The Molecular Biology Database Collection: 2002 update

The Molecular Biology Database Collection is an online resource listing key databases of value to the biological community. This Collection is intended to bring fellow scientists’ attention to high-quality databases that are available throughout the world, rather than just be a lengthy listing of all available databases. As such, this up-to-date listing is intended to serve as the initial point from which to find specialized databases that may be of use in biological research. The databases included in this Collection provide new value to the underlying data by virtue of curation, new data connections or other innovative approaches. Short, searchable summaries and updates for each of the databases included in the Collection are available through the Nucleic Acids Research Web site at http://nar.oupjournals.org.     

The Molecular Biology Database Collection: 2003 update

The Molecular Biology Database Collection is an online resource listing key databases of value to the biological community. This Collection is intended to bring fellow scientists' attention to high-quality databases that are available throughout the world, rather than just be a lengthy listing of all available databases. As such, this up-to-date listing is intended to serve as the jumping-off point from which to find specialized databases that may be of use in advancing biological research. The databases included in this Collection provide new value to the underlying data by virtue of curation, new data connections or other innovative approaches. Short, searchable summaries and updates for each of the databases included in this Collection are available through the Nucleic Acids Research Web site at http://nar.oupjournals.org.     

The Molecular Biology Database Collection: an updated compilation of biological database resources

The Molecular Biology Database Collection is an online resource listing key databases of value to the biological community. This Collection is intended to bring fellow scientists' attention to high-quality databases that are available throughout the world, rather than just be a lengthy listing of all available databases. As such, this up-to-date listing is intended to serve as the initial point from which to find specialized databases that may be of use in biological research. The databases included in this Collection provide new value to the underlying data by virtue of curation, new data connections or other innovative approaches. Short, searchable summaries of each of the databases included in the Collection are available through the Nucleic Acids Research Web site, at http://www. nar.oupjournals.org.     

玉米内生细菌资源研究进展

植物内生菌存在于植物的各个器官,种类繁多,对植物的生长发育具有重要作用。玉米是最重要粮食作物之一,通过研究玉米与其内生细菌的关系,对增产玉米以及生物防治有积极意义。本文归纳了玉米内生细菌资源的分离与收集,包括内生细菌的多样性及新种的鉴定;介绍了玉米内生细菌与宿主的相关性;总结了玉米内生细菌的生物学作用,包括抑菌性、耐受性、固氮作用和促生作用,以及内生细菌对玉米作用机制;并对玉米内生细菌研究趋势进行了前景展望,旨在为玉米内生细菌的研究提供参考。     

Genes identified by visible mutant phenotypes show increased bias toward one of two subgenomes of maize

Not all genes are created equal. Despite being supported by sequence conservation and expression data, knockout homozygotes of many genes show no visible effects, at least under laboratory conditions. We have identified a set of maize (Zea mays L.) genes which have been the subject of a disproportionate share of publications recorded at MaizeGDB. We manually anchored these "classical" maize genes to gene models in the B73 reference genome, and identified syntenic orthologs in other grass genomes. In addition to proofing the most recent version 2 maize gene models, we show that a subset of these genes, those that were identified by morphological phenotype prior to cloning, are retained at syntenic locations throughout the grasses at much higher levels than the average expressed maize gene, and are preferentially found on the maize1 subgenome even with a duplicate copy is still retained on the opposite subgenome. Maize1 is the subgenome that experienced less gene loss following the whole genome duplication in maize lineage 5-12 million years ago and genes located on this subgenome tend to be expressed at higher levels in modern maize. Links to the web based software that supported our syntenic analyses in the grasses should empower further research and support teaching involving the history of maize genetic research. Our findings exemplify the concept of "grasses as a single genetic system," where what is learned in one grass may be applied to another.     

