Globally, maize (Zea mays ssp. mays L.) is an important source of food and nutritional security for millions of people in the developing world, especially in sub-Saharan Africa (SSA) and Latin America . Maize is a staple food in many of the SSA countries and is commonly grown by resource poor, small-scale farmers in rural areas. It covers 25 million hectares in SSA that produce 38 million metric tons  but the average maize yield in the region is estimated at 1.4 tons per hectare, which is about 20%, 37% and 56% of the average maize yield in developed countries, Brazil and Philippines, respectively . Several factors, including high frequency of drought stress, scarcity and high cost of irrigation, and farmers’ inability to obtain quality seeds and fertilizers, contribute to such low productivity in the region. Given the unpredictable nature of drought and climate variability over years, breeders must develop improved maize hybrids that are able to withstand drought stress without significant yield penalty under optimal rainfall conditions [3–5]. For developing drought tolerant maize, selection can be done directly under water stress, indirectly under well-watered (optimal) conditions, or under both optimal and stress conditions . However, heritability of grain yield under water stress has been reported to be lower than yield under optimal environments . Hence, physiologists and breeders have devoted significant efforts in identifying relevant secondary traits correlated to grain yield for indirect selection. These include anthesis silking interval (ASI) between male and female flowering and several other morpho-physiological traits [8, 9].
The ability to transfer target genomic regions associated with trait(s) of interest using molecular markers resulted in extensive QTL mapping experiments in most economically important crops. Such studies aimed at the identification of molecular markers for marker assisted backcrossing (MABC), marker assisted recurrent selection (MARS) and QTL cloning . Using MABC, Ribaut and Ragot  introgressed 5 QTL associated with yield components and flowering in maize from a donor parent into a drought susceptible recurrent parent. The authors reported increased grain yield and reduced ASI under water-limited conditions. The best MABC progeny outperformed the recurrent parent by two to four times under severe drought conditions, with no yield reduction under optimal conditions. However, drought is a complex trait influenced by genetic background and other environmental factors; thus, relying on a few QTL for MABC is unlikely to create optimally drought tolerant lines for target population of environments. Individual drought associated QTL generally explain a very small proportion of the phenotypic variance for grain yield, ASI or barrenness. QTL for drought related traits are also often cross-specific and remain undetected in crosses from different genetic backgrounds. Most QTL are detected under either drought stress or optimal conditions (not both), and there is no assurance that QTL detected from inbred lines will function in the same manner in hybrids. Thus, they must be fully validated in several environmental conditions and hybrid combinations before deployment in a large breeding program.
MARS is another marker based breeding technology that seeks to accumulate favorable alleles from several genomic regions within a single population . In maize, the MARS protocol involves (a) development and evaluation of testcross performance of bi-parental populations in multi-location experiments; (b) genotyping of the F2:3 population (Cycle 0); (c) undertaking an ad hoc significance test to identify a subset of markers that are significantly associated with the target trait; and (d) one generation (cycle) of selection of the best Cycle 0 families based on phenotypic index derived from testcross performance, followed by 2-3 cycles of selection based solely on markers with significant effects [12–15]. Currently, the International Maize and Wheat Improvement Center (CIMMYT), in collaboration with the national agricultural research systems (NARS) from 14 countries in Africa, the International Institute of Tropical Agriculture (IITA), the African Agricultural Technology Foundation (AATF), the Monsanto Company, and several regional and national seed companies in Africa, is working in large scale projects that aim to develop and disseminate drought tolerant maize for SSA using conventional breeding, MARS, and/or transgenic technology. These include the drought tolerant maize for Africa (DTMA) and the water efficient maize for Africa (WEMA) projects. For the MARS component of the WEMA project, CIMMYT developed and evaluated 18 bi-parental mapping populations, which formed the base for this study. All these populations have been phenotyped with common protocols and genotyped under a common single nucleotide polymorphism (SNP) platform.
Comparisons among independent QTL mapping projects usually attempt to determine if loci identified in each are the same by comparing the chromosomal position of a common subset of markers across different studies and/or indirectly by comparing each mapping population to a reference map . Co-localized QTL may not be identical, however, especially when they are associated with large confidence intervals. Meta QTL analysis  is a better method for combining data from independent studies to detect consensus QTL and to shrink the QTL confidence intervals. Meta-analyses have been used in maize, wheat, rice, rapeseed, potato, cotton, soybean, barley, cocoa and apricot [18, 19]. In maize, meta QTL (mQTL) for drought tolerance , flowering time , grain yield components , ear rot resistance [23, 24] and silage quality  have been reported. Hao and colleagues  collected published QTL results and data related to drought tolerance for 12 mapping populations from the MaizeGDB website (http://www.maizegdb.org) and conducted meta-analyses on a total of 239 and 160 QTL detected under water stressed and well watered conditions, respectively. The authors reported 39 consensus mQTL for drought-tolerance related traits under water stress and 36 mQTL under well watered conditions. In most QTL meta-analyses published so far [19, 20, 23, 24], authors compiled published linkage maps and QTL results from independent studies using different phenotyping protocols, constructed consensus linkage maps using a subset of markers common to the different studies, and projected mQTL positions and their confidence intervals onto the consensus map. Limitations of those studies are caused by the use of different phenotyping protocols, different QTL mapping methods, too few common markers, or by too few populations, causing lower confidence in the mQTL and the delimited intervals. The objectives of the present study were to identify mQTL for grain yield and ASI across 18 bi-parental maize populations genotyped with a common SNP platform and phenotyped with a common protocol in multi-location experiments both under water stressed and well watered environments.