Open access peer-reviewed chapter

Breeding Rice for Improved Grain Quality

Written By

Maxwell Darko Asante

Submitted: 26 April 2016 Reviewed: 02 November 2016 Published: 15 March 2017

DOI: 10.5772/66684

From the Edited Volume

Advances in International Rice Research

Edited by Jinquan Li

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Rice grain quality improvement has become very crucial for most breeding programs around the world. Grain quality is a complex trait which comprises milling, appearance (grain size and chalkiness), cooking and eating (starch properties including apparent amylose content (AAC), gelatinization temperature (GT), gel consistency and paste viscosity measured using rapid visco analyzer measured using rapid visco analyzer (RVA) as well as nutritional quality. Many genes/quantitative trait loci (QTLs) for the various quality traits have been identified/cloned. This has enabled the development of functional markers to facilitate the selection for this complex trait. Functional markers, especially those targeting mutations in the BADH2, waxy, alk and GS3 genes, are highly associated with aroma, AAC/RVA, GT and grain size, respectively; and thus effective for marker-assisted breeding. Different alleles can be combined through gene pyramiding to improve rice grain quality for various consumers. To be able to meet future needs, rice breeders must exploit modern marker technologies such as genomic selection (GS) to take care of the effects of both major and minor genes for grain quality as well as high yield, abiotic and biotic stress tolerance.


  • rice
  • grain quality
  • molecular markers
  • aroma
  • waxy gene
  • alk gene
  • GS3 gene

1. Introduction

Rice is the most important source of calories for at least 50% of the world’s population. Consequently, many countries around the world have strategies to achieve self-sufficiency in rice production by expanding the area under cultivation and or increasing yield per unit area. However, for rice, grain quality is as important as yield. This is because unlike other cereals, which are usually processed as food or feed (for animals), rice is mainly eaten as whole cooked grains by humans.

Breeding for consumer-preferred grain qualities have thus become a major goal for breeding programs around the world. To be able to breed for specific consumer preferences, grain quality must be clearly defined and the genes underlying their control deciphered. The rice grain is basically composed of the lemma and palea which form the hull, the bran, embryo and the endosperm (white rice) (Figure 1). Scientists have classified the grain quality of rice as milling, appearance, cooking and eating and nutritional aspects.

Figure 1.

The rice grain. Source: IRRI.

The milling quality of rice determines the yield and appearance of the rice after the milling process. It is thus sometimes classified as under appearance quality. The first step in milling involves the removal of the lemma and palea to obtain de-hulled rice called brown rice. Brown rice has become very important because it has good nutritional value obtained from the presence of the bran. The bran which consists of the aleurone, pericarp and embryo is removed to obtain milled rice. Milling quality thus comprises of brown, milled and head rice recovery (HRY). Brown rice recovery is the percentage of brown rice obtained after de-hulling a sample of paddy. Brown rice absorbs water poorly and does not cook as rapidly as milled rice. Milling recovery is the percentage of milled rice (including brokens) obtained from a sample of paddy. HRY is the percentage of head rice (excluding brokens) obtained from a sample of paddy. It normally includes broken kernels that are 75–80% of the whole kernel. High HRY is one of the most important criteria for measuring milled rice quality. Broken grain has normally only half of the value of head rice. HRY is influenced by genotype (i.e. the potential HRY), production factors and harvesting, drying and milling process. HRY and the degree of milling greatly influence the way the rice appears and thus consumer preference for rice on the market.

Appearance quality is how the rice appears after milling and it is associated with grain length, width, length-width ratio (shape) and translucency/chalkiness of the endosperm. Generally, most markets prefer translucent rice as opposed to chalky ones. Preferences for grain size and shape vary across different countries and cultures. Short grain varieties are preferred in Japan, Korea, Northern China and Sri Lanka while Southern China, India, Pakistan, Thailand, the USA, as well as most African countries prefer long and slender grain rice. Appearance quality has a direct influence on marketability and success of commercial varieties.

Cooking and eating quality is the easiness of cooking as well as texture, springiness, stickiness and chewiness of cooked rice. These characteristics are controlled by starch physicochemical properties comprising of apparent amylose content (AAC), gelatinization temperature (GT), gel consistency (GC) and paste viscosity properties. Starch makes up about 90% of the rice grain.

Rice starch is composed of two classes of glucose polymers—amylopectin and amylose. Amylose is a lightly branched linear molecule with a degree of polymerization (DP) of 1000–5000 glucose units and amylopectin has a much larger polymer unit containing frequent α-1,6 branching linkages [1, 2].

Amylose content is usually referred to as apparent amylose content (AAC) in literature because the iodine-based assay used for measuring it often detects long-chain amylopectin in addition to the “true” amylose [3]. AAC is the most important element influencing the cooking, eating and processing characteristics of rice [4]. The AAC of rice is known to play a crucial role in determining its cooked texture. It is directly related to water absorption, volume expansion, fluffiness and separability of cooked grains and inversely related to cohesiveness, tenderness and glossiness [5]. Typically, cereal grains contains 20–30% amylose with the remainder (70–80%) being amylopectin [6]. Rice can be classified based on its amylose content: waxy rice (0–2% amylose), very low amylose (3–9%), low amylose (10–18%), intermediate amylose (19–23%) and high amylose (>23%) [7, 8]. Synthesis of amylose is catalyzed by the granule-bound starch synthase (GBSS) protein which is encoded by the waxy gene (Wx) [9].

There are three alleles of the waxy gene—Wx, Wxa and Wxb which exist in waxy (sticky) rice, indica and japonica sub-species, respectively [10]. Rice plants with the Wxa allele accumulate more GBSS protein in the endosperm during grain filling than plants with the Wxb allele; hence indica rice generally has a higher content of AAC than japonica. The splicing pattern of the first intron of the waxy gene is reported to be highly correlated to the level of AAC [11]. The authors reported that transcripts in which the intron is completely spliced out would produce high AAC, ones with introns completely un-spliced would produce grains with no AAC (sticky rice) and those with partially spliced introns would produce intermediate AAC.

Gelatinization temperature (GT) is the range of temperature wherein at least 90% of starch granules swell irreversibly in hot water with loss of crystallinity and birefringence [12]. GT ranges from 55 to 85°C and determines the cooking time of rice [13, 14]. According to Tian et al. [72], “GT is a physical-chemical property which directly reflects the cooking quality of rice grain in terms of energy and time needed for cooking”. Cooking time for rice can be reduced by up to four minutes by lowering the GT of the grain [14]. GT of rice can be classified as low (55–69°C), intermediate (70–74°C) or high (75–79°C) [15]. GT also plays an important role in water uptake, volume expansion and kernel elongation of cooked rice [16]. For example, high GT rice elongates less and is likely to be undercooked when standard cooking procedure is applied but its texture could be softened through overcooking. Although rice grain quality preferences differ around the world, varieties with intermediate GT are generally preferred [15]. GT is predominantly determined by amylopectin structure [17]. Three classes of enzymes including starch synthase, starch branching and starch de-branching enzymes have been implicated in the synthesis of amylopectin [9, 18, 19]. The gene, Starch synthase II (SSIIa) has been found to be the major determinant of GT [1, 17].

