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 a

SUBMITTER: Bhandari P 

PROVIDER: S-EPMC9066766 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature