QTL‐seq: rapid mapping of quantitative trait loci in rice by whole genome resequencing of DNA from two bulked populations

The majority of agronomically important crop traits are quantitative, meaning that they are controlled by multiple genes each with a small effect (quantitative trait loci, QTLs). Mapping and isolation of QTLs is important for efficient crop breeding by marker‐assisted selection (MAS) and for a better understanding of the molecular mechanisms underlying the traits. However, since it requires the development and selection of DNA markers for linkage analysis, QTL analysis has been time‐consuming and labor‐intensive. Here we report the rapid identification of plant QTLs by whole‐genome resequencing of DNAs from two populations each composed of 20–50 individuals showing extreme opposite trait values for a given phenotype in a segregating progeny. We propose to name this approach QTL‐seq as applied to plant species. We applied QTL‐seq to rice recombinant inbred lines and F₂ populations and successfully identified QTLs for important agronomic traits, such as partial resistance to the fungal rice blast disease and seedling vigor. Simulation study showed that QTL‐seq is able to detect QTLs over wide ranges of experimental variables, and the method can be generally applied in population genomics studies to rapidly identify genomic regions that underwent artificial or natural selective sweeps.     

Quantitative Trait Locus Mapping and Candidate Gene Analysis for Plant Architecture Traits Using Whole Genome Re-Sequencing in Rice

Plant breeders have focused on improving plant architecture as an effective means to increase crop yield. Here, we identify the main-effect quantitative trait loci (QTLs) for plant shape-related traits in rice (Oryza sativa) and find candidate genes by applying whole genome re-sequencing of two parental cultivars using next-generation sequencing. To identify QTLs influencing plant shape, we analyzed six traits: plant height, tiller number, panicle diameter, panicle length, flag leaf length, and flag leaf width. We performed QTL analysis with 178 F₇ recombinant in-bred lines (RILs) from a cross of japonica rice line ‘SNUSG1’ and indica rice line ‘Milyang23’. Using 131 molecular markers, including 28 insertion/deletion markers, we identified 11 main- and 16 minor-effect QTLs for the six traits with a threshold LOD value > 2.8. Our sequence analysis identified fifty-four candidate genes for the main-effect QTLs. By further comparison of coding sequences and meta-expression profiles between japonica and indica rice varieties, we finally chose 15 strong candidate genes for the 11 main-effect QTLs. Our study shows that the whole-genome sequence data substantially enhanced the efficiency of polymorphic marker development for QTL fine-mapping and the identification of possible candidate genes. This yields useful genetic resources for breeding high-yielding rice cultivars with improved plant architecture.     

Genetic mapping in maize with hybrid progeny across testers and generations: plant height and flowering

DNA markers were used to identify quantitative trait loci (QTLs) for plant height, ear height, and three flowering traits in hybrid progeny of two generations (F_2:3, F_6:8) of lines from a Mo17×H99 maize population. For both generations, testcross (TC) progeny were developed by crossing the lines to three inbred testers (B91, A632, B73). The hybrid progeny from the two generations were evaluated at the same locations but in different years as per an early generation testing program. QTLs were identified within each TC population and for mean testcross (MTC) performance. Overall, more QTLs were detected in the F_6:8 than the F_2:3 generation. Totalled over all five traits, 41 (B91) to 69% (B73) of the QTLs for tester effects and 67% of the QTLs for MTC detected in the F_2:3 generation were verified in the F_6:8 generation. Although differences in relative rank of the QTL effects across generations were observed, especially for the flowering traits, parental contributions were nearly always consistent. Several (8–11) QTLs were identified with effects for all three tester populations and for all traits except the anthesis-silk interval, which had only two such regions. Over all five traits, previous evaluations in this population identified 26 QTLs with consistent effects for two (F_2:3, F_6:8) inbred-progeny evaluations, and 20 (77%) were also associated with MTC in at least one of the generations evaluated herein. In all instances of common inbred and TC QTLs, parental contributions were the same. Received: 26 November 1999 / Accepted: 18 April 2000     

Additive and additive x additive interaction make important contributions to spikelets per panicle in rice near isogenic （Oryza sativa L.） lines