The art and design of genetic screens: maize

Maize (Zea mays) is an excellent model for basic research. Genetic screens have informed our understanding of developmental processes, meiosis, epigenetics and biochemical pathways--not only in maize but also in other cereal crops. We discuss the forward and reverse genetic screens that are possible in this organism, and emphasize the available tools. Screens exploit the well-studied behaviour of transposon systems, and the distinctive chromosomes allow an integration of cytogenetics into mutagenesis screens and analyses. The imminent completion of the maize genome sequence provides the essential resource to move seamlessly from gene to phenotype and back.     

Using satellite and real-time weather data to predict maize production

 Large-scale assessments of crop conditions prior to harvest are critical for providing early estimates of production. Satellite and weather information provide the opportunity for near real-time crop monitoring. The objective of this research was to develop an operational assessment system for crop production utilizing data from these sources. Maize (Zea mays) production was assessed in 42 Crop Reporting Districts (CRDs) across the United States Corn Belt, which produce 60% of all maize grown in the United States. Satellite, climatolocal, and agricultural data were collected for 8 years, 1985–1992, and aggregated into CRDs. A model predicting the normalized maize yields for each CRD was developed that included as independent variables a satellite data variable, the Vegetation Condition Index, and a climatological variable, the Crop Moisture Index. This model explained approximately three-quarters (R ²=0.73) of the variation observed in the normalized yields, and was examined both for its accuracy and its timeliness in providing production estimates. Predicted seasonal yields were summed to provide a maize production estimate for the entire Corn Belt study region. Production estimates deviated from the final USDA statistics, which become available several months after harvest, by less than 10% for all eight growing seasons. In addition, the production estimates were available approximately 2 months prior to the completion of the maize harvest. This system has the potential for providing timely in-formation to organizations monitoring regional or global agricultural production for humanitarian or economic benefits. Received: 23 July 1997 / Accepted: 17 February 1998     

Utility of different gene enrichment approaches toward identifying and sequencing the maize gene space

Maize (Zea mays) possesses a large, highly repetitive genome, and subsequently a number of reduced-representation sequencing approaches have been used to try and enrich for gene space while eluding difficulties associated with repetitive DNA. This article documents the ability of publicly available maize expressed sequence tag and Genome Survey Sequences (GSSs; many of which were isolated through the use of reduced representation techniques) to recognize and provide coverage of 78 maize full-length cDNAs (FLCs). All 78 FLCs in the dataset were identified by at least three GSSs, indicating that the majority of maize genes have been identified by at least one currently available GSS. Both methyl-filtration and high-Cot enrichment methods provided a 7- to 8-fold increase in gene discovery rates as compared to random sequencing. The available maize GSSs aligned to 75% of the FLC nucleotides used to perform searches, while the expressed sequence tag sequences aligned to 73% of the nucleotides. Our data suggest that at least approximately 95% of maize genes have been tagged by at least one GSS. While the GSSs are very effective for gene identification, relatively few (18%) of the FLCs are completely represented by GSSs. Analysis of the overlap of coverage and bias due to position within a gene suggest that RescueMu, methyl-filtration, and high-Cot methods are at least partially nonredundant.     

Molecular marker-assisted breeding options for maize improvement in Asia

Maize is one of the most important food and feed crops in Asia, and is a source of income for several million farmers. Despite impressive progress made in the last few decades through conventional breeding in the “Asia-7” (China, India, Indonesia, Nepal, Philippines, Thailand, and Vietnam), average maize yields remain low and the demand is expected to increasingly exceed the production in the coming years. Molecular marker-assisted breeding is accelerating yield gains in USA and elsewhere, and offers tremendous potential for enhancing the productivity and value of Asian maize germplasm. We discuss the importance of such efforts in meeting the growing demand for maize in Asia, and provide examples of the recent use of molecular markers with respect to (i) DNA fingerprinting and genetic diversity analysis of maize germplasm (inbreds and landraces/OPVs), (ii) QTL analysis of important biotic and abiotic stresses, and (iii) marker-assisted selection (MAS) for maize improvement. We also highlight the constraints faced by research institutions wishing to adopt the available and emerging molecular technologies, and conclude that innovative models for resource-pooling and intellectual-property-respecting partnerships will be required for enhancing the level and scope of molecular marker-assisted breeding for maize improvement in Asia. Scientists must ensure that the tools of molecular marker-assisted breeding are focused on developing commercially viable cultivars, improved to ameliorate the most important constraints to maize production in Asia.     

