Gramene: a bird's eye view of cereal genomes

Rice, maize, sorghum, wheat, barley and the other major crop grasses from the family Poaceae (Gramineae) are mankind's most important source of calories and contribute tens of billions of dollars annually to the world economy (FAO 1999, ; USDA 1997, ). Continued improvement of Poaceae crops is necessary in order to continue to feed an ever-growing world population. However, of the major crop grasses, only rice (Oryza sativa), with a compact genome of ∼400 Mbp, has been sequenced and annotated. The Gramene database () takes advantage of the known genetic colinearity (synteny) between rice and the major crop plant genomes to provide maize, sorghum, millet, wheat, oat and barley researchers with the benefits of an annotated genome years before their own species are sequenced. Gramene is a one stop portal for finding curated literature, genetic and genomic datasets related to maps, markers, genes, genomes and quantitative trait loci. The addition of several new tools to Gramene has greatly facilitated the potential for comparative analysis among the grasses and contributes to our understanding of the anatomy, development, environmental responses and the factors influencing agronomic performance of cereal crops. Since the last publication on Gramene database by D. H. Ware, P. Jaiswal, J. Ni, I. V. Yap, X. Pan, K. Y. Clark, L. Teytelman, S. C. Schmidt, W. Zhao, K. Chang et al. [(2002), Plant Physiol., 130, 1606–1613], the database has undergone extensive changes that are described in this publication

Gramene: a bird's eye view of cereal genomes

Rice, maize, sorghum, wheat, barley and the other major crop grasses from the family Poaceae (Gramineae) are mankind's most important source of calories and contribute tens of billions of dollars annually to the world economy (FAO 1999, ; USDA 1997, ). Continued improvement of Poaceae crops is necessary in order to continue to feed an ever-growing world population. However, of the major crop grasses, only rice (Oryza sativa), with a compact genome of ∼400 Mbp, has been sequenced and annotated. The Gramene database () takes advantage of the known genetic colinearity (synteny) between rice and the major crop plant genomes to provide maize, sorghum, millet, wheat, oat and barley researchers with the benefits of an annotated genome years before their own species are sequenced. Gramene is a one stop portal for finding curated literature, genetic and genomic datasets related to maps, markers, genes, genomes and quantitative trait loci. The addition of several new tools to Gramene has greatly facilitated the potential for comparative analysis among the grasses and contributes to our understanding of the anatomy, development, environmental responses and the factors influencing agronomic performance of cereal crops. Since the last publication on Gramene database by D. H. Ware, P. Jaiswal, J. Ni, I. V. Yap, X. Pan, K. Y. Clark, L. Teytelman, S. C. Schmidt, W. Zhao, K. Chang et al. [(2002), Plant Physiol., 130, 1606–1613], the database has undergone extensive changes that are described in this publication

Gramene: a bird's eye view of cereal genomes

Rice, maize, sorghum, wheat, barley and the other major crop grasses from the family Poaceae (Gramineae) are mankind's most important source of calories and contribute tens of billions of dollars annually to the world economy (FAO 1999, ; USDA 1997, ). Continued improvement of Poaceae crops is necessary in order to continue to feed an ever-growing world population. However, of the major crop grasses, only rice (Oryza sativa), with a compact genome of ∼400 Mbp, has been sequenced and annotated. The Gramene database () takes advantage of the known genetic colinearity (synteny) between rice and the major crop plant genomes to provide maize, sorghum, millet, wheat, oat and barley researchers with the benefits of an annotated genome years before their own species are sequenced. Gramene is a one stop portal for finding curated literature, genetic and genomic datasets related to maps, markers, genes, genomes and quantitative trait loci. The addition of several new tools to Gramene has greatly facilitated the potential for comparative analysis among the grasses and contributes to our understanding of the anatomy, development, environmental responses and the factors influencing agronomic performance of cereal crops. Since the last publication on Gramene database by D. H. Ware, P. Jaiswal, J. Ni, I. V. Yap, X. Pan, K. Y. Clark, L. Teytelman, S. C. Schmidt, W. Zhao, K. Chang et al. [(2002), Plant Physiol., 130, 1606–1613], the database has undergone extensive changes that are described in this publication

Gramene: a bird's eye view of cereal genomes

Rice, maize, sorghum, wheat, barley and the other major crop grasses from the family Poaceae (Gramineae) are mankind’s most important source of calories and contribute tens of billions of dollars annually to the world economy (FAO 1999, http://www.fao.org; USDA 1997, http://www.usda.gov). Continued improvement of Poaceae crops is necessary in order to continue to feed an ever-growing world population. However, of the major crop grasses, only rice (Oryza sativa), with a compact genome of ~400 Mbp, has been sequenced and annotated. The Gramene database (http://www.gramene.org) takes advantage of the known genetic colinearity (synteny) between rice and the major crop plant genomes to provide maize, sorghum, millet, wheat, oat and barley researchers with the benefits of an annotated genome years before their own species are sequenced. Gramene is a one stop portal for finding curated literature, genetic and genomic datasets related to maps, markers, genes, genomes and quantitative trait loci. The addition of several new tools to Gramene has greatly facilitated the potential for comparative analysis among the grasses and contributes to our understanding of the anatomy, development, environmental responses and the factors influencing agronomic performance of cereal crops. Since the last publication on Gramene database by D. H. Ware, P. Jaiswal, J. Ni, I. V. Yap, X. Pan, K. Y. Clark, L. Teytelman, S. C. Schmidt, W. Zhao, K.Changet al. [(2002), Plant Physiol., 130, 1606–1613], the database has undergone extensive changes that are described in this publication.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by Oxford University Press. The published article can be found at: http://nar.oxfordjournals.org/