There two types of rice amylopectin—L- or S-types [19]. For L-type amylopectin the number of short α-1,4-glucan chains with degree of polymerization (DP) of ≤10 was less than 20% with DP ≤24. The amylopectin fine structure of japonica has more DP 7–11 chains and fewer 12 ≤ DP ≥ 24 chains than indica [17]. Amylopectin side chains of DP ≥ 10 form double helices; the length of these double helices determines gelatinization temperatures of starches [20]. Starch granules with amylopectin containing longer A and B1 chains would, therefore, be more resistant to gelatinization [17]. Consequently, japonica starch granules have lower GT than indica starch granules [21]. GT is highly correlated with alkali spreading value (ASV), which reflects the disintegration of milled rice in dilute KOH [8, 21].

Amylose content is the primary measure of the texture of cooked rice, but it fails to describe precisely the texture of certain types of rice [2]. Gel consistency (GC) is, therefore, used to complement AAC. Rice with the same amylose content can be classified as hard gel consistency (26–40 mm); medium gel consistency (41–60 mm); or soft gel consistency (61–100 mm) [22]. The length of gel flow is inversely proportional to GC; therefore, the long gel length corresponds to soft GC and the short gel length means hard GC. Amylopectin, rather than AAC, has been reported to be the major determinant of GC [23]. GC is reported to be affected by milling (lipid content), protein content, aging of milled rice (fat oxidation) and rice flour particle size (efficiency of dispersion) [24].

GC measurements are reported to have poor repeatability, and some laboratories have replaced them with starch paste viscosity parameters [8]. Starch viscosity curves are useful for breeding because the shape of the curve is unique to each class of rice [25]. Their use for breeding purposes was however limited because the traditional equipment used for measurement, Brabender Viscoamylograph (Brabender OHG, Duisberg, Germany), has many disadvantages. It requires a large amount of rice flour (40–50 g) and a long period (80–94 min) is required to run a sample [8, 25]. It has now largely been replaced by Rapid Visco Analyser (RVA, Newport Scientific Pty Ltd., Warriewood, Australia). RVA is popular because it is easy to operate, gives rapid results and requires a small sample (3 g) to run [25]. The rice starch viscosity profile, tested on the Rapid Visco Analyser, is usually called RVA profile. The RVA profile is generated by subjecting rice flour to a “heat-hold-cool-hold” temperature cycle [25]. This cycle (RVA) mimics the process of cooking and monitors the changes to slurry of rice flour and water, during the test [8]. The primary RVA parameters include peak viscosity, PV (first peak viscosity after gelatinization); trough or hot paste viscosity, HPV (paste viscosity at the end of the 95°C holding period) and final or cool paste viscosity, CPV (paste viscosity at the end of the test) [26]. Secondary parameters derived from the primary ones include breakdown (BD = PV − HPV); setback (SB = CPV − PV); consistency (CS = CPV – HPV); set back ratio (SBR = CPV/HPV) and stability (ST = HPV/PV) [2628]. Other parameters include peak time (time required to reach peak viscosity), and pasting temperature (temperature of initial viscosity increase) [29].

Breeding for improved grain is complex because many of the quality traits are phenotyped using subjective and or expensive biochemical methods. Consequently, the scientific community has map/clone many quantitative trait locus (QTLs)/genes for various quality traits and developed molecular marker to facilitate selection for specific grain quality types.

In this chapter, the major breakthroughs in studying the genetics of grain quality will be highlighted and its usefulness in grain quality improvement as well as future trends in rice breeding is discussed.


2. Genetic analyses of grain quality in rice

2.1. Genetics of milling and appearance quality

2.1.1. Genetics of milling quality

Milling quality comprises of brown, milled and head rice recovery. It is a complex trait and its genetics has not been fully deciphered. A good review of studies carried out on milling quality has been done by Bao [30]. At least 20, 19 and 34 QTLs have been identified for brown, milled and head rice recovery, respectively [30].

2.1.2. Genetics of appearance quality

Many QTLs associated with grain size and length have been identified, including one QTL on chromosome 3 with a major effect on grain length/size [3136]. The QTL on chromosome 3 was mapped to a region of 93.8 kb in length [31, 32]. Wan et al. [40] established that a major QTL controlling grain length, qGL-3a, was a single Mendelian gene—long grain was controlled by a recessive gene, gl-3. They further mapped the gl-3 gene to a region of 87.5 kb and suggested that the gl-3 gene could be the same as a grain size gene mapped for rice grain weight [31]. This gene, subsequently referred to as GS3, was cloned through comparative sequence analysis [37] and position cloning confirmed by transformation [38]. The researchers found that all the varieties with large grains had a nonsense mutation, in the second exon of the GS3 gene which caused a 178-aa truncation in the C-terminus of the putative protein [3739]. They found that the GS3 locus was a major QTL for grain length and weight, and a minor QTL for grain width and thickness. Slender shape grain (large length:width ratio) was also reported to be controlled by the GS3 locus (gl-3 gene) [38, 40].

A seed width (SW5) QTL on chromosome 5 [41, 42] and a grain width and weight (GW2) QTL on chromosome 2 [43] have also been cloned. In all these cases, the authors reported that genotypes with the recessive allele(s) have longer, wider and/or heavier seeds than genotypes with the wild type allele and concluded that, in each case, the genes affect cell division.

More recently, a gene on chromosome 3, that controls grain length (GL3) and encodes protein phosphatase with Kelch-like repeat domain (OsPPKL1) has been identified and cloned [4446]. The novel qGL3-1 allele increased in a new variety [46]. Two other genes for grain width chromosomes 5 [47] and 8 [48] have been cloned.

The cloning of these genes provides a basis for marker-aided selection and QTL pyramiding in breeding for appearance-quality. Transgressive segregation for grain dimensions has also been reported by several authors [4951]. This could make gain from selection very feasible.

2.2. Genetics of cooking and eating quality

2.2.1. Genetics of amylose content

Conventional genetic studies have revealed that AAC is controlled by one major gene with several modifiers [5254]. High amylose content was reported to be dominant over low and intermediate amylose content [52, 53]. Other studies concluded that AAC was under the control of two complementary genes [54]. Using diallel analysis, the gene action for AAC was found to be mainly additive although dominance effects were also involved [55]. The authors found no evidence of maternal effects in their study. However, He et al. [60] said that the “inheritance of grain quality is more complicated than that of other agronomic traits in cereals due to epistasis, maternal and cytoplasmic effects, and the triploid nature of endosperm”. The complex nature of inheritance of amylose content is supported by various reports [56, 57].

The advent of molecular marker technology is helping scientists to better understand complex quantitative traits [58]. Grain quality traits, including AAC, have been extensively studied using molecular marker–based quantitative trait locus (QTL) [49, 5962]. AAC is reported to be mainly controlled by the waxy gene locus (Wx) on chromosome 6, which encodes the granule-bound starch synthase (GBSS) [9, 11, 60]. Minor QTLs for AAC have also been detected on chromosomes 1, 3, 4, 7, 8 and 11 [49, 6264]. Using association analysis, the Wx gene was reported to act additively with five minor genes—AGPlar, PUL, SSI, SSII-3 and SSIII-2—to affect AAC [65]. In at least three studies, QTLs for AAC were not associated with the Wx locus [32, 66, 67].This is presumably because the parents shared the same Wx allele, making it easier to detect other loci associated with the trait [32].

