RFLP-facilitated investigation of the quantitative resistance of rice to brown planthopper ( Nilaparvata lugens)

Quantitative trait loci (QTLs), conferring quantitative resistance to rice brown planthopper (BPH), were investigated using 160 F₁₁ recombinant inbred lines (RILs) from the Lemont/Teqing cross, a complete RFLP map, and replicated phenotyping of seedbox inoculation. The paternal indica parent, Teqing, was more-resistant to BPH than the maternal japonica parent, Lemont. The RILs showed transgressive segregation for resistance to BPH. Seven main-effect QTLs and many epistatic QTL pairs were identified and mapped on the 12 rice chromosomes. Collectively, the main-effect and epistatic QTLs accounted for over 70% of the total variation in damage scores. Teqing has the resistance allele at four main-effect QTLs, and the Lemont allele resulted in resistance at the other three. Of the main-effect QTLs identified, QBphr5b was mapped to the vicinity of gl1, a major gene controlling leaf and stem pubescence. The Teqing allele controlling leaf and stem pubescence was associated with resistance, while the Lemont allele for glabrous stem and leaves was associated with susceptibility, indicating that this gene may have contributed to resistance through antixenosis. Similar to the reported BPH resistance genes, the other six detected main-effect QTLs were all mapped to regions where major disease resistance genes locate, suggesting they might have contributed either to antibiosis or tolerance. Our results indicated that marker-aided pyramiding of major resistance genes and QTLs should provide effective and stable control over this devastating pest. Received: 10 December 2000 / Accepted: 7 May 2001     

The main effects, epistatic effects and environmental interactions of QTLs on the cooking and eating quality of rice in a doubled-haploid line population

Amylose content (AC), gel consistency (GC) and gelatinazation temperature (GT) are three important traits that influence the cooking and eating quality of rice. The objective of this study was to characterize the genetic components, including main-effect quantitative trait loci (QTLs), epistatic QTLs and QTL-by-environment interactions (QEs), that are involved in the control of these three traits. A population of doubled haploid (DH) lines derived from a cross between two indica varieties Zhenshan 97 and H94 was used, and data were collected from a field experiment conducted in two different environments. A genetic linkage map consisting of 218 simple sequence repeat (SSR) loci was constructed, and QTL analysis performed using qtlmapper 1.6 resolved the genetic components into main-effect QTLs, epistatic QTLs and QEs. The analysis detected a total of 12 main-effect QTLs for the three traits, with a QTL corresponding to the Wx locus showing a major effect on AC and GC, and a QTL corresponding to the Alk locus having a major effect on GT. Ten digenic interactions involving 19 loci were detected for the three traits, and six main-effect QTLs and two pairs of epistatic QTLs were involved in QEs. While the main-effect QTLs, especially the ones corresponding to known major loci, apparently played predominant roles in the genetic basis of the traits, under certain conditions epistatic effects and QEs also played important roles in controlling the traits. The implications of the findings for rice quality improvement are discussed.     

Genetic mapping and confirmation of quantitative trait loci for grain chalkiness in rice

Grain chalkiness is a highly undesirable trait affecting rice grain quality and milled rice yield. In order to clarify the genetic basis of chalkiness, a recombinant inbred line population (RIL) derived from a cross between Beilu130 (a japonica cultivar with high chalkiness) and Jin23B (an indica cultivar with low chalkiness) was developed for quantitative trait locus (QTL) mapping. A total of 10 QTLs for white belly rate (WBR) and white core rate (WCR) were detected on eight different chromosomes over 2 years. Two QTLs for WBR (qWBR2 and qWBR5) showed similar chromosomal locations with GW2 and qSW5/GW5, which have been cloned previously to control the grain width and should be the right candidate genes. Three novel minor QTLs controlling WCR, qWCR1, qWCR3, and qWCR4 were further validated in near isogenic F₂ populations (NIL-F₂) and explained 26.1, 18.3, and 21.1% of the phenotypic variation, respectively. These QTLs could be targets for map-based cloning of the candidate genes to elucidate the molecular mechanism of chalkiness and for marker-assisted selection (MAS) in rice grain quality improvement.     

