. 2011). Also, an epistatic interaction was noted between the QTLs for grain weight on chromosomes 8 and 9 in the Hwaseongbyeo background (Jin et al. 2011).

Tian et al. (2006) selected an introgression line, SIL040, from the BC4F4 lines of O. rufipogon (Dongxiang) x Guichao 2, an indica rice cultivar. High resolution QTL mapping and analysis in the SIL040 x Guichao 2 F2 progeny for yield components revealed gpa7 (grains per panicle) on the short arm of chromosome 7. This QTL contained five putative genes associated with five panicle traits: panicle length, number of primary and secondary branches per panicle, and number of grains on primary and secondary branches, in the same region. These findings supported the importance of gpa7 in rice domestication and yield enhancement.

Two AB-QTL populations were developed using the O. rufipogon identified as YJCWR (collected from Yuanjiang County, Yunnan Province, P.R. China) as a donor, and TeQing, a popular indica cultivar (Tan et al. 2008) and 93-11, a two-line elite indica restorer (Fu et al. 2010), as the recurrent parents. Both studies revealed QTL attributed to O. rufipogon having a beneficial effect on yield related traits. A CSSL library of 120 lines selected from the TeQing AB-QTL population and evaluated at two locations confirmed a yield advantage associated with O. rufipogon alleles for all traits evaluated except 1000-grain weight (Tan et al. 2007).

Similarly, a CSSL library composed of 133 lines selected from an AB-QTL population with an O. rufipogon collected in Hainan Province, P.R. China, as the donor, and TeQing as the recurrent parent was used to identify spd6, a gene on chromosome 6 responsible for the small panicle and dwarf traits (Shan et al. 2009). This gene, spd6, had pleiotropic effects on panicle number per plant, grain size, grain weight, grain number per panicle and plant height, suggesting it may have played a role in the domestication of rice.

To identify the genetic potential of O. glumaepatula (AgpAgp genome) as a genetic resource for cultivar improvement, Brondani et al. (2002) developed an AB-QTL population using RS-16, an accession of the Amazonian native rice wild species, O. glumaepatula, as the donor parent, and BG90-2, a Latin American indica rice, as the recurrent parent. QTL analysis of 96 BC2F2 progenies for eleven agronomic traits with O. glumaepatula alleles revealed none were positively associated with yield traits. However, several BC2F2 lines indicated the presence of introgressed alleles related to yield improvement which were not detected in the QTL analysis. Later, Rangel et al. (2008) evaluated the agronomic performance of 35 BC2F8 ILs selected from this population over two years and in multiple locations by measuring grain yield and grain quality traits. The six highest yielding ILs had the highest percentage of recurrent parent genomic background. One of the six ILs, CNAi 9930, was recommended for cultivar release due to its good cooking and milling qualities, and high yield stability. Also, all six ILs contained novel alleles, thus were incorporated as parents in the breeding program for developing high yielding cultivars.

BILs in the BC5F5:6 were derived from O. grandiglumis (IRGC101154; CCDD) as the donor parent, and Hwaseongbyeo (Yoon et al. 2006). One BIL, HG101, was backcrossed, and evaluation of the targeted IL, CR1242, revealed the beneficial effect of the O. grandiglumis allele on yield and yield components. Further analysis of the QTL, tgw11, on chromosome 11 associated with 1000-grain weight showed that a single gene in this QTL controls three grain traits: grain weight, grain width and grain thickness (Oh et al. 2011).

To evaluate the effect of O. minuta (IRGC101141) with a BBCC genome on yield components, a single plant, WH79006, was selected from the Hwaseongbyeo x O. minuta BC5F3 families and selfed (Jin et al. 2004). QTL analysis of Hwaseongbyeo x WH79006 F2:3 progeny identified four QTLs, sw7 (seed width), sl11 (seed length), tsw7 (1000-seed weight) and lw10 (seed length to width ratio). Similarly, WH29001 was selected from the BC5F3 families, selfed and by QTL analysis the co-located QTLs for days to heading, dth6 and dth8, and awn length, awn6 and awn8, were identified on chromosomes 6 and 8, respectively (Linh et al. 2006). Subsequently, a new QTL, spp7, for spikelets per panicle, was detected on the long arm of chromosome 7 with the allele attributed to the O. minuta parent and validated in the F3 and F4 progeny (Linh et al. 2008). Similarly the introgression line IR71033-121-15 was selected from the BC3 progeny of the same O. minuta (IRGC101141) x indica cultivar, IR31917. To introgress the O. minuta genome into japonica, IR71033-121-15 was crossed with Junambyeo, a Korean japonica cultivar, and QTL analysis of F2 progeny identified 14 QTLs associated with agronomic traits reported in previous studies and 22 novel QTLs related to yield components (Rahman et al. 2007).

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 An-1 on chromosome 3, An-2 and an-5(t) on chromosome 4, and An-3 on chromosome 5 (Hu et al. 2011; Nagao and Takahashi 1963; Takamure and Kinoshita 1991). O. meridionalis has long awns, ranging in length from 7.8-10.3 cm (Vaughan 1994) and two genes, An7 and An8, associated with the trait, were identified on chromosomes 5 and 4, respectively (Kurakazu et al. 2001). Analysis of O. meridionalis x O. sativa BC4F2:6 and BC4F2:8 revealed the presence of two dominant genes at different locations on chromosome 1, An9 and An10, and a new allele, An6-mer on chromosome 6 (Matsushita et al. 2003a). Another study of an O. sativa x O. glumaepatula population also identified new alleles, An7 and An8 (Matsushita et al. 2003b), thus confirming awn length is controlled by several genes.

A doubled haploid (DH) population was developed from Caiapó (tropical japonica, recurrent parent) x O. glaberrima (donor parent, IRGC103544, MG12) BC3F1 progeny (Aluko et al. 2004). This population was evaluated for agronomic traits including yield and yield components in Colombia and Louisiana, USA (Gutiérrez et al. 2010). Strong segregation distortion was found on chromosomes 3 and 6 indicating the presence of interspecific sterility genes. Evaluation of the phenotypic data revealed transgressive segregation for several traits. A set of 34 CSSLs was selected from Koshihikari, an elite temperate japonica rice cultivar (recurrent parent) x O. glaberrima (donor parent, IRGC104038) BC5F1 progeny, advanced to the F7 generation, and genotyped with 142 SNP markers (Shim et al. 2010). QTL analysis of phenotypic data from field grown plants revealed 105 putative QTL of which 84 were positive with 64 being related to grain yield components, suggesting the possible use of favorable alleles in O. glaberrima for improvement of cultivated rice.

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, O. rufipogon or O. nivara, but also in the more distant tetraploid O. minuta with a BBCC genome. These observations confirm that not only single genes and alleles are affecting these traits but there are epistatic interactions and epigenetic interactions, as well as environmental factors affecting many of these traits, resulting in the phenomenon often described as transgressive variation. Currently, six CSSL libraries are under development with three different O. rufipogon/O. nivara donor accessions, representing the Indica, Japonica and independent groups of O. rufipogon (Huang et al. 2012), and two recurrent parents, IR64, an indica representing the Indica subspecies, and Cybonnet, a U.S. tropical japonica, representing the Japonica subspecies to further explore these interactions resulting in transgressive variation (Tung et al. 2010; SR McCouch, Cornell University and GC Eizenga, personal communication).

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 O. sativa and non-A genome Oryza species. Several studies reported the production of F1 seeds by crossing male sterile lines and Oryza species (Huang et al. 2001; Luo et al. 2000). The crossability rate between O. sativa and other Oryza species vary; however, the rate of crossability between A-genome and non-A genome diploid Oryza species is higher than with tetraploid Oryza species, none of which has an A-genome (Jena and Khush 1989 & 1990; Yasui and Iwata 1991).

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 (siTRD) where one allele is preferentially transmitted through both the male and female parent derived from O. rufipogon (W593). This research showed the S6 allele on chromosome 6 is responsible for the siTRD allele and influenced by other unlinked modifiers. The locus, sa1, conferring pollen sterility was found in O. glaberrima (W025) x T65wx progeny (Sano 1990) where T65wx is an NIL derived from Taichung 65 (japonica rice) x Peiku (indica rice) with a Taichung 65 background and the Peiku waxy gene on chromosome 6. Other studies identified several pollen sterility loci, S-1, S3, S18, S19, S20, S21, s25, s27, S29, S29(t) and s36, in populations resulting from interspecific hybridizations between various O. sativa cultivars and the Oryza species, O. glumaepatula, O. glaberrima and O. nivara (Doi et al. 1998 & 1999; Hu et al. 2006, Sano 1983 & 1986; Taguchi et al. 1999; Win et al. 2009). To overcome such barriers, several methods have been suggested including anther culture, backcrossing, marker-assisted selection (MAS) and asymmetric somatic hybridization, (Fu et al. 2008; Sarla et al. 2005). Also, Deng et al. (2010) demonstrated the fertility in O. glaberrima x O. sativa crosses could be improved by using an O. sativa bridging parent. The bridging parent had the O. glaberrima sterility gene, S1-g on chromosome 6, introgressed into the particular O. sativa cultivar background.

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 Oryza parent affects the apparent amylose content, alkali spreading value, protein content, rice bran percentage, milled rice percentage and seed size. What is desirable for these traits is determined for the most part, by consumer preference and marketing classes. When selecting for these traits, often the grain quality of the recurrent parent is preferred.

Most interesting was the BC3F1 progeny of the Caiapó x O. glaberrima (IRGC103544, MG12) doubled haploid population (Aluko et al. 2004). For this population, the QTL analysis revealed 27 QTLs associated with rice quality of which seven QTLs including percent rice bran, percent milled rice, alkali spreading value (inversely related to gelatinization temperature), percent protein and grain dimensions (length to width ratio), were traced to alleles originating from the O. glaberrima parent.