Epistasis plays an important role in the genetic basis of rice yield traits. Taking interactions into account in breeding programs will help the development of high-yielding rice varieties. In this study, three sets of near isogenic lines (NILs) targeting three QTLs for spikelets per panicle (SPP), namely qSPP1, qSPP2 and qSPP7, which share the same Zhenshan 97 genetic background, were used to produce an F₂ population in which the three QTLs segregated simultaneously. The genotypes of the individual F₂ plants at the three QTLs were replaced with three markers that are closely linked to the corresponding QTLs. These QTLs were validated in the F₂ and F₃ populations at the single marker level. qSPP7 exhibited major pleiotropic effects on SPP, plant height and heading date. Multifactor analysis of variance was performed for the F₂ population and its progeny. Additive (additive interaction between qSPP2 and qSPP7 had significant effects on SPP in both the F₂ population and its progeny. Both additive and additive (additive interactions could explain about 73% of the total SPP phenotypic variance. The SPP performance of 27 three-locus combinations was ranked and favorable combinations were recommended for rice breeding in different ecosystems.     

Identification and Validation of Quantitative Trait Loci for Agronomic Traits in Advanced Backcross Breeding Lines Derived from Oryza rufipogon × Oryza sativa Cultivar MR219

A backcross breeding strategy was used to identify quantitative trait loci (QTLs) associated with 14 traits in a BC₂F₂ population derived from a cross between MR219, an indica rice cultivar and an accession of Oryza rufipogon (IRGC 105491). A total of 261 lines were genotyped with 96 microsatellite markers and evaluated for plant morphology, yield components and growth period. The genetic linkage map generated for this population with an average interval size of 16.2?cM, spanning 1,553.4?cM (Kosambi) of the rice genome. Thirty-eight QTLs were identified with composite interval mapping (CIM), whereas simple interval mapping (SIM) resulted in 47 QTLs (LOD >3.0). The O. rufipogon allele was favourable for 59% of QTLs detected through CIM. Of 261 BC₂F₂ families, 26 advanced backcross breeding lines (BC₂F₅) were used for QTL validation. These lines were selected on the basis of the yield traits potentiality in BC₂F₃ and BC₂F₄ generations. The field trial was conducted at three different locations in Malaysia using randomized complete block design with three replications. Trait based marker analysis was done for QTL determination. Twenty-five QTLs were detected in BC₂F₅ generation whereas 29 QTLs were detected in BC₂F₂ generation of the same population. Two QTLs (qPL-1 and qSPL-7) were not considered for validation due to their low R ² values and two QTLs (qPSS-3-2 and qGW-3-2) were not detected in the BC₂F₅ population. Fifteen QTLs showed the beneficial effect to enhance the trait value of the breeding lines. QTL validation aided to select the promising lines for further utilization.     

Identification of QTLs for Yield and Associated Traits in F₂ Population of Rice

Identification of quantitative trait loci (QTLs) controlling yield and yield-related traits in rice was performed in the F₂ mapping population derived from parental rice genotypes DHMAS and K343. A total of 30 QTLs governing nine different traits were identified using the composite interval mapping (CIM) method. Four QTLs were mapped for number of tillers per plant on chromosomes 1 (2 QTLs), 2 and 3; three QTLs for panicle number per plant on chromosomes 1 (2 QTLs) and 3; four QTLs for plant height on chromosomes 2, 4, 5 and 6; one QTL for spikelet density on chromosome 5; four QTLs for spikelet fertility percentage (SFP) on chromosomes 2, 3 and 5 (2 QTLs); two QTLs for grain length on chromosomes 1 and 8; three QTLs for grain width on chromosomes1, 3 and 8; three QTLs for 1000-grain weight (TGW) on chromosomes 1, 4 and 8 and six QTLs for yield per plant (YPP) on chromosomes 2 (3 QTLs), 4, 6 and 8. Most of the QTLs were detected on chromosome 2, so further studies on chromosome 2 could help unlock some new chapters of QTL for this cross of rice variety. Identified QTLs elucidating high phenotypic variance can be used for marker-assisted selection (MAS) breeding. Further, the exploitation of information regarding molecular markers tightly linked to QTLs governing these traits will facilitate future crop improvement strategies in rice.     

