Jeffrey C. Glaubitz

A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species.

Advances in next generation technologies have driven the costs of DNA sequencing down to the point that genotyping-by-sequencing (GBS) is now feasible for high diversity, large genome species. Here, we report a procedure for constructing GBS libraries based on reducing genome complexity with restriction enzymes (REs). This approach is simple, quick, extremely specific, highly reproducible, and may reach important regions of the genome that are inaccessible to sequence capture approaches. By using methylation-sensitive REs, repetitive regions of genomes can be avoided and lower copy regions targeted with two to three fold higher efficiency. This tremendously simplifies computationally challenging alignment problems in species with high levels of genetic diversity. The GBS procedure is demonstrated with maize (IBM) and barley (Oregon Wolfe Barley) recombinant inbred populations where roughly 200,000 and 25,000 sequence tags were mapped, respectively. An advantage in species like barley that lack a complete genome sequence is that a reference map need only be developed around the restriction sites, and this can be done in the process of sample genotyping. In such cases, the consensus of the read clusters across the sequence tagged sites becomes the reference. Alternatively, for kinship analyses in the absence of a reference genome, the sequence tags can simply be treated as dominant markers. Future application of GBS to breeding, conservation, and global species and population surveys may allow plant breeders to conduct genomic selection on a novel germplasm or species without first having to develop any prior molecular tools, or conservation biologists to determine population structure without prior knowledge of the genome or diversity in the species.

Genetic Properties of the Maize Nested Association Mapping Population

Codifying Maize Modifications Maize, one of our most important crop species, has been the target of genetic investigation and experimentation for more than 100 years. Crossing two inbred lines tends to result in “better” offspring, in a process known as heterosis. Attempts to map the genetic loci that control traits important for farming have been made, but few have been successful (see the Perspective by Mackay). Buckler et al. (p. 714) and McMullen et al. (p. 737) produced a genomic map of maize that relates recombination to genome structure. Even tremendous adaptations in very diverse species were produced by numerous, small additive steps. Differences in flowering time in maize among inbred lines were not caused by a few genes with large effects, but by the cumulative effects of numerous quantitative trait loci—each of which has only a small impact on the trait. Outcrossing vigor in maize is most likely due to retained variability in regions around the centromeres. Maize genetic diversity has been used to understand the molecular basis of phenotypic variation and to improve agricultural efficiency and sustainability. We crossed 25 diverse inbred maize lines to the B73 reference line, capturing a total of 136,000 recombination events. Variation for recombination frequencies was observed among families, influenced by local (cis) genetic variation. We identified evidence for numerous minor single-locus effects but little two-locus linkage disequilibrium or segregation distortion, which indicated a limited role for genes with large effects and epistatic interactions on fitness. We observed excess residual heterozygosity in pericentromeric regions, which suggested that selection in inbred lines has been less efficient in these regions because of reduced recombination frequency. This implies that pericentromeric regions may contribute disproportionally to heterosis.

Relative performance of Bayesian clustering software for inferring population substructure and individual assignment at low levels of population differentiation

Traditional methods for characterizing genetic differentiation among populations rely on a priori grouping of individuals. Bayesian clustering methods avoid this limitation by using linkage and Hardy–Weinberg disequilibrium to decompose a sample of individuals into genetically distinct groups. There are several software programs available for Bayesian clustering analyses, all of which describe a decrease in the ability to detect distinct clusters as levels of genetic differentiation among populations decrease. However, no study has yet compared the performance of such methods at low levels of population differentiation, which may be common in species where populations have experienced recent separation or high levels of gene flow. We used simulated data to evaluate the performance of three Bayesian clustering software programs, PARTITION, STRUCTURE, and BAPS, at levels of population differentiation below FST=0.1. PARTITION was unable to correctly identify the number of subpopulations until levels of FST reached around 0.09. Both STRUCTURE and BAPS performed very well at low levels of population differentiation, and were able to correctly identify the number of subpopulations at FST around 0.03. The average proportion of an individual’s genome assigned to its true population of origin increased with increasing FST for both programs, reaching over 92% at an FST of 0.05. The average number of misassignments (assignments to the incorrect subpopulation) continued to decrease as FST increased, and when FST was 0.05, fewer than 3% of individuals were misassigned using either program. Both STRUCTURE and BAPS worked extremely well for inferring the number of clusters when clusters were not well-differentiated (FST=0.02–0.03), but our results suggest that FST must be at least 0.05 to reach an assignment accuracy of greater than 97%.

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.

Comparative population genomics of maize domestication and improvement

Domestication and plant breeding are ongoing 10,000-year-old evolutionary experiments that have radically altered wild species to meet human needs. Maize has undergone a particularly striking transformation. Researchers have sought for decades to identify the genes underlying maize evolution, but these efforts have been limited in scope. Here, we report a comprehensive assessment of the evolution of modern maize based on the genome-wide resequencing of 75 wild, landrace and improved maize lines. We find evidence of recovery of diversity after domestication, likely introgression from wild relatives, and evidence for stronger selection during domestication than improvement. We identify a number of genes with stronger signals of selection than those previously shown to underlie major morphological changes. Finally, through transcriptome-wide analysis of gene expression, we find evidence both consistent with removal of cis-acting variation during maize domestication and improvement and suggestive of modern breeding having increased dominance in expression while targeting highly expressed genes.

