Effect of quantitative trait loci for seed shattering on abscission layer formation in Asian wild rice Oryza rufipogon

Asian cultivated rice Oryza sativa L. was domesticated from its wild ancestor, O. rufipogon. During domestication, the cultivated rice lost its seed-shattering behaviour. Previous studies have shown that two major quantitative trait loci (QTLs; qSH1 and sh4) are responsible for the seed-shattering degree. Here, we produced introgression lines carrying non-functional alleles from O. sativa ‘Nipponbare’ at the two major QTLs in the genetic background of wild rice O. rufipogon W630, and examined the effects of the two QTLs on seed shattering and abscission layer formation. The introgression lines, with Nipponbare alleles at either or both loci, showed complete or partial abscission layer formation, respectively, indicating that other unknown loci might be involved in enhancing seed shattering in wild rice. We detected a single QTL named qSH3 regulating seed-shattering degree using an F₂ population between Nipponbare and the introgression line carrying Nipponbare alleles at the two QTLs. Although we generated an introgression line for qSH3 alone, no effects on seed shattering were observed. However, a significant effect on seed-shattering degree was observed for the introgression line carrying Nipponbare alleles at qSH3 and the two QTLs, suggesting an important role of qSH3 on seed shattering in coordination with the two QTLs.     

Mapping of quantitative trait loci controlling seed longevity of rice (Oryza sativa L.) after various periods of seed storage

Seed longevity varies considerably in cultivated rice (Oryza sativa L.), but the underlying genetic mechanism of longevity has not been well elucidated. Quantitative trait loci (QTL) that control seed longevity after various periods of seed storage were sought using recombinant inbred lines derived from a combination involving ‘Milyang23’(Indica‐type) and ‘Akihikari’ (Japonica‐type). In all, 12 QTLs for germination and normal seedling growth were detected as indices of seed longevity on chromosome 7 (one region) and chromosome 9 (two regions) in treated seeds that had been stored under laboratory conditions for 1, 2 or 3 years.‘Milyang23’ alleles of all QTLs promoted germination and normal seedling growth after all durations of storage. These QTL regions were detected repeatedly in more than one seed condition. Therefore, we infer that these regions control seed longevity.     

Dissection of genetic effects of quantitative trait loci (QTL) in transgenic cotton

When alien DNA inserts into the cotton genome in a multicopy manner, several quantitative trait loci (QTLs) in the cotton genome are disrupted; these are called dQTL in this study. A transgenic mutant line is near-isogenic to its recipient, which is divergent for the dQTL from the remaining QTLs. Therefore, a set of data from a transgenic QTL line mutated by Agrobacterium-mediated transformation (30074), its recipient and their F1 hybrids, and three elite lines were analyzed under a modified additive-dominance model with genotype × environment interactions in three different environments to separate the genetic effects due to dQTL from whole-genome effects. Our result showed that dQTL had significant additive effects on lint percentage, boll weight, and boll number per square meter, while it had little genetic association with fiber traits, seed cotton yield, and lint yield. The dQTL in 30074 significantly increased lint percentage and boll number, while significantly decreasing boll weight, having little effect on fibre traits, while those from the recipient and three elite lines showed significant genetic effects on lint percentage. In addition, the remaining QTL other than dQTL had significant additive effects on seed cotton yield, fruiting branch number, uniformity index, micronaire, and short fibre index, and significant dominance effects on seed cotton yield, lint yield, and boll number per square meter. The additive and dominance effects under homozygous and heterozygous conditions for each line are also predicted in this study.     

Detection of quantitative trait loci (QTL) associated with resistance to whitebacked planthopper (Sogatella furcifera) in rice (Oryza sativa)

Whitebacked planthopper (WBPH) is an important insect pest of rice. In this study, we report quantitative trait loci (QTL) associated with resistance to WBPH using a doubled‐haploid (DH) mapping population derived from the cross IR64/Azucena. We evaluated a set of 91 DH lines using various screening tests which measure seedling resistance, antibiosis and tolerance to WBPH. QTL analysis involving a RFLP map of 175 markers detected a significant QTL on chromosome 7 (RG511‐RG477) associated with seedling resistance to WBPH. In addition, QTL analysis involving available defence related candidate genes as markers on a sub set of 60 DH lines showed significant association of genomic regions on chromosome 1 (W1‐pMRF1), 2 (XLRfrI7‐RG157) and 7 (RG711‐CDO418) with resistance to WBPH. Several suggestive QTL were detected on chromosomes 2, 3, 6, 7, 8 and 11 showing the possibility of their association with resistance to WBPH. The phenotypic contribution of the QTL ranged from 8.4% to 32.1%. Some of the WBPH resistance QTL detected in this study showed similar map positions with the QTL reported for resistance to brown planthopper (BPH) in the same mapping population. These results would be useful for attempts to trace the genes associated with resistance to planthoppers in rice.     

