A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize (Zea mays L.)

Despite QTL mapping being a routine procedure in plant breeding, approaches that fully exploit data from multi-trait multi-environment (MTME) trials are limited. Mixed models have been proposed both for multi-trait QTL analysis and multi-environment QTL analysis, but these approaches break down when the number of traits and environments increases. We present models for an efficient QTL analysis of MTME data with mixed models by reducing the dimensionality of the genetic variance¿covariance matrix by structuring this matrix using direct products of relatively simple matrices representing variation in the trait and environmental dimension. In the context of MTME data, we address how to model QTL by environment interactions and the genetic basis of heterogeneity of variance and correlations between traits and environments. We illustrate our approach with an example including five traits across eight stress trials in CIMMYT maize. We detected 36 QTLs affecting yield, anthesis-silking interval, male flowering, ear number, and plant height in maize. Our approach does not require specialised software as it can be implemented in any statistical package with mixed model facilities

The CGIAR’s Challenge Program Experiences: A Critical Analysis

This document has been prepared by staff of the four Challenge Programs (CPs) established by the CGIAR in 2002-2004 as a contribution to the first meeting of the Consortium Planning Team (CPT) with the Alliance Executive and Deputy Executive (17-20 February 2009)

Mapping QTLs and QTL x environment interaction for CIMMYT maize drought stress program using factorial regression and partial least squares methods

The study of QTL x environment interaction (QEI) is important for understanding genotype x environment interaction (GEI) in many quantitative traits. For modeling GEI and QEI, factorial regression (FR) models form a powerful class of models. In FR models, covariables (contrasts) defined on the levels of the genotypic and/or environmental factor(s) are used to describe main effects and interactions. In FR models for QTL expression, considerable numbers of genotypic covariables can occur as for each putative QTL an additional covariable needs to be introduced. For large numbers of genotypic and/or environmental covariables, least square estimation breaks down and partial least squares (PLS) estimation procedures become an attractive alternative. In this paper we develop methodology for analyzing QEI by FR for estimating effects and locations of QTLs and QEI and interpreting QEI in terms of environmental variables. A randomization test for the main effects of QTLs and QEI is presented. A population of F-2 derived F-3 families was evaluated in eight environments differing in drought stress and soil nitrogen content and the traits yield and anthesis silking interval (ASI) were measured. For grain yield, chromosomes 1 and 10 showed significant QEI, whereas in chromosomes 3 and 8 only main effect QTLs were observed. For ASI, QTL main effects were observed on chromosomes 1, 2, 6, 8, and 10, whereas QEI was observed only on chromosome 8. The assessment of the QEI at chromosome 1 for grain yield showed that the QTL main effect explained 35.8% of the QTL + QEI variability, while QEI explained 64.2%. Minimum temperature during flowering time explained 77.6% of the QEI. The QEI analysis at chromosome 10 showed that the QTL main effect explained 59.8% of the QTL + QEI variability, while QEI explained 40.2%. Maximum temperature during flowering time explained 23.8% of the QEI. Results of this study show the possibilities of using FR for mapping QTL and for dissecting QEI in terms of environmental variables. PLS regression is efficient in accounting for background noise produced by other QTLs

Simulation Experiments on Efficiencies of Gene Introgression by Backcrossing

Designing a highly efficient backcross (BC) marker-assisted selection (MAS) experiment is not a straightforward exercise, efficiency being defined here as the ratio between the resources that need to be invested at each generation and the number of generations required to achieve the selection. This paper presents results of simulations conducted for different strategies, using the maize genome as a model, to compare allelic introgression with DNA markers through BCs. Simulation results indicate that the selection response in the BC1 could be increased significantly when the selectable population size (N sl) is 100. Selectable population size is defined as the number of individuals with favorable alleles at the target loci from which selection with markers can be carried out on the rest of the genome at nontarget loci, simulations considered the allelic introgression at one to five target loci, with different population sizes, changes in the recombination frequency between target loci and flanking markers, and different numbers of genotypes selected at each generation. For an introgression at one target locus in a partial line conversion, and using MAS at nontarget loci only at one generation, a selection at BC3 would be more efficient than a selection at BC1 or BC2, due to the increase over generations of the ratio of the standard deviation to the mean of the donor genome contribution. With selection only for the presence of a donor allele at one locus in BC1 and BC2, and MAS at BC3, lines with <5% of the donor genome can be obtained with a N sl of 10 in BC1 and BC2, and 100 in BC3 These results are critical in the application of molecular markers to introgress elite alleles as part of plant improvement program