2.2.2. Genetics of gelatinization temperature

Monogenic [54], digenic [68] and polygenic inheritance [69] for gelatinization temperature or ASV, have been reported. Using an 8 × 8 diallel cross, the inheritance of GT was found to fit into additive-dominance with dominance effects being predominant [70]. Crosses in which one parent has high GT have been found to produce many high GT F2 individuals [2, 71]. High GT individuals in an F2 population are essentially homozygous and do not segregate in subsequent generations. F2 individuals with low and intermediate GT will continue to segregate into all three classes of GT for several generations.

Many molecular marker–based QTL analyses have been done for GT [49, 51, 59, 60, 67]. GT is reported to be mainly under the control of the alk locus on chromosome 6 that encodes soluble starch synthase II (SSIIa) [1, 17]. The SSIIa locus was reported to explain 25.5% of the variation in GT [1]. The authors found that, gene interactions as well as the Wx gene (4.7%) were important contributors to the variation in GT. The contribution of the Wx locus to the control of GT has also been reported by other researchers [29, 58, 62]. However, Tian et al. [72] reported that the control of GT was independent of the Wx locus. Other minor QTLs or modifier genes have been found to contribute to variations in GT [49, 58, 63]. Four minor genes including the Wx gene—Wx, SBE3, ISA and SSIV-2—have been reported to act additively with SSII-3 (also referred to as SSIIa by other authors), the major for GT, to affect the trait [65].

2.2.3. Genetics of gel consistency

Some conventional genetic analyses have reported that GC is under the control of one major gene and several minor genes [7375]. The difference between hard and soft gel, hard and medium as well as medium and soft was reported to be under monogenic control [73]. Hard gel was dominant over medium and soft gel; medium gel was dominant over soft. The authors concluded that it was possible to select for GC in early segregating generations. However, multigenic control of GC with genes acting additively has also been reported [76]. Using an 8 × 8 diallel cross, GC was found to fit into an additive-dominance model of inheritance with dominance being predominant [70]. Transgressive segregation has been observed for GC [63, 70, 77]. Using a genetic model for studying quantitative traits of triploid endosperm, cooking quality traits were found to be controlled mainly by genetic effects—seed, maternal and cytoplasmic. Cytoplasmic effects were the main components of GC [78]. Genotype by environment interactions as well as epistatic effects has also been found to play important roles in the control of GC [58, 78].

Many QTLs for GC have been mapped using molecular markers [1, 29, 6163]. Some researchers reported that a single locus in the Wx region and some modifier genes control GC [62, 63]. He et al. [1] also reported that the Wx locus is the major determinant of GC but it explained only 38.9% of the phenotypic variation. Using association analysis, the Wx was confirmed as the major gene controlling GC [65]. A major QTL for GC at the Wx locus has been cloned [79].

In addition to the Wx gene, GC is reported to be affected by three other minor genes in the starch biosynthesis pathway—AGPiso, SBE3 and ISA [65]. Two QTLs with minor effects on GC had earlier been reported [60, 67] and the alk locus has also been found to make minor contributions to GC [29].

2.2.4. Genetics of paste viscosity

The genetics of rice paste viscosity has not been studied as widely as AAC, GC and GT. RVA was reported to be controlled by a single locus [80]. The authors’ finding was based on both F2:3 segregation and diallel analyses. Subsequently, QTL mapping has been used to locate the chromosomal positions of genes controlling various RVA parameters in rice [29, 67, 81, 82].

In general, the major QTLs for most RVA parameters related to eating quality including HPV, CPV, BD, SB and CS were found at the waxy locus [29, 8183]. In at least three studies, QTLs for PV were not found at the Wx locus [29, 67, 81]. Recently, a major QTL for PV was detected at the Wx locus [82].

QTLs related to the cooking process such as pasting temperature and peak time have generally been found at the alk locus on chromosome 6 [29, 82]. Minor QTLs for the various RVA parameters have been found on all 12 chromosomes of rice [29, 8184].

2.2.5. Association between RVA parameters and AAC

RVA parameters including HPV, CPV, SB and CS have been found to be positively correlated with AAC while BD is negatively correlated to AAC [28, 29, 85]. AAC has been reported to be negatively [13, 86], positively [87] and not significantly [28, 29, 85] correlated with PV. It has been suggested that the SNPs in Ex 10 of the waxy gene has a confounding effect that influences the relationship between PV and AAC [88]. The authors found that the effect of AAC on PV would be different depending on the amount of TAC and GAT waxy gene haplotypes in the germplasm used by the various researchers. The significant correlations between AAC and most RVA parameters is not surprising because both traits are mainly controlled by the waxy gene [81, 89]. An SSR marker in the Wx gene (RM 190) explains a large portion of the variation in AAC and most RVA parameters [86, 90, 91]. The RVA parameters and AAC are also highly associated with three SNPs—intron 1 (G→T), exon 6 (A→C) and exon 10 (C→T) substitutions—in the Wx gene [86, 91, 92]. Using the three SNPs together, four waxy SNP haplotypes were found in a collection of world germplasm [86]. These waxy SNP haplotypes include TAC, GCC, GAC and GAT for low, intermediate, high and high AAC plus high RVA, respectively [86, 93].

2.2.6. Relationships between AAC, GC and GT

Since the rice grain is basically composed of starch (approximately 90%), genes involved in starch biosynthesis are naturally expected to affect cooking and eating qualities. Starch biosynthesis is a complex system composed of multiple subunits or isoforms of four classes of enzymes: ADP-glucose pyrophosphorylase (AGP), starch synthase (SS), starch branching enzyme (SBE) and starch debranching enzyme (DBE) [94, 95]. The effect of 18 genes involved in different steps of starch synthesis on AAC, GC and GT was investigated through association analysis [65]. These genes include: AGP, ADP-glucose pyrophosphorylase; AGPlar, AGP large subunit; AGPiso, AGP large subunit isoform; AGPsma, AGP small subunit; GBSS, granule-bound starch synthase; SS (SS-I, SS-II-1, SS-II-2, SS-II-3, SS-III-1, SS-III-2, SS-IV-1 and SS-IV-2) soluble starch synthase; SBE (SBE1, SBE3 and SBE4) starch branching enzyme; ISA, isoamylase; PUL, pullulanase; ISA and PUL belong to starch debranching enzyme (DBE). The authors found that genes related to starch synthesis cooperate with each other to form a fine regulating network that controls the eating and cooking quality of rice.

Tian et al. [65] confirmed earlier reports that Wx gene affects AAC, GC and GT (“three in one” function of the Wx locus)[29, 58, 62] and found strong evidence that the Wx gene does not only have a major effect on AAC, but also regulates GC as a major gene and GT as a minor one. Some authors have proposed that the Alk locus (SSIIa) only affects GT and GC (a “two in one function”) [29, 58]. However, Tian et al. [65] found that the Alk locus (referred to as SSII-3 in their report) plays an essential role not only in controlling GT and GC but also AAC. The authors also showed that some other starch synthesis related genes affect additively AAC, GC and GT as minor genes resulting in the fine complex network controlling cooking and eating qualities of rice grains.