Mapping of quantitative trait loci (QTLs) for rice protein and fat content using doubled haploid lines

Rice protein content (RPC) and rice fatcontent (RFC) are two important componentsof rice nutritional quality. In order toexamine the genetic basis of these traits,a doubled haploid (DH) population and anRFLP linkage map consisting of 232 markerloci were used to search QTLs for thetraits with the computer programQTLMapper1.0. This program is based onmixed linear models and allows simultaneousmapping of both main-effect and digenicepistastic QTLs in a DH population. RPC andRFC were evaluated based on a dry weightbasis of head rice by the Kjeldahl andSoxhlet methods respectively. A total offive main-effect QTLs for RPC wereresolved. The five QTLs collectivelyexplained 74% of the phenotypic variationwith LOD=15.2. Among these QTLs, the majorQTL qRPC-5 with the largest effectwas mapped in the interval of RG435-RG172aon chromosome 5. It accounted for 35% ofthe phenotypic variation with a LOD of16.7. At this locus the allele from theparent `Gui 630' increased RPC by 1.32%.The second QTL qRPC-7 was mapped inthe interval ZG34B-G20 on chromosome 7. Itexplained 23% of the phenotypic variancewith a LOD of 6.1. Its positive alleles,also from the parent `Gui 630', increasedRPC by 1.05%. As for the remaining threeQTLs, their additive effects wererelatively small and their positive alleleswere all inherited from the parent `02428'.Three QTLs for RFC were mapped onchromosome 1, 2 and 5 respectively. Theycollectively explained 44% of thephenotypic variation. Among these loci,QTLs qRFC-2 and qRFC-5 withlarger effects individually accounted for24% and 26% of the phenotypic variancerespectively. At QTL qRFC-2 thepositive allele came from the parent `Gui630', while at QTL qRFC-5 thepositive allele from the parent `02428'.The fact that both parents possess thepositive alleles at the QTLs for the twotraits provides an appropriate explanationfor the large transgressive segregationobserved in the DH lines. Furthermore, onlyone pair of epistatic loci explaining only5.1% of the phenotypic variance wasdetected for RPC, whereas seven pairs ofepistatic loci were resolved for RFC. Thetotal absolute effects of these RFCinteractions amounted to 0.97% which ismuch larger than that (0.42%) of the threemain-effect QTLs for the trait. Alongwith the observation that RPC showed a highheritability (78%), these resultsdemonstrate that RPC in the DH populationcould be mainly controlled by relativelyfew QTLs with large main-effects. As forRFC, epistatic interactions might be aneven more important component of thegenetic basis and the segregation of the DHlines could be largely explained by a fewmain-effect QTLs and many epistatic loci.In addition, a highly negative correlation(r = –0.45) between RPC and RFC inthe DH population was observed. Thiscorrelation could be largely explained bythe linkage of qRPC-5 and qRFC-5 with the directions of effectsopposite and the co-locations of the twoepistatic loci for RFC respectively withtwo different main-effect QTLs for RPC. Theinformation reported in the present papermay be useful for improving ricenutritional quality by means ofmarker-assisted selection.     

Bayesian mapping of quantitative trait loci (QTL) controlling soybean cyst nematode resistant

The soybean cyst nematode (SCN) is one of the most economically important pathogens of soybean. Molecular mapping of quantitative trait loci (QTL) for resistance to SCN is a proven useful strategy in order to assist in the development of resistant soybean cultivars. In the present study, a Bayesian modeling approach was performed to map QTL controlling genetic resistance to SCN races 3 and 14. For this purpose, a population of recombinant inbred lines derived from the cross between line Y23 (susceptible) and cv. Hartwig (resistant) was used. A total of 144 microsatellites markers (Simple Sequence Repeats) were selected and synthesized for mapping purpose. Posterior marginal parameter distributions were computed using the Reversible Jump Markov Chain Monte Carlo (RJ-MCMC) algorithm. It was determined the existence of four QTLs on three linkage groups (LG); that is LG A2 for race 3, LG C2 for race 14, and LG G for both races. The estimates of posterior modes of the heritability were 0.038 and 0.53 for the LGs A2 and G respectively (race 3). For the race 14 the posterior modes of the heritability were 0.044 and 0.05 for the LGs C2 and G. The identified QTLs explained about 57 and 9 % of the total phenotypic variance, for the races 3 and 14, respectively. These results confirm the effectiveness of the Bayesian method to map QTL controlling resistance to SCN in soybean. Accordingly, integrating QTL mapping with Bayesian methods will enable response to selection for quantitative traits of interest in soybean to be improved.     

