Mapping of epistatic quantitative trait loci in four-way crosses

Abstract

Four-way crosses (4WC) involving four different inbred lines often appear in plant and animal commercial breeding programs. Direct mapping of quantitative trait loci (QTL) in these commercial populations is both economical and practical. However, the existing statistical methods for mapping QTL in a 4WC population are built on the single-QTL genetic model. This simple genetic model fails to take into account QTL interactions, which play an important role in the genetic architecture of complex traits. In this paper, therefore, we attempted to develop a statistical method to detect epistatic QTL in 4WC population. Conditional probabilities of QTL genotypes, computed by the multi-point single locus method, were used to sample the genotypes of all putative QTL in the entire genome. The sampled genotypes were used to construct the design matrix for QTL effects. All QTL effects, including main and epistatic effects, were simultaneously estimated by the penalized maximum likelihood method. The proposed method was confirmed by a series of Monte Carlo simulation studies and real data analysis of cotton. The new method will provide novel tools for the genetic dissection of complex traits, construction of QTL networks, and analysis of heterosis.

Development of a multiple-hybrid population for genome-wide association studies: theoretical consideration and genetic mapping of flowering traits in maize

“…The 4WC were widely used in commercial animal and plant breeding, formed by a cross between two hybrids, F 1 (P1 × P2) and F 1 ’(P3 × P4), by which genes and QTL can be identified24 with epistatic QTL mapping developed using penalized maximum likelihood (PML)25.…”

Analytical and numerical comparisons of two methods of estimation of additive × additive interaction of QTL effects

“…Epistatic effects are difficult to detect because an epistatic genetic model potentially contains a large number of model effects (Cheverud and Routman, 1995;He et al, 2011;Zhang and Hu, 2005). Hence, in many QTL studies for quantitative traits were usually assumed absence of epistasis among QTLs (Chen et al, 2010;Frova et al, 1999;Mares and Campbell, 2001;Parker et al, 1998;Sari-Gorla et al, 1999;Ullrich et al, 2009;Xue et al, 2009a,b).…”

Bias correction for estimated QTL effects using the penalized maximum likelihood method

“…As pointed out by Kao et al (1999), the choice of the critical value for claiming a significant QTL becomes complex for multiple QTL test. For simplicity, we use logarithm of odds (LOD)X2.0 as the criterion (Lander and Kruglyak, 1995;Qin et al, 2008;He et al, 2011) for the simulated data and the usual LODX2.5 (He et al, 2011) as the criterion for real data analysis, where LOD¼LR/ln(10).…”