AAC, GC and GT were found to be highly correlated: AAC is negatively correlated with GC (−0.91) and GT value (−0.46), whereas GC is positively correlated with the GT value (0.50) [65]. The negative correlation between AAC and GT was due to the natural occurrence of different haplotype combinations of Wx and SSII-3 in rice germplasm. The Wx gene has three haplotypes: Wx-III is the wild type allele results in high AAC rice, Wx-II is associated with medium level of AAC and Wx-I has a loss-of-function mutation that produces waxy rice varieties [11]. Since AAC is negatively correlated with GC, varieties with Wx-I show high GC values, those with Wx-II show medium GC values, and those with Wx-III have low GC values [65]. The SSII-3 has two allelic states: SSII-3-I, which leads to varieties that have higher GT values and SSII-3-II which leads to phenotypes with lower GT values.

The effect of either Wx or SSII-3 on AAC and GT values was found to fall into a consistent pattern: SSII-3-I contributed to higher AAC under the same Wx background, whereas SSII-3-II led to lower AAC. Wx also combined with SSII-3 to influence GT. For varieties that had the SSII-3-I background, Wx-I caused lower AAC and GT. GT value were however increased by Wx-II and Wx-III. They found five natural haplotype combinations between AAC and GT in their panel—Wx-I/SSII-3-I,Wx-II/SSII-3-I, Wx-II/SSII-3-II,Wx-III/SSII-3-I and Wx-III/SSII-3-II. Varieties belonging to Wx-II/SSII-3-I had medium AAC and high GT values and varieties belonging to Wx-III/SSII-3-II had high AAC and low GT values, these two haplotype combination form 71% of the germplasm. At the same time, varieties with Wx-III/SSII-3-I haplotype had high AAC and high GT values, and those with Wx-II/SSII-3-II had low AAC and low GT values. The Wx-II/SSII-3-II haplotype combination would be most useful for rice breeding in many parts of the world especially if it is combined with long-grain and fragrant/aroma alleles.

2.2.7. Inheritance of aroma

Monogenic, digenic and trigenic control of aroma has been reported by various authors [96100]. However, some authors believed that aroma was quantitatively inherited [15]. The lack of agreement among researchers appears to be related to the different aromatic varieties and methods used in evaluating aroma [101].

However, the use of molecular markers to study the inheritance of fragrance appeared to favor monogenic recessive inheritance of fragrance [102107]. A gene associated with fragrance was originally mapped by Ahn et al. [102] to rice chromosome 8, where it was associated with the RFLP marker, RG28. Bradbury et al. [105] identified a gene encoding betaine aldehyde dehydrogenase 2 (BADH2) as the likely cause of aroma in Basmati and Jasmine styled rices.

An eight base pair deletion (8-bp) and three SNPs in exon 7 of the BADH2 gene distinguished fragrant from non-fragrant rices in that study. These polymorphisms served as the basis for developing an allele-specific marker for fragrant (Bradbury et al. [104]). A new fragrance allele with sequence identical to that of the BADH2 allele in exon 7, but with a 7-bp deletion in exon 2 was identified as the cause of fragrance in some varieties [107]. Based on this information, the authors developed functional markers which can distinguish non-fragrant from fragrant rice and differentiate fragrance caused by 8-bp deletion on exon 7 from that caused by the 7-bp deletion on exon 2 of chromosome 8. In addition, 8 new alleles were discovered at the BADH2 locus, all of which conferred fragrance in 24 accessions that did not carry any of the previously identified alleles [108, 109]. Another molecular marker study reported that three genes, located on chromosomes 3, 4 and 8, caused fragrance in Pusa 1121 [51]. The authors identified a BADH1 gene in the aroma QTL on chromosome 4 and also mapped the QTL on chromosome 8 to the BADH2 region. The BADH2 gene is known to code for 2-acetyl-1-pyrroline, or 2AP [110, 111]. The accumulation of 2AP has been explained by the absence of BADH2 activity leading to increased levels of 4-aminobutyraldehyde/∆1-pyrroline, the immediate precursor of 2AP [112]. However, in another study, it was concluded that BADH2 had no direct role in the synthesis of 2AP [113]. The authors found that ∆1-pyrroline-5-carboxylate, usually the immediate precursor of proline, synthesized from glutamate, reacts directly with methylglyoxal to form 2AP. Fitzgerald et al. [14] declared that “the genetic and biochemical stories of 2AP synthesis are yet to be fully written”.

2.3. Marker-assisted breeding for grain quality in rice

Breeding for improved grain is complex because many of the quality traits are phenotyped using subjective and or expensive biochemical methods. Consequently, the scientific community has map/clone many QTLs/genes for various quality traits and developed molecular markers to facilitate selection for specific grain quality types.

Most fragrant rices including Jasmine and Basmati types have the 8-bp deletion on exon 7 of the BADH2 gene and an allele-specific marker has been developed for selecting rice with this mutation [104]. This marker is being used widely for selecting for aroma. It is a co-dominant marker (Figure 2) and thus very useful for marker-assisted backcrossing for recessive trait such aroma because selection of lines carrying the aroma gene can done in the heterozygote state without progeny testing.

Figure 2.

Agarose gel showing allele-specific marker for the 8-bp deletion in the BADH2 gene. Lanes 1 and 17 = 100 bp DNA ladder. Fragrant individuals (F), non-fragrant individuals (N) and heterozygote types (H) [114].

Other researchers have also developed markers for the 8-bp deletion in exon 7 of chromosome 8 [51, 115]. Functional markers have also been developed for other alleles in the BADH2 gene including a 7-bp deletion in exon 2 [107] and a 3-bp insertion in exon 13 found in aromatic rice varieties from Myanmar [116].

Functional markers for a waxy gene SSR called RM 190, and waxy SNPS on intron (In1), exon 6 (Ex6) and exon 10 (Ex10) are used to select for AAC and RVA around the world [93]. The haplotype across these three SNPs in the waxy gene (waxy SNP haplotypes) have been found to be more efficient in selecting for AAC and RVA than the RM 190 [86, 88]. Across the waxy SNP haplotypes (In1-Ex6-Ex10) TAC, GCC, GAC and GAT is highly associated with low AAC, intermediate AAC, high AAC and high AAC accompanied with high RVA paste viscosity.

The alk gene has been cloned [117] and validated as being the major gene for GT through genetic transformation [118]. Two SNPs (GC/TT and G/A) in the alk gene was found to be highly associated with GT [118]. These two functional SNPs have been used to developed DNA markers for selection of GT [118, 119].

Functional markers have also been developed for grain size [39, 120, 121]. These markers are very highly associated with the C-A SNP mutation in exon 2 of the GS3 gene which is responsible for 80–90% of the variation in kernel length.

These validated markers will facilitate marker-assisted breeding for grain quality in rice. Various alleles of these important genes can be pyramided together to obtain the different consumer preferences for grain quality across countries and regions.

2.4. Future trends in rice breeding and grain quality improvement

By 2050, rice production must double in order to keep pace with population growth. Population growth will come with income growth so consumers will demand even higher quality rice. In addition to this challenge, new biotic and abiotic stresses are merging to due to climate change. Consequently, Rice breeders have to consider a huge number of simple and quantitative traits in combination when developing new lines while, at the same time, maintaining and improving grain quality.

Even though, MAS has been successfully used to improve some biotic, abiotic and quality traits in rice it is based on large effect QTLs/genes and does not take care of epistatic and genetic background effects. Most traits of interest to rice breeders are not controlled only by a few large-effect genes, but by a combination of many genes of small effect and/or major genes.