Keepers of maize in Chiapas,Mexico

This study concerns the knowledge and selection of biological diversity of maize (Zea mays) within its center of domestication in Mesoamerica. Maize farmers in central Chiapas of Southern México keep local varieties (landraces) belonging to six races and four race mixtures. Fifteen local varieties are recognized. In spite of widespread adoption of a modern, high yielding variety, maize farmers continue to select local varieties for specific soils and because of agronomic and use criteria. Farmers maintain maize varieties primarily through seed selection. Spatial and temporal separation do not seem sufficient to maintain varieties. The management of improved varieties can lead from a uniform population to a heterogeneous one as hybridization with local maize populations occurs.     

Entering the second century of maize quantitative genetics

Maize is the most widely grown cereal in the world. In addition to its role in global agriculture, it has also long served as a model organism for genetic research. Maize stands at a genetic crossroads, as it has access to all the tools available for plant genetics but exhibits a genetic architecture more similar to other outcrossing organisms than to self-pollinating crops and model plants. In this review, we summarize recent advances in maize genetics, including the development of powerful populations for genetic mapping and genome-wide association studies (GWAS), and the insights these studies yield on the mechanisms underlying complex maize traits. Most maize traits are controlled by a large number of genes, and linkage analysis of several traits implicates a ‘common gene, rare allele'' model of genetic variation where some genes have many individually rare alleles contributing. Most natural alleles exhibit small effect sizes with little-to-no detectable pleiotropy or epistasis. Additionally, many of these genes are locked away in low-recombination regions that encourage the formation of multi-gene blocks that may underlie maize''s strong heterotic effect. Domestication left strong marks on the maize genome, and some of the differences in trait architectures may be due to different selective pressures over time. Overall, maize''s advantages as a model system make it highly desirable for studying the genetics of outcrossing species, and results from it can provide insight into other such species, including humans.     

The Physical and Genetic Framework of the Maize B73 Genome

Maize is a major cereal crop and an important model system for basic biological research. Knowledge gained from maize research can also be used to genetically improve its grass relatives such as sorghum, wheat, and rice. The primary objective of the Maize Genome Sequencing Consortium (MGSC) was to generate a reference genome sequence that was integrated with both the physical and genetic maps. Using a previously published integrated genetic and physical map, combined with in-coming maize genomic sequence, new sequence-based genetic markers, and an optical map, we dynamically picked a minimum tiling path (MTP) of 16,910 bacterial artificial chromosome (BAC) and fosmid clones that were used by the MGSC to sequence the maize genome. The final MTP resulted in a significantly improved physical map that reduced the number of contigs from 721 to 435, incorporated a total of 8,315 mapped markers, and ordered and oriented the majority of FPC contigs. The new integrated physical and genetic map covered 2,120 Mb (93%) of the 2,300-Mb genome, of which 405 contigs were anchored to the genetic map, totaling 2,103.4 Mb (99.2% of the 2,120 Mb physical map). More importantly, 336 contigs, comprising 94.0% of the physical map (∼1,993 Mb), were ordered and oriented. Finally we used all available physical, sequence, genetic, and optical data to generate a golden path (AGP) of chromosome-based pseudomolecules, herein referred to as the B73 Reference Genome Sequence version 1 (B73 RefGen_v1).     