Trait Donor species Donoraccession Recurrentparent(s) Mapping popu-lation QTL mapping analysis Chromo-some location§ Type of mark-er References
Grain Quality
Apparent amylose content O. glaberrima IRGC103544 V20A AB-QTL SPA, IM, CIM 6, 12 RFLP, SSR Li et al. (2004)
O. grandiglumis IRGC101154 Hwaseong-
byeo
AB-QTL SPA 3, 5, 7 SSR Yoon et al. (2006)
O. nivara IRGC81848 Swarna BC2F2 IM, CIM 2 SSR Swamy et al. (2012)
O. nivara IRGC100195 M-202 AB-QTL MIM 6 SSR Eizenga et al (accepted)
O. rufipogon IRGC105491 IR64 AB-QTL IM, CIM 6 RFLP, SSR Septiningsih et al. (2003b)
Alkali spreading value (ASV) or gel consistency O. glaberrima IRGC103544 Caiapó BC3F1 IM, CIM 6 SSR Aluko et al. (2004)
O. glaberrima IRGC103544 V20A AB-QTL SPA, IM, CIM 12 RFLP, SSR Li et al. (2004)
O. nivara IRGC100195 M-202 AB-QTL MIM 6 SSR Eizenga et al. (accepted)
O. rufipogon IRGC105491 IR64 AB-QTL IM, CIM 6 RFLP, SSR Septiningsih et al. (2003b)
Kernel elongation O. glaberrima IRGC 103544 V20A AB-QTL SPA, IM, CIM 3 RFLP, SSR Li et al. (2004)
Protein O. glaberrima IRGC103544 Caiapó BC3F1 IM, CIM 2, 6 SSR Aluko et al. (2004)
O. glaberrima IRGC103544 V20A AB-QTL SPA, IM, CIM 8 RFLP, SSR Li et al. (2004)
Percent
rice bran
O. glaberrima IRGC103544 Caiapó BC3F1 IM, CIM 4, 7 SSR Aluko et al. (2004)
Percent
milled rice
O. glaberrima IRGC103544 Caiapó BC3F1 IM, CIM 5 SSR Aluko et al. (2004)
O. nivara IRGC81848 Swarna BC2F2 IM, CIM 1 SSR Swamy et al. (2012)
Biotic Stress-Diseases
Bacterial blight O. australiensis 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)
O. latifolia IRGC100914 IR31917-45-3-2 AIL ANOVA 12, others SSR, SNP, STS, InDel Angeles-Shim et al. (accepted)
O. longistaminata WLO2 BS125 NIL 11 RFLP, RAPD Ronald et al. (1992)
O. longistaminata IR24 11 Khush et al. (1990)
O. meyeriana Y73 IR24 RIL CIM 1, 3, 5, 10, 11 SSR, STS Chen et al. (2012)
O. minuta 78-1-5 IR24 F2- BC1 6 RAPD, AFLP Gu et al. (2004)
O. nivara IRGC81825 PR114 RIL, BIL, IL SMA-IM 4 SSR, STS Cheema et al. (2008)
Blast disease O. australiensis IRGC100882 Lijiangxintuan-heigu 6 CAPS, SSR, STS Jeung et al. (2007)
O. minuta IRGC101141 IR31917 F2 6 RAPD Liu et al. (2002)
O. nivara IRGC100898 Bengal AB-QTL MIM 8 SSR Eizenga et al. (2013)
O. nivara IRGC104705 Bengal AB-QTL MIM 8, 12 SSR Eizenga et al. (2013)
O. rufipogon IRGC104812 Koshihikari IL 3, 11 Hirabayashi et al. (2010); Sobrizal et al. (1999)
O. rufipogon IRGC104814 Koshihikari IL 3, 5, 6 Hirabayashi et al. (2010)
Sheath blight disease O. nivara IRGC100898 Bengal AB-QTL MIM 6 SSR Eizenga et al. (2013)
O. nivara IRGC104705 Bengal AB-QTL MIM 3, 6 SSR Eizenga et al. (2013)
Stem rot O. rufipogon IRGC100912
(87-Y-550)
L-201 (long grain-breeding lines) F2 SPA 2, 3 AFLP Ni et al. (2001)
Grassy stunt virus O. nivara IRGC101508 IR8, IR20,
IR22, IR24, IR773A-1-3
F2 Nuque et al. (1982)
Rice stripe necrosis resistance O. glaberrima IRGC103544 Caiapó CSSL IM, CIM 11 SSR Gutiérrez et al. (2010)
Biotic Stress-Insects
Brown planthopper O. australiensis IRGC100882 IR31917-45-3-2 IL 12 RFLP Ishii et al. (1994)
O. australiensis IR65482-7-216-1-2 Jinbubyeo F2, BC2F2 ANOVA 12 SSR, STS Jena et al. (2006)
O. australiensis IR31917-45-3-2 MAAL 12 Multani et al. (1994)
O. eichingeri IRGC105159 2428 F2, BC1F1 2 RFLP, SSR Guoqing et al. (2001)
O. latifolia B14 Taichung RIL 4 SSR, RFLP Yang et al. (2002)
O. minuta 101141 Junambyeo F3 4, 12 SSR, STS Rahman et al. (2009)
O. officinalis IRGC100878, IRGC100896, IRGC101150, IRGC101412, IRGC102385 IR31917-45-3, IR25587-109-3 BC2F8 4, 10, 12 RFLP Jena et al. (1992)
O. officinalis IR54745-2-21-12-17-6 IR50 3 RAPD, STS Renganayaki et al. (2002)
O. officinalis B5 1826, 93-11 3, 4 SSR Li et al. (2006)
O. officinalis IRGC100896 IR31917-45-3-2 F2 11 RAPD Jena et al. (2002)
O. officinalis 3 RFLP Hirabayshi et al. (1998)
O. officinalis IR54745-2-21-12-17-6 IR50 RIL 3 RAPD Renganayaki et al. (2002)
O. officinalis B5 Minghui 63 F2 3 RFLP Huang et al. (2001)
O. officinalis B5 RIL 4 AFLP, RFLP, SSR Yang et al. (2004)
O. rufipogon WBO1 Minghui 63 F2 4, 8 SSR Hou et al. (2011)
Green rice leafhopper O. glaberrima IRGC104038 Taichung 65 NIL IM, CIM 3, 7,
9, 10
SSR Fujita et al. (2010)
O. rufipogon W1962 Taichung 65 NIL, BC4F2 8 SSR Fujita et al. (2006)
White-backed planthopper O. officinalis B5 Minghui 63 RIL 3, 4 SSR Tan et al. (2006b)
O. rufipogon BILs-DWR/Dingxiang XieqingzaoB BIL CIM, MIM 2, 5, 9 SSR Chen et al. (2010)
Abiotic stress
Aluminum tolerance O. rufipogon IRGC106424 IR64 RIL IM 1, 3, 9
(2, 7, 8)
RFLP Nguyen et al. (2003)
Drought tolerance O. rufipogon Guichao 2 IL SMR 2, 12 SSR Zhang et al. (2006)
O. rufipogon W630 Nipponbare BIL IM 1, 5 SSR Thanh et al. (2011)
Heat tolerance O. rufipogon YJCWR TeQing IL CIM 1 SSR Lei et al. (2013)
Low temperature tolerance O. nivara IRGC100195 M-202 AB-QTL MIM 5 SSR Eizenga et al. (accepted)
O. rufipogon W1943 Guang-lu-ai 4 BC4F2 IM, CIM 3, 11 SNP Koseki et al. (2010)
O. rufipogon Dongxiang Nanjing11 BC2F1 CIM 10 SSR Xia et al. (2010)
O. rufipogon W1944 Hwayeong-
byeo
RIL SPA, CIM 2, 5 SSR Lee et al. (2005)
Salt tolerance O. rufipogon TeQing IL SPA 1, 2, 3,
6, 7, 10
SSR Tian et al. (2011)
Submergence stress O. rufipogon 9 Li et al. (2011)
Biomass O. glaberrima IR64 BC2F3 SPA 1, 2, 3,
6, 10
SSR, STS Bimpong et al. (2011)

Table 3.

Summary of QTLs for grain quality, biotic stress tolerance, abiotic stress tolerance and biomass attributed to the non-O. sativa parent.

Abbreviations for mapping population types are: AB-QTL, advanced backcross-quantitative trait locus; AIL, alien introgression line; BIL, backcrossed inbred line; CSSL, chromosome segment substitution line; IL, inbred line; MAAL, monosomic alien addition line; NIL, near isogenic line; RIL, recombinant inbred line.


§ Only the chromosomes where the QTL increase is attributed to the wild parent are listed.



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 Xanthomonas oryzae pv. oryzae Ishiyama Dye (Cheema et al. 2008), blast caused by the fungus Magnaporthe oryzae B. Couch (Couch and Kohn 2002), and sheath blight caused by the soil-borne fungus Rhizoctonia solani Kühn (Zhang 2007). The first reported successful introduction of an agronomically important trait from a wild Oryza species was the introgression of grassy stunt virus resistance from the AA-genome species O. nivara into the cultivated O. sativa genetic background (Khush et al. 1977). The resistance mechanism was subsequently introduced into several other rice cultivars (Sanchez et al. 2013). Since this first introduction, wild Oryza accessions have been screened as a potential source of resistance genes to biotic and abiotic stresses, as well as, yield and yield components, as previously discussed. These screening efforts, including successful introduction of stress resistance genes from Oryza species were recently summarized by Ali et al. (2010), Brar and Singh (2011) and Sanchez et al. (2013). Table 3 summarizes the efforts to identify the chromosome location of stress resistance genes introduced from the wild Oryza species by QTL and fine mapping analyses.

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 O. sativa. These resistance genes were identified in several wild Oryza species, including Xa21 in O. longistaminata, Xa23 in O. rufipogon, Xa27 in O. minuta (IRGC101141), Xa29(t) in O. officinalis (B5), and Xa30(t) in O. nivara (IRGC81825) (Brar and Singh 2011; Cheema et al. 2008b; Gu et al. 2004; Khush et al. 1990; Tan et al. 2004; Zhang et al. 1998). Most recently, Xa34(t) exhibiting broad spectrum resistance, was identified in O. brachyantha (IRGC101232) as a single dominant gene after examining the crossed progeny of two resistant introgression lines derived from IR56 (recurrent parent) and O. brachyantha (Ram et al. 2010a).

Both bacterial blight and blast resistance were identified in the tetraploid CCDD genome species, O. minuta (IRGC101141). To transfer these resistance genes into the background of diploid, cultivated rice, Amante-Bordeos (1992) used embryo rescue and backcrossing to produce interspecific hybrids between the elite O. sativa line, IR31917-45-3-2 (recurrent parent) and O. minuta (donor parent).

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 O. meyeriana and a O. sativa subsp. japonica cultivar (Yan et al. 2004). Subsequently, Chen et al. (2012) developed a RIL population from Y73 x IR24 (O. sativa) and identified five QTLs, two were major QTLs located on chromosomes 1 and 5, and three were minor QTLs on chromosomes 3, 10 and 11. They also mapped 21 additional QTLs related to yield components and yield.

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, Pi9 and Pi40(t), that have been identified in wild Oryza species, O. minuta and O. australiensis, respectively. Pi40(t), which confers broad spectrum of blast resistance, was introgressed into the elite breeding line, IR65482-4-136-2-2 (Liu et al. 2002; Jeung et al. 2007). Qu et al. (2006) cloned the Pi9 gene via a positional (map-based) cloning technique and found the gene is a member of a group of six resistance-like genes, which encodes a nucleotide-binding site (NBS) and leucine-rich repeats (LRRs). Silué et al. (1992) screened 99 O. glaberrima accessions for blast resistance by inoculating with ten M. oryzae strains from Cote d'Ivoire. The results revealed that nine accessions were resistant to all M. oryzae strains and 32 accessions were moderately resistant, suggesting these accessions may be the source of novel resistance genes. Eizenga et al. (2009) identified resistance to U.S. blast races in some Oryza species but no accession exhibited resistance to all races. Subsequently, two AB-QTL populations with two different resistant O. nivara (IRGC100898; IRGC104705) accessions as donor parents x Bengal, a U.S. medium grain tropical japonica, as recurrent parent were evaluated for reaction to two U.S. blast races. QTL analysis of these populations mapped resistance to U.S. leaf blast races IB1 and IB49 to chromosomes 8 and 12 (Eizenga et al. 2013).

Rice sheath blight, Rhizoctonia solani Kühn, was reported for the first time in Japan in 1910 and since then, it has spread to many rice growing areas worldwide (Lee and Rush 1983; Savary et al. 2000). To date, no major resistance gene(s) that confers complete resistance to sheath blight has been discovered, only genes conferring partial resistance (Pinson et al. 2005; Jia et al. 2009). Several Oryza species accessions were screened for sheath blight resistance at the International Rice Research Institute (IRRI) in the Philippines with resistance being identified in accessions of O. minuta and O. rufipogon (Amante et al. 1990), and a resistant O. officinalis accession being the donor of sheath blight resistance genes in O. sativa introgression lines (Lakshmanan, 1991). Prasad and Eizenga (2008) screened a collection of 73 Oryza species accessions using three different methods and identified seven accessions with moderate resistance including three O. nivara accessions and one each of O. barthii, O. meridionalis, O. nivara/O. sativa hybrid, and O. officinalis. Based on these results, Eizenga et al. (2013) developed two AB-QTL populations using two of these O. nivara accessions (IRGC100898; IRGC104705) as the donor parents, and Bengal as the recurrent parent for both populations. QTL analysis identified a major QTL, qShB6, associated with sheath blight attributed to the wild rice parent. Two other minor QTLs, qShB1 and qShB3, were identified but not attributed to the O. nivara parent in all sheath blight tests. A third AB-QTL population with the more distant A-genome species, O. meridionalis (IRGC105306), as the donor parent, and Lemont, a U.S. long grain tropical japonica, as the recurrent parent, was evaluated for reaction to sheath blight disease and the QTL analysis is currently underway (Eizenga, unpublished data).

