QTL mapping for nitrogen-use efficiency and nitrogen-deficiency tolerance traits in rice

Plant and Soil - The improvement of nitrogen-deficiency tolerance (NDT) and nitrogen-use efficiency (NUE) traits is an important objective of many rice breeding programs. A better understanding of...     

Genetic analysis of rice CMS-WA fertility restoration based on QTL mapping

 The inheritance of fertility restoration of rice cytoplasmic male sterility of the wild abortive type was studied by means of QTL mapping. The two segregating populations examined showed high frequencies of highly sterile and highly fertile progenies, but a low frequency of partially sterile and partially fertile progenies. The distributions suggested that fertility restoration was mainly controlled by major genes. Based on a linkage map constructed with 57 RFLP and 61 AFLP markers on a B₁F₁ population, composite interval mapping (CIM) revealed that the fertility was restored by the additive effects of two restorer loci located on chromosome 10. One QTL, tightly linked to RFLP marker C1361 in the middle of the long arm of chromosome 10, explained 71.5% of the phenotypic variance. The second QTL was located between RFLP markers R2309 and RG257 on the short arm and explained 27.3% of the phenotypic variance. Similar results were obtained using the simple interval mapping (SIM) methods. Recived: 8 January 1998/Accepted: 22 April 1998     

QTL mapping of grain weight in rice and the validation of the QTL qTGW3.2

A recombinant inbred line (RIL) population bred from a cross between a javanica type (cv. D50) and an indica type (cv. HB277) rice was used to map seven quantitative trait loci (QTLs) for thousand grain weight (TGW). The loci were distributed on chromosomes 2, 3, 5, 6, 8 and 10. The chromosome 3 QTL qTGW3.2 was stably expressed over two years, and contributed 9–10% of the phenotypic variance. A residual heterozygous line (RHL) was selected from the RIL population and its selfed progeny was used to fine map qTGW3.2. In this “F₂” population, the QTL explained about 23% of the variance, rising to nearly 33% in the subsequent “F_2:3” generation. The physical location of qTGW3.2 was confined to a ~ 556 kb region flanked by the microsatellite loci RM16162 and RM16194. The region also contains other factors influencing certain yield-related traits, although it is also possible that qTGW3.2 affects these in a pleiotropic fashion.     

QTL mapping of protein content in rice using single chromosome segment substitution lines

Protein content (PC) is an important component of rice nutritional quality. In order to better understand the genetic basis of this trait and increase related breeding efficiency, 21 single chromosome segment substitution (SCSS) lines grown in four sites over two growing seasons (regarded as eight environments) were used to associate PC with particular chromosome segments. Segments from 15 chromosomes were found to contain quantitative trait loci (QTLs) for PC in at least one environment. These included segments from chromosome 3 and 8, in which QTLs for PC had not previously been identified. The segment of chromosome 8 in CSSL-48 had the largest positive effect across all environments. The interaction between substitution and environment was highly significant. Some substitutions had large effects in one environment, but no effect in another (i.e. CSSL-08 and CSSL-17), while some substitutions significantly increased PC in one environment but decreased it in another (i.e. CSSL-41 and CSSL-43). By biplot and clustering analysis, the eight environments were grouped into two contrasting environment types, that is, Hainan and Jiangsu. The segment of chromosome 8 in CSSL-48 had PC-enhancing QTLs in both of the environment types. The segments in CSSL-34 had QTLs which increase PC in the Jiangsu environment but have no effect in the Hainan environment. For enhancing PC, CSSL-48 could be explored in breeding for wide adaptation across all environments, while CSSL-12, CSSL-14, CSSL-17, CSSL-41 and CSSL-43, and that in CSSL-34 could be explored in breeding for specific adaptation to the Hainan and Jiangsu environments, respectively. Near isogenic lines are under development to validate the QTLs with large effects in a range of genetic backgrounds relevant to Jiangsu and Hainan breeding programs. Secondary mapping populations are also being developed for further localising the responsible QTLs in CSSL-14, CSSL-34 and CSSL-48.     