Mapping of quantitative trait loci for seminal root morphology and gravitropic response in rice

In order to gain a better understanding of the complex root traits observed in previous studies using a mapping population derived from a Bala × Azucena cross, an experiment was conducted growing plants in agar-filled Perspex chambers with the aim of identifying quantitative trait loci (QTLs) for both seminal root morphology (SRM) and gravitropic response. A total of four main effect QTLs were detected for SRM (a measurement of the degree of a wavy/curly seminal root phenotype); two were located on chromosome 2, one at the top of chromosome 3 and one on chromosome 11. Two main effect QTLs were detected for the gravitropic response (the degree of bending of the growing seminal root when subjected to a 90° rotation); one on chromosome 6 and 1 on chromosome 11. As well as main effect QTLs, an epistatic interaction was observed for each of the traits. For SRM an interaction was detected between the top and the bottom of chromosome 4. For the gravitropic response an interaction was observed between a location on chromosome 6 and 11. Both these interactions were confirmed by analysis of variance using marker classes and the epistatic gravitropic response was also confirmed using a pair of near isogenic lines. All the SRM QTLs detected in this experiment co-localise with root growth QTLs (root penetration or morphology) detected previously in the mapping population. This information could prove valuable in attempts to identify candidate genes for these potentially valuable QTLs because we could postulate that the underlying genes should be involved in the pathway of gravity detection, signal transduction or the growth response to gravity.     

Quantitative trait loci for seed dormancy in rice

Seed dormancy (SD) is controlled by its own complicated genetic factors and environmental factors. SD is an important trait affecting grain yield and quality in cereal crops. A population comprising 240 recombinant inbred lines (RIL) was used for detecting quantitative trait locus (QTL) for SD in rice. To minimize the effect of environment, data for lines for which the optimum temperature during the late ripening stage is either below 20°C or above 30°C were excluded from the analysis, which left 185 lines. In a dynamic germination test of the parents of the population, Minghui 63 showed clear SD and Zhenshan 97 showed none. The seeds of each RIL, harvested 32 days after heading, were divided into two lots: seeds in one lot were sown immediately, without any treatment to break their dormancy, whereas seeds in the other lot were sown after they had been exposed to dry heat (50°C) for 72 h. Composite interval mapping showed the presence of qDGE1, qDGR5a, qDGR5b, and qDGR7 in the first lot whereas only qDGR7 was detected in the second lot––which had been treated to break SD––indicating the strong influence of qDGR7 in controlling SD. A recently cloned Sdr4 is also located in the qDGR7 region. Coincidently, three rice homologues of Arabidopsis SD gene DOG1 were found in qDGE1 and qDGR5b regions where no SD QTL had been reported so far. These results indicate that the QTL found in this study are reliable, and that it would be worthwhile to clone qDGE1 and qDGR5b.     

Mapping of quantitative trait loci controlling seedling vigor in rice (Oryza sativa L.) under submergence

Submergence-induced suppression of seedling vigor is a serious constraint particularly in the direct seeding rice cultivation system. To identify quantitative trait loci (QTLs) associated with seedling vigor in rice under submergence, a mapping population of 98 Backcross Inbred Lines derived from a cross of Nipponbare/Kasalath//Nipponbare was used. Phenotypic evaluation of seedling vigor under submergence was based on shoot length (SHL), root length (RTL) and shoot fresh weight (SFW) using a test tube bioassay method. Thirty-two putative QTLs were detected among which 7 were for SHL, 11 for RTL and 14 for SFW. Phenotypic evaluation was also made of the parental lines and a set of 54 chromosome segment substitution lines in which Nipponbare segments were substituted for by their homologous Kasalath segments covering the entire rice genome. Two QTLs with more than 10 % contribution to the total phenotypic variance were verified for SHL, and at least one for RTL and six for SFW on chromosomes 1, 3, 4, 6 and 7 at the 1 % significance level. Among these, all but two showed reductions in one, two or all three traits. Our present and previous results suggest that the Nipponbare genome has a potential to improve seedling vigor under submergence and that japonica germplasms can be used to breed for this important trait in indica rice.     