Stem rot, a fungal disease caused by Sclerotium oryzae Catt., causes yield losses by reduced tillering, more unfilled grains per panicle, chalky grain, lower milling yields and increased lodging (Ou 1985). The germplasm line 87-Y-550 (PI566666) was derived from a cross between the stem rot resistant O. rufipogon (IRGC100912) and L-201, a long grain California (USA) temperate japonica cultivar (Tseng and Oster 1994). To identify the location of this resistance gene and associated molecular markers, Ni et al. (2001) developed four RIL populations from crosses between 87-Y-550 and four susceptible long grain O. sativa breeding lines. Following the bulk segregant analysis method, QTLs revealed an association between the AFLP marker, TAA/GTA167, on chromosome 2 and SSR marker, RM232, on chromosome 3.

African cultivated rice, O. glaberrima, is an excellent, potential donor of genes to improve the tolerance of Asian cultivated rice, O. sativa, to biotic stresses, including bacterial blight, rice blast, rice stripe necrosis virus (RSNV), nematodes (Meloidogyne graminicola Golden and Birchfield) and especially rice yellow mottle virus, RYMV (Djedatin et al. 2011; Gutiérrez et al. 2010; Ndjiondjop and Alber 1999; Plowright et al. 1999; Silue et al. 1992; Thiémélé et al. 2010). A set of 64 CSSLs selected from a Caiapó x O. glaberrima (IRGC103544) DH, BC3F1 population was used to identify a major QTL controlling RSNV resistance on chromosome 11 (Gutiérrez et al. 2010). RYMV is one of the most destructive rice viral diseases. Few RYMV resistance genes have been found in O. sativa accessions, but 8% of the O. glaberrima accessions that were screened exhibited resistant to the virus with three recessive resistance alleles rymv1-3, rymv1-4, and rymv1-5 and one dominant resistance allele, RYMV1, were identified. Three lines, TOG5681, TOG5672 and TOG7291 initially derived from the wild Oryza glaberrima showed resistance to RYMV, blast, and lodging (Futakuchi et al. 2008; Sié et al. 2002; Thiémélé et al. 2010).

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, Nilaparvata lugens (Stål), one of the most devastating herbivores of rice in Asia, causes damage by feeding on rice plants and transmitting two viruses, rice ragged stunt virus and rice grassy stunt virus. A total of 26 brown leafhopper resistance genes have been reported, of which 10 genes are recessive and 18 are dominant. Of the 26 genes, 12 genes, Bph1, bph2, Bph3, bph4, bph5, Bph6, bph7, bph8, Bph9, bph19, Bph25 and Bph26, are found in O. sativa; two genes, Bph10 and Bph18, are found in O. australiensis; seven genes, Bph11, bph11, bph12, Bph14, Bph15, bph16 and Bph17, in O. officinalis; one gene, Bph13, in O. eichingeri; one gene, Bph17, in O. latifolia; two genes, Bph20(t) and Bph21(t), in O. minuta; one gene, bph22(t) in O. glaberrima; and three genes, Bph22(t), Bph23(t) and Bph24(t), in O. rufipogon (Deen et al. 2010; Jena 2010; Oryzabase 2014; Ram et al. 2010, Zhang 2007).

Early efforts to evaluate the Oryza species accessions as a source of brown planthopper resistance and incorporation of this resistance into O. sativa were in a large part due to the efforts at IRRI. Early reports include introgression of resistance from O. australiensis through backcrossing into an O. sativa background and using RFLP markers to resolve the position of the gene in chromosome 12 (Ishii et al. 1993). Introgression of resistance to three brown planthopper biotypes from five different O. officinalis accessions into cultivated O. sativa breeding lines resulted in 52 BC2F8 ILs (Jena and Khush 1990; Jena et al. 1992). Genotyping of these lines with RFLP markers showed O. officinalis introgressions in 11 of the 12 rice chromosomes with markers on chromosomes 4, 10 and 12 appearing to be associated with BPH resistance but not unequivocally. More recently, a single dominant gene, bph22(t), was discovered in O. glaberrima and transferred into O. sativa (Ram et al. 2010).

The white-backed planthopper, Sogatella furcifera (Horvath), is another serious insect pest of rice in Asia (Chen et al. 2010). Six major QTLs, Wbp1, Wbp2, Wbp3, wbh4, Wbp5 and Wbp6, have been identified (Angeles et al. 1981; Hernandez and Khush 1981; Oryzabase 2014; Ravindar et al. 1982; Sidhu and Khush 1979; Min et al. 1991; Wu and Khush 1985). O. officinalis has been reported as a source of resistance to both white-backed and brown planthoppers. Further study identified two QTLs, Wph8(t) and Bph15 on chromosome 4, with wph7(t) and Bph14 on chromosome 3 (Huang et al. 2001; Oryzabase 2014; Tan et al. 2004). Chen et al. (2010) developed a BIL population derived from O. sativa x O. rufipogon and found three QTLs from wild Oryza including qWph2 on the short arm of chromosome 2, qWph5 on the long arm of chromosome 5, and qWph9, which confers high resistance on the long arm of chromosome 9. In addition, these wild alleles reduced the rate of seedling mortality.

Guo et al. (2013) analyzed 131 BC4F2 ILs resulting from a cross between O. minuta (IRGC101133) x IR24 (O. sativa) by using 164 SSR markers, and estimated the average length of introgressed segments to be 14.78 cM. They observed a range of morphological and yield traits, as well as, resistance to bacterial blight, brown planthopper, and white-backed planthopper among the populations.

Rice monosomic alien addition lines (MAALs) contain the complete O. sativa chromosome complement (2n=24AA) plus an additional, unpaired chromosome from a wild Oryza (alien) donor, thus the designation 2n=24AA+1alien (Jena 2010). MAALs have been used to identify important genes conferring resistance to biotic stresses, such as bacterial blight, brown planthopper and white-backed planthopper, from O. australiensis (EE) and O. latifolia (CCDD) into cultivated O. sativa (Multani et al. 1994 & 2003). Recently, Angeles-Shim (accepted) evaluated a set of 27 alien introgression lines developed from the O. sativa breeding line IR31917-45-3-2 x O. latifolia (IRGC100194) MAALs and identified putative QTLs for resistance to four Philippine races of bacterial blight, as well as, yield components and stem strength.

Green leafhopper [Nephotettix virescens (Distant)] is an insect found in wetlands including irrigated and rainfed environments. Six resistance loci, Grh1, Grh2, Grh3, Grh4, Grh5 and Grh6, and one QTL, qGRH4, conferring resistance to green leafhopper have been reported. Grh5 was identified in the wild rice, O. rufipogon, for the first time. Ghr6 was identified in both cultivated rice and O. nivara (Fujita et al. 2003, 2004 & 2010; Fukuta et al. 1998; Saka et al. 1997; Tamura et al. 1999; Yasui and Yoshimura 1999; Yazawa et al. 1998).

The wild Oryza species have been used successfully as a source of novel alleles conferring resistance to both rice diseases and insect pests because in many instances these alleles could be transferred to O. sativa by backcrossing and screening the progeny. In fact, several of these alleles were successfully transferred even before the advent of molecular markers. With molecular markers, it is now possible to expedite the introduction of these novel alleles because marker-assisted breeding techniques can be used. With the molecular tools currently available, it should be possible to unravel those resistances which are quantitatively inherited like sheath blight.

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 (O. sativa) x CG14 (O. glaberrima) to identify the effect of drought on rice agronomic traits. Despite the fact that African rice (O. glaberrima) has low productivity and grain yield, it is an excellent source of genes associated with drought tolerance (Blum 1998; Hanamaratti et al. 2008; Manneh et al. 2007; Ndjiondjop et al. 2010).

Bocco et al. (2012) evaluated the morphological and agronomical traits of 60 genotypes including 54 BC3F6 introgression lines from IR64 (recurrent parent, elite indica cultivar) x TOG5681 (O. glaberrima), two parents (IR64 and TOG5681) and four NERICA-L cultivars derived from the same parents, for comparison as controls. These genotypes were evaluated in two time periods corresponding to the dry season under irrigated (control) and drought conditions to identify the most drought tolerance introgression lines. Plant height, spikelet fertility, grain yield and leaf area at harvesting were consistently reduced by drought and values for leaf temperature, leaf rolling, leaf tip drying, leaf blast disease, days to flowering and days to maturity were increased under drought conditions. From this evaluation, five BC3F6 lines were identified that out yielded the four NERICA-L cultivars described as drought tolerant.

Several accessions of O. barthii, O. meridionalis and O. australiensis were screened for heat and drought tolerance at the University of Arizona, which is located in a desert environment at Tuscon, Arizona, USA (Sanchez et al. 2013). One of the most tolerant O. meridionalis accessions was crossed with M-202, a California (USA) medium grain, temperate japonica cultivar. From the backcross progeny, two heat-tolerant advanced backcross lines resembling the M-202 parent were selected for variety release as ‘Arizona Rice-1’ and ‘Arizona Rice-2’.

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 Indica subspecies is more sensitive to cold stress than Japonica rice. Several QTLs associated with cold tolerance have been identified, especially in populations derived from crosses between Japonica and Indica cultivars (Lu et al. 2007; Zhang et al. 2005).

Wild rice species, such as O. rufipogon, contain QTLs that can be integrated into cultivated rice to improve cold tolerance (Koseki et al. 2010). Lee et al. (2005) constructed a RIL population consisting of 120 BC1F7 lines derived from a cross between the japonica cultivar, Hwayeongbyeo and O. rufipogon (W1944). The population was genotyped with 124 SSR markers and evaluated for 20 agronomic traits including leaf discoloration which is associated with cold stress. Of the 63 QTLs identified, there were two QTLs for decreased leaf discoloration, in other words, increased cold tolerance, attributed to the O. rufipogon parent. These QTLs, dc2 located on chromosome 2 and dc5 on chromosome 5, accounted for 11.2% and 11.1% of the phenotypic variation, respectively. The O. rufipogon parent also contributed favorable alleles to panicle length, spikelets per panicle and days to heading.

Koseki et al. (2010) analyzed 184 F2 introgression lines from crosses of Guang-lu-ai 4 (cold sensitive, indica cultivar) x W1943 (cold tolerant, O. rufipogon) for cold tolerance at the seedling stage (CTSS). Three Ctss-QTLs were detected with those on chromosomes 3 (qCtss 3) and 11 (qCtss11) attributed to the O. rufipogon parent, and on chromosome 10 (qCtss10) to Guang-lu-ai 4. The major QTL, qCtss11, explained 40% of the phenotypic variation and using backcross progenies, it was fine-mapped to a 60kb candidate region defined by markers AK24 and GP0030 with Os11g0615600 and/or Os11g0615900 hypothesized as the causal gene(s) for cold tolerance.

Seedling cold tolerance was measured in the M-202 (medium grain, U.S. temperate japonica) x O. nivara (IRGC100195) AB-QTL population using a slant board method [Jones and Petersen 1976; Eizenga et al. (accepted)]. In this study, QTLs for increasing coleoptile length and shoot length were identified in the same region on chromosome 5 and attributed to the O. nivara parent. QTLs for increased shoot length and root length were found on chromosome 8 and 6, respectively, and attributed to the M-202 parent.

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 O. rufipogon (IRGC106424) accession found growing in an acid sulfate soil in Vietnam (Sanchez et al. 2013) has proven to be valuable for improving tolerance to both aluminum and acid sulfate soils in cultivated rice. Initially, Nguyen et al. (2003) evaluated 171 F6 RILs derived from IR64 (indica, susceptible) x O. rufipogon (IRGC106424, tolerant) for aluminum tolerance. QTL analysis revealed QTLs for root length under stress conditions attributed to the O. rufipogon parent in six different chromosomal regions on chromosomes 1, 2, 3, 7, 8 and 9 that individually explained 9.0–24.9% of the phenotypic variation and were controlled by additive effects. The major QTL on chromosome 3, explaining 24.9% of the variation, was found to be conserved across cereal species. During the same time period, the tolerance to acid sulfate soils identified in this O. rufipogon accession was introgressed into the IR64 background through breeding efforts. The selected introgression line, IR73678-6-9-B, was released by IRRI as variety AS996 (Sanchez et al. 2013). AS996 is currently grown on 100,000 ha in the Mekong Delta and described as moderately tolerant to acid sulfate soils and tolerant to brown planthopper and blast.

