Linkage disequilibrium can be used for identifying associations between traits of interest and genetic markers. This study used mapped diversity array technology (DArT) markers to find associations with resistance to stem rust, leaf rust, yellow rust, and powdery mildew, plus grain yield in five historical wheat international multienvironment trials from the International Maize and Wheat Improvement Center (CIMMYT). Two linear mixed models were used to assess marker-trait associations incorporating information on population structure and covariance between relatives. An integrated map containing 813 DArT markers and 831 other markers was constructed. Several linkage disequilibrium clusters bearing multiple host plant resistance genes were found. Most of the associated markers were found in genomic regions where previous reports had found genes or quantitative trait loci (QTL) influencing the same traits, providing an independent validation of this approach. In addition, many new chromosome regions for disease resistance and grain yield were identified in the wheat genome. Phenotyping across up to 60 environments and years allowed modeling of genotype 3 environment interaction, thereby making possible the identification of markers contributing to both additive and additive 3 additive interaction effects of traits.A useful new tool for crop genetic improvement is the identification of polymorphic markers associated with phenotypic variation for important traits by means of linkage disequilibrium (LD) between loci ( Thornsberry et al. 2001;Flint-Garcia et al. 2003). A major advantage of this approach over conventional linkage mapping is that it does not require the timeconsuming and expensive generation of specific genetic populations. LD is determined by the physical distance of the loci across chromosomes and has proven useful for dissecting complex traits because it offers fine-scale mapping due to the inclusion of historical recombination (Lynch and Walsh 1998). However, false positive correlation between markers and traits can arise in the absence of physical proximity due to population structure caused by admixture, mating system, and genetic drift or by artificial or natural selection during evolution, domestication, or plant improvement ( Jannink and Walsh 2002). False associations can also be caused by alleles occurring at very low frequencies in the initial population (Breseghello and Sorrells 2006a,b). These factors create LD between loci that are not physically linked and cause a high rate of false positives when relating polymorphic markers to phenotypic trait variation. Thus, separating LD due to physical linkage from LD due to population structure is a critical prerequisite in association analyses.Population structure can be quantified using Bayesian analysis, which has been effective for assigning individuals to subpopulations (Q matrix) using unlinked markers (Pritchard et al. 2000). Other multivariate statistical analyses such as classification (clustering) and ordination (scaling) can al...

Genomic selection incorporates all the available marker information into a model to predict genetic values of breeding progenies for selection. The objective of this study was to estimate genetic gains in grain yield from genomic selection (GS) in eight bi‐parental maize populations under managed drought stress environments. In each population, 148 to 300 F2:3 (C0) progenies were derived and crossed to a single‐cross tester from a complementary heterotic group. The resulting testcrosses of each population were evaluated under two to four managed drought stress and three to four well‐watered conditions in different locations and genotyped with 191 to 286 single nucleotide polymorphism (SNP) markers. The top 10% families were selected from C0 using a phenotypic selection index and were intermated to form C1. Selections both at C1 and C2 were based on genomic estimated breeding values (GEBVs). The best lines from C0 were also advanced using a pedigree selection scheme. For genetic gain studies, a total of 55 entries representing the eight populations were crossed to a single‐cross tester, and evaluated in four managed drought stress environments. Each population was represented by bulk seed containing equal amounts of seed of C0, C1, C2, C3, parents, F1s, and lines developed via pedigree selection. Five commercial checks were included for comparison. The average gain from genomic selection per cycle across eight populations was 0.086 Mg ha–1. The average grain yield of C3–derived hybrids was significantly higher than that of hybrids derived from C0. Hybrids derived from C3 produced 7.3% (0.176 Mg ha–1) higher grain yield than those developed through the conventional pedigree breeding method. The study demonstrated that genomic selection is more effective than pedigree‐based conventional phenotypic selection for increasing genetic gains in grain yield under drought stress in tropical maize.

A recombinant inbred line (RIL) population was evaluated in seven field experiments representing four environments: water stress at flowering (WS) and well-watered (WW) conditions in Mexico and Zimbabwe. The QTLs were identified for each trait in each individual experiment (single-experiment analysis) as well as per environment, per water regime across locations and across all experiments (joint analyses). For the six target traits (male flowering, anthesis-to-silking interval, grain yield, kernel number, 100-kernel fresh weight and plant height) 81, 57, 51 and 34 QTLs were identified in the four step-wise analyses, respectively. Despite high values of heritability, the phenotypic variance explained by QTLs was reduced, indicating epistatic interactions. About 80, 60 and 6% of the QTLs did not present significant QTL-by-environment interactions (QTL x E) in the joint analyses per environment, per water regime and across all experiments. The expression of QTLs was quite stable across years at a given location and across locations under the same water regime. However, the stability of QTLs decreased drastically when data were combined across water regimes, reflecting a different genetic basis of the target traits in the drought and well-watered trials. Several clusters of QTLs for different traits were identified by the joint analyses of the WW (chromosomes 1 and 8) and WS (chromosomes 1, 3 and 5) treatments and across water regimes (chromosome 1). Those regions are clear targets for future marker-assisted breeding, and our results confirm that the best approach to breeding for drought tolerance includes selection under water stress.

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