Mapping of quantitative trait loci controlling broomrape (Orobanche crenata Forsk.) resistance in faba bean (Vicia faba L.)

Genome ◽  
2002 ◽  
Vol 45 (6) ◽  
pp. 1057-1063 ◽  
Author(s):  
Belén Román ◽  
Ana M Torres ◽  
Diego Rubiales ◽  
Jose Ignacio Cubero ◽  
Zlatko Satovic
Keyword(s):  
Quantitative Trait Loci ◽  
Vicia Faba ◽  
Faba Bean ◽  
Quantitative Trait ◽  
Composite Interval Mapping ◽  
Interval Mapping ◽  
Phenotypic Variance ◽  
Orobanche Crenata ◽  
Resistant Parent ◽  
Trait Loci

Orobanche crenata Forsk. is a root parasite that produces devastating effects on many crop legumes and has become a limiting factor for faba bean production in the Mediterranean region. The efficacy of available control methods is minimal and breeding for broomrape resistance remains the most promising method of control. Resistance seems to be scarce and complex in nature, being a quantitative characteristic difficult to manage in breeding programmes. To identify and map the QTLs (quantitative trait loci) controlling the trait, 196 F2 plants derived from the cross between a susceptible and a resistant parent were analysed using isozymes, RAPD, seed protein genes, and microsatellites. F2- derived F3 lines were studied for broomrape resistance under field conditions. Of the 130 marker loci segregating in the F2 population, 121 could be mapped into 16 linkage groups. Simple interval mapping (SIM) and composite interval mapping (CIM) were performed using QTL Cartographer. Composite interval mapping using the maximum number of markers as cofactors was clearly the most efficient way to locate putative QTLs. Three QTLs for broomrape resistance were detected. One of the three QTLs explained more than 35% of the phenotypic variance, whereas the others accounted for 11.2 and 25.5%, respectively. This result suggests that broomrape resistance in faba bean can be considered a polygenic trait with major effects of a few single genes.Key words: Orobanche crenata, Vicia faba, QTL, broomrape resistance.

Quantitative Trait Loci That Control Vector Competence for Dengue-2 Virus in the Mosquito Aedes aegypti

Genetics ◽  
2000 ◽  
Vol 156 (2) ◽  
pp. 687-698
Author(s):  
Christopher F Bosio ◽  
Ruth E Fulton ◽  
Mike L Salasek ◽  
Barry J Beaty ◽  
William C Black
Keyword(s):  
Quantitative Trait Loci ◽  
Aedes Aegypti ◽  
Quantitative Trait ◽  
Composite Interval Mapping ◽  
Dengue Infection ◽  
Interval Mapping ◽  
Polymorphism Analysis ◽  
Minor Effect ◽  
Phenotypic Variance ◽  
Trait Loci

Abstract Quantitative trait loci (QTL) affecting the ability of the mosquito Aedes aegypti to become infected with dengue-2 virus were mapped in an F1 intercross. Dengue-susceptible A. aegypti aegypti were crossed with dengue refractory A. aegypti formosus. F2 offspring were analyzed for midgut infection and escape barriers. In P1 and F1 parents and in 207 F2 individuals, regions of 14 cDNA loci were analyzed with single-strand conformation polymorphism analysis to identify and orient linkage groups with respect to chromosomes I–III. Genotypes were also scored at 57 RAPD-SSCP loci, 5 (TAG)n microsatellite loci, and 6 sequence-tagged RAPD loci. Dengue infection phenotypes were scored in 86 F2 females. Two QTL for a midgut infection barrier were detected with standard and composite interval mapping on chromosomes II and III that accounted for ∼30% of the phenotypic variance (σp2) in dengue infection and these accounted for 44 and 56%, respectively, of the overall genetic variance (σg2). QTL of minor effect were detected on chromosomes I and III, but these were not detected with composite interval mapping. Evidence for a QTL for midgut escape barrier was detected with standard interval mapping but not with composite interval mapping on chromosome III.

scholarly journals Time-Related Mapping of Quantitative Trait Loci Underlying Tiller Number in Rice

