Connected populations for detecting quantitative trait loci and testing for epistasis: an application in maize

Abstract

Quantitative trait loci (QTL) detection experiments have often been restricted to large biallelic populations. Use of connected multiparental crosses has been proposed to increase the genetic variability addressed and to test for epistatic interactions between QTL and the genetic background. We present here the results of a QTL detection performed on six connected F(2) populations of 150 F(2:3) families each, derived from four maize inbreds and evaluated for three traits of agronomic interest. The QTL detection was carried out by composite interval mapping on each population separately, then on the global design either by taking into account the connections between populations or not. Epistatic interactions between loci and with the genetic background were tested. Taking into account the connections between populations increased the number of QTL detected and the accuracy of QTL position estimates. We detected many epistatic interactions, particularly for grain yield QTL (R(2) increase of 9.6%). Use of connections for the QTL detection also allowed a global ranking of alleles at each QTL. Allelic relationships and epistasis both contribute to the lack of consistency for QTL positions observed among populations, in addition to the limited power of the tests. The potential benefit of assembling favorable alleles by marker-assisted selection are discussed.

Identification of Quantitative Trait Loci Controlling Symptom Development During Viral Infection inArabidopsis thaliana

“…Despite those potentialities, we detected only weak epistatic effect between the above QTL and in one single population, Col × Ler. Similarly to what was observed in our study, others reported poor QTL congruency when analyzing six connected maize populations (Blanc et al 2006).…”

QTL for yield components and protein content: a multienvironment study of two pea (Pisum sativum L.) populations

“…The mean number of first-order QTL-QTL effects was almost three times larger for the regions showing inconsistency of additive effects than for the regions with consistent effects, which shows some positive relationship between the applied measure of the QTL by genetic background interaction and the measure of the pairwise QTL-QTL interaction. This relation was not consistently such for all traits, so although some correspondence between the two types of non-allelic interaction was proved, it is not universal, as already noted by Blanc et al (2006).…”

“…Later, Jannink and Jansen (2001) considered a similar contrast approach to build linear models and a likelihood method to test for the presence of epistasis. More recently, Blanc et al (2006) performed a study of QTLs in six populations obtained in a diallel design. In the papers cited here (as well as others), two types of epistasis are discussed (Carlborg and Haley 2004): the QTL by genetic background interaction and the QTL-QTL first order interaction.…”

Comparison of three QTL detection models on biochemical, sensory, and yield characters in Coffea canephora

“…Recently, an approach by crossing designs composed of biparental populations that are connected by common parents was developed to increase genetic diversity under QTL studies (Blanc et al 2006). The alleles of different parents can be compared within a single model using MCQTL software (Jourjon et al 2005).…”

“…The alleles of different parents can be compared within a single model using MCQTL software (Jourjon et al 2005). Studies on maize (Blanc et al 2006;Blanc et al 2007) and on ryegrass (Pauly et al 2012) reported a higher number of QTLs detected with this connected model than in single-population analyses. The multi-parent approach also made QTL positioning more precise for flowering time QTLs in three connected populations of Medicago truncatula (Pierre et al 2008).…”