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.

Characterization of genetic diversity is of great value to assist breeders in parental line selection and breeding system design. We screened 770 maize inbred lines with 1,034 single nucleotide polymorphism (SNP) markers and identified 449 high-quality markers with no germplasm-specific biasing effects. Pairwise comparisons across three distinct sets of germplasm, CIMMYT (394), China (282), and Brazil (94), showed that the elite lines from these diverse breeding pools have been developed with only limited utilization of genetic diversity existing in the center of origin. Temperate and tropical/subtropical germplasm clearly clustered into two separate groups. The temperate germplasm could be further divided into six groups consistent with known heterotic patterns. The greatest genetic divergence was observed between temperate and tropical/subtropical lines, followed by the divergence between yellow and white kernel lines, whereas the least divergence was observed between dent and flint lines. Long-term selection for hybrid performance has contributed to significant allele differentiation between heterotic groups at 20% of the SNP loci. There appeared to be substantial levels of genetic variation between different breeding pools as revealed by missing and unique alleles. Two SNPs developed from the same candidate gene were associated with the divergence between two opposite Chinese heterotic groups. Associated allele frequency change at two SNPs and their allele missing in Brazilian germplasm indicated a linkage disequilibrium block of 142 kb. These results confirm the power of SNP markers for diversity analysis and provide a feasible approach to unique allele discovery and use in maize breeding programs.

BackgroundKnowledge of germplasm diversity and relationships among elite breeding materials is fundamentally important in crop improvement. We genotyped 450 maize inbred lines developed and/or widely used by CIMMYT breeding programs in both Kenya and Zimbabwe using 1065 SNP markers to (i) investigate population structure and patterns of relationship of the germplasm for better exploitation in breeding programs; (ii) assess the usefulness of SNPs for identifying heterotic groups commonly used by CIMMYT breeding programs; and (iii) identify a subset of highly informative SNP markers for routine and low cost genotyping of CIMMYT germplasm in the region using uniplex assays.ResultsGenetic distance for about 94% of the pairs of lines fell between 0.300 and 0.400. Eighty four percent of the pairs of lines also showed relative kinship values ≤ 0.500. Model-based population structure analysis, principal component analysis, neighbor-joining cluster analysis and discriminant analysis revealed the presence of 3 major groups and generally agree with pedigree information. The SNP markers did not show clear separation of heterotic groups A and B that were established based on combining ability tests through diallel and line x tester analyses. Our results demonstrated large differences among the SNP markers in terms of reproducibility, ease of scoring, polymorphism, minor allele frequency and polymorphic information content. About 40% of the SNPs in the multiplexed chip-based GoldenGate assays were found to be uninformative in this study and we recommend 644 of the 1065 for low to medium density genotyping in tropical maize germplasm using uniplex assays.ConclusionsThere were high genetic distance and low kinship coefficients among most pairs of lines, clearly indicating the uniqueness of the majority of the inbred lines in these maize breeding programs. The results from this study will be useful to breeders in selecting best parental combinations for new breeding crosses, mapping population development and marker assisted breeding.

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BackgroundIdentification of QTL with large phenotypic effects conserved across genetic backgrounds and environments is one of the prerequisites for crop improvement using marker assisted selection (MAS). The objectives of this study were to identify meta-QTL (mQTL) for grain yield (GY) and anthesis silking interval (ASI) across 18 bi-parental maize populations evaluated in the same conditions across 2-4 managed water stressed and 3-4 well watered environments.ResultsThe meta-analyses identified 68 mQTL (9 QTL specific to ASI, 15 specific to GY, and 44 for both GY and ASI). Mean phenotypic variance explained by each mQTL varied from 1.2 to 13.1% and the overall average was 6.5%. Few QTL were detected under both environmental treatments and/or multiple (>4 populations) genetic backgrounds. The number and 95% genetic and physical confidence intervals of the mQTL were highly reduced compared to the QTL identified in the original studies. Each physical interval of the mQTL consisted of 5 to 926 candidate genes.ConclusionsMeta-analyses reduced the number of QTL by 68% and narrowed the confidence intervals up to 12-fold. At least the 4 mQTL (mQTL2.2, mQTL6.1, mQTL7.5 and mQTL9.2) associated with GY under both water-stressed and well-watered environments and detected up to 6 populations may be considered for fine mapping and validation to confirm effects in different genetic backgrounds and pyramid them into new drought resistant breeding lines. This is the first extensive report on meta-analysis of data from over 3100 individuals genotyped using the same SNP platform and evaluated in the same conditions across a wide range of managed water-stressed and well-watered environments.

