Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-03T08:16:50.895Z Has data issue: false hasContentIssue false

Inferences regarding the numbers and locations of QTLs under multiple-QTL models using interval mapping and composite interval mapping

Published online by Cambridge University Press:  11 November 2003

THEODORE W. CORNFORTH
Affiliation:
Department of Ecology and Evolutionary Biology, University of California, Irvine, California 92697-2525, USA Present address: Institute of Neuroscience, 1254 University of Oregon, Eugene, OR 97403-1254, USA.
ANTHONY D. LONG
Affiliation:
Department of Ecology and Evolutionary Biology, University of California, Irvine, California 92697-2525, USA

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

This paper examines the properties of likelihood maps generated by interval mapping (IM) and composite interval mapping (CIM), two widely used methods for detecting quantitative trait loci (QTLs). We evaluate the usefulness of interpretations of entire maps, rather than only evaluating summary statistics that consider isolated features of maps. A simulation study was performed in which traits with varying genetic architectures, including 20–40 QTLs per chromosome, were examined with both IM and CIM under different marker densities and sample sizes. IM was found to be an unreliable tool for precise estimation of the number and locations of individual QTLs, although it has greater power for simply detecting the presence of QTLs than CIM. The ability of CIM to resolve the correct number of QTLs and to estimate their locations correctly is good if there are three or fewer QTLs per 100 centiMorgans, but can lead to erroneous inferences for more complex architectures. When the underlying genetic architecture of a trait consists of several QTLs with randomly distributed effects and locations likelihood profiles were often indicative of a few underlying genes of large effect. Studies that have detected more than a few QTLs per chromosome should be interpreted with caution.

Type
Research Article
Copyright
© 2003 Cambridge University Press