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.

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5. Conclusions

The repositories of Oryza species accessions found around the world are a storehouse of novel alleles and traits lost during the evolution and domestication of cultivated rice as we know it today. The fact that introgression lines derived from crosses between Asian rice and it’s ancestral species, O. rufipogon and O. nivara, exhibited notable improvement in yield and yield components through the phenomenon known as transgressive variation, was surprising and unexpected. The identification of novel alleles related to biotic stress, especially insect pests like brown planthopper and bacterial leaf blight, and more recently abiotic stresses like acid sulfate soils and drought, underscore the importance of mining these collections. The advent of molecular marker technology and development of mapping populations, especially AB-QTL and CSSL, have made it possible to map many of these alleles to chromosome location and begin to dissect the interactions between various genes. The fact that high quality genome sequences are now available or will soon be available, make it possible to interrogate the wild Oryza species accessions at a level that was not possible before. These resources will allow us to move swiftly beyond the first step of QTL identification to fine mapping traits of interest; introgressing desirable traits into elite breeding lines using markers within the gene, thus decreasing linkage drag; identifying genotype by environment interactions; determining the effect of epistasis (non-allelic genes) on traits of interest; discovering epigenetic effects such as histone modification or DNA methylation; and finally unraveling other genetic phenomenon like gene silencing. In summary, the interspecific and intergenomic mapping populations available or soon to be available, and the increased availability of SNP data, resequencing data and advanced statistical software, create even more opportunities to investigate novel alleles for agronomically important traits discovered in the Oryza species and increase our understanding of the mechanisms underlying these traits to deal with the challenges of climate change and feeding nine billion people.

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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.