Genomic selection (GS) has been projected as alternative to conventional MAS. GS has huge potential to enhance breeding efficiency by increasing gain per selection per unit time [122]. GS breeding allows breeders to select the most desirable parents for the next generation using genome-wide DNA marker data. These parents are selected based on the relationship between the genome-wide markers and phenotypes of the individuals undergoing selection. The major advantage of GS over MAS is that genotyping is not restricted to selected markers that target genes with large effects, but rather all available marker data are used to predict breeding value. This helps to prevent loss of information. Genes with small effect can be tracked and selected for using information on all the marker data. GS would become more effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline [122].

2.5. Conclusion

Since rice is eaten mainly by humans as whole grain in cooked form, its grain quality is extremely important. The quality of the rice grain can be classified as milling, appearance, cooking and eating as well as nutritional quality. Different consumers around the world demand very specific measurements and combinations of the various aspects of rice grain quality. Breeding for these specific consumer demands can be challenging because grain quality is phenotyped using subjective, biochemical analyses that can be very expensive. Marker- assisted selection is thus a very good option for breeding for grain quality. The sequencing of the rice genome over a decade ago has made it possible for researchers to identify genes for the various grain quality traits. Functional molecular markers have been developed that are highly efficient in selecting for grain size, aroma, AAC, GT and paste viscosity parameters. These markers are increasingly being used for breeding for consumer-preferred grain qualities around the world. Modern genome-wide marker technologies which will take care of genes with small effect and allow breeders to simultaneously select for grain quality, yield and stress tolerance are recommended for future rice breeding work.



The Alliance for a Green Revolution in Africa (AGRA) funded the author’s research on rice grain quality.


