SummaryImprovement of grain yield is an essential long-term goal of maize (Zea mays) breeding to meet continual and increasing food demands worldwide, but the genetic basis remains unclear.We used 10 different recombination inbred line (RIL) populations genotyped with highdensity markers and phenotyped in multiple environments to dissect the genetic architecture of maize ear traits.Three methods were used to map the quantitative trait loci (QTLs) affecting ear traits. We found 17-34 minor-or moderate-effect loci that influence ear traits, with little epistasis and environmental interactions, totally accounting for 55.4-82% of the phenotypic variation. Four novel QTLs were validated and fine mapped using candidate gene association analysis, expression QTL analysis and heterogeneous inbred family validation.The combination of multiple different populations is a flexible and manageable way to collaboratively integrate widely available genetic resources, thereby boosting the statistical power of QTL discovery for important traits in agricultural crops, ultimately facilitating breeding programs.

Metabolic genome-wide association studies (mGWAS), whereupon metabolite levels are regarded as traits, can help unravel the genetic basis of metabolic networks. A total of 309 Arabidopsis accessions were grown under two independent environmental conditions (control and stress) and subjected to untargeted LC-MS-based metabolomic profiling; levels of the obtained hydrophilic metabolites were used in GWAS. Our two-condition-based GWAS for more than 3000 semi-polar metabolites resulted in the detection of 123 highly resolved metabolite quantitative trait loci (p ≤ 1.0E-08), 24.39% of which were environment-specific. Interestingly, differently from natural variation in Arabidopsis primary metabolites, which tends to be controlled by a large number of small-effect loci, we found several major large-effect loci alongside a vast number of small-effect loci controlling variation of secondary metabolites. The two-condition-based GWAS was followed by integration with network-derived metabolite-transcript correlations using a time-course stress experiment. Through this integrative approach, we selected 70 key candidate associations between structural genes and metabolites, and experimentally validated eight novel associations, two of them showing differential genetic regulation in the two environments studied. We demonstrate the power of combining large-scale untargeted metabolomics-based GWAS with time-course-derived networks both performed under different abiotic environments for identifying metabolite-gene associations, providing novel global insights into the metabolic landscape of Arabidopsis.

SummaryMeiotic recombination is a major driver of genetic diversity, species evolution, and agricultural improvement. Thus, an understanding of the genetic recombination landscape across the maize (Zea mays) genome will provide insight and tools for further study of maize evolution and improvement.Here, we used c. 50 000 single nucleotide polymorphisms to precisely map recombination events in 12 artificial maize segregating populations. We observed substantial variation in the recombination frequency and distribution along the ten maize chromosomes among the 12 populations and identified 143 recombination hot regions.Recombination breakpoints were partitioned into intragenic and intergenic events. Interestingly, an increase in the number of genes containing recombination events was accompanied by a decrease in the number of recombination events per gene. This kept the overall number of intragenic recombination events nearly invariable in a given population, suggesting that the recombination variation observed among populations was largely attributed to intergenic recombination. However, significant associations between intragenic recombination events and variation in gene expression and agronomic traits were observed, suggesting potential roles for intragenic recombination in plant phenotypic diversity.Our results provide a comprehensive view of the maize recombination landscape, and show an association between recombination, gene expression and phenotypic variation, which may enhance crop genetic improvement.

Maize (Zea mays) is a major staple crop. Maize kernel size and weight are important contributors to its yield. Here, we measured kernel length, kernel width, kernel thickness, hundred kernel weight, and kernel test weight in 10 recombinant inbred line populations and dissected their genetic architecture using three statistical models. In total, 729 quantitative trait loci (QTLs) were identified, many of which were identified in all three models, including 22 major QTLs that each can explain more than 10% of phenotypic variation. To provide candidate genes for these QTLs, we identified 30 maize genes that are orthologs of 18 rice (Oryza sativa) genes reported to affect rice seed size or weight. Interestingly, 24 of these 30 genes are located in the identified QTLs or within 1 Mb of the significant single-nucleotide polymorphisms. We further confirmed the effects of five genes on maize kernel size/weight in an independent association mapping panel with 540 lines by candidate gene association analysis. Lastly, the function of ZmINCW1, a homolog of rice GRAIN INCOMPLETE FILLING1 that affects seed size and weight, was characterized in detail. ZmINCW1 is close to QTL peaks for kernel size/weight (less than 1 Mb) and contains significant single-nucleotide polymorphisms affecting kernel size/weight in the association panel. Overexpression of this gene can rescue the reduced weight of the Arabidopsis (Arabidopsis thaliana) homozygous mutant line in the AtcwINV2 gene (Arabidopsis ortholog of ZmINCW1). These results indicate that the molecular mechanisms affecting seed development are conserved in maize, rice, and possibly Arabidopsis.Maize (Zea mays) is one of the most important crops and is cultivated worldwide as a source of staple food, animal feed, and industrial materials. According to the Food and Agriculture Organization, the production of maize was 1,016.7 million tons in 2013, which was far more than rice (Oryza sativa) and wheat (Triticum aestivum; 745.7 and 713.1 million tons, respectively). Yield improvement is a central goal of maize breeding. Kernel size and weight are two significant components of maize yield, and many attempts have been made to elucidate the genetic basis of kernel size and weight.Many studies have mapped quantitative trait loci (QTLs) for natural variations in kernel size and weight. (2015) mapped 28 QTLs in a test cross population. Most of these studies used two diverse inbred lines to develop the segregating population and used a limited number of genetic markers to construct the linkage map, which greatly limited the resolution and power to detect rare and/or small-effect QTLs. Large-scale QTL mapping studies including more diverse genetic backgrounds and dense genetic markers would provide more insight into the number and effect of QTLs controlling the natural variations of kernel size and weight in maize.