Genetics ◽  
1999 ◽  
Vol 151 (1) ◽  
pp. 297-303 ◽  
Author(s):  
Wei-Ren Wu ◽  
Wei-Ming Li ◽  
Ding-Zhong Tang ◽  
Hao-Ran Lu ◽  
A J Worland
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Composite Interval Mapping ◽  
Interval Mapping ◽  
Tiller Number ◽  
Multiple Trait ◽  
Phenotypic Data ◽  
Recombinant Inbred Population ◽  
Trait Loci ◽  
Final Observation

Abstract Using time-related phenotypic data, methods of composite interval mapping and multiple-trait composite interval mapping based on least squares were applied to map quantitative trait loci (QTL) underlying the development of tiller number in rice. A recombinant inbred population and a corresponding saturated molecular marker linkage map were constructed for the study. Tiller number was recorded every 4 or 5 days for a total of seven times starting at 20 days after sowing. Five QTL were detected on chromosomes 1, 3, and 5. These QTL explained more than half of the genetic variance at the final observation. All the QTL displayed an S-shaped expression curve. Three QTL reached their highest expression rates during active tillering stage, while the other two QTL achieved this either before or after the active tillering stage.

Bayesian Mapping of Multiple Quantitative Trait Loci From Incomplete Inbred Line Cross Data

Genetics ◽  
1998 ◽  
Vol 148 (3) ◽  
pp. 1373-1388
Author(s):  
Mikko J Sillanpää ◽  
Elja Arjas
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Composite Interval Mapping ◽  
Backcross Progeny ◽  
Random Variable ◽  
Interval Mapping ◽  
Mapping Method ◽  
Structure Mapping ◽  
Standard Interval ◽  
Trait Loci

Abstract A novel fine structure mapping method for quantitative traits is presented. It is based on Bayesian modeling and inference, treating the number of quantitative trait loci (QTLs) as an unobserved random variable and using ideas similar to composite interval mapping to account for the effects of QTLs in other chromosomes. The method is introduced for inbred lines and it can be applied also in situations involving frequent missing genotypes. We propose that two new probabilistic measures be used to summarize the results from the statistical analysis: (1) the (posterior) QTL-intensity, for estimating the number of QTLs in a chromosome and for localizing them into some particular chromosomal regions, and (2) the location wise (posterior) distributions of the phenotypic effects of the QTLs. Both these measures will be viewed as functions of the putative QTL locus, over the marker range in the linkage group. The method is tested and compared with standard interval and composite interval mapping techniques by using simulated backcross progeny data. It is implemented as a software package. Its initial version is freely available for research purposes under the name Multimapper at URL http://www.rni.helsinki.fi/~mjs.

scholarly journals Quantitative Trait Loci Affecting Survival and Fertility-Related Traits in Caenorhabditis elegans Show Genotype-Environment Interactions, Pleiotropy and Epistasis

Genetics ◽  
1999 ◽  
Vol 153 (3) ◽  
pp. 1233-1243 ◽  
Author(s):  
David R Shook ◽  
Thomas E Johnson
Keyword(s):  
Caenorhabditis Elegans ◽  
Quantitative Trait Loci ◽  
Population Growth ◽  
Quantitative Trait ◽  
Age Of Onset ◽  
Inbred Strains ◽  
Interval Mapping ◽  
Phenotypic Variance ◽  
Epistatic Interactions ◽  
Trait Loci

Abstract We have identified, using composite interval mapping, quantitative trait loci (QTL) affecting a variety of life history traits (LHTs) in the nematode Caenorhabditis elegans. Using recombinant inbred strains assayed on the surface of agar plates, we found QTL for survival, early fertility, age of onset of sexual maturity, and population growth rate. There was no overall correlation between survival on solid media and previous measures of survival in liquid media. Of the four survival QTL found in these two environments, two have genotype-environment interactions (GEIs). Epistatic interactions between markers were detected for four traits. A multiple regression approach was used to determine which single markers and epistatic interactions best explained the phenotypic variance for each trait. The amount of phenotypic variance accounted for by genetic effects ranged from 13% (for internal hatching) to 46% (for population growth). Epistatic effects accounted for 9–11% of the phenotypic variance for three traits. Two regions containing QTL that affected more than one fertility-related trait were found. This study serves as an example of the power of QTL mapping for dissecting the genetic architecture of a suite of LHTs and indicates the potential importance of environment and GEIs in the evolution of this architecture.