Money matters (I): costs of field and laboratory procedures associated with conventional and marker-assisted maize breeding at CIMMYT

This article presents selected results of a study carried out in Mexico at the International Maize and Wheat Improvement Center (CIMMYT) to compare the cost-effectiveness of conventional and marker-assisted maize breeding. Costs associated with use of conventional and marker-assisted selection (MAS) methods were estimated using a spreadsheet-based budgeting approach. This information was used to compare the cost of using conventional screening and MAS to achieve a well-defined breeding objective—identification of plants carrying a mutant recessive form of the opaque2 gene in maize that is associated with Quality Protein Maize (QPM). In addition to generating empirical cost information that will be of use to CIMMYT research managers, the study produced four important insights. First, for any given breeding project, detailed budget analysis will be needed to determine the cost-effectiveness of MAS relative to conventional selection. Second, direct comparisons of unit costs for MAS methods and conventional selection methods provide useful information for research managers, but factors other than cost are likely to play an important role in driving the choice of screening methods. Third, the choice between MAS and conventional selection may be complicated by the fact that the two are not always direct substitutes. Fourth, when used with empirical data from actual breeding programs, spreadsheet-based budgeting tools can be used by research managers to improve the efficiency of existing protocols and to inform decisions about future technology choices.     

玉米/花生间作对土壤微生物和土壤养分状况的影响

通过大田试验,研究了玉米/花生间作对玉米和花生根区土壤微生物和土壤养分状况的影响.结果表明：与单作相比,间作能显著提高玉米和花生根区的土壤细菌数量;间作花生根区土壤真菌和放线菌数量与单作无显著差异;间作玉米根区土壤真菌和放线菌数量比单作明显提高;间作作物根区微生物群落功能多样性和代谢活性比单作有所改善.玉米/花生间作不同程度提高了整个间作系统根区的土壤碱解氮、速效磷、有机质含量及EC值,其中,间作玉米根区土壤养分的增加更为明显,说明玉米/花生间作可以较明显地改善两种作物根区的微生物和养分状况,土壤微生态环境的改善又会促进作物地上部的生长.     

Impact of 9 years of Bt-maize cultivation on the distribution of maize viruses

This study assesses the effect of Bt-maize on the distribution of maize viruses. Random surveys were conducted in Spain between 2001 and 2006 to evaluate the occurrence of maize viruses in Bt-maize cultivation areas and in areas where this crop had not been introduced. Maize dwarf mosaic virus (MDMV) was the predominant virus in Bt-areas, and Maize rough dwarf virus (MRDV) was the most predominant one in non-Bt-areas, with MRDV an emergent virus in both types of areas. A decline in the occurrence of MDMV and an increase in that of Sugarcane mosaic virus was observed in Bt-areas. Additionally, data obtained over 6 years in experimental fields showed non-significant differences between the infection rates exhibited by two generations of Bt varieties and the non-transformed isogenics varieties for any of the viruses. Our data suggest that differences in virus distribution are linked to the genetic background of the maize varieties and the distribution of virus reservoirs rather than to Bt-maize cultivation.     

Meta-analysis combined with syntenic metaQTL mining dissects candidate loci for maize yield

Yield potential and stability improvement with the goal of ensuring global food security is an important priority. Yield has a quantitative nature and is controlled by quantitative trait loci (QTL) and environmental factors. An increasingly large number of maize yield QTL have been identified, and how to integrate and re-analyze them is challenging. To this end, we tried to combine QTL meta-analysis with homology-based cloning techniques to dissect candidate loci/genes for maize yield. We first collected maize yield-related QTL from public resources. Then, 351 collected QTL were iteratively projected and meta-analyzed to obtain metaQTL (MQTL). A total of 54 MQTL were identified and tended to cluster in the maize genome. Seven MQTL containing ten maize orthologs of rice yield genes were dissected and temporarily termed syntenic MQTL. Maize orthologs of three functionally-characterized rice yield genes, GIF1, WFP/IPA1, and DEP1, were specially selected to undergo phylogenetic, proliferation, and selective pattern analysis. The results showed that maize orthologs were closely related to rice yield genes and subjected to mixed selective pressures, including positive selection during selective sweeps. The power of the combined techniques reported here was primarily validated not only by the congruency of MQTL and recently reported maize yield QTL but also by mined syntenic MQTL containing the well-characterized Miniature1 (Mn1) gene for maize kernel size and weight determination. Maize MQTL, especially syntenic MQTL regions, could serve not only for QTL fine-mapping and cloning but also for the marker-assisted selection breeding program. The maize yield candidate loci/genes presented here also deserve further investigation and will provide clues to the molecular bases of grain yield. Additionally, the combined technique described here will find its way into further quantitative trait research.     