Mapping quantitative trait loci and expressed sequence tags related to brown planthopper resistance in rice

To map genes responsible for brown planthopper (BPH) resistance in rice, a rice genetic map was constructed based on a recombinant inbred line population from a cross between a BPH‐resistant line ‘B5’ and a susceptible variety ‘Minghui 63’. Four quantitative trait loci (QTLs) for BPH resistance were detected. ESTs differentially regulated by BPH feeding were isolated by suppressive subtractive hybridization (SSH) and assigned to chromosomes based on RFLP mapping and searches of the rice genome database. The distribution of ESTs showed some clustering, and some ESTs are related to known QTLs and known BPH resistance genes. These findings suggest that the mapping of differentially induced ESTs may be a useful strategy for the identification of candidate plant defence genes, which could be beneficial in the development of a BPH‐resistant rice variety.     

Dynamic quantitative trait loci underlies isoflavone accumulation in soybean seed

Most of quantitative trait loci (QTL) underlying soybean seed isoflavone contents were derived from the harvest stage of plant development, which uncover the genetic effects that were expressed in earlier seed developmental stages. The aim of this study was to detect conditional QTL associated with isoflavone accumulation during the entire seed development. A total of 112 recombinant inbred lines developed from the cross between ‘Zhongdou 27’ (higher seed isoflavone content) and ‘Jiunong 20’ (lower seed isoflavone content) were used for the conditional QTL analysis of daidzein (DZ), genistein (GT), glycitein (GC) and total isoflavone (TI) accumulations through composite interval mapping with mixed genetic model. The results indicated that the number and type of QTL and their additive effects for individual and total isoflavone accumulations were different among R3 to R8 developmental stages. Three unconditional QTL and six conditional QTL for DZ, four unconditional QTL and five conditional QTL for GT, six unconditional QTL and five conditional QTL for GC, six unconditional QTL and seven conditional QTL for TI were identified at different developmental stages, respectively. Unconditional and conditional QTL that affect individual and total isoflavone accumulations exhibited multiple expression patterns, implying that some QTL are active for long period and others are transient. Two genomic regions, Satt144‐Satt569 in linkage group F (LG F; chromosome 13, chr 13) for DZ, GC, GT and TI accumulations and Satt540‐Sat_240 in LG M (chr 07) for TI and GC accumulations, were found to significantly affect individual and total isoflavone accumulations in multiple developmental stages, suggesting that the accumulation of soybean seed isoflavones is governed by time‐dependent gene expression.     

Identification of quantitative trait loci underlying milling quality of rice (Oryza sativa) grains

Milling quality of rice grains is important to both producers and consumers. In this study, quantitative trait loci (QTLs) controlling brown rice rate (BR), milled rice recovery (MR) and head rice recovery (HR) were analysed by composite interval mapping over 2 years using 98 backcross inbred lines (BILs). A total of 12 QTLs for the three traits were detected, of which five were for BR, four for MR and three for HR. The proportion of phenotypic variation explained by individual QTLs ranged from 7.5 to 19.9%, and additive effects contributed by a single QTL accounted for 0.46 to 2.34% of the variation. QTL‐by‐environment interactions were observed by comparing QTL mapping of the same population grown in two consecutive years. Three of five QTLs for BR and two of four QTLs for MR were detected in 2 years, and all three QTLs for HR were detected in 1 year only. BR was significantly correlated with MR, and all four QTLs of MR were located in the same regions as those of BR. This indicated that QTLs for highly correlated traits could often be detected in the same interval.     

Mapping of quantitative trait loci for phytic acid and phosphorus contents in seed and seedling of mungbean (Vigna radiata (L.) Wilczek)

Phytic acid (PA) is the storage form of phosphorus (P) in seeds and plays an important role in the nutritional quality of food crops. There is little information on the genetics of seed and seedling PA in mungbean [Vigna radiata (L.) Wilczek]. Quantitative trait loci (QTL) were identified for phytic acid P (PAP), total P (TP), and inorganic P (IP) in mungbean seeds and seedlings, and for flowering, maturity and seed weight, in an F₂ population developed from a cross between low PAP cultivated mungbean (V1725BG) and high PAP wild mungbean (AusTRCF321925). Seven QTLs were detected for P compounds in seed; two for PAP, four for IP and one for TP. Six QTLs were identified for P compounds in seedling; three for PAP, two for TP and one for IP. Only one QTL co-localized between P compounds in seed and seedling suggesting that low PAP seed and low PAP seedling must be selected for at different QTLs. Seed PAP and TP were positively correlated with days to flowering and maturity, indicating the importance of plant phenology to seed P content.     