TASSEL: software for association mapping of complex traits in diverse samples

Association analyses that exploit the natural diversity of a genome to map at very high resolutions are becoming increasingly important. In most studies, however, researchers must contend with the confounding effects of both population and family structure. TASSEL (Trait Analysis by aSSociation, Evolution and Linkage) implements general linear model and mixed linear model approaches for controlling population and family structure. For result interpretation, the program allows for linkage disequilibrium statistics to be calculated and visualized graphically. Database browsing and data importation is facilitated by integrated middleware. Other features include analyzing insertions/deletions, calculating diversity statistics, integration of phenotypic and genotypic data, imputing missing data and calculating principal components. Availability: The TASSEL executable, user manual, example data sets and tutorial document are freely available at http://www.maizegenetics.net/tassel. The source code for TASSEL can be found at http://sourceforge.net/projects/tassel. Contact: [email protected]

TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline.

Genotyping by sequencing (GBS) is a next generation sequencing based method that takes advantage of reduced representation to enable high throughput genotyping of large numbers of individuals at a large number of SNP markers. The relatively straightforward, robust, and cost-effective GBS protocol is currently being applied in numerous species by a large number of researchers. Herein we describe a bioinformatics pipeline, TASSEL-GBS, designed for the efficient processing of raw GBS sequence data into SNP genotypes. The TASSEL-GBS pipeline successfully fulfills the following key design criteria: (1) Ability to run on the modest computing resources that are typically available to small breeding or ecological research programs, including desktop or laptop machines with only 8-16 GB of RAM, (2) Scalability from small to extremely large studies, where hundreds of thousands or even millions of SNPs can be scored in up to 100,000 individuals (e.g., for large breeding programs or genetic surveys), and (3) Applicability in an accelerated breeding context, requiring rapid turnover from tissue collection to genotypes. Although a reference genome is required, the pipeline can also be run with an unfinished "pseudo-reference" consisting of numerous contigs. We describe the TASSEL-GBS pipeline in detail and benchmark it based upon a large scale, species wide analysis in maize (Zea mays), where the average error rate was reduced to 0.0042 through application of population genetic-based SNP filters. Overall, the GBS assay and the TASSEL-GBS pipeline provide robust tools for studying genomic diversity

Within NAM family allele frequency distributions of chromosome 10 SNPs after different levels of filtering.

<p>Allele frequencies were calculated in each of the 25 Nested Association Mapping (NAM) families (collectively comprising 5,254 RILs) after application of the filters to the entire set of 31,978 maize samples in the AllZeaGBSv2.6 build. Allele frequencies were only estimated in a NAM family if at least 19 RILs had non-missing genotypes. Each histogram shows the allele frequency distribution for all the SNP-NAM family combinations with n > =  19. (A, B) No filter other than minimum MAF of 0.001. (C, D) A minimal filter only for MAF > =  0.01. (E, F) “Standard” maize build filters of MAF > =  0.001, minimum F_IT in inbred samples of 0.8, inbred coverage >0.15, and inbred heterozygosity score <0.21. (A, C, E) All SNP-family combinations: the error-free, monomorphic SNP-family combinations dwarf the segregating SNPs in all three cases. (B, D, F) Polymorphic SNP-family combinations only: omitting the monomorphic SNP-family combinations permits visualization of the remaining allele frequency distribution.</p

Comparison of error rates for chromosome 10 SNPs from the AllZeaGBSv2.6 build for different levels of filtering by the Discovery SNP caller.

1<p>Filters applied to the entire build (31,978 non-blank samples)</p>2<p>Minimum sample size of 19 in at least one maize Nested Association Mapping (NAM) family</p>3<p>Average error rates estimated from 5,254 NAM RILs. Median error rates were zero for all three filters.</p>4<p>Average number of chromosome 10 SNPs with n > =  19 and MAF between 0.25 and 0.75 across the 25 NAM families.</p>5<p>MAF > =  0.001, minimum F_IT in inbred samples of 0.8, inbred coverage >0.15, inbred heterozygosity score <0.21.</p

Size of the key data structures used by the tassel-gbs pipeline for a recent maize “Discovery Build” (AllZeaGBSv2.6).

1<p>Giganucleotides</p>2<p>Read depths for 97,502,532 tags across 32,736 taxa (including 758 blank negative controls.</p>3<p>105 data points per tag (with each base counted as one data point) times 97,502,532 tags.</p

Comprehensive genotyping of the USA national maize inbred seed bank

Background: Genotyping by sequencing, a new low-cost, high-throughput sequencing technology was used to genotype 2,815 maize inbred accessions, preserved mostly at the National Plant Germplasm System in the USA. The collection includes inbred lines from breeding programs all over the world. Results: The method produced 681,257 single-nucleotide polymorphism (SNP) markers distributed across the entire genome, with the ability to detect rare alleles at high confidence levels. More than half of the SNPs in the collection are rare. Although most rare alleles have been incorporated into public temperate breeding programs, only a modest amount of the available diversity is present in the commercial germplasm. Analysis of genetic distances shows population stratification, including a small number of large clusters centered on key lines. Nevertheless, an average fixation index of 0.06 indicates moderate differentiation between the three major maize subpopulations. Linkage disequilibrium (LD) decays very rapidly, but the extent of LD is highly dependent on the particular group of germplasm and region of the genome. The utility of these data for performing genome-wide association studies was tested with two simply inherited traits and one complex trait. We identified trait associations at SNPs very close to known candidate genes for kernel color, sweet corn, and flowering time; however, results suggest that more SNPs are needed to better explore the genetic architecture of complex traits. Conclusions: The genotypic information described here allows this publicly available panel to be exploited by researchers facing the challenges of sustainable agriculture through better knowledge of the nature of genetic diversity