Multi-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices

Genomic prediction models are often calibrated using multi-generation data. Over time, as data accumulates, training data sets become increasingly heterogeneous. Differences in allele frequency and linkage disequilibrium patterns between the training and prediction genotypes may limit prediction accuracy. This leads to the question of whether all available data or a subset of it should be used to calibrate genomic prediction models. Previous research on training set optimization has focused on identifying a subset of the available data that is optimal for a given prediction set. However, this approach does not contemplate the possibility that different training sets may be optimal for different prediction genotypes. To address this problem, we recently introduced a sparse selection index (SSI) that identifies an optimal training set for each individual in a prediction set. Using additive genomic relationships, the SSI can provide increased accuracy relative to genomic-BLUP (GBLUP). Non-parametric genomic models using Gaussian kernels (KBLUP) have, in some cases, yielded higher prediction accuracies than standard additive models. Therefore, here we studied whether combining SSIs and kernel methods could further improve prediction accuracy when training genomic models using multi-generation data. Using four years of doubled haploid maize data from the International Maize and Wheat Improvement Center (CIMMYT), we found that when predicting grain yield the KBLUP outperformed the GBLUP, and that using SSI with additive relationships (GSSI) lead to 5–17% increases in accuracy, relative to the GBLUP. However, differences in prediction accuracy between the KBLUP and the kernel-based SSI were smaller and not always significant.Subject terms: Quantitative trait, Genetic models     

A powerful tool for genome analysis in maize: development and evaluation of the high density 600 k SNP genotyping array

Background

High density genotyping data are indispensable for genomic analyses of complex traits in animal and crop species. Maize is one of the most important crop plants worldwide, however a high density SNP genotyping array for analysis of its large and highly dynamic genome was not available so far.

Results

We developed a high density maize SNP array composed of 616,201 variants (SNPs and small indels). Initially, 57 M variants were discovered by sequencing 30 representative temperate maize lines and then stringently filtered for sequence quality scores and predicted conversion performance on the array resulting in the selection of 1.2 M polymorphic variants assayed on two screening arrays. To identify high-confidence variants, 285 DNA samples from a broad genetic diversity panel of worldwide maize lines including the samples used for sequencing, important founder lines for European maize breeding, hybrids, and proprietary samples with European, US, semi-tropical, and tropical origin were used for experimental validation. We selected 616 k variants according to their performance during validation, support of genotype calls through sequencing data, and physical distribution for further analysis and for the design of the commercially available Affymetrix® Axiom® Maize Genotyping Array. This array is composed of 609,442 SNPs and 6,759 indels. Among these are 116,224 variants in coding regions and 45,655 SNPs of the Illumina® MaizeSNP50 BeadChip for study comparison. In a subset of 45,974 variants, apart from the target SNP additional off-target variants are detected, which show only a minor bias towards intermediate allele frequencies. We performed principal coordinate and admixture analyses to determine the ability of the array to detect and resolve population structure and investigated the extent of LD within a worldwide validation panel.

Conclusions

The high density Affymetrix® Axiom® Maize Genotyping Array is optimized for European and American temperate maize and was developed based on a diverse sample panel by applying stringent quality filter criteria to ensure its suitability for a broad range of applications. With 600 k variants it is the largest currently publically available genotyping array in crop species.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-823) contains supplementary material, which is available to authorized users.