Fine mapping a major QTL for flag leaf size and yield-related traits in rice

Leaf size is a major determinant of plant architecture and yield potential in crops. A previous study showed that the genomic region of chromosome 1 contains a major quantitative trait locus (QTL) for flag leaf size in a set of backcross recombinant inbred lines derived from two elite parental lines (Zhenshan 97 and 93-11). In the present study, the QTL (qFL1) was shown to explain a large proportion of the variation in flag leaf size (leaf length, width and area) in derived populations (BC₂F₃ and BC₃F₂) in multiple environments. Using a large segregating population, we narrowed the location of qFL1 to a 31 kb region containing four predicted genes. Expression of one of these genes, OsFTL1, differed between leaves in near-isogenic lines carrying alleles of Zhenshan 97 and 93-11. qFL1 had a pleiotropic effect on flag leaf size and yield-related traits. Conditional QTL analysis of the derived population (BC₃F₂) supports the assertion that qFL1 is the QTL for flag leaf length and exhibits pleiotropy. Pyramiding of qFL1 with two known genes (GS3 and Wx) from 93-11 into Zhenshan 97 enlarged flag leaves, improved grain size and amylose content, and increased yield per plant, but slightly delayed heading date. These results provide a foundation for the functional characterization of the gene underlying the pleiotropic effects of qFL1 and for genetic improvement of the plant architecture and yield potential of rice.     

QTL mapping for the paste viscosity characteristics in rice (Oryza sativa L.)

In order to understand the genetic basis of the paste viscosity characteristics (RVA profile, which is tested on the Rapid Visco Analyser) of the rice grain, we mapped QTLs for RVA profile parameters using a DH population derived from a cross between an indica variety, Zai-Ye-Qing 8 (ZYQ8), and a japonica variety, Jing-Xi 17 (JX17). Evidence of genotype-by-environment interaction was found by comparing the mapped QTLs between two locations, Hainan (HN) and Hangzhou (HZ). A total of 20 QTLs for six parameters of the RVA profiles were identified at least one location. Only the waxy locus (wx) located on chromosome 6 was detected significantly at both environments for five traits, i.e. hot paste viscosity (HPV), cool paste viscosity (CPV), breakdown viscosity (BDV), consistency viscosity (CSV) and setback viscosity (SBV). This locus explained 19.5%–63.7% of the total variations at both environments, suggesting that the RVA profiles were mainly controlled by the wx gene. HPV, CPV, BDV, CSV and SBV were also controlled by other QTLs whose effects on the respective parameter were detected only in one environment, while for the peak viscosity (PKV), only 2 QTLs, 1 at HN,the other at HZ, were identified. These results indicate that RVA profiles are obviously affected by environment. Received: 18 July 1999 / Accepted: 27 August 1999     

QTL analysis for rice grain length and fine mapping of an identified QTL with stable and major effects

Grain length in rice plays an important role in determining rice appearance, milling, cooking and eating quality. In this study, the genetic basis of grain length was dissected into six main-effect quantitative trait loci (QTLs) and twelve pairs of epistatic QTLs. The stability of these QTLs was evaluated in four environments using an F₇ recombinant inbred line (RIL) population derived from the cross between a Japonica variety, Asominori, and an Indica variety, IR24. Moreover, chromosome segment substitution lines (CSSLs) harboring each of the six main-effect QTLs were used to evaluate gene action of QTLs across eight environments. A major QTL denoted as qGL-3a, was found to express stably not only in the isogenic background of Asominori but also in the recombinant background of Asominori and IR24 under multiple environments. The IR24 allele at qGL-3a has a positive effect on grain length. Based on the test of advanced backcross progenies, qGL-3a was dissected as a single Mendelian factor, i.e., long rice grain was controlled by a recessive gene gl-3. High-resolution genetic and physical maps were further constructed for fine mapping gl-3 by using 11 simple sequence repeat (SSR) markers designed using sequence information from seven BAC/PAC clones and a BC₄F₂ population consisting of 2,068 individuals. Consequently, the gl-3 gene was narrowed down to a candidate genomic region of 87.5 kb long defined by SSR markers RMw357 and RMw353 on chromosome 3, which provides a basis for map-based cloning of this gene and for marker-aided QTL pyramiding in rice quality breeding.     