References

  1. 1. Ali ML, Sanchez P, Yu SB, Lorieux M, Eizenga GC. Chromosome segment substitution lines: a powerful tool for the introgression of valuable genes from Oryza wild species into cultivated rice (O. sativa). Rice 2010;3(4):218-34.
  2. 2. Aluko G, Martinez C, Tohme J, Castano C, Bergman C, Oard J. QTL mapping of grain quality traits from the interspecific cross Oryza sativa x O. glaberrima. Theoretical and Applied Genetics 2004;109(3):630-9.
  3. 3. Amante A, De L, Sitch L, Leung H, Mew T. Sheath blight (ShB) resistance in wild rices. International Rice Research Institute Newsletter 1990;15(3).
  4. 4. Amante-Bordeos A, Sitch L, Nelson R, Dalmacio R, Oliva N, Aswidinnoor H, et al. Transfer of bacterial blight and blast resistance from the tetraploid wild rice Oryza minuta to cultivated rice, Oryza sativa. Theoretical and Applied Genetics 1992;84(3-4):345-54.
  5. 5. Ammiraju JS, Song X, Luo M, Sisneros N, Angelova A, Kudrna D, et al. The Oryza BAC resource: a genus-wide and genome scale tool for exploring rice genome evolution and leveraging useful genetic diversity from wild relatives. Breeding Science 2010;60(5):536-43.
  6. 6. Andaya V, Tai T. Fine mapping of the qCTS4 locus associated with seedling cold tolerance in rice (Oryza sativa L.). Molecular Breeding 2007;20(4):349-58.
  7. 7. Angeles E, Khush G, Heinrichs E. New genes for resistance to whitebacked planthopper [Sogatella furcifera] in rice. Crop Science 1981;21(1):47-50.
  8. 8. Angeles-Shim RB, Vinarao RB, Marathi B, Jena KK. Molecular analysis of Oryza latifolia Desv. (CCDD genome)-derived introgression lines and identification of value-added traits for rice (O. sativa L.) improvement. Journal of Heredity (provisional acceptance).
  9. 9. Balkunde S, Le HL, Lee HS, Kim DM, Kang JW, Ahn SN. Fine mapping of a QTL for the number of spikelets per panicle by using near‐isogenic lines derived from an interspecific cross between Oryza sativa and Oryza minuta. Plant Breeding 2013;132(1):70-6.
  10. 10. Bandillo N, Raghavan C, Muyco PA, Sevilla MAL, Lobina IT, Dilla-Ermita CJ, et al. Multi-parent advanced generation inter-cross (MAGIC) populations in rice: progress and potential for genetics research and breeding. Rice 2013;6(1):1-15.
  11. 11. Bernacchi D, Tanksley S, Beck-Bunn T, Uhlig J, Eshed Y, Zamir D, et al. Advanced backcross QTL analysis in tomato. I. Identification of QTLs for traits of agronomic importance from Lycopersicon hirsutum. Theoretical and Applied Genetics 1998;97(3):381-97.
  12. 12. Bhuiyan M, Narimah M, Rahim HA, Abdullah M, Wickneswari R. Transgressive variants for red pericarp grain with high yield potential derived from Oryza rufipogon x Oryza sativa: field evaluation, screening for blast disease, QTL validation and background marker analysis for agronomic traits. Field Crops Research 2011;121(2):232-9.
  13. 13. Bimpong IK, Serraj R, Chin JH, Ramos J, Mendoza EM, Hernandez JE, et al. Identification of QTLs for drought-related traits in alien introgression lines derived from crosses of rice (Oryza sativa cv. IR64) x O. glaberrima under lowland moisture stress. Journal of Plant Biology 2011;54(4):237-50.
  14. 14. Blanco A, Gadaleta A, Simeone R. Variation for yield and quality components in durum wheat backcross inbred lines derived from ssp. dicoccoides. Bodenkultur 2004;54(3):163-70.
  15. 15. Blum A. Plant breeding for stress environments. BocaRaton, Forida, USA: CRC Press;1998.
  16. 16. Bocco R, Lorieux M, Seck P, Futakuchi K, Manneh B, Baimey H, et al. Agro-morphological characterization of a population of introgression lines derived from crosses between IR64 (Oryza sativa Indica) and TOG5681 (Oryza glaberrima) for drought tolerance. Plant Science 2012;183:65-6.
  17. 17. Brar D, Khush G. Alien introgression in rice. Plant Molecular Biology 1997;35(1-2):35-47.
  18. 18. Brar D, Singh K. Oryza. In: Kole C, editor. Wild crop relatives: Genomic and breeding resources: Cereals. Dordrecht London, New York: Springer Heidelberg;2011. p 321-36.
  19. 19. Brar D, Toriyama K, Heong K, Hardy B. Broadening the gene pool of rice through introgression from wild species. Rice is life: Scientific perspectives for the 21st century. Proceedings of the World Rice Research Conference. Tsukuba, Japan, 4-7 November 2004. 2005:157-60.
  20. 20. Brondani C, Rangel P, Brondani R, Ferreira M. QTL mapping and introgression of yield-related traits from Oryza glumaepatula to cultivated rice (Oryza sativa) using microsatellite markers. Theoretical and Applied Genetics 2002;104(6-7):1192-203.
  21. 21. Brooker RW. Plant–plant interactions and enviromental change. New Phytologist 2006;171(2):271-84.
  22. 22. Cavanagh C, Morell M, Mackay I, Powell W. From mutations to MAGIC: resources for gene discovery, validation and delivery in crop plants. Current Opinion in Plant Biology 2008;11(2):215-21.
  23. 23. Cheema K, Bains N, Mangat G, Das A, Vikal Y, Brar D, et al. Development of high yielding IR64 x Oryza rufipogon (Griff.) introgression lines and identification of introgressed alien chromosome segments using SSR markers. Euphytica 2008a;160(3):401-9.
  24. 24. Cheema KK, Grewal NK, Vikal Y, Sharma R, Lore JS, Das A, et al. A novel bacterial blight resistance gene from Oryza nivara mapped to 38kb region on chromosome 4L and transferred to Oryza sativa L. Genetical Research 2008b;90(5):397.
  25. 25. Chen J, Huang DR, Wang L, Liu GJ, Zhuang JY. Identification of quantitative trait loci for resistance to whitebacked planthopper, Sogatella furcifera, from an interspecific cross Oryza sativa x O. rufipogon. Breeding Science 2010;60(2):153-9.
  26. 26. Chen L, Zhang W, Cheng X, Yang Y, Yan C, Wang X, et al. Identification of quantitative trait loci for bacterial blight resistance derived from Oryza meyeriana and agronomic traits in recombinant inbred lines of Oryza sativa. Journal of Phytopathology 2012;160(9):461-8.
  27. 27. Chen Z, Hu F, Xu P, Li J, Deng X, Zhou J, et al. QTL analysis for hybrid sterility and plant height in interspecific populations derived from a wild rice relative, Oryza longistaminata. Breeding Science 2009;59(4):441-5.
  28. 28. Cho Y, Suh J, Choi I, Hong H, Baek M, Kang K, et al. QTLs analysis of yield and its related traits in wild rice relative Oryza rufipogon. Trends in Crop Research 2003;4:19-29.
  29. 29. Couch B, Kohn L. A multilocus gene genealogy concordant with host preference indicates segregation of a new species, Magnaporthe oryzae, from M. grisea. Mycologia 2002;94(4):683-93.
  30. 30. Deen R, Ramesh K, Gautam S, Rao Y, Lakshmi V, Viraktamath B, et al. Identification of new gene for BPH resistance introgressed from O. rufipogon. Rice Genetics Newsletter 2010;25:70-1.
  31. 31. Doi K, Taguchi A, Yoshimura A. A new locus affecting high F1 pollen sterility found in backcross progenies between Japonica rice and African rice. Rice Genetics Newsletter 1998;15:146-8.
  32. 32. Doi K, Taguchi K, Yoshimura A. RFLP mapping of S20 and S21 for F1 pollen semi-sterility found in backcross progeny of Oryza sativa and O. glaberrima. Rice Genetics Newsletter 1999;16:65-8.
  33. 33. Eizenga G, Jia Y, Agrama H, Lee F. Exploring genetic diversity and potential novel disease resistance genes in a collection of rice (Oryza spp.) wild relatives. Genetic Resources and Crop Evolution 2009;56(1):65-76.
  34. 34. Eizenga GC, Prasad B, Jackson AK, Jia MH. Identification of rice sheath blight and blast quantitative trait loci in two different O. sativa/O. nivara advanced backcross populations. Molecular Breeding 2013;31:889–907.
  35. 35. Eizenga G, Neves P, Bryant RJ, Agrama H, Mackill DJ. Evaluation of a M-202 x Oryza nivara advanced backcross mapping population for seedling vigor, agronomic traits, yield components, yield and grain quality. Euphytica (provisional acceptance).
  36. 36. Eshed Y, Zamir D. An introgression line population of Lycopersicon pennellii in the cultivated tomato enables the identification and fine mapping of yield-associated QTL. Genetics 1995;141(3):1147.
  37. 37. Foy CD. Soil chemical factors limiting plant root growth. Limitations to plant root growth: Springer; 1992. p 97-149.
  38. 38. Fu Q, Tan L, Zhu Z, Ma D, Fu Y, Cai H, et al. Analysis of QTLs for yield-related traits in Yuanjiang common wild rice (Oryza rufipogon Griff.). Journal of Genetics and Genomics 2010;37(2):147-57.
  39. 39. Fu X, Lu Y, Liu X, Li J. Progress on transferring elite genes from non-AA genome wild rice into Oryza sativa through interspecific hybridization. Rice Science 2008;15(2):79-87.
  40. 40. Fujino K, Sekiguchi H, Sato T, Kiuchi H, Nonoue Y, Takeuchi Y, et al. Mapping of quantitative trait loci controlling low-temperature germinability in rice (Oryza sativa L.). Theoretical and Applied Genetics 2004;108(5):794-9.
  41. 41. Fujita D, Doi K, Yoshimura A, Yasui H. Mapping a new resistance gene for green rice leafhopper introgressed from Oryza rufipogon Griff. into cultivated rice, Oryza sativa L. Rice Genetics Newletter 2003;20:79-80.
  42. 42. Fujita D, Doi K, Yoshimura A, Yasui H. Introgression of a resistance gene for green rice leafhopper from Oryza nivara into cultivated rice, Oryza sativa L. Rice Genetics Newsletter 2004;21: 64–6.
  43. 43. Fujita D, Doi K, Yoshimura A, Yasui H. Molecular mapping of a novel gene, Grh5, conferring resistance to green rice leafhopper (Nephotettix cincticeps Uhler) in rice, Oryza sativa L. Theoretical and Applied Genetics 2006;113(4):567-73.
  44. 44. Fujita D, Doi K, Yoshimura A, Yasui H. A major QTL for resistance to green rice leafhopper (Nephotettix cincticeps Uhler) derived from African rice (Oryza glaberrima Steud.). Breeding Science 2010;60(4):336-41.
  45. 45. Fujita D, Yoshimura A, Yasui H. Development of near-isogenic lines and pyramided lines carrying resistance genes to green rice leafhopper (Nephotettix cincticeps Uhler) with the Taichung 65 genetic background in rice (Oryza sativa L.). Breeding Science 2010;60(1):18-27.
  46. 46. Fukuoka S, Nonoue Y, Yano M. Germplasm enhancement by developing advanced plant materials from diverse rice accessions. Breeding Science 2010;60(5):509-17.
  47. 47. Fukuta Y, Tamura K, Hirae M, Oya S. Genetic analysis of resistance to green rice leafhopper (Nephotettix cincticeps Uhler) in rice parental line, Norin-PL6, using RFLP markers. Breeding Science 1998;48:243–9.
  48. 48. Fuller D, Castillo C, Weisskopf A, Kingwell-Banham E, Song J, Sato Y, et al. Consilience of genetics and archaeobotany in the entangled history of rice. Archaeological and Anthropological Sciences 2010:1-17.
  49. 49. Fulton T, Beck-Bunn T, Emmatty D, Eshed Y, Lopez J, Petiard V, et al. QTL analysis of an advanced backcross of Lycopersicon peruvianum to the cultivated tomato and comparisons with QTLs found in other wild species. Theoretical and Applied Genetics 1997;95(5-6):881-94.
  50. 50. Futakuchi K, Fofana M, Sié M. Varietal differences in lodging resistance of African rice (Oryza glaberrima Steud.). Asian Journal of Plant Sciences 2008;7(6):569-73.
  51. 51. Garris A, Coburn J, Kresovich S, McCouch S, Tai T. Genetic structure and diversity in Oryza sativa L. Genetics 2005;169(3):1631-8.
  52. 52. Ghesquière A, Séquier J, Second G, Lorieux M. First steps towards a rational use of African rice, Oryza glaberrima, in rice breeding through a ‘contig line’concept. Euphytica 1997;96(1):31-9.
  53. 53. Godfray HCJ, Beddington JR, Crute IR, Haddad L, Lawrence D, Muir JF, et al. Food security: the challenge of feeding 9 billion people. Science 2010;327(5967):812-8.
  54. 54. Goff SA, Ricke D, Lan TH, Presting G, Wang R, Dunn M, Glazebrook J, Sessions A, Oeller P, Varma H et al. A draft sequence of the rice genome (Oryza sativa L. ssp. japonica). Science 2002;296:92–100.
  55. 55. Grandillo S, Tanksley SD. Advanced backcross QTL analysis: Results and perspectives. In: Tuberosa R, Phillips RL, Gale M. (editors) In the Wake of the Double Helix: From the Green Revolution to the Gene Revolution, Proceedings of the International Congress, 27-31 May 2003 Bologna, Italy; 2005.
  56. 56. Grennan AK. Abiotic stress in rice. An “omic” approach. Plant Physiology 2006;140(4):1139-41.
  57. 57. Gu K, Tian D, Yang F, Wu L, Sreekala C, Wang D, et al. High-resolution genetic mapping of Xa27(t), a new bacterial blight resistance gene in rice, Oryza sativa L. Theoretical and Applied Genetics 2004;108:800-7.
