Interactions Between Markers Can Be Caused by the Dominance Effect of Quantitative Trait Loci

Abstract

F 2 populations are commonly used in genetic studies of animals and plants. For simplicity, most quantitative trait locus or loci (QTL) mapping methods have been developed on the basis of populations having two distinct genotypes at each polymorphic marker or gene locus. In this study, we demonstrate that dominance can cause the interactions between markers and propose an inclusive linear model that includes marker variables and marker interactions so as to completely control both additive and dominance effects of QTL. The proposed linear model is the theoretical basis for inclusive compositeinterval QTL mapping (ICIM) for F 2 populations, which consists of two steps: first, the best regression model is selected by stepwise regression, which approximately identifies markers and marker interactions explaining both additive and dominance variations; second, the interval mapping approach is applied to the phenotypic values adjusted by the regression model selected in the first step. Due to the limited mapping population size, the large number of variables, and multicollinearity between variables, coefficients in the inclusive linear model cannot be accurately determined in the first step. Interval mapping is necessary in the second step to fine tune the QTL to their true positions. The efficiency of including marker interactions in mapping additive and dominance QTL was demonstrated by extensive simulations using three QTL distribution models with two population sizes and an actual rice F 2 population.

Identification of QTLs for tolerance to hypoxia during germination in rice

“…Missing phenotypic data values were excluded from QTL mapping. QTL analysis was conducted using the phenotypic data and SNP genotypic data with the conventional Interval Mapping for additive QTL (IM-ADD) and the inclusive composite interval mapping for additive QTL (ICIM-ADD) (Zhang et al 2008). Significant threshold values of LOD scores for QTL detection were determined using permutation tests (Churchill and Doerge 1994) with 1000 replicates.…”

Statistical properties of QTL linkage mapping in biparental genetic populations

“…Statistical properties of QTL linkage mapping H Li et al some Bayesian models (Li et al, 2007Zhang et al, 2008). In practice, ICIM has been successfully applied to identify flowering time QTL in maize nested association mapping population (Buckler et al, 2009).…”

“…ICIM was used to conduct QTL mapping, which consists of two steps (Li et al, 2007;Zhang et al, 2008). In the first step of ICIM, marker selection is conducted through stepwise regression by considering all marker information simultaneously.…”

“…In this sense, a confidence interval (CI) has to be used to indicate which significant QTLs belong to which predefined QTLs in simulation. As an example, a CI of 10 cM was used by Li et al (2007) and Zhang et al (2008) to compare the detection power of various QTL mapping methods.…”

“…Inclusive composite interval mapping (ICIM) was then proposed (Li et al, 2007;Wang, 2009) to deal with this problem while retaining other advantages related to CIM. Major advantages of ICIM were summarized as follows: (1) ICIM controls the sampling variance better; (2) it makes the background marker selection process easier and simpler; (3) it gives clearly high logarithm of the odds (LOD) scores at chromosomal regions with QTL but rather low LOD scores (that is, close to 0) in which no QTLs are located, thereby increasing mapping power and decreasing the false discovery rate (FDR); (4) it is robust for mapping parameters; (5) it can be extended to map digenic epistatic QTLs regardless of whether the two interacting QTLs have significant additive effects or not; and (6) the expectation and maximization (EM) algorithm used in ICIM has a high convergence speed and is therefore less computing intensive (Li et al, 2007Zhang et al, 2008).…”