QTL mapping and molecular marker analysis for the resistance of rice to ozone

The resistance of rice to ozone (O3) is a quantitative trait controlled by nuclear genes. The identification of quantitative trait loci (QTL) and analysis of molecular markers of O3 resistance is important for increasing the resistance of rice to O3 stress. QTL associated with the O3 resistance of rice were mapped on chromosomes 1, 7 and 11 using 164 recombinant inbred (RI) lines from a cross between 'Milyang 23' and 'Gihobyeo'. The quantitative trait loci were tightly linked to the markers RG109, C507 and RG1094 and were detected in each of three replications. The association between these markers and O3 resistance in 26 rice cultivars and doubled haploid (DH) populations was analysed. The markers permit the screening of rice germplasm for O3 resistance and the introduction of resistance into elite lines in breeding programs.     

Human QTL linkage mapping

Human quantitative trait locus (QTL) linkage mapping, although based on classical statistical genetic methods that have been around for many years, has been employed for genome-wide screening for only the last 10–15 years. In this time, there have been many success stories, ranging from QTLs that have been replicated in independent studies to those for which one or more genes underlying the linkage peak have been identified to a few with specific functional variants that have been confirmed in in vitro laboratory assays. Despite these successes, there is a general perception that linkage approaches do not work for complex traits, possibly because many human QTL linkage studies have been limited in sample size and have not employed the family configurations that maximize the power to detect linkage. We predict that human QTL linkage studies will continue to be productive for the next several years, particularly in combination with RNA expression level traits that are showing evidence of regulatory QTLs of large effect sizes and in combination with high-density genome-wide SNP panels. These SNP panels are being used to identify QTLs previously localized by linkage and linkage results are being used to place informative priors on genome-wide association studies.     

Fine mapping and candidate gene analysis of a major QTL for panicle structure in rice

Key message

A gene not only control tiller and plant height, but also regulate panicle structure by QTL dissection in rice.

Abstract

An ideal panicle structure is important for improvement of plant architecture and rice yield. In this study, using recombinant inbred lines (RILs) of PA64s and 93-11, we identified a quantitative trait locus (QTL), designated qPPB3 for primary panicle branch number. With a BC₃F₂ population derived from a backcross between a resequenced RIL carrying PA64s allele and 93-11, qPPB3 was fine mapped to a 34.6-kb genomic region. Gene prediction analysis identified four putative genes, among which Os03g0203200, a previously reported gene for plant height and tiller number, Dwarf 88 (D88)/Dwarf 14 (D14), had three nucleotide substitutions in 93-11 compared with PA64s. The T to G substitution resulted in one amino acid change from valine in 93-11 to glycine in PA64s. Real-time PCR analysis showed expression level of D88 was higher in 93-11 than PA64s. The expression of APO1 and IPA1 increased, while GN1a and DST decreased in 93-11 compared with PA64s. Therefore, D88/D14 is not only a key regulator for branching, but also affects panicle structure.     

Advances in QTL mapping in pigs

Over the past 15 years advances in the porcine genetic linkage map and discovery of useful candidate genes have led to valuable gene and trait information being discovered. Early use of exotic breed crosses and now commercial breed crosses for quantitative trait loci (QTL) scans and candidate gene analyses have led to 110 publications which have identified 1,675 QTL. Additionally, these studies continue to identify genes associated with economically important traits such as growth rate, leanness, feed intake, meat quality, litter size, and disease resistance. A well developed QTL database called PigQTLdb is now as a valuable tool for summarizing and pinpointing in silico regions of interest to researchers. The commercial pig industry is actively incorporating these markers in marker-assisted selection along with traditional performance information to improve traits of economic performance. The long awaited sequencing efforts are also now beginning to provide sequence available for both comparative genomics and large scale single nucleotide polymorphism (SNP) association studies. While these advances are all positive, development of useful new trait families and measurement of new or underlying traits still limits future discoveries. A review of these developments is presented.     