  58. 58. Guo SB, Wei Y, Li XQ, Liu KQ, Huang FK, Chen CH, et al. Development and identification of introgression lines from cross of Oryza sativa and Oryza minuta. Rice Science 2013;20(2):95-102.
  59. 59. Gutierrez A, Carabalí S, Giraldo O, Martínez C, Correa F, Prado G, et al. Identification of a Rice stripe necrosis virus resistance locus and yield component QTLs using Oryza sativa x O. glaberrima introgression lines. BMC Plant Biology 2010;10(1):6.
  60. 60. Haley CS, Knott SA. A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 1992;69(4):315-24.
  61. 61. Hanamaratti N, Prashanthi S, Salimath P, Hanchinal R, Mohankumar H, Parameshwarappa K, et al. Traditional and races of rice in Karnataka: reservoirs of valuable traits. Current Science 2008;94:242–7.
  62. 62. Hechanova S. Molecular characterization of introgression and putative genes for bacterial blight resistance in wide-cross derivatives of rice (Oryza sativa). Food and Agriculture Organization of the United Nations. 2008. http://agris.fao.org/agris-search/search.do?f=2009/PH/PH0909.xml;PH2009000711 (accessed 24 January 2014).
  63. 63. Hernandez JE, Khush GS. Genetics of resistance to white-backed planthopper in some rice (Oryza sativa L.) varieties. Oryza 1981;18(1):44-50.
  64. 64. Hirabayashi H, Angeles E, Kaji R, Ogawa T, Brar D, Khush G. Identification of brown planthopper resistance gene derived from O. officinalis using molecular markers in rice. Breeding Science 1998;48(Suppl 1):82.
  65. 65. Hirabayashi H, Sato H, Nonoue Y, Kuno-Takemoto Y, Takeuchi Y, Kato H, et al. Development of introgression lines derived from Oryza rufipogon and O. glumaepatula in the genetic background of japonica cultivated rice (O. sativa L.) and evaluation of resistance to rice blast. Breeding Science 2010;60(5):604-12.
  66. 66. Hou Ly, Yu P, Xu Q, Yuan XP, Yu HY, Wang YP, et al. Genetic analysis and preliminary mapping of two recessive resistance genes to brown planthopper, Nilaparvata lugens Stål in rice. Rice Science 2011;18(3):238-42.
  67. 67. Hu F, Xu P, Deng X, Zhou J, Li J, Tao D. Molecular mapping of a pollen killer gene S29(t) in Oryza glaberrima and co-linear analysis with S22 in O. glumaepatula. Euphytica 2006;151(3):273-8.
  68. 68. Hu G, Zhang D, Pan H, Li B, Wu J, Zhou X, et al. Fine mapping of the awn gene on chromosome 4 in rice by association and linkage analyses. Chinese Science Bulletin 2011;56(9):835-9.
  69. 69. Hu H, You J, Fang Y, Zhu X, Qi Z, Xiong L. Characterization of transcription factor gene SNAC2 conferring cold and salt tolerance in rice. Plant Molecular Biology 2008;67(1-2):169-81.
  70. 70. Huang X, Kurata N, Wei X, Wang ZX, Wang A, Zhao Q, et al. A map of rice genome variation reveals the origin of cultivated rice. Nature 2012;490(7421):497-501.
  71. 71. Huang Z, He G, Shu L, Li X, Zhang Q. Identification and mapping of two brown planthopper resistance genes in rice. Theoretical and Applied Genetics 2001;102(6-7):929-34.
  72. 72. Imai I, Kimball JA, Conway B, Yeater KM, McCouch SR, McClung A. Validation of yield-enhancing quantitative trait loci from a low-yielding wild ancestor of rice. Molecular Breeding 2013;32:1-20.
  73. 73. Ingram J, Bartels D. The molecular basis of dehydration tolerance in plants. Annual Review of Plant Biology 1996;47(1):377-403.
  74. 74. Ishii T, Brar D, Multani D, Khush G. Molecular tagging of genes for brown planthopper resistance and earliness introgressed from Oryza australiensis into cultivated rice, O. sativa. Genome 1994;37(2):217-21.
  75. 75. Jacquemin J, Bhatia D, Singh K, Wing RA. The international Oryza map alignment project: development of a genus-wide comparative genomics platform to help solve the 9 billion-people question. Current Opinion in Plant Biology 2013;16:147-56.
  76. 76. Jena KK. The species of the genus Oryza and transfer of useful genes from wild species into cultivated rice, O. sativa. Breeding Science 2010;60(5):518-23.
  77. 77. Jena K, Jeung J, Lee J, Choi H, Brar D. High-resolution mapping of a new brown planthopper (BPH) resistance gene, Bph18(t), and marker-assisted selection for BPH resistance in rice (Oryza sativa L.). Theoretical and Applied Genetics 2006;112(2):288-97.
  78. 78. Jena K, Khush GS. Monosomic alien addition lines of rice: production, morphology, cytology, and breeding behavior. Genome 1989;32(3):449-55.
  79. 79. Jena KK, Khush G. Introgression of genes from Oryza officinalis Well Ex. Watt to cultivated rice, O. sativa L. Theoretical and Applied Genetics 1990;80:737-45.
  80. 80. Jena K, Kochert G, Khush G. RFLP analysis of rice (Oryza sativa L.) introgression lines. Theoretical and Applied Genetics 1992;84(5-6):608-16.
  81. 81. Jena K, Pasalu I, Rao Y, Varalaxmi Y, Krishnaiah K, Khush G, et al. Molecular tagging of a gene for resistance to brown planthopper in rice (Oryza sativa L.). Euphytica 2002;129(1):81-8.
  82. 82. Jeung JU, Kim BR, Cho YC, Han SS, Moon HP, Lee YT, Jena KK. A novel gene, Pi40(t), linked to the DNA markers derived from NBS-LRR motifs confers broad spectrum of blast resistance in rice. Theoretical and Applied Genetics 2007;115(8):1163-77.
  83. 83. Jia Y, Liu G, Costanzo S, Lee S, Dai Y. Current progress on genetic interactions of rice with rice blast and sheath blight fungi. Frontiers of Agriculture in China 2009;3(3):231–9.
  84. 84. Jin F, Kwon S, Kang K, Jeong O, Le L, Yoon D, et al. Introgression for grain traits from Oryza minuta into rice, O. sativa. Rice Genetics Newsletter 2004; 21:15.
  85. 85. Jin FX, Ji SD, Xie XB, Kang JW, Ju HG, Ahn SN. Detection of epistatic interaction of two QTLs, gw8.1 and gw9.1, underlying grain weight using nearly isogenic lines in rice. Breeding Science 2011;61(1):69-75.
  86. 86. Jin FX, Kim DM, Ju HG, Ahn SN. Mapping quantitative trait loci for awnness and yield component traits in isogenic lines derived from an Oryza sativa/O. rufipogon cross. Journal of Crop Science and Biotechnology 2009;12(1):9-15.
  87. 87. Joehanes R, Nelson JC. QGene 4.0, an extensible Java QTL-analysis platform. Bioinformatics 2008;24(23):2788-9.
  88. 88. Jones DB, Peterson ML. Rice seedling vigor at sub-optimal temperatures. Crop Science 1976;16(1):102-5.
  89. 89. Kang J, Suh J, Kim D, Oh C, Oh J, Ahn S. QTL mapping of agronomic traits in an advanced backcross population from a cross between Oryza sativa L. cv. Milyang 23 and O. glaberrima. Korean Journal of Breeding Science 2008;40(3):243-9.
  90. 90. Kao CH, Zeng ZB, Teasdale RD. Multiple interval mapping for quantitative trait loci. Genetics 1999;152(3):1203-16.
  91. 91. Kellogg EA. The evolutionary history of Ehrhartoideae, Oryzeae, and Oryza. Rice 2009;2(1):1-14.
  92. 92. Keurentjes JJB, Bentsink L, Hanhart CJ, Vries HBD, Koornneef M, Vreugdenhil D, et al. Development of a near-isogenic line population of Arabidopsis thaliana and comparison of mapping power with a recombinant inbred line population. Genetics 2007;175(2):891-905.
  93. 93. Khush GS. Disease and insect resistance in rice. Advances in Agronomy 1977;29:265-341.
  94. 94. Khush G, Ling K, Aquino R, Aguiero V. Breeding for resistance to grassy stunt in rice. Plant Breeding Papers; International Congress of the Society for the Advancement of Breeding Researches in Asia & Oceania. 1977.
  95. 95. Khush GS, Bacalangco E, Ogawa T. A new gene for resistance to bacterial blight from O. longistaminata. Rice Genetic Newsletter 1990;7:121-2.
  96. 96. Koide Y, Shinya Y, Ikenaga M, Sawamura N, Matsubara K, Onishi K, et al. Complex genetic nature of sex-independent transmission ratio distortion in Asian rice species: the involvement of unlinked modifiers and sex-specific mechanisms. Heredity 2012;108(3):242-7.
  97. 97. Koseki M, Kitazawa N, Yonebayashi S, Maehara Y, Wang ZX, Minobe Y. Identification and fine mapping of a major quantitative trait locus originating from wild rice, controlling cold tolerance at the seedling stage. Molecular Genetics and Genomics 2010;284(1):45-54.
  98. 98. Kover PX, Scarcelli N, Valdar W, Trakalo J, Durrant C, Mott R, et al. A multiparent advanced generation inter-cross to fine-map quantitative traits in Arabidopsis thaliana. PLoS Genetics 2009;5(7). DOI: 10.1371/journal.pgen.1000551
  99. 99. Kurakazu T, Sobrizal, Yoshimura A. RFLP mapping of genes for awn on chromosomes 4 and 5 in rice using Oryza meridionalis introgression lines. Rice Genetics Newsletter 2001(18):28-30.
  100. 100. Lakshmanan P, Velusamy R. Resistance to sheath blight (ShB) and brown spot (BS) in lines derived from Oryza officinalis. International Rice Resesarch Institute Newsletter 1991;16:8.
  101. 101. Lander ES, Botstein D. Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 1989;121(1):185-99.
  102. 102. Lee FN, Rush MC. Rice sheath blight: a major rice disease. Plant Disease 1983;67:829–32.
  103. 103. Lee H, Sasaki K, Higashitani A, Ahn S, Sato T. Mapping and characterization of quantitative trait loci for mesocotyl elongation in rice (Oryza sativa L.). Rice 2012;5(13).
  104. 104. Lee SJ, Oh CS, Suh JP, McCouch S, Ahn SN. Identification of QTLs for domestication‐related and agronomic traits in an Oryza sativa x O. rufipogon BC1F7 population. Plant Breeding 2005;124(3):209-219.
  105. 105. Lei D, Tan L, Liu F, Chen L, Sun C. Identification of heat-sensitive QTL derived from common wild rice (Oryza rufipogon, Griff.). Plant Science 2013;201-202:123-7.
  106. 106. Lexer C, Fay M. Adaptation to environmental stress: a rare or frequent driver of speciation? Journal of Evolutionary Biology 2005;18(4):893-900.
  107. 107. Li C, Zhou A, Sang T. Genetic analysis of rice domestication syndrome with the wild annual species, Oryza nivara. New Phytologist 2006;170(1):185-94.
  108. 108. Li J, Thomson M, McCouch SR. Fine mapping of a grain-weight quantitative trait locus in the pericentromeric region of rice chromosome 3. Genetics 2004;168(4):2187-95.
  109. 109. Li J, Xiao J, Grandillo S, Jiang L, Wan Y, Deng Q, et al. QTL detection for rice grain quality traits using an interspecific backcross population derived from cultivated Asian (O. sativa L.) and African (O. glaberrima S.) rice. Genome 2004;47(4):697-704.
  110. 110. Li JB, Xia MY, Qi HX, He GC, Wan BL, Zha ZP. Marker assisted selection for brown planthopper (Nilaparvata lugens Stål) resistance genes Bph14 and Bph15 in rice. Scientia Agricultura Sinica 2006;39(10):2132-7.
  111. 111. Li Z, Pinson SR, Stansel JW, Paterson AH. Genetic dissection of the source-sink relationship affecting fecundity and yield in rice (Oryza sativa L.). Molecular Breeding 1998;4(5):419-26.
  112. 112. Li ZX, Septiningsih EM, Quilloy-Mercado SM, McNally KL, Mackill DJ. Identification of SUB1A alleles from wild rice Oryza rufipogon Griff. Genetic Resources and Crop Evolution 2011;58(8):1237-42.
  113. 113. Linh LH, Hang NT, Jin FX, Kang KH, Lee YT, Kwon SJ, et al. Introgression of a quantitative trait locus for spikelets per panicle from Oryza minuta to the O. sativa cultivar Hwaseongbyeo. Plant Breeding 2008;127(3):262-7.
  114. 114. Linh LH, Jin FX, Kang KH, Lee YT, Kwon SJ, Ahn SN. Mapping quantitative trait loci for heading date and awn length using an advanced backcross line from a cross between Oryza sativa and O. minuta. Breeding Science 2006;56(4):341-9.
  115. 115. Liu G, Lu G, Zeng L, Wang GL. Two broad-spectrum blast resistance genes, Pi9(t) and Pi2(t), are physically linked on rice chromosome 6. Molecular Genetics and Genomics. 2002;267(4):472-80.
  116. 116. Liu G, Yan H, Fu Q, Qian Q, Zhang Z, Zhai W, et al. Mapping of a new gene for brown planthopper resistance in cultivated rice introgressed from Oryza eichingeri. Chinese Science Bulletin 2001;46(17):1459-62.
  117. 117. Liu G, Yang J, Xu H, Hayat Y, Zhu J. Genetic analysis of grain yield conditioned on its component traits in rice (Oryza sativa L.). Crop and Pasture Science 2008;59(2):189-95.
  118. 118. Lorieux M. MapDisto: Fast and efficient computation of genetic linkage maps. Molecular Breeding 2012;30(2):1231-5.
  119. 119. Lu BR, Jackson M, Vaughn D. Wild rice taxonomy. In: Rice knowledge bank, International Rice Research Institute, Philippines. 2014; http://www.knowledgebank.irri.org/extension/index.php/ wild-rice-taxonomy (accessed 23 January 2014).
  120. 120. Luo DG, Zhang SP. Preliminary study on simplified distant cross techniques by using male-sterile lines as maternal parent and O. gradiglumis as paternal parent. Seed 2000;3:49–50.