scholarly journals Mapping Quantitative Trait Loci for Resistance to Rice Blast

2011 ◽  
Vol 101 (2) ◽  
pp. 176-181 ◽  
Author(s):  
Y. Jia ◽  
G. Liu
Keyword(s):  
Quantitative Trait Loci ◽  
Phenotypic Variation ◽  
Quantitative Trait ◽  
Composite Interval Mapping ◽  
Rice Blast ◽  
Ssr Marker ◽  
Interval Mapping ◽  
Chromosome 8 ◽  
Chromosome 12 ◽  
Trait Loci

Quantitative trait loci (QTLs) conferring resistance to rice blast, caused by Magnaporthe oryzae, have been under-explored. In the present study, composite interval mapping was used to identify the QTLs that condition resistance to the 6 out of the 12 common races (IB1, IB45, IB49, IB54, IC17, and ID1) of M. oryzae using a recombinant inbred line (RIL) population derived from a cross of the moderately susceptible japonica cultivar Lemont with the moderately resistant indica cultivar Jasmine 85. Disease reactions of 227 F7 RILs were determined using a category scale of ratings from 0, representing the most resistant, to 5, representing the most susceptible. A total of nine QTLs responsive to different degrees of phenotypic variation ranging from 5.17 to 26.53% were mapped on chromosomes 3, 8, 9, 11, and 12: qBLAST3 at 1.9 centimorgans (cM) to simple sequence repeat (SSR) marker RM282 on chromosome 3 to IB45 accounting for 5.17%; qBLAST8.1 co-segregated with SSR marker RM1148 to IB49 accounting for 6.69%, qBLAST8.2 at 0.1 cM to SSR marker RM72 to IC17 on chromosome 8 accounting for 7.22%; qBLAST9.1 at 0.1 cM to SSR marker RM257 to IB54, qBLAST9.2 at 2.1 cM to SSR marker RM108, and qBLAST9.3 at 0.1 cM to SSR marker RM215 to IC17 on chromosome 9 accounting for 4.64, 7.62, and 4.49%; qBLAST11 at 2.2 cM to SSR marker RM244 to IB45 and IB54 on chromosome 11 accounting for 26.53 and 19.60%; qBLAST12.1 at 0.3 cM to SSR marker OSM89 to IB1 on chromosome 12 accounting for 5.44%; and qBLAST12.2 at 0.3 and 0.1 cM to SSR marker OSM89 to IB49 and ID1 on chromosome 12 accounting for 9.7 and 10.18% of phenotypic variation, respectively. This study demonstrates the usefulness of tagging blast QTLs using physiological races by composite interval mapping.

Mapping quantitative trait loci for pre-harvest sprouting resistance in white-grained winter wheat line CA 0431

2013 ◽  
Vol 64 (6) ◽  
pp. 573 ◽  
Author(s):  
X. L. Miao ◽  
Y. J. Zhang ◽  
X. C. Xia ◽  
Z. H. He ◽  
Y. Zhang ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Winter Wheat ◽  
Quantitative Trait ◽  
Interval Mapping ◽  
Field Trials ◽  
Economic Losses ◽  
Phenotypic Variance ◽  
Wheat Line ◽  
F2 Population ◽  
Trait Loci

Pre-harvest sprouting (PHS) in wheat severely reduces yield and end-use quality, resulting in substantial economic losses. The Chinese winter wheat line CA 0431, with white grain, showed high PHS resistance for many years. To identify quantitative trait loci (QTLs) of PHS resistance in this line, 220 F2 plants and the corresponding F2 : 3 lines derived from a cross between CA 0431 and the PHS-susceptible cultivar Zhongyou 206 were used for PHS testing and QTL analysis. Field trials were conducted in Beijing during the 2010–11 and 2011–12 cropping seasons, and in Anyang during 2011–12. PHS resistance was evaluated by assessing the sprouting responses of intact spikes. In total, 1444 molecular markers were used to screen the parents, and 31 markers with polymorphisms between the resistant and susceptible bulks were used to genotype the entire F2 population. Broad-sense heritability of sprouting rate was 0.71 across environments. Inclusive composite interval mapping identified four QTLs, QPhs.caas-2BL, QPhs.caas-3AS.1, QPhs.caas-3AS.2, and QPhs.caas-3AL, each explaining 2.8–27.7% of the phenotypic variance across environments. The QTLs QPhs.caas-3AS.1, QPhs.caas-3AS.2, and QPhs.caas-3AL were located at similar positions to QTLs reported previously, whereas QPhs.caas-2BL is likely a new QTL flanked by markers Xbarc1042 and Xmag3319. Line CA 0431 and the identified markers can be used in breeding programs targeting improvement of PHS resistance for white-kernel wheat.

