BackgroundCotton, with a large genome, is an important crop throughout the world. A high-density genetic linkage map is the prerequisite for cotton genetics and breeding. A genetic map based on simple polymerase chain reaction markers will be efficient for marker-assisted breeding in cotton, and markers from transcribed sequences have more chance to target genes related to traits. To construct a genome-wide, functional marker-based genetic linkage map in cotton, we isolated and mapped expressed sequence tag-simple sequence repeats (EST-SSRs) from cotton ESTs derived from the A1, D5, (AD)1, and (AD)2 genome.ResultsA total of 3177 new EST-SSRs developed in our laboratory and other newly released SSRs were used to enrich our interspecific BC1 genetic linkage map. A total of 547 loci and 911 loci were obtained from our EST-SSRs and the newly released SSRs, respectively. The 1458 loci together with our previously published data were used to construct an updated genetic linkage map. The final map included 2316 loci on the 26 cotton chromosomes, 4418.9 cM in total length and 1.91 cM in average distance between adjacent markers. To our knowledge, this map is one of the three most dense linkage maps in cotton. Twenty-one segregation distortion regions (SDRs) were found in this map; three segregation distorted chromosomes, Chr02, Chr16, and Chr18, were identified with 99.9% of distorted markers segregating toward the heterozygous allele. Functional analysis of SSR sequences showed that 1633 loci of this map (70.6%) were transcribed loci and 1332 loci (57.5%) were translated loci.ConclusionsThis map lays groundwork for further genetic analyses of important quantitative traits, marker-assisted selection, and genome organization architecture in cotton as well as for comparative genomics between cotton and other species. The segregation distorted chromosomes can be a guide to identify segregation distortion loci in cotton. The annotation of SSR sequences identified frequent and rare gene ontology items on each chromosome, which is helpful to discover functions of cotton chromosomes.

A detailed understanding of genetic architecture of mRNA expression by millions of genetic variants is important for studying quantitative trait variation. In this study, we identified 1.25M SNPs with a minor allele frequency greater than 0.05 by combining reduced genome sequencing (GBS), high-density array technologies (600K), and previous deep RNA-sequencing data from 368 diverse inbred lines of maize. The balanced allelic frequencies and distributions in a relatively large and diverse natural panel helped to identify expression quantitative trait loci (eQTLs) associated with more than 18 000 genes (63.4% of tested genes). We found that distant eQTLs were more frequent (∼75% of all eQTLs) across the whole genome. Thirteen novel associated loci affecting maize kernel oil concentration were identified using the new dataset, among which one intergenic locus affected the kernel oil variation by controlling expression of three other known oil-related genes. Altogether, this study provides resources for expanding our understanding of cellular regulatory mechanisms of transcriptome variation and the landscape of functional variants within the maize genome, thereby enhancing the understanding of quantitative variations.

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.

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.

Background In maize hybrid breeding, complementary pools of parental lines with reshuffled genetic variants are established for superior hybrid performance. To comprehensively decipher the genetics of heterosis, we present a new design of multiple linked F1 populations with 42,840 F1 maize hybrids, generated by crossing a synthetic population of 1428 maternal lines with 30 elite testers from diverse genetic backgrounds and phenotyped for agronomic traits. Results We show that, although yield heterosis is correlated with the widespread, minor-effect epistatic QTLs, it may be resulted from a few major-effect additive and dominant QTLs in early developmental stages. Floral transition is probably one critical stage for heterosis formation, in which epistatic QTLs are activated by paternal contributions of alleles that counteract the recessive, deleterious maternal alleles. These deleterious alleles, while rare, epistatically repress other favorable QTLs. We demonstrate this with one example, showing that Brachytic2 represses the Ubiquitin3 locus in the maternal lines; in hybrids, the paternal allele alleviates this repression, which in turn recovers the height of the plant and enhances the weight of the ear. Finally, we propose a molecular design breeding by manipulating key genes underlying the transition from vegetative-to-reproductive growth. Conclusion The new population design is used to dissect the genetic basis of heterosis which accelerates maize molecular design breeding by diminishing deleterious epistatic interactions.

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.

Plant height (PH) is a key factor in maize ( Zea mays L.) yield, biomass, and plant architecture. We investigated the PH of diverse maize inbred lines (117 temperate lines, 135 tropical lines) at four growth stages using unmanned aerial vehicle high-throughput phenotypic platforms (UAV-HTPPs). We extracted PH data using an automated pipeline based on crop surface models and orthomosaic model. The correlation between UAV and manually measured PH data reached 0.95. Under temperate field conditions, temperate maize lines grew faster than tropical maize lines at early growth stages, but tropical lines grew faster at later growth stages and ultimately became taller than temperate lines. A genome-wide association study identified 68 unique quantitative trait loci (QTLs) for seven PH-related traits, and 35% of the QTLs coincided with those previously reported to control PH. Generally, different QTLs controlled PH at different growth stages, but eight QTLs simultaneously controlled PH and growth rate at multiple growth stages. Based on gene annotations and expression profiles, we identified candidate genes controlling PH. The PH data collected by the UAV-HTPPs were credible and the genetic mapping power was high. Therefore, UAV-HTPPs have great potential for use in studies on PH.

Background: Identifying genotype-phenotype links and causative genes from quantitative trait loci (QTL) is challenging for complex agronomically important traits. To accelerate maize gene discovery and breeding, we present the Complete-diallel design plus Unbalanced Breeding-like Inter-Cross (CUBIC) population, consisting of 1404 individuals created by extensively inter-crossing 24 widely used Chinese maize founders. Results: Hundreds of QTL for 23 agronomic traits are uncovered with 14 million high-quality SNPs and a high-resolution identity-by-descent map, which account for an average of 75% of the heritability for each trait. We find epistasis contributes to phenotypic variance widely. Integrative cross-population analysis and cross-omics mapping allow effective and rapid discovery of underlying genes, validated here with a case study on leaf width. Conclusions: Through the integration of experimental genetics and genomics, our study provides useful resources and gene mining strategies to explore complex quantitative traits.