  121. 121. Manneh B, Kiepe P, Sie M, Ndjiondjop M, Drame N, Traore K, et al. Exploiting partnerships in research and development to help African rice farmers cope with climate variability. Journal of SAT Agricultural Research 2007;4(1):1-10.
  122. 122. Marri PR, Sarla N, Reddy LV, Siddiq E. Identification and mapping of yield and yield related QTLs from an Indian accession of Oryza rufipogon. BMC Genetics 2005;6(1):33.
  123. 123. Matsushita S, Kurakazu T, Sobrizal, Doi K, Yoshimura A. Mapping genes for awn in rice using Oryza meridionalis introgression lines. Rice Genetics Newsletter 2003;20:17.
  124. 124. Matsushita S, Sanchez P, Sobrizal, Doi K, Yoshimura A. Identification of new alleles of awnness genes, An7 and An8, in rice using Oryza glumaepatula introgression lines. Rice Genetics Newsletter 2003;20:19-20.
  125. 125. McCombie R. New whole genome de-novo assemblies of three divergent strains of rice document novel gene space in aus and indica missed by previous reference assemblies. 7th International Rice Genetics Symposium Program and Abstract Book; Manila, Philippines: 5-8 November 2013; p 120.
  126. 126. McCouch SR, McNally KL, Wang W, Hamilton RS. Genomics of gene banks: a case study in rice. American Journal of Botany 2012;99(2):407–23.
  127. 127. McCouch SR, Sweeney M, Jiang H, Thomson M, Septiningsih E, Edwards J, et al. Through the genetic bottleneck: O. rufipogon as a source of trait-enhancing alleles for O. sativa. Euphytica 2007;154(3):317-39.
  128. 128. McCouch SR, Zhao K, Wright M, Tung CW, Ebana K, Thomson M, et al. Development of genome-wide SNP assays for rice. Breeding Science 2010;60(5):524-35.
  129. 129. Mew T, Alvarez A, Leach J, Swings J. Focus on bacterial blight of rice. Plant Disease 1993;77(1):5-12.
  130. 130. Miura K, Yamamoto E, Morinaka Y, Takashi T, Kitano H, Matsuoka M, et al. The hybrid breakdown 1 (t) locus induces interspecific hybrid breakdown between rice Oryza sativa cv. Koshihikari and its wild relative O. nivara. Breeding Science 2008;58(2):99-105.
  131. 131. Moncada P, Martinez C, Borrero J, Châtel M, Gauch Jr H, Guimaraes E, et al. Quantitative trait loci for yield and yield components in an Oryza sativa x Oryza rufipogon BC2F2 population evaluated in an upland environment. Theoretical and Applied Genetics 2001;102(1):41-52.
  132. 132. Multani D, Jena K, Brar D, de los Reyes B, Angeles E, Khush G. Development of monosomic alien addition lines and introgression of genes from Oryza australiensis Domin. to cultivated rice O. sativa L. Theoretical and Applied Genetics 1994;88(1):102-109.
  133. 133. Multani D, Khush G, de los Reyes B, Brar D. Alien genes introgression and development of monosomic alien addition lines from Oryza latifolia Desv. to rice, Oryza sativa L. Theoretical and Applied Genetics 2003;107(3):395-405.
  134. 134. Hanamaratti NG, Prashanthi SK, Salimath PM, Hanchinal RR, Mohankumar HD, Parameshwarappa KG, Raikar SD. Traditional land races of rice in Karnataka: reservoirs of valuable traits. Current Science 2008;94:242-7.
  135. 135. Nagao S, Takahashi T. Trial construction of twelve linkage groups in Japanese rice. Genetical studies on rice plant, XXVII Journal of the Faculty of Agriculture, Hokkaido University. 1963:72-130.
  136. 136. Ndjiondjop MN, Manneh B, Cissoko M, Drame NK, Kakai RG, Bocco R, et al. Drought resistance in an interspecific backcross population of rice (Oryza spp.) derived from the cross WAB56-104 (O. sativa) x CG14 (O. glaberrima). Plant Science 2010;179(4):364-73.
  137. 137. Ndjiondjop MN, Manneh B, Cissoko M, Drame NK, Kakai RG, Bocco R, et al. Drought resistance in an interspecific backcross population of rice (Oryza spp.) derived from the cross WAB56-104 (O. sativa) x CG14 (O. glaberrima). Plant Science 2010;179(4):364-73.
  138. 138. Ndjiondjop MN, Albar L, Fargette D, Fauquet C, Ghesquière A. The genetic basis of high resistance to rice yellow mottle virus (RYMV) in cultivars of two cultivated rice species. Plant Disease 1999;83(10):931-35.
  139. 139. Nguyen BD, Brar DS, Bui BC, Nguyen TV, Pham LN, Nguyen HT. Identification and mapping of the QTL for aluminum tolerance introgressed from the new source, Oryza rufipogon Griff., into indica rice (Oryza sativa L.). Theoretical and Applied Genetics 2003;106(4):583-93.
  140. 140. Ni J, Colowit P, Oster J, Xu K, Mackill D. Molecular markers linked to stem rot resistance in rice. Theoretical and Applied Genetics 2001;102(4):511-6.
  141. 141. Niño‐Liu DO, Ronald PC, Bogdanove AJ. Xanthomonas oryzae pathovars: model pathogens of a model crop. Molecular Plant Pathology 2006;7(5):303-24.
  142. 142. Nuque F, Aguiero V, Ou S. Inheritance of resistance to grassy stunt virus in rice. Plant Disease 1982;66(1):63-4.
  143. 143. Oh JM, Balkunde S, Yang P, Yoon DB, Ahn SN. Fine mapping of grain weight QTL, tgw11 using near isogenic lines from a cross between Oryza sativa and O. grandiglumis. Genes and Genomics 2011;33(3):259-65.
  144. 144. Orjuela J, Garavito A, Bouniol M, Arbelaez JD, Moreno L, Tohme J, et al. A universal core genetic map for rice. Theoretical and Applied Genetics 2010;120(3):563-72.
  145. 145. Oryzabase. List of Gene 2014 (Updated 20 Jan. 2014) http://www.shigen.nig.ac.jp/rice/oryzabaseV4/gene/list (accessed 24 January 2014).
  146. 146. Ou SH. Rice Diseases. Commonwealth Agricultural Bureau, Kew, UK. 1985.
  147. 147. Pastori GM, Foyer CH. Common components, networks, and pathways of cross-tolerance to stress. The central role of “redox” and abscisic acid-mediated controls. Plant Physiology 2002;129(2):460-8.
  148. 148. Pinson SRM, Capdevielle FM, Oard JH. Confirming QTLs and finding additional loci conditioning sheath blight resistance in rice using recombinant inbred lines. Crop Science 2005; 45:503–10
  149. 149. Plowright RA, Nash P, Coyne DL, Jones MP. Resistance to the rice nematodes Heterodera sacchari, Meloidogyne graminicola and M. incognita in Oryza glaberrima and O. glaberrima x O. sativa interspecific hybrids. Nematology 1999;1(7-8):745-51.
  150. 150. Poehlman JM, Sleper DA. Breeding Field Crops, Fourth edition: Iowa State University Press 1995.
  151. 151. Prasad B, Eizenga GC. Rice sheath blight disease resistance identified in Oryza spp. accessions. Plant Disease 2008;92(11):1503-9.
  152. 152. Qu S, Liu G, Zhou B, Bellizzi M, Zeng L, Wang GL, et al. The broad-spectrum blast resistance gene Pi9 encodes a nucleotide-binding site-leucine-rich repeat protein and is a member of a multigene family in rice. Genetics 2006;172(3):1901-14.
  153. 153. Wing R. 15 million years of Oryza evolution as seen through the analysis of 12 high quality genome assemblies. 7th International Rice Genetics Symposium Program and Abstract Book; Manila, Philippines: 5-8 November 2013; p 121-2.
  154. 154. Rahman ML, Jiang W, Chu SH, Qiao Y, Ham TH, Woo MO, Lee J, Khanam MS, Chin JH, Jeung JU, Brar DS, Jena KK, Koh HJ. High-resolution mapping of two rice brown planthopper resistance genes, Bph20 (t) and Bph21 (t), originating from Oryza minuta. Theoretical and Applied Genetics 2009;119(7):1237-46.
  155. 155. Rahman L, Koh HJ, Khanam MS. QTL analysis for yield related traits using populations derived from an indica-japonica hybrid in rice (Oryza sativa L.). Czech Journal of Genetics and Plant Breeding 2008;44(3):93-104.
  156. 156. Ram T, Deen R, Gautam S, Ramesh K, Rao Y, Brar D. Identification of new genes for brown planthopper resistance in rice introgressed from O. glaberrima and O. minuta. Rice Genetics Newsletter 2010;25:67.
  157. 157. Ram T, Laha G, Gautam S, Deen R, Madhav MS, Brar D, et al. Identification of new gene introgressed from Oryza brachyantha with broad-spectrum resistance to bacterial blight of rice in India. Rice Genetics Newsletter. 2010;25:57-8.
  158. 158. Rangel PN, Brondani RPV, Rangel PHN, Brondani C. Agronomic and molecular characterization of introgression lines from the interspecific cross Oryza sativa (BG 90-2) x Oryza glumaepatula (RS-16). Genetic and Molecular Research 2008;7(1):184-95.
  159. 159. Rangel PN, Vianello RP, Melo ATO, Rangel PHN, Mendonça JA, Brondani C. Yield QTL analysis of Oryza sativa x O. glumaepatula introgression lines. Pesquisa Agropecuária Brasileira 2013;48(3):280-6.
  160. 160. Rausher MD. Co-evolution and plant resistance to natural enemies. Nature 2001;411(6839):857-64.
  161. 161. Ravinder SS, Khush GS, Heinrichs EA. Genetic analysis of resistance to whitebacked planthopper, Sogatella furcifera (Horvath), in some rice varieties. Crop Protection 1982;1:289-97.
  162. 162. Renganayaki K, Fritz AK, Sadasivam S, Pammi S, Harrington SE, McCouch SR, et al. Mapping and progress toward map-based cloning of brown planthopper biotype-4 resistance gene introgressed from Oryza officinalis into cultivated rice. Crop Science 2002;42(6):2112-2117.
  163. 163. Ronald PC, Albano B, Tabien R, Abenes L, Wu KS, McCouch S, et al. Genetic and physical analysis of the rice bacterial blight disease resistance locus, Xa21. Molecular and General Genetics 1992;236(1):113-20.
  164. 164. Saka N, Toyama T, Tsuji T, Nakamae H, Izawa T. Fine mapping of green ricehopper resistant gene Grh-3(t) and screening of Grh-3(t) among green ricehopper resistant and green leafhopper resistant cultivars in rice. Breeding Science 1997; 47(1):55.
  165. 165. Sanchez PL, Sobrizal, Ikeda K, Yasui H, Yoshimura A. Identifying late heading genes in rice using Oryza glumaepatula introgression lines. In: Advances in Rice Genetics. International Rice Research Institute, Los Baños, Laguna, Philippines. 2003; p 153-4.
  166. 166. Sanchez PL, Wing RA, Brar DS. The wild relative of rice: genomes and genomics. In: Zhang Q, Wing RA, editors. Genetics and Genomics of Rice, Plant Genetics and Genomics: Crops and Models 5. New York: Springer Science: 2013; p 9-25.
  167. 167. Sano Y. A new gene controlling sterility in F1 hybrids of two cultivated rice species: Its association with photoperiod sensitivity. Journal of Heredity 1983;74(6):435-9.
  168. 168. Sano Y. Sterility barriers between Oryza sativa and O. glaberrima. In: Rice genetics, Proceedings of the International Rice Genetics Symposium, International Rice Research Institute, Los Baños, Philippines: 27–31 May 1985; p 109–18.
  169. 169. Sano Y. The genic nature of gamete eliminator in rice. Genetics 1990;125(1):183-91.
  170. 170. Sarla N, Swamy BM. Oryza glaberrima: a source for the improvement of Oryza sativa. Current Science 2005;89(6):955-63.
  171. 171. Savary S, Willocquet L, Elazegui FA, Castilla NP, Teng PS. Rice pest constraints in tropical Asia: quantification of yield losses due to rice pests in a range of production situations. Plant Disease 2000;84(3):357-69.
  172. 172. Septiningsih E, Prasetiyono J, Lubis E, Tai T, Tjubaryat T, Moeljopawiro S, et al. Identification of quantitative trait loci for yield and yield components in an advanced backcross population derived from the Oryza sativa variety IR64 and the wild relative O. rufipogon. Theoretical and Applied Genetics 2003;107(8):1419-32.
  173. 173. Shan JX, Zhu MZ, Shi M, Gao JP, Lin HX. Fine mapping and candidate gene analysis of spd6, responsible for small panicle and dwarfness in wild rice (Oryza rufipogon Griff.). Theoretical and Applied Genetics 2009;119(5):827-36.
  174. 174. Shim RA, Ashikari M, Angeles ER, Takashi T. Development and evaluation of Oryza glaberrima Steud. chromosome segment substitution lines (CSSLs) in the background of O. sativa L. cv. Koshihikari. Breeding Science 2010;60(5):613-9.
  175. 175. Sidhu G, Khush G. Linkage relationships of some genes for disease and insect resistance and semidwarf stature in rice. Euphytica 1979;28(2):233-7.
  176. 176. Sié M, Blaise K, Drissa S, Yonnelle D. Selection of intraspecific (Oryza sativa x O. sativa) and inter-specific (O. sativa x O. glaberrima) lines for their tolerance to blast in Burkina Faso. Institut de l'Environnement et de Recherches Agricoles (INERA) 01 BP 910 Bobo-Dioulasso 1:23-37.
  177. 177. Silué D, Nottéghem JL, Chaume J. Etude de la résistance spécifique d'Oryza glaberrima Steud à la pyriculariose du riz (translation Study of the specific resistance of Oryza glaberrima Steud to rice blast). Agronomie Tropicale 1992;42:121-9.
  178. 178. Sobrizal, Ikeda K, Sanchez PL, Yasui H, Yoshimura A. Development and evaluation Oryza glumaepatula introgression lines in rice, Oryza sativa L. Rice Genetics Newsletter 1999;16:107-8.
