scholarly journals Multiple trait multiple interval mapping of quantitative trait loci from inbred line crosses

BMC Genetics ◽  
2012 ◽  
Vol 13 (1) ◽  
pp. 67 ◽  
Author(s):  
Luciano Costa E Silva ◽  
Shengchu Wang ◽  
Zhao-Bang Zeng
Keyword(s):  
Quantitative Trait Loci ◽  
Inbred Line ◽  
Quantitative Trait ◽  
Interval Mapping ◽  
Multiple Trait ◽  
Multiple Interval Mapping ◽  
Trait Loci

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.

scholarly journals Multiple Interval Mapping for Quantitative Trait Loci

Genetics ◽  
1999 ◽  
Vol 152 (3) ◽  
pp. 1203-1216
Author(s):  
Chen-Hung Kao ◽  
Zhao-Bang Zeng ◽  
Robert D Teasdale
Keyword(s):  
Genetic Variation ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Genetic Parameters ◽  
Interval Mapping ◽  
Ratio Test ◽  
Data Set ◽  
Multiple Interval Mapping ◽  
Total Genetic Variation ◽  
Trait Loci

Abstract A new statistical method for mapping quantitative trait loci (QTL), called multiple interval mapping (MIM), is presented. It uses multiple marker intervals simultaneously to fit multiple putative QTL directly in the model for mapping QTL. The MIM model is based on Cockerham's model for interpreting genetic parameters and the method of maximum likelihood for estimating genetic parameters. With the MIM approach, the precision and power of QTL mapping could be improved. Also, epistasis between QTL, genotypic values of individuals, and heritabilities of quantitative traits can be readily estimated and analyzed. Using the MIM model, a stepwise selection procedure with likelihood ratio test statistic as a criterion is proposed to identify QTL. This MIM method was applied to a mapping data set of radiata pine on three traits: brown cone number, tree diameter, and branch quality scores. Based on the MIM result, seven, six, and five QTL were detected for the three traits, respectively. The detected QTL individually contributed from ∼1 to 27% of the total genetic variation. Significant epistasis between four pairs of QTL in two traits was detected, and the four pairs of QTL contributed ∼10.38 and 14.14% of the total genetic variation. The asymptotic variances of QTL positions and effects were also provided to construct the confidence intervals. The estimated heritabilities were 0.5606, 0.5226, and 0.3630 for the three traits, respectively. With the estimated QTL effects and positions, the best strategy of marker-assisted selection for trait improvement for a specific purpose and requirement can be explored. The MIM FORTRAN program is available on the worldwide web (http://www.stat.sinica.edu.tw/~chkao/).

scholarly journals Quantitative Trait Loci (QTL) that Underlie SCN Resistance in Soybean [Glycine max (L.) Merr.] PI438489B by ‘Hamilton’ Recombinant Inbred Line (RIL) Population

2017 ◽  
Vol 1 (3) ◽  
pp. 29-38 ◽  
Author(s):  
Kassem My Abdelmajid ◽  
Laura Ramos ◽  
David Hyten ◽  
Jason Bond ◽  
Abdelhafid Bendahmane ◽  
...  
Keyword(s):  
Glycine Max ◽  
Quantitative Trait Loci ◽  
Inbred Line ◽  
Recombinant Inbred Line ◽  
Quantitative Trait ◽  
Recombinant Inbred ◽  
Interval Mapping ◽  
Trait Loci ◽  
Glycine Max L ◽  
Scn Resistance

Soybean cyst nematode caused by Heterodera glycines Ichinohe is the most devastating pest in soybean [Glycine max (L.) Merr.]. Resistance to SCN is complex, polygenic, race and cultivar specific, and it is controlled by several quantitative trait loci (QTL). Our objective was to identify and map QTL for SCN resistance to races 3 (HG Type 0) and 5 (HG Type 2.5.7) using a high density SNP-based genetic linkage map based on the PI438489B by ‘Hamilton’ (PIxH, n=50) recombinant inbred line population. The PI438489B by Hamilton map contained 648 SNPs distributed on 31 LGs with coverage of 1,524.7 cM and an average distance of 2.35 cM between two markers (Kassem et al., 2011). Using interval mapping (IM) and composite interval mapping (CIM), eight QTL were identified for SCN resistance to races 3 and 5 on 7 different soybean chromosomes. Four QTL for resistance to SCN race 3 were identified and mapped on chromosomes 7, 13, 15, and 16. Similarly, four QTL for resistance to SCN race 5 were identified and mapped on chromosomes 5, 8, and 11. The QTL identified here will be highly beneficial in breeding programs to develop cultivars with resistance to both SCN races 3 and 5.

scholarly journals On the Differences Between Maximum Likelihood and Regression Interval Mapping in the Analysis of Quantitative Trait Loci

Genetics ◽  
2000 ◽  
Vol 156 (2) ◽  
pp. 855-865 ◽  
Author(s):  
Chen-Hung Kao
Keyword(s):  
Quantitative Trait Loci ◽  
Maximum Likelihood ◽  
Qtl Mapping ◽  
Quantitative Trait ◽  
Mean Squared Error ◽  
Interval Mapping ◽  
Size Difference ◽  
Ratio Test ◽  
Trait Loci ◽  
Variance Explained

AbstractThe differences between maximum-likelihood (ML) and regression (REG) interval mapping in the analysis of quantitative trait loci (QTL) are investigated analytically and numerically by simulation. The analytical investigation is based on the comparison of the solution sets of the ML and REG methods in the estimation of QTL parameters. Their differences are found to relate to the similarity between the conditional posterior and conditional probabilities of QTL genotypes and depend on several factors, such as the proportion of variance explained by QTL, relative QTL position in an interval, interval size, difference between the sizes of QTL, epistasis, and linkage between QTL. The differences in mean squared error (MSE) of the estimates, likelihood-ratio test (LRT) statistics in testing parameters, and power of QTL detection between the two methods become larger as (1) the proportion of variance explained by QTL becomes higher, (2) the QTL locations are positioned toward the middle of intervals, (3) the QTL are located in wider marker intervals, (4) epistasis between QTL is stronger, (5) the difference between QTL effects becomes larger, and (6) the positions of QTL get closer in QTL mapping. The REG method is biased in the estimation of the proportion of variance explained by QTL, and it may have a serious problem in detecting closely linked QTL when compared to the ML method. In general, the differences between the two methods may be minor, but can be significant when QTL interact or are closely linked. The ML method tends to be more powerful and to give estimates with smaller MSEs and larger LRT statistics. This implies that ML interval mapping can be more accurate, precise, and powerful than REG interval mapping. The REG method is faster in computation, especially when the number of QTL considered in the model is large. Recognizing the factors affecting the differences between REG and ML interval mapping can help an efficient strategy, using both methods in QTL mapping to be outlined.

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