Estimating the genetic architecture of quantitative traits

Zeng, Zhao‐Bang; Kao, C. R.; Basten, Christopher J.

doi:10.1017/s0016672399004255

Cited by 284 publications

(213 citation statements)

References 52 publications

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“…Multiple interval mapping (MIM) was proposed to map multiple QTL simultaneously Zeng et al 1999). MIM may have avoided the complicated background selection process associated with CIM, but introduced various model selection methods Wang et al 2005b).…”

Section: Discussionmentioning

confidence: 99%

A Modified Algorithm for the Improvement of Composite Interval Mapping

Ye²,

2007

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Composite interval mapping (CIM) is the most commonly used method for mapping quantitative trait loci (QTL) with populations derived from biparental crosses. However, the algorithm implemented in the popular QTL Cartographer software may not completely ensure all its advantageous properties. In addition, different background marker selection methods may give very different mapping results, and the nature of the preferred method is not clear. A modified algorithm called inclusive composite interval mapping (ICIM) is proposed in this article. In ICIM, marker selection is conducted only once through stepwise regression by considering all marker information simultaneously, and the phenotypic values are then adjusted by all markers retained in the regression equation except the two markers flanking the current mapping interval. The adjusted phenotypic values are finally used in interval mapping (IM). The modified algorithm has a simpler form than that used in CIM, but a faster convergence speed. ICIM retains all advantages of CIM over IM and avoids the possible increase of sampling variance and the complicated background marker selection process in CIM. Extensive simulations using two genomes and various genetic models indicated that ICIM has increased detection power, a reduced false detection rate, and less biased estimates of QTL effects.T HE rapid increase in availability of fine-scale genetic marker maps has led to the intensive use of QTL mapping in the genetic study of quan-

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Section: Discussionmentioning

confidence: 99%

A Modified Algorithm for the Improvement of Composite Interval Mapping

Ye²,

2007

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“…Detailed descriptions and discussions of the multiple interval mapping methodology can be found in Kao et al (1999), Zeng et al (1999) and Kao (2000).…”

Section: Multiple Interval Mappingmentioning

confidence: 99%

A comparison of regression interval mapping and multiple interval mapping for linked QTL

Mayer

2005

Heredity

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Regression interval mapping and multiple interval mapping are compared with regard to mapping linked quantitative trait loci (QTL) in inbred-line cross experiments. For that purpose, a simulation study was performed using genetic models with two linked QTL. Data were simulated for F 2 populations of different sizes and with all QTL and marker alleles fixed for alternative alleles in the parental lines. The criteria for comparison are power of QTL identification and the accuracy of the QTL position and effect estimates. Further, the estimates of the relative QTL variance are assessed. There are distinct differences in the QTL position estimates between the two methods. Multiple interval mapping tends to be more powerful as compared to regression interval mapping. Multiple interval mapping further leads to more accurate QTL position and QTL effect estimates. The superiority increased with wider marker intervals and larger population sizes. If QTL are in repulsion, the differences between the two methods are very pronounced. For both methods, the reduction of the marker interval size from 10 to 5 cM increases power and greatly improves QTL parameter estimates. This contrasts with findings in the literature for single QTL scenarios, where a marker density of 10 cM is generally considered as sufficient. The use of standard (asymptotic) statistical theory for the computation of the standard errors of the QTL position and effect estimates proves to give much too optimistic standard errors for regression interval mapping as well as for multiple interval mapping. Heredity (2005) 94, 599-605.

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“…There are several different strategies to map quantitative trait loci (Kearsey and Farquhar 1998), e.g., single-marker locus analysis (Liu 1998); simple interval mapping (Lander and Botstein 1989); composite interval mapping (Zeng 1993(Zeng , 1994Krajewski et al 2012); marker regression (Kearsey and Hyne 1994;Wu and Li 1994;Bocianowski and Krajewski 2009); Bayesian methods (Sillanpää and Arjas 1998); and multiple interval mapping Zeng et al 1999). The latter methods have been shown to yield better power of QTL detection than interval mapping and single-marker locus analysis (Liu 1998;Piepho 2000).…”

Section: Discussionmentioning

confidence: 99%

Mixed linear model approaches in mapping QTLs with epistatic effects by a simulation study

Bocianowski

Nowosad

2014

Euphytica

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Epistasis refers to the phenotypic effects of interactions between alleles of different loci. Statistical detection of such interactions remains to be the subject of presented studies. A method based on mixed linear model was developed for mapping QTLs with digenic epistasis. Reliable estimates of QTL additive and epistasis effects can be obtained by the maximum-likelihood estimate methods. Likelihood ratio and t statistics were combined for testing hypotheses about QTL effects (additive and epistasis effects). Monte Carlo simulations were conducted for evaluating the unbiasedness, accuracy and power for parameter estimation in QTL mapping. The results indicated that the mixed-model approaches could provide unbiased estimates for effects of QTLs. Additionally, the mixed-model approaches also showed high accuracy in mapping QTLs with epistasis effects. The information obtained in this study will be useful for manipulating the QTLs for plant breeding by marker assisted selection.

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Estimating the genetic architecture of quantitative traits

Cited by 284 publications

References 52 publications

A Modified Algorithm for the Improvement of Composite Interval Mapping

A Modified Algorithm for the Improvement of Composite Interval Mapping

A comparison of regression interval mapping and multiple interval mapping for linked QTL

Mixed linear model approaches in mapping QTLs with epistatic effects by a simulation study

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