  1. 1. He Y, Han Y, Jiang L, Xu C, Lu J, Xu M. Functional analysis of starch-synthesis genes in determining rice eating and cooking qualities. Molecular Breeding. 2006;18(4):277–90.
  2. 2. Juliano BO. Rice chemistry and quality. Philippine Rice Research Institute, Manila; 2003.
  3. 3. Takeda Y, Hizukuri S, Juliano BO. Structures of rice amylopectins with low and high affinities for iodine. Carbohydrate Research. 1987;168(1):79–88.
  4. 4. Juliano BO. Amylose analysis in rice—a review. In Proceedings of the workshop on chemical aspects of rice grain quality, International Rice Research Institute, Los Banos, Laguna, Philippines. 1979;251–60.
  5. 5. Juliano BO. A simplified assay for milled-rice amylose. Cereal Science Today. 1971;16: 334–40,60.
  6. 6. Preiss J. Biology and molecular biology of starch synthesis and its regulation. Oxford Surveys of Plant Molecular and Cell Biology. 1991;7:59–114.
  7. 7. Fitzgerald M. Starch. In Champagne ET, editor. Rice: Chemistry and Technology. AACC: St Paul, USA; 2004. 109–41.
  8. 8. Bergman C, Bhattcharya K, Ohtsubo K. Rice end-use quality analysis. In Champagne E, editor. Rice Chemistry and Technology. AACC: St Paul; 2004. 415–72.
  9. 9. Smith AM, Denyer K, Martin C. The synthesis of the starch granule. Annual Review of Plant Physiology and Plant Molecular Biology. 1997;48(1):67–87.
  10. 10. Sano Y, Katsumata M, Okuno K. Genetic studies of speciation in cultivated rice. 5. Inter- and intraspecific differentiation in the waxy gene expression of rice. Euphytica. 1986;35(1):1–9.
  11. 11. Wang Z-Y, Zheng F-Q, Shen G-Z, Gao J-P, Snustad DP, Li M-G, et al. The amylose content in rice endosperm is related to the post-transcriptional regulation of the waxy gene. The Plant Journal. 1995;7(4):613–22.
  12. 12. R.K. Singh ,U.S. Singh G.S. Khush. Grain quality evaluation procedures. In Aromatic rices. Science Publishers, Inc.: Oxford; 2000. 15–28.
  13. 13. Tan Y, Corke H. Factor analysis of physicochemical properties of 63 rice varieties. Journal of the Science of Food and Agriculture. 2002;82(7):745–52.
  14. 14. Fitzgerald MA, McCouch SR, Hall RD. Not just a grain of rice: The quest for quality. Trends in Plant Science. 2009;14(3):133–9.
  15. 15. Khush GS, Paule CM, DeLaCruz MN. Rice quality evaluation and improvement at IRRI. In Proceedings of the workshop on chemical aspects of rice grain quality, International Rice Research Institute, Los Banos, Laguna, Philippines. 1979;21–31.
  16. 16. Tomar JB, Nanda JS. Genetics and association studies of kernel shape in rice. Indian Journal of Genetics and Plant Breeding. 1985;45(2):278–83.
  17. 17. Umemoto T, Yano M, Satoh H, Shomura A, Nakamura Y. Mapping of a gene responsible for the difference in amylopectin structure between japonica-type and indica-type rice varieties. Theoretical and Applied Genetics. 2002;104(1):1–8. Epub 2003/02/13.
  18. 18. Myers AM, Morell MK, James MG, Ball SG. Recent progress toward understanding biosynthesis of the amylopectin crystal. Plant Physiology. 2000;122(4):989–98.
  19. 19. Nakamura Y, Sakurai A, Inaba Y, Kimura K, Iwasawa N, Nagamine T. The fine structure of amylopectin in endosperm from Asian cultivated rice can be largely classified into two classes. Starch-Starke. 2002;54(3–4):117–31.
  20. 20. Safford R, Jobling SA, Sidebottom CM, Westcott RJ, Cooke D, Tober KJ, et al. Consequences of antisense RNA inhibition of starch branching enzyme activity on properties of potato starch. Carbohydrate Polymers. 1998;35(3–4):155–68.
  21. 21. Little RR, Hilder GB, Dawson EH. Differential effect of dilute alkali on 25 varieties of milled white rice. Cereal Chemistry. 1958;35:111–26
  22. 22. Cagampang GB, Perez CM, Juliano BO. A gel consistency test for eating quality of rice. Journal of the Science of Food and Agriculture. 1973;24(12):1589–94.
  23. 23. Juliano BO, Perdon AA. Gel and molecular properties of nonwaxy rice starch. Starch-Starke. 1975;27(4):115–20.
  24. 24. Perez CM. Gel consistency and viscosity of rice. In Proceeding of the workshop on chemical aspects of rice grain quality, International Rice Research Institute, Los Banos, Laguna, Philippines. 1979; 303–11.
  25. 25. Juliano BO. Rice quality screening with the rapid visco analyser. In Walker CE, Hazelton JL, editor. Applications of the rapid visco analyser. Newport Scientific: Sydney; 1996. pp. 19–24.
  26. 26. Bao JS, Xia YW. Genetic control of paste viscosity characteristics in indica rice (Oryza sativa L.). Theoretical and Applied Genetics. 1999;98(6):1120–4.
  27. 27. Collado LS, Corke H. Properties of starch noodles as affected by sweet potato genotype. Cereal Chemistry Journal. 1997;74(2):182–7.
  28. 28. Bao J, Shen S, Sun M, Corke H. Analysis of genotypic diversity in the starch physicochemical properties of nonwaxy rice: apparent amylose content, pasting viscosity and gel texture. Starch—Starke. 2006;58(6):259–67.
  29. 29. Wang L, Liu W, Xu Y, He Y, Luo L, Xing Y, et al. Genetic basis of 17 traits and viscosity parameters characterizing the eating and cooking quality of rice grain. Theoretical and Applied Genetics. 2007;115(4):463–76.
  30. 30. Jinsong Bao (2014). Genes and QTLs for Rice Grain Quality Improvement, Rice - Germplasm, Genetics and Improvement, Wengui Yan (Ed.), InTech, DOI: 10.5772/56621.
  31. 31. 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. Epub 2004/12/22.
  32. 32. 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/National Research Council Canada. 2004;47(4):697–704. Epub 2004/07/31.
  33. 33. Redoña ED, Mackill DJ. Quantitative trait locus analysis for rice panicle and grain characteristics. Theoretical and Applied Genetics. 1998;96(6):957–63.
  34. 34. Tan YF, Xing YZ, Li JX, Yu SB, Xu CG, Zhang Q. Genetic bases of appearance quality of rice grains in shanyou 63, an elite rice hybrid. Theoretical and Applied Genetics. 2000;101(5–6):823–9.
  35. 35. Thomson MJ, Tai TH, McClung AM, Lai XH, Hinga ME, Lobos KB, et al. 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. Epub 2003/05/09.
  36. 36. Bai X, Luo L, Yan W, Kovi MR, Zhan W, Xing Y. Genetic dissection of rice grain shape using a recombinant inbred line population derived from two contrasting parents and fine mapping a pleiotropic quantitative trait locus qgl7. BMC Genetics. 2010;11(1):16. Epub 2010/02/27.
  37. 37. Fan C, Xing Y, Mao H, Lu T, Han B, Xu C, et al. Gs3, a major qtl for grain length and weight and minor qtl for grain width and thickness in rice, encodes a putative transmembrane protein. Theoretical and Applied Genetics. 2006;112(6):1164–71. Epub 2006/02/03.
  38. 38. Takano-Kai N, Jiang H, Kubo T, Sweeney M, Matsumoto T, Kanamori H, et al. Evolutionary history of gs3, a gene conferring grain length in rice. Genetics. 2009;182(4):1323–34. Epub 2009/06/10.
  39. 39. Fan C, Yu S, Wang C, Xing Y. A causal c-a mutation in the second exon of gs3 highly associated with rice grain length and validated as a functional marker. Theoretical and Applied Genetics. 2009;118(3):465–72. Epub 2008/11/21.
  40. 40. Wan X, Wan J, Jiang L, Wang J, Zhai H, Weng J, et al. Qtl analysis for rice grain length and fine mapping of an identified qtl with stable and major effects. Theoretical and Applied Genetics. 2006;112(7):1258–70.
  41. 41. Shomura A, Izawa T, Ebana K, Ebitani T, Kanegae H, Konishi S, et al. Deletion in a gene associated with grain size increased yields during rice domestication. Nature Genetics. 2008;40(8):1023–8.
  42. 42. Weng J, Gu S, Wan X, Gao H, Guo T, Su N, et al. Isolation and initial characterization of gw5, a major qtl associated with rice grain width and weight. Cell Research. 2008;18(12):1199–209. Epub 2008/11/19.
  43. 43. Song XJ, Huang W, Shi M, Zhu MZ, Lin HX. A qtl for rice grain width and weight encodes a previously unknown ring-type e3 ubiquitin ligase. Nature Genetics. 2007;39(5):623–30. Epub 2007/04/10.