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.     

Locating QTLs controlling overwintering seedling rate in perennial glutinous rice 89-1 (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

A new cold tolerant germplasm resource named glutinous rice 89-1 (Gr89-1, Oryza sativa L.) can overwinter using axillary buds, with these buds being ratooned the following year. The overwintering seedling rate (OSR) is an important factor for evaluating cold tolerance. Many quantitative trait loci (QTLs) controlling cold tolerance at different growth stages in rice have been identified, with some of these QTLs being successfully cloned. However, no QTLs conferring to the OSR trait have been located in the perennial O. sativa L. To identify QTLs associated with OSR and to evaluate cold tolerance. 286 F₁₂ recombinant inbred lines (RILs) derived from a cross between the cold tolerant variety Gr89-1 and cold sensitive variety Shuhui527 (SH527) were used. A total of 198 polymorphic simple sequence repeat (SSR) markers that were distributed uniformly on 12 chromosomes were used to construct the linkage map. The gene ontology (GO) annotation of the major QTL was performed through the rice genome annotation project system. Three main-effect QTLs (qOSR2, qOSR3, and qOSR8) were detected and mapped on chromosomes 2, 3, and 8, respectively. These QTLs were located in the interval of RM14208 (35,160,202 base pairs (bp))–RM208 (35,520,147 bp), RM218 (8,375,236 bp)–RM232 (9,755,778 bp), and RM5891 (24,626,930 bp)–RM23608 (25,355,519 bp), and explained 19.6%, 9.3%, and 11.8% of the phenotypic variations, respectively. The qOSR2 QTL displayed the largest effect, with a logarithm of odds score (LOD) of 5.5. A total of 47 candidate genes on the qOSR2 locus were associated with 219 GO terms. Among these candidate genes, 11 were related to cell membrane, 7 were associated with cold stress, and 3 were involved in response to stress and biotic stimulus. OsPIP1;3 was the only one candidate gene related to stress, biotic stimulus, cold stress, and encoding a cell membrane protein. After QTL mapping, a total of three main-effect QTLs—qOSR2, qOSR3, and qOSR8—were detected on chromosomes 2, 3, and 8, respectively. Among these, qOSR2 explained the highest phenotypic variance. All the QTLs elite traits come from the cold resistance parent Gr89-1. OsPIP1;3 might be a candidate gene of qOSR2.     

Identification of major QTLs and epistatic interactions for seed protein concentration in soybean under multiple environments based on a high-density map

The concentration of protein in soybean is an important trait that drives successful soybean quality. A recombinant inbred line derived from a cross between the Charleston and Dongnong594 cultivars was planted in one location across 10 years and two locations across 5 years in China (20 environments in total), and the genetic effects were partitioned into additive main effects, epistatic main effects and their environmental interaction effects using composite interval mapping and inclusive composite interval mapping models based on a high-density genetic map. Ten main-effect quantitative trait loci (QTLs) were identified on chromosomes 3, 6, 7, 13, 15 and 20 and detected in more than three environments, with each of the main-effect QTLs contributing a phenotypic variation of around 10 %. Between the intervals of the main-effect QTLs, 93 candidate genes were screened for their involvement in seed protein storage and/or amino acid biosynthesis and metabolism processes based on gene ontology and annotation information. Furthermore, an analysis of epistatic interactions showed that three epistatic QTL pairs were detected, and could explain approximately 50 % of the phenotypic variation. The additive main-effect QTLs and epistatic QTL pairs contributed to high phenotypic variation under multiple environments, and the results were also validated and corroborated with previous research, indicating that marker-assisted selection can be used to improve soybean protein concentrations and that the candidate genes can also be used as a foundation data set for research on gene function.     

The genetic basis of flowering time and photoperiod sensitivity in rapeseed <Emphasis Type="Italic">Brassica napus</Emphasis> L.