QTL mapping of panicle blast resistance in japonica landrace heikezijing and its application in rice breeding

The rice blast caused by Magnaporthe oryzae is one of the most devastating diseases worldwide, and the panicle blast could result in more loss of yield in rice production. However, the quantitative trait loci (QTLs) and genes related to panicle-blast resistance have not been well studied due to the time-consuming screening methodology involved and variation in symptoms. The QTLs for panicle blast resistance have been mapped in a population of 162 RILs (recombination inbreeding lines), derived from a cross between a highly blast-resistant rice landrace, Heikezijing, and a susceptible variety, Suyunuo. Two QTLs for panicle-blast resistance, qPbh-11–1 and qPbh-7-1, were identified, which were distributed on chromosomes 11 and 7. The QTL qPbh-11–1 was stably detected in three independent experiments, at Nanjing in 2013 and 2014 and at Hainan in 2014, located between the region of RM27187 and RM27381 on the distal end of chromosome 11 far from the reported resistant loci Pb1 and qPbm11 for panicle blast. The QTL qPbh-7-1 was detected only at Nanjing in 2013 and located between the region of M18 and RM3555 on chromosome 7. With marker-assisted selection (MAS) three introgression lines with the major panicle blast-resistance QTL qPbh-11–1 were developed from a recurrent parent Nanjing 44 (NJ44) and the panicle resistance of introgression lines was improved 46.36–55.47 % more than NJ44. Based on the results provided, Heikezijing appears to be a valuable source for panicle blast resistance.     

Saturation mapping of QTL regions and identification of putative candidate genes for drought tolerance in rice

We have developed 85 new markers (50 RFLPs, 5 SSRs, 12 DD cDNAs, 9 ESTs, 8 HSP-encoding cDNAs and one BSA-derived AFLP marker) for saturation mapping of QTL regions for drought tolerance in rice, in our efforts to identify putative candidate genes. Thirteen of the markers were localized in the close vicinity of the targeted QTL regions. Fifteen of the additional markers mapped, respectively, inside one QTL region controlling osmotic adjustment on chromosome 3 ( oa3.1) and 14 regions that affect root traits on chromosomes 1, 2, 4, 5, 6, 7, 8, 9, 10 and 12. Differential display was used to identify more putative candidate genes and to saturate the QTL regions of the genetic map. Eleven of the isolated cDNA clones were found to be derived from drought-inducible genes. Two of them were unique and did not match any genes in the GenBank, while nine were highly similar to cDNAs encoding known proteins, including a DnaJ-related protein, a zinc-finger protein, a protease inhibitor, a glutathione-S-transferase, a DNA recombinase, and a protease. Twelve new cDNA fragments were mapped onto the genetic linkage map; seven of these mapped inside, or in close proximity to, the targeted QTL regions determining root thickness and osmotic adjustment capacity. The gene I12A1, which codes for a UDP-glucose 4-epimerase homolog, was identified as a putative target gene within the prt7.1/brt7.1 QTL region, as it is involved in the cell wall biogenesis pathway and hence may be implicated in modulating the ability of rice roots to penetrate further into the substratum when exposed to drought conditions. RNAs encoding elongation factor 1, a DnaJ-related protein, and a homolog of wheat zinc-finger protein were more prominently induced in the leaves of IR62266 (the lowland rice parent of the mapping materials used) than in those of CT9993 (the upland rice parent) under drought conditions. Homologs of 18S ribosomal RNA, and mRNAs for a multiple-stress induced zinc-finger protein, a protease inhibitor, and a glutathione-S-transferase were expressed at significantly higher levels in CT9993 than in IR62266. Thus several genes involved in the regulation of DNA structure and mRNA translation were found to be drought-regulated, and may be implicated in drought resistance.Communicated by R. Hagemann     

Fine mapping of the grain chalkiness QTL qPGWC-7 in rice (Oryza sativa L.)