Mapping quantitative trait loci associated with starch paste viscosity in rice (Oryza sativa L.) under different environmental conditions

A recombinant inbred line (RIL) population consisting of 153 lines derived from a cross between indica ‘Zhongyouzao8’ and japonica ‘Toyonishiki’ rice was used to detect stable quantitative trait loci (QTL) for rapid visco analyser (RVA) characteristics under four environmental conditions. We identified 93 QTL for RVA profiles, and four pleiotropic regions harbouring stably expressed QTL were detected on chromosomes 2, 6, 7 and 11. These newly identified and stable QTL will facilitate further research into the genetic mechanism regulating RVA profiles. Amylose content (AC) was correlated with RVA traits. AC and RVA traits were unaffected by indica–japonica subspecies differentiation, suggesting that RVA profiles were mainly influenced by the Wx gene. The RIL population was divided into two subpopulations according to Wx genotypes. A total of 106 QTL associated with RVA profiles were detected in the subpopulations. These QTL differed from those detected in the whole population in terms of their genomic location, number, logarithm of odds values and amount of phenotypic variance explained. Using this strategy, we detected QTL with minor effects and eliminated false due to the Wx gene.     

Mapping quantitative trait loci influencing panicle‐related traits from Chinese common wild rice (Oryza rufipogon) using introgression lines

Panicle‐related traits are important agronomic traits which directly associated with grain yield. In this study, we investigated quantitative trait loci (QTLs) associated with panicle‐related traits using a set of 265 introgression lines (ILs) of common wild rice (Oryza rufipogon Griff.) in the background of Indica high‐yielding cultivar Guichao 2 (O. sativa L.). A total of 39 QTLs associated with panicle‐related traits including panicle length (PL), primary branch number (PBN), secondary branch number (SBN), spikelet number per panicle (SPP) and spikelet density (SD), were detected in the ILs with single‐point analysis. The alleles of 20 QTLs derived from wild rice showed positive effects, and some QTLs, such as, QPl1b for PL, QPbn8 for PBN, QSd4 and QSd11b for SD and QSpp4 for SPP showed larger positive effects, providing good candidates and useful information for marker‐aided improvement of yield potential of rice. Most of the QTLs controlling SPP, SBN and SD were located in cluster or closely linked on chromosomes, and the directions of their additive effects were consistent, which explained the genetic basis of significant correlations between their phenotypic characters.     

Mapping quantitative trait loci for yield-related traits in soybean (Glycine max L.)

Development of soybean cultivars with high seed yield is a major focus in soybean breeding programs. This study was conducted to identify genetic loci associated with seed yield-related traits in soybean and also to clarify consistency of the detected QTLs with QTLs found by previous researchers. A population of 135 F_2:3 lines was developed from a cross between a vegetable soybean line (MJ0004-6) and a landrace cultivar from Myanmar ({"type":"entrez-nucleotide","attrs":{"text":"R18500","term_id":"772110","term_text":"R18500"}}R18500). They were evaluated in the experimental field of Kasetsart University, Kamphaeng Saen, Nakhon Pathom, Thailand in a randomized complete block design with two replications each in 2011 and 2012 growing seasons. The two parents exhibited contrasting characteristics for most of the traits that were mapped. Analysis of variance showed that the main effects of genotype and environment (year) were significant for all studied traits. Genotype by environment interaction was also highly significant for all the traits. The population was genotyped by 149 polymorphic SSR markers and the genetic map consisted of 129 SSR loci which converged into 38 linkage groups covering 1156 cM of soybean genome. There were 10 QTLs significantly associated with seed yield-related traits across two seasons with single QTLs explaining between 5.0% to 21.9% of the phenotypic variation. Three of these QTLs were detected in both years for days to flowering, days to maturity and 100 seed weight. Most of the detected QTLs in our research were consistent with earlier QTLs reported by previous researchers. However, four novel QTLs including SF1, SF2 and SF3 on linkage groups L and N for seed filling period and PN1 on linkage group D1b for pod number were identified in the present study.