  179. 179. Sobrizal, Matsuzaki Y, Sanchez PL, Ikeda K, Yoshimura A. Identification of a gene for male gamete abortion in backcross progeny of Oryza sativa L. and Oryza glumaepatula Steud. Rice Genetics Newsletter 2000a;17:59-61.
  180. 180. Sobrizal, Matsuzaki Y, Sanchez PL, Ikeda K, Yoshimura A. Mapping of F1 pollen semi-sterility found in backcross progeny of Oryza sativa L. and Oryza glumaepatula Steud. Rice Genetics Newsletter 2000b;17:61-3.
  181. 181. Suh JP, Ahn SN, Cho YC, Kang KH, Choi IS, Kim YG, Suh HS, Hwang HG. Mapping of QTLs for yield traits using an advanced backcross population from a cross between Oryza sativa and O. glaberrima. Korean Journal of Breeding Science. 2005;37(4):214-20.
  182. 182. Swamy BM, Kaladhar K, Rani NS, Prasad G, Viraktamath B, Reddy GA, et al. QTL analysis for grain quality traits in 2 BC2F2 populations derived from crosses between Oryza sativa cv Swarna and 2 accessions of O. nivara. Journal of Heredity 2012;103(3):442-52.
  183. 183. Takamure I, Kinoshita T. Linkage analysis in chromosomes 3 and 6. Rice Genetics Newsletter 1991;8:98-100.
  184. 184. Talamè V, Sanguineti M, Chiapparino E, Bahri H, Salem M, Forster B, et al. Identification of Hordeum spontaneum QTL alleles improving field performance of barley grown under rainfed conditions. Annals of Applied Biology 2004;144(3):309-319.
  185. 185. Tamura K, Fukuta Y., Hirae M, Oya S, Ashikawa I, Yagi.T. Mapping of the Grh1 locus for green rice leafhopper resistance in rice using RFLP markers. Breeding Science 1999; 49:11–14.
  186. 186. Tamura K, Fukuta Y, Hirae M, Oya S, Ashikawa I., Yagi T. RFLP mapping of a new resistance gene for green rice leafhopper in Kanto PL10. Rice Genetics Newsletter 2004;21: 62–4.
  187. 187. Tan G, Weng Q, Ren X, Huang Z, Zhu L, He G. Two whitebacked planthopper resistance genes in rice share the same loci with those for brown planthopper resistance. Heredity 2004;92(3):212-7.
  188. 188. Tan GX, Ren X, Weng QM, Shi ZY, Zhu LL, He GC. Mapping of a new resistance gene to bacterial blight in rice line introgressed from Oryza officinalis. Acta Genetica Sinica 2004;31(7):724-9.
  189. 189. Tan L, Liu F, Xue W, Wang G, Ye S, Zhu Z, et al. Development of Oryza rufipogon and O. sativa introgression lines and assessment for yield-related quantitative trait loci. Journal of Integrative Plant Biology 2007;49(6):871-84.
  190. 190. Tan L, Zhang P, Liu F, Wang G, Ye S, Zhu Z, et al. Quantitative trait loci underlying domestication-and yield-related traits in an Oryza sativa x Oryza rufipogon advanced backcross population. Genome 2008;51(9):692-704.
  191. 191. Tan LB, Fu YC, Liu FX, Wang XK, Sun CQ, Zhang PJ. Identification of quantitative trait loci controlling plant height and days to heading from Yuanjiang common wild rice (Oryza rufipogon Griff.). Acta Genetica Sinica 2004;31(10):1123-8.
  192. 192. Tanksley S, Grandillo S, Fulton T, Zamir D, Eshed Y, Petiard V, et al. Advanced backcross QTL analysis in a cross between an elite processing line of tomato and its wild relative L. pimpinellifolium. Theoretical and Applied Genetics 1996;92(2):213-24.
  193. 193. Tanksley S, Nelson J. Advanced backcross QTL analysis: a method for the simultaneous discovery and transfer of valuable QTLs from unadapted germplasm into elite breeding lines. Theoretical and Applied Genetics 1996;92(2):191-203.
  194. 194. Tanksley SD, McCouch SR. Seed banks and molecular maps: unlocking genetic potential from the wild. Science 1997;277(5329):1063-6.
  195. 195. Tanksley SD, Medina-Filho H, Rick CM. Use of naturally-occurring enzyme variation to detect and map genes controlling quantitative traits in an interspecific backcross of tomato. Heredity 1982;49 (1):11-25.
  196. 196. Thanh PT, Phan PDT, Mori N, Ishikawa R, Ishii T. Development of backcross recombinant inbred lines between Oryza sativa Nipponbare and O. rufipogon and QTL detection on drought tolerance. Breeding Science 2011;61(1):76-9.
  197. 197. Thiémélé D, Boisnard A, Chéron S, Ghesquière A, Albar L, Aké S, et al. Identification of a second major resistance gene to rice yellow mottle virus, RYMV2, in the African cultivated rice species, O. glaberrima. Theoretical and Applied Genetics 2010;121(1):169-79.
  198. 198. Thomson MJ, Tai TH, McClung AM, Lai XH, Hinga ME, Lobos KB, Xu Y, Martinez CP, McCouch SR. Mapping quantitative trait loci for yield, yield components and morphological traits in an advanced backcross population between Oryza rufipogon and the Oryza sativa cultivar Jefferson. Theoretical and Applied Genetics 2003;107(3):479-93.
  199. 199. Tian F, Zhu Z, Zhang B, Tan L, Fu Y, Wang X, et al. Fine mapping of a quantitative trait locus for grain number per panicle from wild rice (Oryza rufipogon Griff.). Theoretical and Applied Genetics 2006;113(4):619-29.
  200. 200. Tian L, Tan L, Liu F, Cai H, Sun C. Identification of quantitative trait loci associated with salt tolerance at seedling stage from Oryza rufipogon. Journal of Genetics and Genomics 2011;38(12):593-601.
  201. 201. Tilman D, Lehman C. Human-caused environmental change: impacts on plant diversity and evolution. Proceedings of the National Academy of Sciences 2001;98(10):5433-40.
  202. 202. Tseng S, Oster J. Registration of 87-Y-550, a rice germplasm line resistant to stem rot disease. Crop Science 1994;34(1)314.
  203. 203. Tung CW, Zhao K, Wright MH, Ali ML, Jung J, Kimball J, et al. Development of a research platform for dissecting phenotype–genotype associations in rice (Oryza spp.). Rice 2010;3(4):205-17.
  204. 204. Van Ooijen J. JoinMap 4. Software for the calculation of genetic linkage maps in experimental populations Kyazma BV, Wageningen, Netherlands: 2006.
  205. 205. Vaughan DA. The wild relatives of rice: a genetic resources handbook: Manila, Philippines: International Rice Research Institute;1994.
  206. 206. Vaughan DA, Ge S, Kaga A, Tomooka N. Phylogeny and biogeography of the genus Oryza. Rice Biology in the Genomics Era. Berlin-Heidelberg: Springer; 2008; p 219-34.
  207. 207. Vaughan DA, Morishima H. Biosystematics of the genus Oryza In: Smith CW, Dilday RH, editors. Rice: Origin, History, Technology, and Production 6: John Wiley & Sons; 2002; p 27-65.
  208. 208. Wang J, Wan X, Li H, Pfeiffer WH, Crouch J, Wan J. Application of identified QTL-marker associations in rice quality improvement through a design-breeding approach. Theoretical and Applied Genetics 2007;115(1):87-100.
  209. 209. Wang S, Basten CJ, Zeng ZB. Windows QTL Cartographer 2.5. Department of Statistics, North Carolina State University, Raleigh, NC.2012. http://statgen.ncsu.edu/qtlcart/WQTLCart.htm (accessed 24 January 2014).
  210. 210. Wang YM, Dong ZY, Zhang ZJ, Lin XY, Shen Y, Zhou D, et al. Extensive de novo genomic variation in rice induced by introgression from wild rice (Zizania latifolia Griseb.). Genetics 2005;170(4):1945-56.
  211. 211. Wickneswari R, Bhuiyan M, Lim LS, Thomson MJ, Narimah MK, Abdullah MZ. Identification and validation of quantitative trait loci for agronomic traits in advanced backcross breeding lines derived from Oryza rufipogon x Oryza sativa cultivar MR219. Plant Molecular Biology Reporter 2012;30(4):929-39.
  212. 212. Win KT, Kubo T, Miyazaki Y, Doi K, Yamagata Y, Yoshimura A. Identification of two loci causing F1 pollen sterility in inter-and intraspecific crosses of rice. Breeding Science 2009;59(4):411-8.
  213. 213. Win KT, Yamagata Y, Miyazaki Y, Doi K, Yasui H, Yoshimura A. Independent evolution of a new allele of F1 pollen sterility gene S27 encoding mitochondrial ribosomal protein L27 in Oryza nivara. Theoretical and Applied Genetics 2011;122(2):385-94.
  214. 214. Wu P, Zhao B, Yan J, Luo A, Wu Y, Senadihra D. Genetic control of seedling tolerance to aluminum toxicity in rice. Euphytica 1997;97:289-293.
  215. 215. Xia R, Xiao N, Hong Y, Zhang C, Su Y, Zhang X, et al. QTLs mapping for cold tolerance at seedling stage in Dongxiang wild rice (Oryza rufipogon Griff.). Scientia Agricultura Sinica 2010;43:443-51.
  216. 216. Xiao J, Grandillo S, Ahn SN, McCouch SR, Tanksley SD, Li J, et al. Genes from wild rice improve yield. Nature (London) 1996;384(6606):223-4.
  217. 217. Xiao J, Li J, Grandillo S, Ahn SN, Yuan L, Tanksley SD, et al. Identification of trait-improving quantitative trait loci alleles from a wild rice relative, Oryza rufipogon. Genetics 1998;150(2):899-909.
  218. 218. Xie X, Song MH, Jin F, Ahn SN, Suh JP, Hwang HG, et al. Fine mapping of a grain weight quantitative trait locus on rice chromosome 8 using near-isogenic lines derived from a cross between Oryza sativa and Oryza rufipogon. Theoretical and Applied Genetics 2006;113(5):885-94.
  219. 219. Yan CQ, Qian KX, Yan QS, Zhang XQ, Xue GP, Huangfu WG, et al. Use of asymmetric somatic hybridization for transfer of the bacterial blight resistance trait from Oryza meyeriana L. to O. sativa L. ssp. japonica. Plant Cell Reports 2004;22(8):569-75.
  220. 220. Yang J, Hu C, Hu H, Yu R, Xia Z, Ye X, et al. QTL Network: mapping and visualizing genetic architecture of complex traits in experimental populations. Bioinformatics 2008;24(5):721-3.
  221. 221. Yang H, Ren X, Weng Q, Zhu L, He G. Molecular mapping and genetic analysis of a rice brown planthopper (Nilaparvata lugens Stål) resistance gene. Hereditas 2002;136(1):39-43.
  222. 222. Yang H, You A, Yang Z, Zhang F, He R, Zhu L, He G. High-resolution genetic mapping at the Bph15 locus for brown planthopper resistance in rice (Oryza sativa L.). Theoretical and Applied Genetics 2004;110(1):182-91.
  223. 223. Yasui H, Iwata N. Production of monosomic alien addition lines of Oryza sativa having a single O. punctata chromosome. In: Rice Genetics II: Proceedings of the 2nd International Rice Genetics Symposium. 14-18 May 1990. International Rice Research Institute;1991; p 147-55.
  224. 224. Yasui H, Yoshimura A. QTL mapping of antibiosis to green leafhopper, Nephotettix virescens Distant and green rice leafhopper, Nephotettix cincticeps Uhler in rice, Oryza sativa L. Rice Genetics Newsletter 1999;16:96-8.
  225. 225. Yazawa S, Yasui H, Yoshimura A, Iwata N. RFLP mapping of genes for resistance to green rice leafhopper (Nephotettix cincticeps Uhler) in rice cultivar DV85 using near isogenic lines. Science Bulletin of the Faculty of Agriculture, Kyushu University 1988; 52:169–75.
  226. 226. Yoon DB, Kang KH, Kim HJ, Ju HG, Kwon SJ, Suh JP, et al. Mapping quantitative trait loci for yield components and morphological traits in an advanced backcross population between Oryza grandiglumis and the O. sativa japonica cultivar Hwaseongbyeo. Theoretical and Applied Genetics 2006;112(6):1052-62.
  227. 227. Yu J, Hu S, Wang J, Wong GKS, Li S, Liu B, et al. A draft sequence of the rice genome (Oryza sativa L. ssp. indica). Science 2002;296(5565):79-92.
  228. 228. Yuan PR, Kim HJ, Chen QH, Ju HG, Lee SJ, Ji SD, et al. QTL dissection of agronomic and domestication traits using introgression lines carrying wild rice (Oryza rufipogon Griff.) segments in cultivated rice (O. sativa L.) background. Journal of Crop Science and Biotechnology 2009;12(4):245-52.
  229. 229. Zeng ZB. Precision mapping of quantitative trait loci. Genetics 1994;136:1457-68.
  230. 230. Zhang Q, Lin SC, Zho BY, Wang CL, Yang WC, Zhou YL, Li DY, Chen CB,. Zhu LH. Identification and tagging a new gene for resistance to bacterial blight (Xanthomonas oryzae pv. oryzae) from O. rufipogon. Rice Genetics Newsletter 1998;15: 138-42.
  231. 231. Zhang Q. Strategies for developing green super rice. Proceedings of the National Academy of Sciences (USA) 2007;104(42):16402-9.
  232. 232. Zhang X, Zhou S, Fu Y, Su Z, Wang X, Sun C. Identification of a drought tolerant introgression line derived from Dongxiang common wild rice (O. rufipogon Griff.). Plant Molecular Biology 2006;62(1-2):247-59.
  233. 233. Zhao K, Tung CW, Eizenga GC, Wright MH, Ali ML, Price AH, Norton GJ, Islam MR, Reynolds A, Mezey J, McClung AM, Bustamante CD, McCouch SR. Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nature Communications 2011;2:467. DOI: 10.1038/ncomms1467
  234. 234. Zhou F, He H, Chen H, Yu H, Lorieux M, He Y. Genomics-based breeding technology.In: Zhang Q, Wing RA, editors. Genetics and Genomics of Rice, Plant Genetics and Genomics: Crops and models 5. New York: Springer; 2013; p 329-48.

Written By

Ehsan Shakiba and Georgia C. Eizenga

Published: 23 April 2014