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.

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.

BackgroundGenotyping 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.ResultsThe 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.ConclusionsThe 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.

Now in its 10th year, the Gramene database (http://www.gramene.org) has grown from its primary focus on rice, the first fully-sequenced grass genome, to become a resource for major model and crop plants including Arabidopsis, Brachypodium, maize, sorghum, poplar and grape in addition to several species of rice. Gramene began with the addition of an Ensembl genome browser and has expanded in the last decade to become a robust resource for plant genomics hosting a wide array of data sets including quantitative trait loci (QTL), metabolic pathways, genetic diversity, genes, proteins, germplasm, literature, ontologies and a fully-structured markers and sequences database integrated with genome browsers and maps from various published studies (genetic, physical, bin, etc.). In addition, Gramene now hosts a variety of web services including a Distributed Annotation Server (DAS), BLAST and a public MySQL database. Twice a year, Gramene releases a major build of the database and makes interim releases to correct errors or to make important updates to software and/or data.

Gramene (www.gramene.org) is a curated resource for genetic, genomic and comparative genomics data for the major crop species, including rice, maize, wheat and many other plant (mainly grass) species. Gramene is an open-source project. All data and software are freely downloadable through the ftp site (ftp.gramene.org/pub/gramene) and available for use without restriction. Gramene's core data types include genome assembly and annotations, other DNA/mRNA sequences, genetic and physical maps/markers, genes, quantitative trait loci (QTLs), proteins, ontologies, literature and comparative mappings. Since our last NAR publication 2 years ago, we have updated these data types to include new datasets and new connections among them. Completely new features include rice pathways for functional annotation of rice genes; genetic diversity data from rice, maize and wheat to show genetic variations among different germplasms; large-scale genome comparisons among Oryza sativa and its wild relatives for evolutionary studies; and the creation of orthologous gene sets and phylogenetic trees among rice, Arabidopsis thaliana, maize, poplar and several animal species (for reference purpose). We have significantly improved the web interface in order to provide a more user-friendly browsing experience, including a dropdown navigation menu system, unified web page for markers, genes, QTLs and proteins, and enhanced quick search functions.

Mixed models improve the ability to detect phenotype-genotype associations in the presence of population stratification and multiple levels of relatedness in genome-wide association studies (GWAS), but for large data sets the resource consumption becomes impractical. At the same time, the sample size and number of markers used for GWAS is increasing dramatically, resulting in greater statistical power to detect those associations. The use of mixed models with increasingly large data sets depends on the availability of software for analyzing those models. While multiple software packages implement the mixed model method, no single package provides the best combination of fast computation, ability to handle large samples, flexible modeling and ease of use. Key elements of association analysis with mixed models are reviewed, including modeling phenotype-genotype associations using mixed models, population stratification, kinship and its estimation, variance component estimation, use of best linear unbiased predictors or residuals in place of raw phenotype, improving efficiency and software-user interaction. The available software packages are evaluated, and suggestions made for future software development.

Successful management and utilization of increasingly large genomic datasets is essential for breeding programs to increase genetic gain and accelerate cultivar development. To help with data management and storage, we developed a sorghum Practical Haplotype Graph (PHG) pangenome database that stores all identified haplotypes and variant information for a given set of individuals. We developed two PHGs in sorghum, one with 24 individuals and another with 398 individuals, that reflect the diversity across genic regions of the sorghum genome. 24 founders of the Chibas sorghum breeding program were sequenced at low coverage (0.01x) and processed through the PHG to identify genome-wide variants. The PHG called SNPs with only 5.9% error at 0.01x coverage -only 3% lower than its accuracy when calling SNPs from 8x coverage sequence. Additionally, 207 progeny from the Chibas genomic selection (GS) training population were sequenced and processed through the PHG. Missing genotypes in the progeny were imputed from the parental haplotypes available in the PHG and used for genomic prediction. Mean prediction accuracies with PHG SNP calls range from 0.57-0.73 for different traits, and are similar to prediction accuracies obtained with genotyping-by-sequencing (GBS) or markers from sequencing targeted amplicons (rhAmpSeq). This study provides a proof of concept for using a sorghum PHG to call and impute SNPs from low-coverage sequence data and also shows that the PHG can unify genotype calls from different sequencing platforms. By reducing the amount of input sequence needed, the PHG has the potential to decrease the cost of genotyping for genomic selection, making GS more feasible and facilitating larger breeding populations that can capture maximum recombination. Our results demonstrate that the PHG is a useful research and breeding tool that can maintain variant information from a diverse group of taxa, store sequence data in a condensed but readily accessible format, unify genotypes from different genotyping methods, and provide a cost-effective option for genomic selection for any species.

Strong directional selection occurred during the domestication of maize from its wild ancestor teosinte, reducing its genetic diversity, particularly at genes controlling domestication-related traits. Nevertheless, variability for some domestication-related traits is maintained in maize. The genetic basis of this could be sequence variation at the same key genes controlling maize-teosinte differentiation (due to lack of fixation or arising as new mutations after domestication), distinct loci with large effects, or polygenic background variation. Previous studies permit annotation of maize genome regions associated with the major differences between maize and teosinte or that exhibit population genetic signals of selection during either domestication or postdomestication improvement. Genome-wide association studies and genetic variance partitioning analyses were performed in two diverse maize inbred line panels to compare the phenotypic effects and variances of sequence polymorphisms in regions involved in domestication and improvement to the rest of the genome. Additive polygenic models explained most of the genotypic variation for domesticationrelated traits; no large-effect loci were detected for any trait. Most trait variance was associated with background genomic regions lacking previous evidence for involvement in domestication. Improvement sweep regions were associated with more trait variation than expected based on the proportion of the genome they represent. Selection during domestication eliminated large-effect genetic variants that would revert maize toward a teosinte type. Small-effect polygenic variants (enriched in the improvement sweep regions of the genome) are responsible for most of the standing variation for domestication-related traits in maize.KEYWORDS quantitative trait loci; nested association mapping; genome-wide association study; variance components; Zea mays T HE domestication of all major crop plants occurred in a relatively short period in human history, starting 10,000 years ago (Harlan 1992). During the domestication process, seeds of preferred forms were selected and saved to plant subsequent generations. Some alleles favored under domestication may have been neutral or even deleterious for the survival of wild plant species; for example, seed shattering promotes seed dispersal in wild grasses, but alleles for nondisarticulating seed structures were strongly selected for under domestication (Galinat 1983). Consequently, rare alleles favorable for growth and development under agricultural conditions or for traits desired by humans increased in frequency, often reaching fixation and reducing genetic variation very near causal sequence sites (Wang et al. 1999). In addition, domestication was often accompanied by severe genetic bottlenecks from the use of small founder populations. The reduction in effective population sizes also resulted in reduced genetic diversity genome-wide. Population genetics methods to model the strength and duration of bottlenecks provide a means to ...