scholarly journals Inclusive Composite Interval Mapping of Quantitative Trait Genes

2009 ◽  
Vol 35 (2) ◽  
pp. 239-245 ◽  
Author(s):  
Jian-Kang WANG
Keyword(s):  
Quantitative Trait ◽  
Composite Interval Mapping ◽  
Interval Mapping ◽  
Inclusive Composite Interval Mapping

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 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 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.

scholarly journals Inferences regarding the numbers and locations of QTLs under multiple-QTL models using interval mapping and composite interval mapping

2003 ◽  
Vol 82 (2) ◽  
pp. 139-149 ◽  
Author(s):  
THEODORE W. CORNFORTH ◽  
ANTHONY D. LONG
Keyword(s):  
Simulation Study ◽  
Quantitative Trait ◽  
Composite Interval Mapping ◽  
Genetic Architecture ◽  
Interval Mapping ◽  
Summary Statistics ◽  
Precise Estimation ◽  
Multiple Qtl ◽  
Complex Architectures ◽  
Erroneous Inferences

This paper examines the properties of likelihood maps generated by interval mapping (IM) and composite interval mapping (CIM), two widely used methods for detecting quantitative trait loci (QTLs). We evaluate the usefulness of interpretations of entire maps, rather than only evaluating summary statistics that consider isolated features of maps. A simulation study was performed in which traits with varying genetic architectures, including 20–40 QTLs per chromosome, were examined with both IM and CIM under different marker densities and sample sizes. IM was found to be an unreliable tool for precise estimation of the number and locations of individual QTLs, although it has greater power for simply detecting the presence of QTLs than CIM. The ability of CIM to resolve the correct number of QTLs and to estimate their locations correctly is good if there are three or fewer QTLs per 100 centiMorgans, but can lead to erroneous inferences for more complex architectures. When the underlying genetic architecture of a trait consists of several QTLs with randomly distributed effects and locations likelihood profiles were often indicative of a few underlying genes of large effect. Studies that have detected more than a few QTLs per chromosome should be interpreted with caution.

scholarly journals Mapping quantitative trait loci for yield-related traits and predicting candidate genes for grain weight in maize

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Yanming Zhao ◽  
Chengfu Su
Keyword(s):  
Quantitative Trait Loci ◽  
Candidate Genes ◽  
Quantitative Trait ◽  
Composite Interval Mapping ◽  
Developmental Stages ◽  
Interval Mapping ◽  
Kernel Weight ◽  
Grain Weight ◽  
Trait Loci ◽  
Stable Qtls

Abstract Quantitative trait loci (QTLs) mapped in different genetic populations are of great significance for marker-assisted breeding. In this study, an F2:3 population were developed from the crossing of two maize inbred lines SG-5 and SG-7 and applied to QTL mapping for seven yield-related traits. The seven traits included 100-kernel weight, ear length, ear diameter, cob diameter, kernel row number, ear weight, and grain weight per plant. Based on an ultra-high density linkage map, a total of thirty-three QTLs were detected for the seven studied traits with composite interval mapping (CIM) method, and fifty-four QTLs were indentified with genome-wide composite interval mapping (GCIM) methods. For these QTLs, Fourteen were both detected by CIM and GCIM methods. Besides, eight of the thirty QTLs detected by CIM were identical to those previously mapped using a F2 population (generating from the same cross as the mapping population in this study), and fifteen were identical to the reported QTLs in other recent studies. For the fifty-four QTLs detected by GCIM, five of them were consistent with the QTLs mapped in the F2 population of SG-5 × SG-7, and twenty one had been reported in other recent studies. The stable QTLs associated with grain weight were located on maize chromosomes 2, 5, 7, and 9. In addition, differentially expressed genes (DEGs) between SG-5 and SG-7 were obtained from the transcriptomic profiling of grain at different developmental stages and overlaid onto the stable QTLs intervals to predict candidate genes for grain weight in maize. In the physical intervals of confirmed QTLs qKW-7, qEW-9, qEW-10, qGWP-6, qGWP-8, qGWP-10, qGWP-11 and qGWP-12, there were 213 DEGs in total. Finally, eight genes were predicted as candidate genes for grain size/weight. In summary, the stable QTLs would be reliable and the candidate genes predicted would be benefit for maker assisted breeding or cloning.

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.

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