Genetic basis of grain yield heterosis relies on the cumulative effects of dominance, overdominance, and epistasis in maize hybrid Yuyu22. Heterosis, i.e., when F1 hybrid phenotypes are superior to those of the parents, continues to play a critical role in boosting global grain yield. Notwithstanding our limited insight into the genetic and molecular basis of heterosis, it has been exploited extensively using different breeding approaches. In this study, we investigated the genetic underpinnings of grain yield and its components using "immortalized F2" and recombinant inbred line populations derived from the elite hybrid Yuyu22. A high-density linkage map consisting of 3,184 bins was used to assess (1) the additive and additive-by-additive effects determined using recombinant inbred lines; (2) the dominance and dominance-by-dominance effects from a mid-parent heterosis dataset; and (3) the various genetic effects in the "immortalized F2" population. Compared with a low-density simple sequence repeat map, the bin map identified more quantitative trait loci, with higher LOD scores and better accuracy of detecting quantitative trait loci. The bin map showed that, among all traits, dominance was more important to heterosis than other genetic effects. The importance of overdominance/pseudo-overdominance was proportional to the amount of heterosis. In addition, epistasis contributed to heterosis as well. Phenotypic variances explained by the QTLs detected were close to the broad-sense heritabilities of the observed traits. Comparison of the analyzed results obtained for the "immortalized F2" population with those for the mid-parent heterosis dataset indicated identical genetic modes of action for mid-parent heterosis and grain yield performance of the hybrid.

The temperate-tropical division of early maize germplasms to different agricultural environments was arguably the greatest adaptation process associated with the success and near ubiquitous importance of global maize production. Deciphering this history is challenging, but new insight has been gained from examining 558 529 single nucleotide polymorphisms, expression data of 28 769 genes, and 662 traits collected from 368 diverse temperate and tropical maize inbred lines in this study. This is a new attempt to systematically exploit the mechanisms of the adaptation process in maize. Our results indicate that divergence between tropical and temperate lines apparently occurred 3400-6700 years ago. Seven hundred and one genomic selection signals and transcriptomic variants including 2700 differentially expressed individual genes and 389 rewired co-expression network genes were identified. These candidate signals were found to be functionally related to stress responses, and most were associated with directionally selected traits, which may have been an advantage under widely varying environmental conditions faced by maize as it was migrated away from its domestication center. Our study also clearly indicates that such stress adaptation could involve evolution of protein-coding sequences as well as transcriptome-level regulatory changes. The latter process may be a more flexible and dynamic way for maize to adapt to environmental changes along its short evolutionary history.

The current trends of crop yield improvements are not expected to meet the projected rise in demand. Genomic selection uses molecular markers and machine learning to identify superior genotypes with improved traits, such as growth. Plant growth directly depends on rates of metabolic reactions which transform nutrients into the building blocks of biomass. Here, we predict growth of Arabidopsis thaliana accessions by employing genomic prediction of reaction rates estimated from accession-specific metabolic models. We demonstrate that, comparing to classical genomic selection on the available data sets for 67 accessions, our approach improves the prediction accuracy for growth within and across nitrogen environments by 32.6% and 51.4%, respectively, and from optimal nitrogen to low carbon environment by 50.4%. Therefore, integration of molecular markers into metabolic models offers an approach to predict traits directly related to metabolism, and its usefulness in breeding can be examined by gathering matching datasets in crops.

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