Chalkiness of rice grain is an important quality component of rice, as it has a profound influence on eating and milling qualities. We has determined the inheritance of percentage of grain with chalkiness (PGWC) using a set of chromosome segment substitution lines, made from a cross between cv. PA64s and cv. 9311. Two loci controlling PGWC, designated as qPGWC-6 and qPGWC-7, were located on, respectively, chromosomes 6 and 7. Comparisons were made between C-51 (a CSSL harbouring qPGWC-7 and having a chalky endosperm) and the recurrent parent 9311 (translucent endosperm) to characterize the physical and chemical differences between translucent and chalky endosperm. Unlike the translucent endosperm, the chalky endosperm contains loosely packed starch granules, and there were significant difference between C-51 and 9311 for amylopectin structure and degree of crystallinity, but not for either amylose content or starch viscosity. Segregation analysis of the F₂ population from the cross between C-51 and 9311 showed PGWC is a semi-dominant trait, controlled by single nuclear gene. A large F₂ population was constructed from the cross C51 × 9311, and used for the fine mapping of qPGWC-7, which was located to a 44-kb DNA fragment, containing thirteen predicted genes. This result provides a springboard for the map-based cloning of qPGWC-7 and allowed for marker-assisted selection for endosperm texture.     

QTL analysis and mapping of pi21, a recessive gene for field resistance to rice blast in Japanese upland rice

Field resistance is defined as the resistance that allows effective control of a parasite under natural field conditions and is durable when exposed to new races of that parasite. To identify the genes for field resistance to rice blast, quantitative trait loci (QTLs) conferring field resistance to rice blast in Japanese upland rice were detected and mapped using RFLP and SSR markers. QTL analysis was carried out in F₄ progeny lines from the cross between Nipponbare (moderately susceptible, lowland) and Owarihatamochi (resistant, upland). Two QTLs were detected on chromosome 4 and one QTL was detected on each of chromosomes 9 and 12. The phenotypic variation explained by each QTL ranged from 7.9 to 45.7% and the four QTLs explained 66.3% of the total phenotypic variation. Backcrossed progeny lines were developed to transfer the QTL with largest effect using the susceptible cultivar Aichiasahi as a recurrent parent. Among 82 F₃ lines derived from the backcross, resistance segregated in the expected ratio of resistant 1 : heterozygous 2 : susceptible 1. The average score for blast resistance measured in the field was 4.2 ± 0.67, 7.5 ± 0.51and 8.2 ± 0.66, for resistant, heterozygous and susceptible groups, respectively. The resistance gene, designated pi21, was mapped on chromosome 4 as a single recessive gene between RFLP marker loci G271 and G317 at a distance of 5.0 cM and 8.5 cM, respectively. The relationship to previously reported major genes and QTLs conferring resistance to blasts, and the significance of marker-assisted selection to improve field resistance, are discussed. Received: 8 June 2000 / Accepted: 24 November 2000     

Genetic dissection of rice blast resistance by QTL mapping approach using an F3 population

Rice blast is one of the major fungal diseases that badly reduce rice production in Asia including Malaysia. There is not much information on identification of QTLs as well as linked markers and their association with blast resistance within local rice cultivars. In order to understanding of the genetic control of blast in the F₃ families from indica rice cross Pongsu seribu2/Mahsuri, an analysis of quantitative trait loci against one of the highly virulent Malaysian rice blast isolate Magnaporthe oryzae, P5.0 was carried out. Result indicated that partial resistance to this pathotype observed in the present study was controlled by multiple loci or different QTLs. In QTL analysis in F₃ progeny fifteen QTLs on chromosomes 1, 2, 3, 5, 6, 11 and 12 for resistance to blast nursery tests was identified. Three of detected QTLs (qRBr-6.1, qRBr-11.4, and qRBr-12.1) had significant threshold (LOD >3) and approved by both IM and CIM methods. Twelve suggestive QTLs, qRBr-1.2, qRBr-2.1, qRBr-4.1, qRBr-5.1, qRBr-6.2, qRBr-6.3, qRBr-8.1, qRBr-10.1, qRBr-10.2, qRBr-11.1, qRBr-11.2 and qRBr-11.3) with Logarithmic of Odds (LOD) <3.0 or LRS <15) were distributed on chromosomes 1, 2, 4, 5, 6, 8, 10, and 11. Most of the QTLs detected using single isolate had the resistant alleles from Pongsu seribu 2 which involved in the resistance in the greenhouse. We found that QTLs detected for deferent traits for the using isolate were frequently located in similar genomic regions. Inheritance study showed among F₃ lines resistance segregated in the expected ratio of 15: 1 for resistant to susceptible. The average score for blast resistance measured in the green house was 3.15, 1.98 and 29.95 % for three traits, BLD, BLT and % DLA, respectively.     