Identification of QTLs affecting traits of agronomic importance in a recombinant inbred population derived from a subspecific rice cross

To detect QTLs controlling traits of agronomic importance in rice, two elite homozygous lines 9024 and LH422, which represent the indica and japonica subspecies of rice (Oryza sativa), were crossed. Subsequently a modified single-seed-descent procedure was employed to produce 194 recombinant inbred lines (F₈). The 194 lines were genotyped at 141 RFLP marker loci and evaluated in a field trial for 13 quantitative traits including grain yield. Transgressive segregants were observed for all traits examined. The number of significant QTLs (LOD 2.0) detected affecting each trait ranged from one to six. The percentage of phenotypic variance explained by each QTL ranged from 5.1% to 73.7%. For those traits for which two or more QTLs were detected, increases in the traits were conditioned by indica alleles at some QTLs Japonica alleles at others. No significant evidence was found for epistasis between markers associated with QTLs and all the other markers. Pleitropic effects of single QTLs on different traits are suggested by the observation of clustering of QTLs. No QTL for traits was found to map to the vicinity of major gene loci governing the same traits qualitatively. Evidence for putative orthologous QTLs across rice, maize, oat, and barley is discussed.     

Identification of QTLs for grain yield and grain-related traits of maize (Zeamays L.) using an AFLP map, different testers, and cofactor analysis

We exploited the AFLP®¹(AFLP® is a registered trademark of Keygene, N.V.) technique to map and characterise quantitative trait loci (QTLs) for grain yield and two grain-related traits of a maize segregating population. Two maize elite inbred lines were crossed to produce 229 F₂ individuals which were genotyped with 66 RFLP and 246 AFLP marker loci. By selfing the F₂ plants 229 F₃ lines were produced and subsequently crossed to two inbred testers (T1 and T2). Each series of testcrosses was evaluated in field trials for grain yield, dry matter concentration, and test weight. The efficiency of generating AFLP markers was substantially higher relative to RFLP markers in the same population, and the speed at which they were generated showed a great potential for application in marker-assisted selection. AFLP markers covered linkage group regions left uncovered by RFLPs; in particular at telomeric regions, previously almost devoided of markers. This increase of genome coverage afforded by the inclusion of the AFLPs revealed new QTL locations for all the traits investigated and allowed us to map telomeric QTLs with higher precision. The present study has also provided an opportunity to compare simple (SIM) and composite interval mapping (CIM) for QTL analysis. Our results indicated that the method of CIM employed in this study has greater power in the detection of QTLs, and provided more precise and accurate estimates of QTL positions and effects than SIM. For all traits and both testers we detected a total of 36 QTLs, of which only two were in common between testers. This suggested that the choice of a tester for identifying QTL alleles for use in improving an inbred is critical and that the expression of QTL alleles identified may be tester-specific.     

Molecular mapping and location of QTLs for drought-resistance traits in <Emphasis Type="Italic">indica</Emphasis> rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.) lines adapted to target environments

Drought is a major limitation for rice production in rainfed ecosystems. Identifying quantitative trait loci (QTLs) linked to drought resistance provides opportunity to breed high yielding rice varieties suitable for drought-prone areas. Although considerable efforts were made in mapping QTLs associated with drought-resistance traits in rice, most of the studies involved indica × japonica crosses and hence, the drought-resistance alleles were contributed mostly by japonica ecotypes. It is desirable to look for genetic variation within indica ecotypes adapted to target environment (TE) as the alleles from japonica ecotype may not be expressed under lowland conditions. A subset of 250 recombinant inbred lines (RILs) of F₈ generation derived from two indica rice lines (IR20 and Nootripathu) with contrasting drought-resistance traits were used to map the QTLs for morpho-physiological and plant production traits under drought stress in the field in TE. A genetic linkage map was constructed using 101 polymorphic PCR-based markers distributed over the 12 chromosomes covering a total length of 1,529 cM in 17 linkage groups with an average distance of 15.1 cM. Composite interval mapping analysis identified 22 QTLs, which individually explained 4.8–32.2% of the phenotypic variation. Consistent QTLs for drought-resistance traits were detected using locally adapted indica ecotypes, which may be useful for rainfed rice improvement.     

Mapping QTL for Biomass Yield and Its Components in Rice (Oryza sativa L.)

Addicive effects, additive by additive epistatic effects, and their environmental interactions of QTLs are important genetic components of quantitative traits. Genetic architecture underlying rice biomass yield and its two component traits (straw yield and grain yield) were analyzed for a population of 125 DH lines from an inter-subspecific cross of IR64/Azucena. The mixed-model based composite interval mapping approach (MCIM) was used to detect QTLs, There were 12 QTLs detected with additive main effects, 27 QTLs involved in digenic interaction with aa and/or aae effects, and 18 QTLs affected by environments with ae and/or aae effects. It was revealed that epistatic effects and QE interaction effects existed on biomass yield and its component traits in rice. In addition, the genetic basis of relationships among these traits were investigated. Four QTLs and one pair of epistatic QTLs were detected to be responsible for the positive correlation between biomass yield and straw yield. Three QTLs might be responsible for the negative correlation between straw yield and grain yield. This result could partially explain the genetic basis of correlation among the three traits, and provide useful information for genetic improvement of these traits by marker-assisted selection.     