Key message Analysis of the genetic architecture of MCMV and MLN resistance in maize doubled-haploid populations revealed QTLs with major effects on chromosomes 3 and 6 that were consistent across genetic backgrounds and environments. Two major-effect QTLs, qMCMV3 - 108/qMLN3 - 108 and qMCMV6 - 17/qMLN6 - 17 , were identified as conferring resistance to both MCMV and MLN. Abstract Maize lethal necrosis (MLN) is a serious threat to the food security of maize-growing smallholders in sub-Saharan Africa. The ability of the maize chlorotic mottle virus (MCMV) to interact with other members of the Potyviridae causes severe yield losses in the form of MLN. The objective of the present study was to gain insights and validate the genetic architecture of resistance to MCMV and MLN in maize. We applied linkage mapping to three doubled-haploid populations and a genome-wide association study (GWAS) on 380 diverse maize lines. For all the populations, phenotypic variation for MCMV and MLN was significant, and heritability was moderate to high. Linkage mapping revealed 13 quantitative trait loci (QTLs) for MCMV resistance and 12 QTLs conferring MLN resistance. One major-effect QTL, qMCMV3 - 108/qMLN3 - 108 , was consistent across populations for both MCMV and MLN resistance. Joint linkage association mapping (JLAM) revealed 18 and 21 main-effect QTLs for MCMV and MLN resistance, respectively. Another major-effect QTL, qMCMV6 - 17/qMLN6 - 17 , was detected for both MCMV and MLN resistance. The GWAS revealed a total of 54 SNPs (MCMV-13 and MLN-41) significantly associated ( P ≤ 5.60 × 10 −05 ) with MCMV and MLN resistance. Most of the GWAS-identified SNPs were within or adjacent to the QTLs detected through linkage mapping. The prediction accuracy for within populations as well as the combined populations is promising; however, the accuracy was low across populations. Overall, MCMV resistance is controlled by a few major and many minor-effect loci and seems more complex than the genetic architecture for MLN resistance. Electronic supplementary material The online version of this article (10.1007/s00122-019-03360-x) contains supplementary material, which is available to authorized users.

Quality control (QC) genotyping is an important component in breeding, but to our knowledge there are not well established protocols for its implementation in practical breeding programs. The objectives of our study were to (a) ascertain genetic identity among 2-4 seed sources of the same inbred line, (b) evaluate the extent of genetic homogeneity within inbred lines, and (c) identify a subset of highly informative single-nucleotide polymorphism (SNP) markers for routine and low-cost QC genotyping and suggest guidelines for data interpretation. We used a total of 28 maize inbred lines to study genetic identity among different seed sources by genotyping them with 532 and 1,065 SNPs using the KASPar and GoldenGate platforms, respectively. An additional set of 544 inbred lines was used for studying genetic homogeneity. The proportion of alleles that differed between seed sources of the same inbred line varied from 0.1 to 42.3 %. Seed sources exhibiting high levels of genetic distance are mis-labeled, while those with lower levels of difference are contaminated or still segregating. Genetic homogeneity varied from 68.7 to 100 % with 71.3 % of the inbred lines considered to be homogenous. Based on the data sets obtained for a wide range of sample sizes and diverse genetic backgrounds, we recommended a subset of 50-100 SNPs for routine and low-cost QC genotyping, verified them in a different set of double haploid and inbred lines, and outlined a protocol that could be used to minimize errors in genetic analyses and breeding.

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