  44. 44. Hu Z, He H, Zhang S, Sun F, Xin X, Wang W, et al. A kelch motif-containing serine/threonine protein phosphatase determines the large grain qtl trait in rice. Journal of Integrative Plant Biology. 2012;54(12):979–90.
  45. 45. Zhang X, Wang J, Huang J, Lan H, Wang C, Yin C, et al. Rare allele of osppkl1 associated with grain length causes extra-large grain and a significant yield increase in rice. Proceedings of the National Academy of Sciences. 2012;109(52):21534–9.
  46. 46. Qi P, Lin Y-S, Song X-J, Shen J-B, Huang W, Shan J-X, et al. The novel quantitative trait locus gl3.1 controls rice grain size and yield by regulating cyclin-t1;3. Cell Research. 2012;22(12):1666–80.
  47. 47. Li Y, Fan C, Xing Y, Jiang Y, Luo L, Sun L, et al. Natural variation in gs5 plays an important role in regulating grain size and yield in rice. Nature Genetics. 2011;43(12):1266–9.
  48. 48. Wang S, Wu K, Yuan Q, Liu X, Liu Z, Lin X, et al. Control of grain size, shape and quality by osspl16 in rice. Nature Genetics. 2012;44(8):950–4.
  49. 49. Aluko G, Martinez C, Tohme J, Castano C, Bergman C, Oard JH. Qtl mapping of grain quality traits from the interspecific cross Oryza sativa x O. glaberrima. Theoretical and Applied Genetics. 2004;109(3):630–9. Epub 2004/04/24.
  50. 50. Asante MD, Dartey PKA, Akromah R, Ofori J. Genetic analysis of grain size and shape in two rice crosses. Journal of Ghana Science Association. 2007;9(1):20–7.
  51. 51. Amarawathi Y, Singh R, Singh AK, Singh VP, Mohapatra T, Sharma TR, et al. Mapping of quantitative trait loci for basmati quality traits in rice (Oryza sativa l.). Molecular Breeding. 2008;21(1):49–65.
  52. 52. Kumar I, Khush GS. Genetic analysis of different amylose levels in rice. Crop Science. 1987;27(6):1167–72.
  53. 53. Kumar I, Khush GS. Inheritance of amylose content in rice (Oryza sativa L.). Euphytica. 1988;38(3):261–9.
  54. 54. McKenzie KS, Rutger JN. Genetic analysis of amylose content, alkali spreading score, and grain dimensions in rice. Crop Science. 1983;23:306–11.
  55. 55. Kuo Y-C, Webb BD, Stansel JW. Griffing and hayman diallel analysis of variance for eating and processing quality parameters of miled rice. Journal of Agriculture Research China. 1997;46(1):15–31.
  56. 56. Pooni HS, Kumar I, Khush GS. Genetical control of amylose content in a diallel set of rice crosses. Heredity. 1993;71(6):603–13.
  57. 57. Bollich CN, Webb BD. Inheritance of amylose in two hybrid populations of rice. Cereal Chemistry. 1973;50:631–6.
  58. 58. Fan CC, Yu XQ, Xing YZ, Xu CG, Luo LJ, Zhang Q. The main effects, epistatic effects and environmental interactions of qtls on the cooking and eating quality of rice in a doubled-haploid line population. Theoretical and Applied Genetics. 2005;110(8):1445–52.
  59. 59. Govindaraj P, Vinod K, Arumugachamy S, Maheswaran M. Analysing genetic control of cooked grain traits and gelatinization temperature in a double haploid population of rice by quantitative trait loci mapping. Euphytica. 2009;166(2):165–76.
  60. 60. He P, Li SG, Qian Q, Ma YQ, Li JZ, Wang WM, et al. Genetic analysis of rice grain quality. Theoretical and Applied Genetics. 1999;98(3–4):502–8.
  61. 61. Septiningsih EM, Trijatmiko KR, Moeljopawiro S, McCouch SR. Identification of quantitative trait loci for grain quality 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):1433–41. Epub 2003/09/27.
  62. 62. Tan YF, Li JX, Yu SB, Xing YZ, Xu CG, Zhang Q. The three important traits for cooking and eating quality of rice grains are controlled by a single locus in an elite rice hybrid, shanyou 63. Theoretical and Applied Genetics. 1999;99(3–4):642–8.
  63. 63. Lanceras JC, Huang Z-L, Naivikul O, Vanavichit A, Ruanjaichon V, Tragoonrung S. Mapping of genes for cooking and eating qualities in Thai jasmine rice (kdml105). DNA Research. 2000;7(2):93–101.
  64. 64. Zheng X, Wu JG, Lou XY, Xu HM, Shi CH. The qtl analysis on maternal and endosperm genome and their environmental interactions for characters of cooking quality in rice (Oryza sativa l.). Theoretical and Applied Genetics. 2008;116(3):335–42. Epub 2007/11/09.
  65. 65. Tian Z, Qian Q, Liu Q, Yan M, Liu X, Yan C, et al. Allelic diversities in rice starch biosynthesis lead to a diverse array of rice eating and cooking qualities. Proceedings of the National Academy of Sciences. 2009;106(51):21760–5.
  66. 66. Cho Y-G, Kang H-J, Lee Y-T, Jong S-K, Eun M-Y, McCouch SR. Identification of quantitative trait loci for physical and chemical properties of rice grain. Plant Biotechnology Reports. 2010;4(1):61–73.
  67. 67. Bao JS, Wu YR, Hu B, Wu P, Cui HR, Shu QY. Qtl for rice grain quality based on a dh population derived from parents with similar apparent amylose content. Euphytica. 2002;128(3):317–24.
  68. 68. Stansel JW. Influence of heredity and environment on endosperm characteristics of rice (Oryza sativa L.). Microfilms. Purdue University: Ann Arbor, Michigan; 1965, Dissertation Abstract, 27:48B.
  69. 69. Singh NB, Singh HG, Singh P. Heterosis and combining ability for quality components in rice. Indian Journal of Genetics and Plant Breeding. 1977;37(2):347–52.
  70. 70. Leng Y, Hong D-L. Grain quality and genetic analysis derived from different ecological types in japonica rice (Oryza sativa) (abstract). Rice Science 2004;2:165–70.
  71. 71. Jennings PR, Coffman WR, Kauffman HE. Rice improvement: International Rice Research Institute. Los Banos: Philippines; 1979.
  72. 72. Tian R, Jiang G-H, Shen L-H, Wang L-Q, He Y-Q. Mapping quantitative trait loci underlying the cooking and eating quality of rice using a dh population. Molecular Breeding. 2005;15(2):117–24.
  73. 73. Tang SX, Khush G, Juliano BO. Genetics of gel consistency in rice (Oryza sativa L.). Journal of Genetics. 1991;70:69–78.
  74. 74. Tang SX, Zhang YK, Yu HY. Genetics of gel consistency in the crosses between indica and japonica rice. Scientia Agricultura Sinica. 1996;29:51–5.
  75. 75. Tang SX, Khush GS, Juliano BO. Diallel analysis of gel consistency in rice (Oryza sativa L.). SABRAO Journal. 1989;21:135–42.
  76. 76. Zaman FU, Siddiq EA, Phasod AB. Genetical analysis of gel consistency in rice (Oryza sativa L.). Indian Journal of Genetics and Plant Breeding. 1985; 45:111–8.
  77. 77. Harrington SE, Bligh HFJ, Park WD, Jones CA, McCouch SR. Linkage mapping of starch branching enzyme iii in rice (Oryza sativa l.) and prediction of location of orthologous genes in other grasses. Theoretical and Applied Genetics. 1997;94(5):564–8.
  78. 78. Shi CH, Zhu J, Zang RC, Chen GL. Genetic and heterosis analysis for cooking quality traits of indica rice in different environments. Theoretical and Applied Genetics. 1997;95(1–2):294–300.
  79. 79. Su Y, Rao Y, Hu S, Yang Y, Gao Z, Zhang G, et al. Map-based cloning proves qgc-6, a major qtl for gel consistency of japonica/indica cross, responds by waxy in rice (Oryza sativa L.). Theoretical and Applied Genetics. 2011;123(5):859–67. Epub 2011/06/24.
  80. 80. Gravois K, Webb B. Inheritance of long grain rice amylograph viscosity characteristics. Euphytica. 1997;97(1):25–9.
  81. 81. Bao JS, Zheng XW, Xia YW, He P, Shu QY, Lu X, et al. Qtl mapping for the paste viscosity characteristics in rice (Oryza sativa L.). Theoretical and Applied Genetics. 2000;100(2):280–4.
  82. 82. Zheng L, Zhang W, Liu S, Chen L, Liu X, Chen X, et al. Genetic relationship between grain chalkiness, protein content, and paste viscosity properties in a backcross inbred population of rice. Journal of Cereal Science. 2012;56(2):153–60.
  83. 83. Liu X, Wan X, Ma X, Wan J. Dissecting the genetic basis for the effect of rice chalkiness, amylose content, protein content, and rapid viscosity analyzer profile characteristics on the eating quality of cooked rice using the chromosome segment substitution line population across eight environments. Genome/National Research Council Canada. 2011;54(1):64–80.