Quantitative trait loci for yield and related traits in the wheat population Ning7840 × Clark

Grain yield and associated agronomic traits are important factors in wheat (Triticum aestivum L.) improvement. Knowledge regarding the number, genomic location, and effect of quantitative trait loci (QTL) would facilitate marker-assisted selection and the development of cultivars with desirable characteristics. Our objectives were to identify QTLs directly and indirectly affecting grain yield expression. A population of 132 F₁₂ recombinant inbred lines (RILs) was derived by single-seed descent from a cross between the Chinese facultative wheat Ning7840 and the US soft red winter wheat Clark. Phenotypic data were collected for 15 yield and other agronomic traits in the RILs and parental lines from three locations in Oklahoma from 2001 to 2003. Twenty-nine linkage groups, consisting of 363 AFLP and 47 SSR markers, were identified. Using composite interval mapping (CIM) analysis, 10, 16, 30, and 14 QTLs were detected for yield, yield components, plant adaptation (shattering and lodging resistance, heading date, and plant height), and spike morphology traits, respectively. The QTL effects ranged from 7 to 23%. Marker alleles from Clark were associated with a positive effect for the majority of QTLs for yield and yield components, but gene dispersion was the rule rather than the exception for this RIL population. Often, QTLs were detected in proximal positions for different traits. Consistent, co-localized QTLs were identified in linkage groups 1AL, 1B, 4B, 5A, 6A, and 7A, and less consistent but unique QTLs were identified on 2BL, 2BS, 2DL, and 6B. Results of this study provide a benchmark for future efforts on QTL identification for yield traits.     

QTLs for Tolerance of Drought and Breeding for Tolerance of Abiotic and Biotic Stress: An Integrated Approach

Background

The coupling of biotic and abiotic stresses leads to high yield losses in rainfed rice (Oryza sativa L.) growing areas. While several studies target these stresses independently, breeding strategies to combat multiple stresses seldom exist. This study reports an integrated strategy that combines QTL mapping and phenotypic selection to develop rice lines with high grain yield (GY) under drought stress and non-stress conditions, and tolerance of rice blast.

Methodology

A blast-tolerant BC₂F₃-derived population was developed from the cross of tropical japonica cultivar Moroberekan (blast- and drought-tolerant) and high-yielding indica variety Swarna (blast- and drought-susceptible) through phenotypic selection for blast tolerance at the BC₂F₂ generation. The population was studied for segregation distortion patterns and QTLs for GY under drought were identified along with study of epistatic interactions for the trait.

Results

Segregation distortion, in favour of Moroberekan, was observed at 50 of the 59 loci. Majority of these marker loci co-localized with known QTLs for blast tolerance or NBS-LRR disease resistance genes. Despite the presence of segregation distortion, high variation for DTF, PH and GY was observed and several QTLs were identified under drought stress and non-stress conditions for the three traits. Epistatic interactions were also detected for GY which explained a large proportion of phenotypic variance observed in the population.

Conclusions

This strategy allowed us to identify QTLs for GY along with rapid development of high-yielding purelines tolerant to blast and drought with considerably reduced efforts. Apart from this, it also allowed us to study the effects of the selection cycle for blast tolerance. The developed lines were screened at IRRI and in the target environment, and drought and blast tolerant lines with high yield were identified. With tolerance to two major stresses and high yield potential, these lines may provide yield stability in rainfed rice areas.     