Inclusive composite-interval mapping reveals quantitative trait loci for plant architectural traits in sorghum (Sorghum bicolor)

2019 ◽  
Vol 70 (8) ◽  
pp. 659
Author(s):  
Huawen Zhang ◽  
Runfeng Wang ◽  
Bin Liu ◽  
Erying Chen ◽  
Yanbing Yang ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Sorghum Bicolor ◽  
Quantitative Trait ◽  
Composite Interval Mapping ◽  
Recombinant Inbred Lines ◽  
Interval Mapping ◽  
Environment Interaction ◽  
Trait Loci ◽  
Inclusive Composite Interval Mapping ◽  
Architectural Traits

Architecture-efficient sorghum (Sorghum bicolor (L.) Moench) has erect leaves forming a compact canopy that enables highly effective utilisation of solar radiation; it is suitable for high-density planting, resulting in an elevated overall production. Development of sorghum ideotypes with optimal plant architecture requires knowledge of the genetic basis of plant architectural traits. The present study investigated seven production-related architectural traits by using 181 sorghum recombinant inbred lines (RILs) with contrasting architectural phenotypes developed from the cross Shihong 137 × L-Tian. Parents along with RILs were phenotyped for plant architectural traits for two consecutive years (2012, 2013) at two locations in the field. Analysis of variance revealed significant (P ≤ 0.05) differences among RILs for architectural traits. All traits showed medium to high broad-sense heritability estimates (0.43–0.94) and significant (P ≤ 0.05) genotype × environment effects. We employed 181 simple sequence repeat markers to identify quantitative trait loci (QTLs) and the effects of QTL × environment interaction based on the inclusive composite interval mapping algorithm. In total, 53 robust QTLs (log of odds ≥4.68) were detected for these seven traits and explained 2.11–12.11% of phenotypic variation. These QTLs had small effects of QTL × environment interaction and yet significant epistatic effects, indicating that they could stably express across environments but influence phenotypes through strong interaction with non-allelic loci. The QTLs and linked markers need to be verified through function and candidate-gene analyses. The new knowledge of the genetic regulation of architectural traits in the present study will provide a theoretical basis for the genetic improvement of architectural traits in sorghum.

scholarly journals Quantitative Trait Loci Mapping of Resistance to Sugarcane Mosaic Virus in Maize

1999 ◽  
Vol 89 (8) ◽  
pp. 660-667 ◽  
Author(s):  
Xianchun Xia ◽  
Albrecht E. Melchinger ◽  
Lissy Kuntze ◽  
Thomas Lübberstedt
Keyword(s):  
Quantitative Trait Loci ◽  
Mosaic Virus ◽  
Quantitative Trait ◽  
Virus Disease ◽  
Interval Mapping ◽  
Field Trials ◽  
Sugarcane Mosaic Virus ◽  
Phenotypic Variance ◽  
Environment Interaction ◽  
Trait Loci

Sugarcane mosaic virus (SCMV) is an important virus disease of maize (Zea mays) in Europe. In this study, we mapped and characterized quantitative trait loci (QTL) affecting resistance to SCMV in a maize population consisting of 219 F3 or immortalized F2 families from the cross of two European maize inbreds, D32 (resistant) × D145 (susceptible). Resistance was evaluated in replicated field trials across two environments under artificial inoculation. The method of composite interval mapping was employed for QTL detection with a linkage map based on 87 restriction fragment length polymorphism and 7 mapped microsatellite markers. Genotypic and genotype × environment interaction variances for SCMV resistance were highly significant in the population. Heritabilities ranged from 0.77 to 0.94 for disease scores recorded on seven consecutive dates. Five QTL for SCMV resistance were identified on chromosomes 1, 3, 5, 6, and 10 in the joint analyses. Two major QTL on chromosomes 3 and 6 were detected consistently in both environments. Significant epistatic effects were found among some of these QTL. A simultaneous fit with all QTL in the joint analyses explained between 70 and 77% of the phenotypic variance observed at various stages of plant development. Resistance to SCMV was correlated with plant height and days to anthesis.

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