Multiple trait analysis of genetic mapping for quantitative trait loci.

TLDR

Testing pleiotropy versus close linkage at a QTL locus informs genetic correlation understanding and breeding strategies aimed at breaking unfavorable linkage. The paper develops composite interval mapping–based models and statistical methods for multiple‑trait QTL analysis and formal tests of genetic correlation hypotheses. The authors implement composite interval mapping and devise procedures for joint mapping, pleiotropy, QTL‑by‑environment interaction, and distinguishing pleiotropy from close linkage. Joint analysis of correlated traits increases QTL detection power and parameter‑estimation precision, as shown by extensive simulation studies, and the pleiotropy‑versus‑linkage test yields insights for breeding.

Abstract

Abstract We present in this paper models and statistical methods for performing multiple trait analysis on mapping quantitative trait loci (QTL) based on the composite interval mapping method. By taking into account the correlated structure of multiple traits, this joint analysis has several advantages, compared with separate analyses, for mapping QTL, including the expected improvement on the statistical power of the test for QTL and on the precision of parameter estimation. Also this joint analysis provides formal procedures to test a number of biologically interesting hypotheses concerning the nature of genetic correlations between different traits. Among the testing procedures considered are those for joint mapping, pleiotropy, QTL by environment interaction, and pleiotropy vs. close linkage. The test of pleiotropy (one pleiotropic QTL at a genome position) vs. close linkage (multiple nearby nonpleiotropic QTL) can have important implications for our understanding of the nature of genetic correlations between different traits in certain regions of a genome and also for practical applications in animal and plant breeding because one of the major goals in breeding is to break unfavorable linkage. Results of extensive simulation studies are presented to illustrate various properties of the analyses.