Efficacy of microarray profiling data combined with QTL mapping for the identification of a QTL gene controlling the initial growth rate in rice

Seedling vigor, which is controlled by many quantitative trait loci (QTLs), is one of several important agronomic traits for direct-seedling rice systems. However, isolating these QTL genes is laborious and expensive. Here, we combined QTL mapping and microarray profiling to identify QTL genes for seedling vigor. By performing QTL mapping using 82 backcross inbred lines (BILs) of the Koshihikari (japonica) and Habataki (indica) cultivars for the rice initial growth, we identified two QTLs, early-stage plant development1/2 (qEPD1 and qEPD2), whose Koshihikari alleles promote plant height and/or leaf sheath length. Phenotypic analysis of the two substituted lines carrying the Habataki qEPD1 or qEPD2 allele revealed that qEPD2 functioned more dominantly for the initial growth of rice. From the microarray experiment, 55 and 45 candidate genes were found in the qEPD1 and qEPD2 genomic regions, which are expressed differentially between each substitution line (SL) and Koshihikari. Gibberellin 20 oxidase-2 (OsGA20ox2), which is identical to Semi Dwarf1 (SD1), was included among the 55 candidate genes of qEPD1, whereas its paralog, OsGA20ox1, was included among the 45 candidate genes of qEPD2. Consistently, introduction of the Koshihikari OsGA20ox1 allele into SL(qEPD2) increaseed its plant height and leaf sheath length significantly relative to the introduction of the Habataki OsGA20ox1 allele. Therefore, microarray profiling could be useful for rapidly screening QTL candidate genes. We concluded that OsGA20ox1 and OsGA20ox2 (SD1) function during the initial growth of rice, but OsGA20ox1 plays a dominant role in increasing plant height and leaf sheath length at the initial growth stage.     

Haplotypic QTL mapping in an outbred pedigree

An offspring genome can be viewed as a mosaic of chromosomal segments or haplotypes contributed by multiple founders in any quantitative trait locus (QTL) detection study but tracing these is especially complex to achieve for outbred pedigrees. QTL haplotypes can be traced from offspring back to individual founders in outbred pedigrees by combining founder-origin probabilities with fully informative flanking markers. This haplotypic method was illustrated for QTL detection using a three-generation pedigree for a woody perennial plant, Pinus taeda L. Growth rate was estimated using height measurements from ages 2 to 10 years. Using simulated and actual datasets, power of the experimental design was shown to be efficient for detecting QTLs of large effect. Using interval mapping and fully informative markers, a large QTL accounting for 11.3% of the phenotypic variance in the growth rate was detected. This same QTL was expressed at all ages for height, accounting for 7.9-12.2% of the phenotypic variance. A mixed-model inheritance was more appropriate for describing genetic architecture of growth curves in P. taeda than a strictly polygenic model. The positive QTL haplotype was traced from the offspring to its contributing founder, GP3, then the haplotypic phase for GP3 was determined by assaying haploid megagametophytes. The positive QTL haplotype was a recombinant haplotype contributed by GP3. This study illustrates the combined power of fully informative flanking markers and founder origin probabilities for (1) estimating QTL haplotype magnitude, (2) tracing founder origin and (3) determining haplotypic transmission frequency.