postQTL: a QTL mapping R workflow to improve the accuracy of true positive loci identification

Abstract

Objective: The determination of the location of quantitative trait loci (QTL) (i.e., QTL mapping) is essential for identifying new genes. Various statistical methods are being incorporated into different QTL mapping functions. However, statistical errors and limitations may often occur in a QTL mapping, implying the risk of false positive errors and/or failing to detect a true positive QTL effect. We simulated the power to detect four simulated QTL in tomato using cim() and stepwiseqtl(), widely adopted QTL mapping functions, and QTL.gCIMapping(), a derivative of the composite interval mapping method. While there is general agreement that those three functions identified simulated QTL, missing or false positive QTL were observed, which were prevalent when more realistic data (such as smaller population size) were provided.

Results: To address this issue, we developed postQTL, a QTL mapping R workflow that incorporates (i) both cim() and stepwiseqtl(), (ii) widely used R packages developed for model selection, and (iii) automation to increase the accuracy, efficiency, and accessibility of QTL mapping. QTL mapping experiments on tomato F₂ populations in which QTL effects were simulated or calculated showed advantages of postQTL in QTL detection.

Keywords: Mapping; Model search; QTL; R workflow; Regularization.

Fig. 1

An overview of the postQTL R workflow

Fig. 2

Comparisons of QTL identified by cim(), QTL.gCIMapping(), and stepwiseqtl() for two differently sized populations. Each figure shows the frequency of QTL identified by 100 iterations. A, B Show simulated QTL (a total of four simulated QTL) identified (LOD > 3.0) for populations of 1000 or 100 F₂ individuals, respectively (e.g., the left panel in A shows all four simulated QTL were identified by cim() in at least 70 iterations, while two out of four simulated QTL were identified in less than 10 iterations). C, D Show potential false positive QTL identified (LOD > 3.0) for populations of 1000 or 100 F₂ individuals, respectively