The objective of this study was to dissect the genetic control of days to flowering (DTF) and photoperiod sensitivity (PS) into the various components including the main-effect quantitative trait loci (QTLs), epistatic QTLs and QTL-by-environment interactions (QEs). Doubled haploid (DH) lines were produced from an F₁ between two spring Brassica napus cultivars Hyola 401 and Q2. DTF of the DH lines and parents were investigated in two locations, one location with a short and the other with a long photoperiod regime over two years. PS was calculated by the delay in DTF under long day as compared to that under short day. A genetic linkage map was constructed that comprised 248 marker loci including SSR, SRAP, and AFLP markers. Further QTL analysis resolved the genetic components of flowering time and PS into the main-effect QTLs, epistatic QTLs, and QEs. A total of 7 main-effect QTLs and 11 digenic interactions involving 21 loci located on 13 out of the 19 linkage groups were detected for the two traits. Three main-effect QTLs and four pairs of epistatic QTLs were involved in QEs conferring DTF. One QTL on linkage group (LG) 18 was revealed to simultaneously affect DTF and PS and explain for the highest percentage of the phenotypic variation. The implications of the results for B. napus breeding have been discussed. The text was submitted by the authors in English.     

Effects of genetic background and environment on QTLs and epistasis for rice (Oryza sativa L.) panicle number

A double-haploid (DH) population and a recombinant inbred (RI) line population, derived from a cross between a tropical japonica variety, Azucena, as male parent and two indica varieties, IR64 and IR1552, as female parents respectively, were used in both field and pot experiments for detecting QTLs and epistasis for rice panicle number in different genetic backgrounds and different environments. Panicle number (PN) was measured at maturity. A molecular map with 192 RFLP markers for the DH population and a molecular map with 104 AFLP markers and 103 RFLP markers for the RI population were constructed, in which 70 RFLP markers were the same. Six QTLs were identified in the DH population, including two detected from field experiments and four from pot experiments. The two QTLs, mapped on chromosomes 1 and 12, were identical in both field and pot experiments. In the RI population, nine QTLs were detected, five QTLs from field conditions and four from the pot experiments. Three of these QTLs were identical in both experimental conditions. Only one QTL, linked to CDO344 on chromosome 12, was detected across the populations and experiments. Different epistasitic interaction loci on PN were found under different populations and in different experimental conditions. One locus, flanked by RG323 and RZ801 on chromosome 1, had an additive effect in the DH population, but epistatic effects in the RI population. These results indicate that the effect of genetic background on QTLs is greater than that of environments, and epistasis is more sensitive to genetic background and environments than main-effect QTLs. QTL and epistatic loci could be interchangeable depending on the genetic backgrounds and probably on the environments where they are identified. Received: 26 May 2000 / Accepted: 19 October 2000     

The genetic basis of stay-green in rice analyzed in a population of doubled haploid lines derived from an<Emphasis Type="Italic"> indica</Emphasis> by<Emphasis Type="Italic"> japonica</Emphasis> cross

Delayed leaf-senescence, or stay-green, has been regarded as a desired characteristic for the production of a number of crops including rice. In this study, we analyzed the genetic basis of stay-green using a population of 190 doubled haploid lines from the cross between an indica parent Zhenshan 97 and a stay-green japonica parent Wuyujing 2. The population was tested in replicated field trials in 2 consecutive years, and six traits were defined to evaluate the stay-green characteristics. A genetic linkage map with 179 SSR (simple sequence repeat) marker loci was constructed. The software QTLMapper, based on a mixed linear model approach, was applied to detect QTLs, epistatic effects and their environmental interactions for these traits. A total of 46 main-effect QTLs was detected for the six traits that can be localized to 25 chromosomal regions. The individual effects of all the QTLs were small. Fifty digenic interactions were resolved that involved 66 loci distributed on all 12 chromosomes. Environmental interactions were detected for 18 of the main-effect QTLs and 14 of the epistatic interactions. Collectively, the epistatic effects and QTL by year interactions accounted for large proportions of the phenotypic variations. The results also showed that most of the stay-green traits were negatively correlated with yield and its component traits. The implications of the results in crop improvement were discussed.Communicated by C. Möllers     

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.     