Combination of Eight Alleles at Four Quantitative Trait Loci Determines Grain Length in Rice

Grain length is an important quantitative trait in rice (Oryza sativa L.) that influences both grain yield and exterior quality. Although many quantitative trait loci (QTLs) for grain length have been identified, it is still unclear how different alleles from different QTLs regulate grain length coordinately. To explore the mechanisms of QTL combination in the determination of grain length, five mapping populations, including two F₂ populations, an F₃ population, an F₇ recombinant inbred line (RIL) population, and an F₈ RIL population, were developed from the cross between the U.S. tropical japonica variety ‘Lemont’ and the Chinese indica variety ‘Yangdao 4’ and grown under different environmental conditions. Four QTLs (qGL-3-1, qGL-3-2, qGL-4, and qGL-7) for grain length were detected using both composite interval mapping and multiple interval mapping methods in the mapping populations. In each locus, there was an allele from one parent that increased grain length and another allele from another parent that decreased it. The eight alleles in the four QTLs were analyzed to determine whether these alleles act additively across loci, and lead to a linear relationship between the predicted breeding value of QTLs and phenotype. Linear regression analysis suggested that the combination of eight alleles determined grain length. Plants carrying more grain length-increasing alleles had longer grain length than those carrying more grain length-decreasing alleles. This trend was consistent in all five mapping populations and demonstrated the regulation of grain length by the four QTLs. Thus, these QTLs are ideal resources for modifying grain length in rice.     

Genetic mapping and QTL analysis for yield and agronomic traits with an F2:3 population derived from a waxy corn × sweet corn cross

We constructed a framework map using SSR markers in the F₂ population derived from a cross between a waxy corn inbred line and a sweet corn inbred line. We constructed a genetic linkage map of the F_2:3 population employing 295 SSR markers on 158 F₂ individuals produced from the cross. The map comprised a total genomic length of 2,626.5 cM in 10 linkage groups and an average distance between markers of 8.9 cM. The number of loci per linkage group ranged from 27 (chr. 5) to 34 (chr. 7). The genetic distance per linkage group ranged from 213.6 cM (chr. 10) to 360.6 cM (chr. 2). Χ ² tests revealed that 254 markers (86.1 %) distributed over all 10 chromosomes exhibited a Mendelian segregation ratio of 1:2:1. A total of 14 quantitative trait loci (QTLs) for days to silking (DTS), plant height (PH), ear height (EH), ear height ratio (ER), ear length (L-ear), and setted ear length (L-sear) were found in the 158 F₂ progeny. They were mapped to chromosomes 1, 2, 3, 7, 8, and 10. Among them, one QTL was associated with DTS, three with PH, six with EH, one with ER, two with L-ear, and one QTL was related to L-sear. In our study, we found that four QTLs: qDTS1, qEH1a, qEH1b, and qPH1, were clustered between umc2390 and umc1603 on chromosome 1. These new QTLs identified by the present study could serve as useful molecular markers in selecting for yield and agronomic traits in maize. The results of this study may improve the identification and characterization of genes responsible for yield and agronomic traits in waxy corn and sweet corn.     

Identification of main effects, epistatic effects and their environmental interactions of QTLs for yield traits in rice

Quantitative trait loci (QTLs) controlling yield and yield components were identified by using a doubled haploid (DH) population of 120 lines from a sub-specific cross between ‘Samgang’ (Indica) and ‘Nagdong’ (Japonica). Main effects, epistatic effects, their environment interactions of QTLs were analyzed via mixed linear model approach across different environments. A total of 17 putative QTLs were identified on 8 chromosomes and five QTLs were detected over two years. 7 QTLs of main effects and 23 epistatic interactions were observed for five traits. Epistatic interactions played an important role in controlling the expression of yield related traits. The epistatic effects explained higher percentages of phenotype variation for panicles per plant, seed set percentage and yield. Significant QTL×environment (QE) interactions effects were identified for all traits, including 5 main effect QTLs. However, the present study failed to identify the significant interactions between epistatic loci containing main effect QTLs and the environment. The information provided in the present study could be used in the marker-assisted selection to enhance selection efficiency and to improve yield in rice.     

Identification of quantitative trait loci for grain size and the contributions of major grain-size QTLs to grain weight in rice

Grain weight, one of the three major components of rice yield, is largely determined by grain size, which is controlled by quantitative trait loci (QTLs). In a previous study, we identified qGS5 as a major QTL for grain width. Here, we report our identification of two more major grain-size QTLs (qGL3 and qGW2a) by using a recombinant inbred line (RIL) population from a cross of two indica varieties, ‘Zhenshan 97’ and ‘SLG’. To investigate the contribution of the three grain-size QTLs to final grain weight, we developed near-isogenic lines (NILs) NIL-qGL3, NIL-qGW2a, and NIL-qGS5 and used these to build the combined QTLs–NIL in the genetic background of ‘Zhenshan 97’ by marker-assisted selection and conventional backcrossing, respectively. A BCF₂ population of 957 individuals was developed from the combined QTLs-NIL for further study of the genetic control of grain size. The QTL analysis revealed that qGW2a and qGL3 played more important roles in grain weight gain than qGS5. All three QTLs showed additive effects with respect to grain weight, with no interaction. These results clearly indicate that pyramiding of major grain-size QTLs is a useful approach for improving rice yield.     

