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.

Identification of quantitative trait loci underlying fatty acid content of soybean (Glycine max), including main, epistatic and QTL×environment effects across multiple environments

2017 ◽  
Vol 68 (9) ◽  
pp. 842 ◽  
Author(s):  
Ning Xia ◽  
Depeng Wu ◽  
Xia Li ◽  
Weili Teng ◽  
Xue Zhao ◽  
...  
Keyword(s):  
Glycine Max ◽  
Fatty Acid ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Acid Content ◽  
Recombinant Inbred Lines ◽  
Fatty Acid Content ◽  
Environment Interaction ◽  
Epistatic Qtls ◽  
Trait Loci

The uses and nutritional value of soybean (Glycine max (L.) Merrill) oil are largely influenced by the levels and relative proportions in the seed of the five major fatty acids: oleic (OA), palmitic (PA), stearic (SA), linoleic (LLA), linolenic (LNA). The present study was undertaken to identify quantitative trait loci (QTLs) that are associated with fatty acid content (particularly OA) and to determine the effects of epistasis and the environment. The mapping population included 134 recombinant inbred lines (RILs) derived from soybean varieties Suinong10 and L-9. Phenotypic data of the two parents and their RILs were obtained at Harbin in 2013, 2014 and 2015. Nineteen QTLs associated with individual fatty acid content (six for OA, four for LNA, three for PA, two for SA, four for LLA) were identified. Twelve of these QTLs (four for OA, three for LNA, two for PA, one for SA, two for LLA) were detected with an additive main effect and/or additive × environment interaction effect in certain environments. Epistatic QTLs were identified for contents of OA (two QTLs), LNA (one QTL) and LLA (one QTL) in different environments, and which exhibited significant epistatic effects. Our observation of these additive and epistatic QTLs suggested that soybean possesses a complex network for fatty acid accumulation, which is valuable for marker-assisted selection.

scholarly journals Genetic Analysis of Recombinant Inbred Lines for Sorghum bicolor × Sorghum propinquum

2013 ◽  
Vol 3 (1) ◽  
pp. 101-108 ◽  
Author(s):  
Wenqian Kong ◽  
Huizhe Jin ◽  
Cleve D Franks ◽  
Changsoo Kim ◽  
Rajib Bandopadhyay ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Sorghum Bicolor ◽  
Genetic Map ◽  
Quantitative Trait ◽  
Recombinant Inbred ◽  
Growth And Development ◽  
Recombinant Inbred Lines ◽  
Sorghum Propinquum ◽  
Ril Population ◽  
Trait Loci

Abstract We describe a recombinant inbred line (RIL) population of 161 F5 genotypes for the widest euploid cross that can be made to cultivated sorghum (Sorghum bicolor) using conventional techniques, S. bicolor × Sorghum propinquum, that segregates for many traits related to plant architecture, growth and development, reproduction, and life history. The genetic map of the S. bicolor × S. propinquum RILs contains 141 loci on 10 linkage groups collectively spanning 773.1 cM. Although the genetic map has DNA marker density well-suited to quantitative trait loci mapping and samples most of the genome, our previous observations that sorghum pericentromeric heterochromatin is recalcitrant to recombination is highlighted by the finding that the vast majority of recombination in sorghum is concentrated in small regions of euchromatin that are distal to most chromosomes. The advancement of the RIL population in an environment to which the S. bicolor parent was well adapted (indeed bred for) but the S. propinquum parent was not largely eliminated an allele for short-day flowering that confounded many other traits, for example, permitting us to map new quantitative trait loci for flowering that previously eluded detection. Additional recombination that has accrued in the development of this RIL population also may have improved resolution of apices of heterozygote excess, accounting for their greater abundance in the F5 than the F2 generation. The S. bicolor × S. propinquum RIL population offers advantages over early-generation populations that will shed new light on genetic, environmental, and physiological/biochemical factors that regulate plant growth and development.

scholarly journals Genotype-Environment Interaction at Quantitative Trait Loci Affecting Sensory Bristle Number in Drosophila melanogaster

Genetics ◽  
1998 ◽  
Vol 149 (4) ◽  
pp. 1883-1898 ◽  
Author(s):  
Marjorie C Gurganus ◽  
James D Fry ◽  
Sergey V Nuzhdin ◽  
Elena G Pasyukova ◽  
Richard F Lyman ◽  
...  
Keyword(s):  
Drosophila Melanogaster ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Rank Order ◽  
Recombinant Inbred Lines ◽  
Environment Interaction ◽  
Genotype Environment Interaction ◽  
Bristle Number ◽  
A Genome ◽  
Trait Loci

AbstractThe magnitude of segregating variation for bristle number in Drosophila melanogaster exceeds that predicted from models of mutation-selection balance. To evaluate the hypothesis that genotype-environment interaction (GEI) maintains variation for bristle number in nature, we quantified the extent of GEI for abdominal and sternopleural bristles among 98 recombinant inbred lines, derived from two homozygous laboratory strains, in three temperature environments. There was considerable GEI for both bristle traits, which was mainly attributable to changes in rank order of line means. We conducted a genome-wide screen for quantitative trait loci (QTLs) affecting bristle number in each sex and temperature environment, using a dense (3.2-cM) marker map of polymorphic insertion sites of roo transposable elements. Nine sternopleural and 11 abdominal bristle number QTLs were detected. Significant GEI was exhibited by 14 QTLs, but there was heterogeneity among QTLs in their sensitivity to thermal and sexual environments. To further evaluate the hypothesis that GEI maintains variation for bristle number, we require estimates of allelic effects across environments at genetic loci affecting the traits. This level of resolution may be achievable for Drosophila bristle number because candidate loci affecting bristle development often map to the same location as bristle number QTLs.

Identification of quantitative trait loci underlying seed oil content of soybean including main, epistatic and QTL×environment effects in different regions of Northeast China

2017 ◽  
Vol 68 (7) ◽  
pp. 625 ◽  
Author(s):  
Weili Teng ◽  
Binbin Zhang ◽  
Qi Zhang ◽  
Wen Li ◽  
Depeng Wu ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Oil Content ◽  
Recombinant Inbred Lines ◽  
Soybean Seed ◽  
Environment Interaction ◽  
Additive Effects ◽  
Linkage Groups ◽  
Soybean Breeding ◽  
Trait Loci

Oil content is a primary trait in soybean and determines the quality of soy food, feed and oil product. Increasing oil content is a major objective of soybean breeding. The aims of the present study were to identify quantitative trait loci (QTLs) and epistatic QTLs associated with oil content in soybean seed by using 129 recombinant inbred lines derived from a cross between cultivar Dongnong 46 (oil content 22.53%) and the semi-wild line L-100 (oil content 17.33%). Phenotypic data were collected from 10 tested environments including Harbin in the years 2012–15, Hulan in 2013–15 and Acheng in 2013–15. A genetic linkage map including 213 simple sequence repeat markers in 18 chromosomes (or linkage groups) was constructed, covering ~3623.39 cM. Seven QTLs, located on five chromosomes (or linkage groups), were identified to be associated with oil content, explaining 2.24–17.54% of the phenotypic variation in multi-environments. Among these identified QTLs, five (qOIL-2, qOIL-4, qOIL-5, qOIL-6 and qOIL-7) were detected in more than five environments. Seven QTLs had additive and/or additive × environment interaction effects. QTLs with higher additive effects were more stable in multi-environments than those with lower additive effects. Moreover, five epistatic, pairwise QTLs were identified in different environments. The findings with respect to genetic architecture for oil content could be valuable for marker-assisted selection in soybean breeding programs for high oil content.

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.

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.

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