Exploring the quantitative resistance to <Emphasis Type="Italic">Pseudomonas syringae</Emphasis> pv. <Emphasis Type="Italic">phaseolicola</Emphasis> in common bean (<Emphasis Type="Italic">Phaseolus vulgaris</Emphasis> L.)

Pseudomonas syringae pv. phaseolicola is an important disease that causes halo blight in common bean. The genetic mechanisms underlying quantitative halo blight resistance are poorly understood in this species, as most disease studies have focused on qualitative resistance. The present work examines the genetic basis of quantitative resistance to the nine halo blight races in different organs (primary and trifoliate leaf, stem and pod) of an Andean recombinant inbred line (RIL) progeny. Using a multi-environment quantitative trait locus (QTL) mapping approach, 76 and 101 main-effect and epistatic QTLs were identified, respectively. Most of the epistatic interactions detected were due to loci without detectable QTL additive main effects. Main and epistatic QTLs detected were mainly consistent across the environment conditions. The homologous genomic regions corresponding to 26 of the 76 main-effect detected QTLs were positive for the presence of resistance-associated gene cluster encoding nucleotide-binding and leucine-rich repeat (NL) proteins and known defence genes. Main-effect QTLs for resistance to races 3, 4 and 5 in leaf, stem and pod were located on chromosome 2 within a 3.01-Mb region, where a cluster of nine NL genes was detected. The NL gene Phvul.002G323300 is located in this region, which can be considered an important putative candidate gene for the non-organ-specific QTL identified here. The present research provides essential information not only for the better understanding of the plant-pathogen interaction but also for the application of genomic assisted breeding for halo blight resistance in common bean.     

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.     

QTL detection and MAS selection efficiency for lipid content in brown rice (Oryza sativa L.)

Rice lipid content as one of important ingredients of functional food and industrial products has become an entirely new target in the rice breeding programs worldwide. A genetic linkage map spanning 12 rice chromosomes with an average interval of 10.51 cM between markers was created using 172 DNA markers, which intended to elucidate genetic basis of lipid content in brown rice by QTL detection. Eight QTLs related to lipid content with LOD from 2.52 to 7.86 were mapped on chromosome1, 2, 3, 5, 6, 7 and 9 using a doubled haploid (DH) population from a cross of ‘Samgang/Nagdong’ with field experiments for five years. Two QTLs of qLC5.1 and qLC6.1 in the intervals 5014-5024 and 6011-RM19696 were repeatedly detected over four years at average LOD scores of 4.85 and 4.21, respectively. Five of eight QTLs tend to increase the lipid content from ‘Samgang’ alleles. Epistatic and environmental effects played important roles and explained 42.20% of phenotype variations. Three QTLs of qLC6.1, qLC7.1 and qLC9.1 collectively explained much than 27% of phenotype variations and increased 0.25% of lipid content and, showed much than 85% of selection efficiency for the lines with high lipid contents in the F7 population from a cross of ‘Samgang/Nagdong’. Thus it provides the sufficient possibility to realize QTLs pyramiding and to promote process of rice breeding.     

Association mapping of grain color,phenolic content,flavonoid content and antioxidant capacity in dehulled rice

Phytochemicals such as phenolics and flavonoids in rice grain are antioxidants that are associated with reduced risk of developing chronic diseases including cardiovascular disease, type-2 diabetes and some cancers. Understanding the genetic basis of these traits is necessary for the improvement of nutritional quality by breeding. Association mapping based on linkage disequilibrium has emerged as a powerful strategy for identifying genes or quantitative trait loci (QTL) underlying complex traits in plants. In this study, genome-wide association mapping using models controlling both population structure (Q) and relative kinship (K) were performed to identify the marker loci/QTLs underlying the naturally occurring variations of grain color and nutritional quality traits in 416 rice germplasm accessions including red and black rice. A total of 41 marker loci were identified for all the traits, and it was confirmed that Ra (i.e., Prp-b for purple pericarp) and Rc (brown pericarp and seed coat) genes were main-effect loci for rice grain color and nutritional quality traits. RM228, RM339, fgr (fragrance gene) and RM316 were important markers associated with most of the traits. Association mapping for the traits of the 361 white or non-pigmented rice accessions (i.e., excluding the red and black rice) revealed a total of 11 markers for four color parameters, and one marker (RM346) for phenolic content. Among them, Wx gene locus was identified for the color parameters of lightness (L*), redness (a*) and hue angle (H ^o). Our study suggested that the markers identified in this study can feasibly be used to improve nutritional quality or health benefit properties of rice by marker-assisted selection if the co-segregations of the marker–trait associations are validated in segregating populations.     

