Inclusive Composite Interval Mapping (ICIM) of Quantitative Trait Genes

Quantitative trait gene or locus (QTL) mapping is routinely used in genetic analysis of complex traits. Interval Mapping (IM) and Composite Interval Mapping (CIM) are two most commonly used methods for QTL mapping with populations derived from biparental crosses. The QTL detection power of IM is low and the estimates of QTL locations and effects may be biased when QTLs are linked. Composite interval mapping (CIM) aims to separate linked QTL by controlling the effects of QTLs on other intervals or chromosomes onto the QTL that is being tested. However, the algorithm used in CIM cannot completely ensure that the effect of QTL at current testing interval is not absorbed by the background marker variables, and may result in biased estimation of QTL effect. We proposed a statistical method for QTL mapping, which was called Inclusive Composite Interval Mapping (ICIM). Two steps were included in ICIM. In the first step, stepwise regression was applied to identify the most significant regression variables. In the second step, a one-dimensional scanning or interval mapping was conducted for detecting additive (and dominance) QTL. In ICIM, marker selection is conducted only once through stepwise regression by considering all marker information simultaneously, and the phenotypic values are then adjusted by all markers retained in the regression equation except the two markers flanking the current mapping interval. The adjusted phenotypic values are finally used in interval mapping. The modified algorithm has a simpler form than that used in CIM, but a faster convergence speed. ICIM retains all advantages of CIM over interval mapping (IM), and avoids the possible increase of sampling variance and the complicated background marker selection process in CIM. Extensive simulations using two genomes and various genetic models indicated that ICIM has increased detection power, reduced false detection rate and less biased estimates of QTL effects. ICIM provides intuitive statistics for testing additive, dominance and epistasis, and can be used for most experimental populations derived from two inbred parental lines. ICIM is a useful step forward that highlights the importance of model selection and interval testing in QTL linkage mapping. ICIM can be easily extended to epistasis and QTL by environment interaction.