Dynamic QTL analysis of the Na+ content,K+ content,and Na+/K+ ratio in rice roots during the field growth under salt stress

Rice (Oryza sativa L.) is seriously impacted by global soil salinization. To determine the quantitative trait loci (QTLs) related to salt tolerance in rice roots, F_2:3 and BC₁F_2:3 populations derived from a cross between the cv. Dongnong 425 of high quality and yield and the salt-tolerant cv. Changbai 10, were studied at different development stages. Two genetic linkage maps of F_2:3 and BC1F_2:3 populations were constructed. A 66 mM NaCl solution was used to irrigate the field and to analyze the dynamic QTL of some rice root traits. Using unconditional and conditional QTL mapping methods, 30 unconditional QTLs and 16 conditional QTLs related to the 6 root traits were detected on the 9 rice chromosomes during different developmental stages. Fourteen pairs of unconditional and conditional QTLs were detected at the identical developmental stage in the identical population. A number of QTLs were detected at different developmental stages, however, many did not appear at the last stage. Remarkably, qRKC1 appeared continuously at multiple stages in both the populations suggesting its key role in regulating the salt tolerance of rice roots.     

Identification of QTLs for Drought-Related Traits in Alien Introgression Lines Derived from Crosses of Rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> cv. IR64) × <Emphasis Type="Italic">O. glaberrima</Emphasis> under Lowland Moisture Stress

Drought is a major abiotic stress that limits rice productivity in rain-fed and upland ecosystems. African rice, Oryza glaberrima, has low yields but is tolerant to drought and other stresses. We evaluated 513 BC₂F₃ progenies from alien introgression lines (AILs) that were derived from crosses of Oryza sativa (IR64) × O. glaberrima. They were assessed for yield and other traits when grown under drought at two locations. Such conditions reduced grain production by 59% compared with the recurrent parent (IR64). However, 33 AILs had higher yields, thus demonstrating their potential as genetic material for transferring drought-related traits from O. glaberrima to O. sativa. A set of 200 AILs was selectively genotyped with 173 simple sequence repeat and sequenced tagged site markers. Molecular analysis showed that a mean of 4.5% of the O. glaberrima genome was introgressed in BC₂F₃ AILs. Our analysis revealed 33 quantitative trait loci (QTLs; including 10 novel) for different traits. O. glaberrima contributed 50% of the alleles to those newly identified QTLs, with one for grain yield per plant (ypp9.1) being new. A QTL at RM208 on chromosome 2 positively affected yield under stress, accounting for 22% of the genetic variation. Our identification of drought-related QTLs for yield and yield components will be useful to future research efforts in marker-assisted selection.     

Analysis of quantitative trait loci affecting chlorophyll content of rice leaves in a double haploid population and two backcross populations

Chlorophyll content, one of the most important physiological parameters related to plant photosynthesis, is usually used to predict yield potential. To map the quantitative trait loci (QTLs) underlying the chlorophyll content of rice leaves, a double haploid (DH) population was developed from an indica/japonica (Zhenshan 97/Wuyujing 2) crossing and two backcross populations were established subsequently by backcrossing DH lines with each of their parents. The contents of chlorophyll a and chlorophyll b were determined by using a spectrophotometer to directly measure the leaf chlorophyll extracts. To determine the leaf chlorophyll retention along with maturation, all measurements were performed on the day of heading and were repeated 30 days later. A total of 60 QTLs were resolved for all the traits using these three populations. These QTLs were distributed on 10 rice chromosomes, except chromosomes 5 and 10; the closer the traits, the more clustering of the QTLs residing on common rice chromosomal regions. In general, the majority of QTLs that specify chlorophyll a content also play a role in determining chlorophyll b content. Strangely, chlorophyll content in this study was found mostly to be lacking or to have a negative correlation with yield. In both backcross F₁ populations, overdominant (or underdominant) loci were more important than complete or partially dominant loci for main-effect QTLs and epistatic QTLs, thereby supporting previous findings that overdominant effects are the primary genetic basis for depression in inbreeding and heterosis in rice.