水稻抗褐飞虱种质Swarnalata抽穗期和休眠性相关QTL检测

为有效利用抗褐飞虱水稻Swarnalata,对2013年南京种植的Swarnalata/02428 F2分离群体进行抽穗期和种子休眠性考察,利用172个分子标记构建了Swarnalata/02428 F2的分子遗传连锁图谱,图谱全长为3311.4c M,标记间平均图距为19.22c M。利用Windows QTL Cartographer V2.5软件对该分离群体进行抽穗期和种子休眠性相关QTL检测,共检测到7个抽穗期相关QTL,分别位于第2、3、6、11染色体,其中位于第11染色体的q HD-11-1贡献率最高,为28.85%;检测到3个种子休眠性相关QTL,分别位于第3、6、9染色体,其中位于第9染色体的q Sd-9贡献率最高,为22.11%。分析表明,本研究检测到的抽穗期QTL与种子休眠QTL所在位置不同,说明该群体中种子休眠与抽穗期没有直接关系,它们分别由不同基因控制。本研究不仅为水稻休眠基因的精细定位及克隆奠定基础,也为更有效利用Swarnalata中的抗褐飞虱基因提供基础和一些优良的中间材料。     

Gene actions of QTLs affecting several agronomic traits resolved in a recombinant inbred rice population and two backcross populations

To understand the types of gene action controlling seven quantitative traits in rice, we carried out quantitative trait locus (QTL) mapping in order to distinguish between the main-effect QTLs (M-QTLs) and digenic epistatic QTLs (E-QTLs) responsible for the trait performance of 254 recombinant inbred lines (RILs) from rice varieties Lemont/Teqing and two backcross hybrid (BCF₁) populations derived from these RILs. We identified 44 M-QTL and 95 E-QTL pairs in the RI and BCF₁ populations as having significant effects on the mean values and mid-parental heterosis of heading date, plant height, flag leaf length, flag leaf width, panicle length, spikelet number and spikelet fertility. The E-QTLs detected collectively explained a larger portion of the total phenotypic variation than the M-QTLs in both the RI and BCF₁ populations. In both BCF₁ populations, over-dominant (or under-dominant) loci were more important than additive and complete or partially dominant loci for M-QTLs and E-QTL pairs, thereby supporting prior findings that overdominance resulting from epistatic loci are the primary genetic basis of inbreeding depression and heterosis in rice.     

水稻籼粳交DH群体收获指数及源库性状的QTL分析

以 1个水稻籼粳交 (圭 6 30 0 2 4 2 8)来源的DH群体为材料 ,利用 1张含有 2 32个标记的RFLP连锁图谱和基于混合线性模型的定位软件QTLMapper1 0对水稻收获指数及生物量、籽粒产量、库容量和株高 5个性状进行QTL分析 ,共检测到 2 1个主效应QTLs和 9对上位性互作位点。其中 ,控制籽粒产量的 3个QTLs合计贡献率为 4 2 % ,LOD值为 7 10 ;这 3个QTLs或者与收获指数的QTL同位 ,或者与生物量的QTL同位 ,且加性效应的方向一致 ,从而揭示了“籽粒产量 =生物量×收获指数”的遗传基础所在。控制收获指数的 4个QTLs合计贡献率为 4 6 % ,LOD值为 10 3;控制生物量的 4个QTLs合计贡献率为 6 4 % ,LOD值为 14 0 9;收获指数的 4个QTLs与生物量的 4个QTLs均不同位。因此 ,通过基因重组 ,可能实现控制收获指数和生物量的增效基因的聚合 ,由此获得收获指数和生物量“双高”的基因型。检测到 5个株高QTLs,其合计贡献率为 6 4 % ,LOD值为 11 6 2 ;其中 ,有 3个效应较小的QTLs与生物量、库容量和 或籽粒产量QTLs同位 ,且同位QTLs的加性效应方向一致 ;未发现株高QTLs与收获指数QTLs的同位性。由此表明 ,株高与“源 流 库”概念中的“源”和“库”在遗传上有一定程度的关联 ,而与“流”无关联。此外还发现 ,在上述同位性QTL     

