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Mapping epistatic quantitative trait loci.

Laurie C, Wang S, Carlini-Garcia LA, Zeng ZB - BMC Genet. (2014)

Bottom Line: In the first stage, main effect QTL are searched and mapped.The search for main effect QTL is robust and does not bias the search for epistatic QTL due to a genetic property associated with the orthogonal genetic model that the additive and additive by additive variances are independent despite of linkage.This method provides an effective and powerful solution to map multiple QTL with complex epistatic pattern.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, University of Alabama, Tuscaloosa AL, USA. claurie@bama.ua.edu.

ABSTRACT

Background: How to map quantitative trait loci (QTL) with epistasis efficiently and reliably has been a persistent problem for QTL mapping analysis. There are a number of difficulties for studying epistatic QTL. Linkage can impose a significant challenge for finding epistatic QTL reliably. If multiple QTL are in linkage and have interactions, searching for QTL can become a very delicate issue. A commonly used strategy that performs a two-dimensional genome scan to search for a pair of QTL with epistasis can suffer from low statistical power and also may lead to false identification due to complex linkage disequilibrium and interaction patterns.

Results: To tackle the problem of complex interaction of multiple QTL with linkage, we developed a three-stage search strategy. In the first stage, main effect QTL are searched and mapped. In the second stage, epistatic QTL that interact significantly with other identified QTL are searched. In the third stage, new epistatic QTL are searched in pairs. This strategy is based on the consideration that most genetic variance is due to the main effects of QTL. Thus by first mapping those main-effect QTL, the statistical power for the second and third stages of analysis for mapping epistatic QTL can be maximized. The search for main effect QTL is robust and does not bias the search for epistatic QTL due to a genetic property associated with the orthogonal genetic model that the additive and additive by additive variances are independent despite of linkage. The model search criterion is empirically and dynamically evaluated by using a score-statistic based resampling procedure. We demonstrate through simulations that the method has good power and low false positive in the identification of QTL and epistasis.

Conclusion: This method provides an effective and powerful solution to map multiple QTL with complex epistatic pattern. The method has been implemented in the user-friendly computer software Windows QTL Cartographer. This will greatly facilitate the application of the method for QTL mapping data analysis.

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Related in: MedlinePlus

Comparison of score statistic and threshold with likelihood ratio statistic and permutation threshold.A, compares threshold values (y-axis) across significance levels α (x-axis) with score threshold indicated by the dotted curve and permutation threshold indicated by the solid curve. B, compares the likelihood ratio profile (solid) and score statistic profile (dotted) for one replication; the solid and dotted horizontal lines represent the permutation threshold and score threshold (α = 5%), respectively.
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Fig1: Comparison of score statistic and threshold with likelihood ratio statistic and permutation threshold.A, compares threshold values (y-axis) across significance levels α (x-axis) with score threshold indicated by the dotted curve and permutation threshold indicated by the solid curve. B, compares the likelihood ratio profile (solid) and score statistic profile (dotted) for one replication; the solid and dotted horizontal lines represent the permutation threshold and score threshold (α = 5%), respectively.

Mentions: Interval mapping for detecting one QTL using the likelihood ratio statistic and permutation threshold is well established in the literature [15]. In a sperate simulation of the model of no QTL, the score procedure for selecting the first QTL behaved as expected with respect to Type 1 error, as that for the permutation test (Figure 1), reinforcing the comparison results of [15]. Of couse, in this study we extended the score statistic for all the three-stages and multiple steps within the statges. It turns out that score thresholds are very similar at different steps of search process for QTL as shwon in Figure 2 for stage 1, demonstaing that the score statistic genome-wide threshold mainly depends on genome size and not on model size (QTL number).Figure 1


Mapping epistatic quantitative trait loci.

Laurie C, Wang S, Carlini-Garcia LA, Zeng ZB - BMC Genet. (2014)

Comparison of score statistic and threshold with likelihood ratio statistic and permutation threshold.A, compares threshold values (y-axis) across significance levels α (x-axis) with score threshold indicated by the dotted curve and permutation threshold indicated by the solid curve. B, compares the likelihood ratio profile (solid) and score statistic profile (dotted) for one replication; the solid and dotted horizontal lines represent the permutation threshold and score threshold (α = 5%), respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4226885&req=5

Fig1: Comparison of score statistic and threshold with likelihood ratio statistic and permutation threshold.A, compares threshold values (y-axis) across significance levels α (x-axis) with score threshold indicated by the dotted curve and permutation threshold indicated by the solid curve. B, compares the likelihood ratio profile (solid) and score statistic profile (dotted) for one replication; the solid and dotted horizontal lines represent the permutation threshold and score threshold (α = 5%), respectively.
Mentions: Interval mapping for detecting one QTL using the likelihood ratio statistic and permutation threshold is well established in the literature [15]. In a sperate simulation of the model of no QTL, the score procedure for selecting the first QTL behaved as expected with respect to Type 1 error, as that for the permutation test (Figure 1), reinforcing the comparison results of [15]. Of couse, in this study we extended the score statistic for all the three-stages and multiple steps within the statges. It turns out that score thresholds are very similar at different steps of search process for QTL as shwon in Figure 2 for stage 1, demonstaing that the score statistic genome-wide threshold mainly depends on genome size and not on model size (QTL number).Figure 1

Bottom Line: In the first stage, main effect QTL are searched and mapped.The search for main effect QTL is robust and does not bias the search for epistatic QTL due to a genetic property associated with the orthogonal genetic model that the additive and additive by additive variances are independent despite of linkage.This method provides an effective and powerful solution to map multiple QTL with complex epistatic pattern.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, University of Alabama, Tuscaloosa AL, USA. claurie@bama.ua.edu.

ABSTRACT

Background: How to map quantitative trait loci (QTL) with epistasis efficiently and reliably has been a persistent problem for QTL mapping analysis. There are a number of difficulties for studying epistatic QTL. Linkage can impose a significant challenge for finding epistatic QTL reliably. If multiple QTL are in linkage and have interactions, searching for QTL can become a very delicate issue. A commonly used strategy that performs a two-dimensional genome scan to search for a pair of QTL with epistasis can suffer from low statistical power and also may lead to false identification due to complex linkage disequilibrium and interaction patterns.

Results: To tackle the problem of complex interaction of multiple QTL with linkage, we developed a three-stage search strategy. In the first stage, main effect QTL are searched and mapped. In the second stage, epistatic QTL that interact significantly with other identified QTL are searched. In the third stage, new epistatic QTL are searched in pairs. This strategy is based on the consideration that most genetic variance is due to the main effects of QTL. Thus by first mapping those main-effect QTL, the statistical power for the second and third stages of analysis for mapping epistatic QTL can be maximized. The search for main effect QTL is robust and does not bias the search for epistatic QTL due to a genetic property associated with the orthogonal genetic model that the additive and additive by additive variances are independent despite of linkage. The model search criterion is empirically and dynamically evaluated by using a score-statistic based resampling procedure. We demonstrate through simulations that the method has good power and low false positive in the identification of QTL and epistasis.

Conclusion: This method provides an effective and powerful solution to map multiple QTL with complex epistatic pattern. The method has been implemented in the user-friendly computer software Windows QTL Cartographer. This will greatly facilitate the application of the method for QTL mapping data analysis.

Show MeSH
Related in: MedlinePlus