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, 180 (2), 1177-90

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

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

Luyan Zhang et al. Genetics.

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 composite-interval 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.

Figures

F<sc>igure</sc> 1.—
Figure 1.—
Power analysis of CIM and ICIM from 100 simulations. (A) QTL distribution model I and population size 200; (B) QTL distribution model I and population size 500; (C) QTL distribution model II and population size 200; (D) QTL distribution model II and population size 500; (E) QTL distribution model III and population size 200; (F) QTL distribution model III and population size 500. The confidence interval for each predefined QTL was set at 15 cM, and the LOD threshold was 3.0. The last bar in each section represents the false discovery rate (FDR).
F<sc>igure</sc> 2.—
Figure 2.—
Power analysis of every marker interval. (A) QTL distribution model I and population size 200; (B) QTL distribution model I and population size 500; (C) QTL distribution model II and population size 200; (D) QTL distribution model II and population size 500; (E) QTL distribution model III and population size 200; (F) QTL distribution model III and population size 500. The LOD threshold was set at 3.0. Powers were present for all marker intervals in eight chromosomes in A and B, but for marker intervals on the first four chromosomes in C–F.
F<sc>igure</sc> 3.—
Figure 3.—
Phenotypic distributions of the three simulated and one actual F2 populations. (A) QTL distribution model I and population size 500; (B) QTL distribution model II and population size 500; (C) QTL distribution model III and population size 500; (D) rice F2 population derived from PA64s and Nipponbare and population size 180.
F<sc>igure</sc> 4.—
Figure 4.—
Mapping results from ICIM for the three simulated and one actual F2 populations. (A and B) The first simulated F2 population from QTL distribution model I and population size 500; (C and D) the first simulated F2 population from QTL distribution model II and population size 500; (E and F) the first simulated F2 population from QTL distribution model III and population size 500; (G and H) rice F2 population derived from PA64s and Nipponbare, population size 180. The scanning step was 1 cM.

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