Maize HapMap2 identifies extant variation from a genome in flux

Whereas breeders have exploited diversity in maize for yield improvements, there has been limited progress in using beneficial alleles in undomesticated varieties. Characterizing standing variation in this complex genome has been challenging, with only a small fraction of it described to date. Using a population genetics scoring model, we identified 55 million SNPs in 103 lines across pre-domestication and domesticated Zea mays varieties, including a representative from the sister genus Tripsacum. We find that structural variations are pervasive in the Z. mays genome and are enriched at loci associated with important traits. By investigating the drivers of genome size variation, we find that the larger Tripsacum genome can be explained by transposable element abundance rather than an allopolyploid origin. In contrast, intraspecies genome size variation seems to be controlled by chromosomal knob content. There is tremendous overlap in key gene content in maize and Tripsacum, suggesting that adaptations from Tripsacum (for example, perennialism and frost and drought tolerance) can likely be integrated into maize.

Population genomic and genome-wide association studies of agroclimatic traits in sorghum

Accelerating crop improvement in sorghum, a staple food for people in semiarid regions across the developing world, is key to ensuring global food security in the context of climate change. To facilitate gene discovery and molecular breeding in sorghum, we have characterized ∼265,000 single nucleotide polymorphisms (SNPs) in 971 worldwide accessions that have adapted to diverse agroclimatic conditions. Using this genome-wide SNP map, we have characterized population structure with respect to geographic origin and morphological type and identified patterns of ancient crop diffusion to diverse agroclimatic regions across Africa and Asia. To better understand the genomic patterns of diversification in sorghum, we quantified variation in nucleotide diversity, linkage disequilibrium, and recombination rates across the genome. Analyzing nucleotide diversity in landraces, we find evidence of selective sweeps around starch metabolism genes, whereas in landrace-derived introgression lines, we find introgressions around known height and maturity loci. To identify additional loci underlying variation in major agroclimatic traits, we performed genome-wide association studies (GWAS) on plant height components and inflorescence architecture. GWAS maps several classical loci for plant height, candidate genes for inflorescence architecture. Finally, we trace the independent spread of multiple haplotypes carrying alleles for short stature or long inflorescence branches. This genome-wide map of SNP variation in sorghum provides a basis for crop improvement through marker-assisted breeding and genomic selection.

Comprehensive genotyping of the USA national maize inbred seed bank

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.

Population structure and genetic diversity of New World maize races assessed by DNA microsatellites

Because of the economic importance of maize and its scientific importance as a model system for studies of domestication, its evolutionary history is of general interest. We analyzed the population genetic structure of maize races by genotyping 964 individual plants, representing almost the entire set of ∼350 races native to the Americas, with 96 microsatellites. Using Bayesian clustering, we detected four main clusters consisting of highland Mexican, northern United States (US), tropical lowland, and Andean races. Phylogenetic analysis indicated that the southwestern US was an intermediary stepping stone between Mexico and the northern US. Furthermore, southeastern US races appear to be of mixed northern flint and tropical lowland ancestry, while lowland middle South American races are of mixed Andean and tropical lowland ancestry. Several cases of post-Columbian movement of races were detected, most notably from the US to South America. Of the four main clusters, the highest genetic diversity occurs in highland Mexican races, while diversity is lowest in the Andes and northern US. Isolation by distance appears to be the main factor underlying the historical diversification of maize. We identify highland Mexico and the Andes as potential sources of genetic diversity underrepresented among elite lines used in maize breeding programs.

Genetic signals of origin, spread, and introgression in a large sample of maize landraces

The last two decades have seen important advances in our knowledge of maize domestication, thanks in part to the contributions of genetic data. Genetic studies have provided firm evidence that maize was domesticated from Balsas teosinte (Zea mays subspecies parviglumis), a wild relative that is endemic to the mid- to lowland regions of southwestern Mexico. An interesting paradox remains, however: Maize cultivars that are most closely related to Balsas teosinte are found mainly in the Mexican highlands where subspecies parviglumis does not grow. Genetic data thus point to primary diffusion of domesticated maize from the highlands rather than from the region of initial domestication. Recent archeological evidence for early lowland cultivation has been consistent with the genetics of domestication, leaving the issue of the ancestral position of highland maize unresolved. We used a new SNP dataset scored in a large number of accessions of both teosinte and maize to take a second look at the geography of the earliest cultivated maize. We found that gene flow between maize and its wild relatives meaningfully impacts our inference of geographic origins. By analyzing differentiation from inferred ancestral gene frequencies, we obtained results that are fully consistent with current ecological, archeological, and genetic data concerning the geography of early maize cultivation.