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.     

The effect of epistasis between linked genes on quantitative trait locus analysis

The effect of epistasis between linked genes on quantitative trait locus (QTL) analysis was studied as a function of their contribution to the phenotypic variance and their genetic distance by simulation of F2 (at least 200 individuals) and recombinant inbred line (RIL) populations. Data sets were replicated 100 times. For F2 populations, the presence of epistasis improves the detection of QTLs having effects in opposite directions. Epistasis between linked QTLs (26.5 cM) was poorly detected even when its contribution was relatively high compared to the main effects, and was null for heritabilities lower than 0.10. The detection of false-positive main effects is strongly affected by the distance between epistatic QTLs. The closer they are (≤11.5 cM), the higher the probability of detecting false-positive main-effect QTLs and the lower the probability of detecting epistatic effects. In this case, the presence of main-effect QTLs is due to the deviation of the heterozygote from the homozygotes at each linked interacting QTL and is algebraically explained by the joint effect of the linkage and the additive-by-additive interaction, resulting in a heterosis at a single genomic region in the absence of simulated dominant genetic effects. The number of false-positive main effects only reached nominal levels at about 100 cM. For RIL populations, the number of false positives or the detection of existing epistasis does not depend on the distance, and the power to detect epistatic QTLs is much higher even with small sample sizes and low contributions to the trait. RIL populations are highly recommended to detect epistatic QTLs and to better infer the genetic architecture of a quantitative trait.     

Retracted: Screening of candidate genes and fine mapping of drought tolerance quantitative trait loci on chromosome 4 in rice (Oryza sativa L.) under drought stress

Due to severe water resource shortage, genetics of and breeding for DT (drought tolerance) in rice (Oryza sativa L.) have become one of the hot research topics. Identification of grain yield QTLs (quantitative trait loci) directly related to the DT trait of rice can provide useful information for breeding new drought‐resistant and water‐saving rice varieties via marker‐assisted selection. A population of 105 advanced BILs (backcross introgression lines) derived from a cross between Zhenshan97B and IRAT109 in Zhenshan97B background were grown under drought stress in a field experiment and phenotypic traits were investigated. The results showed that in the target interval of RM273‐RM255 on chromosome 4, three main‐effect QTLs related to panicle length, panicle number, and spikelet number per panicle were identified (LOD [logarithm of the odds] > 2.0). The panicle length‐related QTL had two loci located in the neighboring intervals of RM17308‐RM17305 and RM17349‐RM17190, which explained 18.80% and 20.42%, respectively, of the phenotypic variation, while the panicle number‐related QTL was identified in the interval of RM1354‐RM17308, explaining 11.47% of the phenotypic variation. As far as the spikelet number per panicle‐related QTL was concerned, it was found to be located in the interval of RM17308‐RM17305, which explained 28.08% of the phenotypic variation. Using the online Plant‐GE query system, a total of 13 matched ESTs (expressed sequence tags) were found in the target region, and of the 13 ESTs, 12 had corresponding predicted genes. For instance, the two ESTs CB096766 and CA765747 were corresponded to the same predicted gene LOC_Os04g46370, while the other four ESTs, CA754286, CB000011, CX056247, and CX056240, were corresponded to the same predicted gene LOC_Os04g46390.