  84. 84. Bao J, He P, Xia Y, Chen Y, Zhu L. Starch RVA profile parameters of rice are mainly controlled bywx gene. Chinese Science Bulletin. 1999;44(22):2047–51.
  85. 85. Bao J, Kong X, Xie J, Xu L. Analysis of genotypic and environmental effects on rice starch. 1. Apparent amylose content, pasting viscosity, and gel texture. Journal of Agricultural and Food Chemistry. 2004;52(19):6010–6.
  86. 86. Chen M-H, Bergman CJ, Pinson SRM, Fjellstrom RG. Waxy gene haplotypes: associations with pasting properties in an international rice germplasm collection. Journal of Cereal Science. 2008;48(3):781–8.
  87. 87. Singh N, Kaur L, Sandhu KS, Kaur J, Nishinari K. Relationships between physicochemical, morphological, thermal, rheological properties of rice starches. Food Hydrocolloids. 2006;20(4):532–42.
  88. 88. Asante MD, Offei SK, Gracen V, Adu-Dapaah H, Danquah EY, Bryant R, et al. Starch physicochemical properties of rice accessions and their association with molecular markers. Starch—Starke. 2013;65:1022–8.
  89. 89. Larkin P, McClung A, Ayres N, Park W. The effect of the waxy locus (granule bound starch synthase) on pasting curve characteristics in specialty rices (Oryza sativa L.). Euphytica. 2003;131(2):243–53.
  90. 90. Bergman CJ, Delgado JT, McClung AM, Fjellstrom RG. An improved method for using a microsatellite in the rice waxy gene to determine amylose class. Cereal Chemistry. 2001;78(3):257–60.
  91. 91. Chen M-H, Bergman C, Pinson S, Fjellstrom R. waxy gene haplotypes: Associations with apparent amylose content and the effect by the environment in an international rice germplasm collection. Journal of Cereal Science. 2008;47(3):536–45.
  92. 92. Larkin PD, Park WD. Association of waxy gene single nucleotide polymorphisms with starch characteristics in rice (Oryza sativa L.). Molecular Breeding. 2003;12(4):335–9.
  93. 93. Chen M-H, Fjellstrom RG, Christensen EF, Bergman CJ. Development of three allele-specific codominant rice waxy gene pcr markers suitable for marker-assisted selection of amylose content and paste viscosity. Molecular Breeding. 2010;26(3):513–23.
  94. 94. James MG, Denyer K, Myers AM. Starch synthesis in the cereal endosperm. Current Opinion in Plant Biology. 2003;6(3):215–22.
  95. 95. Nakamura Y. Towards a better understanding of the metabolic system for amylopectin biosynthesis in plants: rice endosperm as a model tissue. Plant and Cell Physiology. 2002;43(7):718–25.
  96. 96. Reddy PR, Sathyanarayanaiah K. Inheritance of aroma in rice. Indian Journal of Genetics and Plant Breeding. 1980;(40):327–9.
  97. 97. Sood BC, Siddiq EA. A rapid technique for scent determination in rice. Indian Journal of Genetics and Plant Breeding. 1978;38:268–71.
  98. 98. Berner DK, Hoff BJ. Inheritance of scent in American long grain rice. Crop Science. 1986;26(5):876–8.
  99. 99. Pinson SRM. Inheritance of aroma in six rice cultivars. Crop Science. 1994;34(5):1151.
  100. 100. Dong Y, Tsuzuki E, Terao H. Trisomic genetic analysis of aroma in three Japanese native rice varieties (Oryza sativa L.). Euphytica. 2001;117(3):191–6.
  101. 101. Tsuzuki E, Shimokawa E. Inheritance of aroma in rice. Euphytica. 1990;46(2):157–9.
  102. 102. Ahn SN, Bollich CN, Tanksley SD. RFLP tagging of a gene for aroma in rice. Theoretical and Applied Genetics. 1992;84(7):825–8.
  103. 103. Bourgis F, Guyot R, Gherbi H, Tailliez E, Amabile I, Salse J, et al. Characterization of the major fragance gene from an aromatic japonica rice and analysis of its diversity in Asian cultivated rice. Theoretical and Applied Genetics. 2008;117(3):353–68. Epub 2008/05/21.
  104. 104. Bradbury L, Henry R, Jin Q, Reinke RF, Waters DLE. A perfect marker for fragrance genotyping in rice. Molecular Breeding. 2005;16(4):279–83.
  105. 105. Bradbury LMT, Fitzgerald TL, Henry RJ, Jin Q, Waters DLE. The gene for fragrance in rice. Plant Biotechnology Journal. 2005;3(3):363–70.
  106. 106. Chen S, Wu J, Yang Y, Shi W, Xu M. The fgr gene responsible for rice fragrance was restricted within 69kb. Plant Science. 2006;171(4):505–14.
  107. 107. Shi W, Yang Y, Chen S, Xu M. Discovery of a new fragrance allele and the development of functional markers for the breeding of fragrant rice varieties. Molecular Breeding. 2008;22(2):185–92.
  108. 108. Fitzgerald MA, Sackville Hamilton NR, Calingacion MN, Verhoeven HA, Butardo VM. Is there a second fragrance gene in rice? Plant Biotechnology Journal. 2008;6(4):416–23. Epub 2008/03/12.
  109. 109. Kovach MJ, Calingacion MN, Fitzgerald MA, McCouch SR. The origin and evolution of fragrance in rice (Oryza sativa L.). Proceedings National Academy of Science. 2009;106(34):14444–9. Epub 2009/08/27.
  110. 110. Buttery RG, Ling LC, Juliano BO, Turnbaugh JG. Cooked rice aroma and 2-acetyl-1-pyrroline. Journal of Agricultural and Food Chemistry. 1983;31(4):823–6.
  111. 111. Lorieux M, Petrov M, Huang N, Guiderdoni E, Ghesquière A. Aroma in rice: genetic analysis of a quantitative trait. Theoretical and Applied Genetics. 1996;93(7):1145–51.
  112. 112. Bradbury L, Gillies S, Brushett D, Waters D, Henry R. Inactivation of an aminoaldehyde dehydrogenase is responsible for fragrance in rice. Plant Molecular Biology. 2008;68(4):439–49.
  113. 113. Huang TC, Teng CS, Chang JL, Chuang HS, Ho CT, Wu ML. Biosynthetic mechanism of 2-acetyl-1-pyrroline and its relationship with delta1-pyrroline-5-carboxylic acid and methylglyoxal in aromatic rice (Oryza sativa L.) callus. Journal Agriculture and Food Chemistry. 2008;56(16):7399–404. Epub 2008/08/06.
  114. 114. Asante MD, Kovach MJ, Huang L, Harrington S, Dartey PK, Akromah R, et al. The genetic origin of fragrance in nerica1. Molecular Breeding. 2010;26(3):419–24.
  115. 115. Sakthivel K, Shobha Rani N, Pandey MK, Sivaranjani AKP, Neeraja CN, Balachandran SM, et al. Development of a simple functional marker for fragrance in rice and its validation in Indian basmati and non-basmati fragrant rice varieties. Molecular Breeding. 2009;24(2):185–90.
  116. 116. Myint K, Arikit S, Wanchana S, Yoshihashi T, Choowongkomon K, Vanavichit A. A pcr-based marker for a locus conferring the aroma in Myanmar rice (Oryza sativa l.). TAG Theoretical and Applied Genetics. 2012;125(5):887–96.
  117. 117. Gao ZY, Zheng DL, Cui X, Zhou YH, Yan MX, Huang DN, et al. Map-based cloning of the alk gene, which controls the gelatinization temperature of rice. Science China (Series C). 2003;46:661–8.
  118. 118. Gao Z, Zeng D, Cheng F, Tian Z, Guo L, Su Y, et al. Alk, the key gene for gelatinization temperature, is a modifier gene for gel consistency in rice. Journal of Integrative Plant Biology. 2011;53(9):756–65. Epub 2011/06/30.
  119. 119. Bao JS, Corke H, Sun M. Nucleotide diversity in starch synthase iia and validation of single nucleotide polymorphisms in relation to starch gelatinization temperature and other physicochemical properties in rice (Oryza sativa L.). Theoretical and Applied Genetics. 2006;113(7):1171–83. Epub 2006/07/20.
  120. 120. Wang C, Chen S, Yu S. Functional markers developed from multiple loci in gs3 for fine marker-assisted selection of grain length in rice. Theoretical and Applied Genetics. 2011;122(5):905–13. Epub 2010/11/26.
  121. 121. Ramkumar G, Sivaranjani A, Pandey M, Sakthivel K, Shobha Rani N, Sudarshan I, et al. Development of a 2010 PCR-based SNP marker system for effective selection of kernel length and kernel elongation in rice. Molecular Breeding. 26(4):735–40.
  122. 122. Spindel J, Begum H, Akdemir D, Virk P, Collard B, Redoña E, et al. Genomic selection and association mapping in rice (oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines. PLoS Genetics. 2015;11(2):e1004982.

Written By

Maxwell Darko Asante

Submitted: 26 April 2016 Reviewed: 02 November 2016 Published: 15 March 2017