Statistical properties of QTL linkage mapping in biparental genetic populations

Abstract

Quantitative trait gene or locus (QTL) mapping is routinely used in genetic analysis of complex traits. Especially in practical breeding programs, questions remain such as how large a population and what level of marker density are needed to detect QTLs that are useful to breeders, and how likely it is that the target QTL will be detected with the data set in hand. Some answers can be found in studies on conventional interval mapping (IM). However, it is not clear whether the conclusions obtained from IM are the same as those obtained using other methods. Inclusive composite interval mapping (ICIM) is a useful step forward that highlights the importance of model selection and interval testing in QTL linkage mapping. In this study, we investigate the statistical properties of ICIM compared with IM through simulation. Results indicate that IM is less responsive to marker density and population size (PS). The increase in marker density helps ICIM identify independent QTLs explaining 45% of phenotypic variance. When PS is 4200, ICIM achieves unbiased estimations of QTL position and effect. For smaller PS, there is a tendency for the QTL to be located toward the center of the chromosome, with its effect overestimated. The use of dense markers makes linked QTL isolated by empty marker intervals and thus improves mapping efficiency. However, only large-sized populations can take advantage of densely distributed markers. These findings are different from those previously found in IM, indicating great improvements with ICIM.

Linkage map construction and quantitative trait loci (QTL) mapping using intermated vs. selfed recombinant inbred maize line (Zea maysL.)

“…If a large number of QTL are segregating for a given trait, only a fraction will be identified in a given experiment and the probability that any two independent populations will identify the same set of QTL is low (Melchinger et al 1998). In addition, the power to detect a moderate effect QTL explaining 10% of the trait variance given our population size and marker density is approximately 0.7 (Li et al 2010). The identification of QTL in only one population suggests that flowering time and other agronomic trait variation is due to many small effect loci.…”

“…where a 1 and a 2 are the additive effects of QTL1 and QTL2, respectively, and r is the recombination frequency between QTL1 and QTL2 (Li et al 2010). The Haldane mapping function was used to convert genetic distances to recombination frequencies.…”

Estimation of statistical power and false discovery rate of QTL mapping methods through computer simulation

“…The asymptotical properties of test statistics used in most QTL mapping methods are hardly known, therefore the statistical power and the FDR cannot be estimated theoretically [8,16]. When using actual mapping populations, the true QTL positions and effects are usually unknown.…”

“…To appropriately evaluate the efficiency of these methods, we need to compare the true QTL positions and effects with their estimate and calculate the statistical power and false discovery rate (FDR). As with any statistical test, two types of error, Type I and Type II, can occur in QTL mapping [5,8,11]. (1) A Type I error is a false positive, in which a segregating QTL is detected when in fact it is not present.…”

“…Composite interval mapping (CIM), proposed by Zeng [4], is one of the most commonly used methods, but CIM can result in biased mapping because it simultaneously estimates QTLs and background effects [5]. Inclusive composite interval mapping (ICIM) is a critical step forward that highlights the importance of model selection and interval testing in QTL linkage mapping, and is able to separate linked QTLs through a two-step mapping strategy [5][6][7][8]. In addition, ICIM can be readily extended to epistasis mapping and QTL mapping in multi-parental populations [9][10][11].…”