Combined linkage and linkage disequilibrium QTL mapping in multiple families of maize (Zea mays L.) line crosses highlights complementarities between models based on parental haplotype and single locus polymorphism

Abstract

Advancements in genotyping are rapidly decreasing marker costs and increasing marker density. This opens new possibilities for mapping quantitative trait loci (QTL), in particular by combining linkage disequilibrium information and linkage analysis (LDLA). In this study, we compared different approaches to detect QTL for four traits of agronomical importance in two large multi-parental datasets of maize (Zea mays L.) of 895 and 928 testcross progenies composed of 7 and 21 biparental families, respectively, and genotyped with 491 markers. We compared to traditional linkage-based methods two LDLA models relying on the dense genotyping of parental lines with 17,728 SNP: one based on a clustering approach of parental line segments into ancestral alleles and one based on single marker information. The two LDLA models generally identified more QTL (60 and 52 QTL in total) than classical linkage models (49 and 44 QTL in total). However, they performed inconsistently over datasets and traits suggesting that a compromise must be found between the reduction of allele number for increasing statistical power and the adequacy of the model to potentially complex allelic variation. For some QTL, the model exclusively based on linkage analysis, which assumed that each parental line carried a different QTL allele, was able to capture remaining variation not explained by LDLA models. These complementarities between models clearly suggest that the different QTL mapping approaches must be considered to capture the different levels of allelic variation at QTL involved in complex traits.

Dissecting quantitative trait variation in the resequencing era: complementarity of bi-parental, multi-parental and association panels

“…For the MAGIC data, we used a regression of phenotypic values on the predicted haplotypes of the lines, as the percentage of prediction was high. We could have used the SNP alleles as in GWAs approaches [14], or intermediate approach grouping the haplotypes [18]. These approaches may lead to small differences as shown for FW in tomato [16].…”

“…Evaluating GWAs offers unique information by allowing the analysis of a wider range of diversity, and usually provide greater precision, as they are based on recombination that has taken place during a greater number of generations. Other connected population designs were proposed [18,19] with related interests. We recently developed a tomato MAGIC population based on eight cultivated lines and showed its potential to map QTLs for fruit weight [16].…”

How do the type of QTL effect and the form of the residual term influence QTL detection in multi-parent populations? A case study in the maize EU-NAM population

“…2006) and factorial designs (Bardol et al. 2013), as well as more complicated MPPs created by intercrossing multiple founders followed by inbreeding, such as in multi-parent advanced generation inter-cross (MAGIC) populations (Cavanagh et al. 2008).…”

Usefulness of Multiparental Populations of Maize (Zea mays L.) for Genome-Based Prediction

“…Especially if the multiparental population consists of several families connected by common parents, they can provide greater power of QTL detection and better resolution of QTL localization compared to individual families (Rebai and Goffinet 1993;Jannink and Jansen 2001;Blanc et al 2006;Yu et al 2008;Bardol et al 2013;Mackay et al 2014). In the context of genome-based prediction (Meuwissen et al 2001), accuracies achieved within large biparental families are assumed to be the maximum that can be obtained with a given sample size (Crossa et al 2014), because of medium allele frequencies, absence of genetic substructure, and equal linkage phases between markers and functional polymorphisms.…”