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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.

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.

Jeff Ross-Ibarra and colleagues report a population genomic analysis of maize evolution. They analyze genome-wide evidence for selection during the initial domestication of wild maize and during the improvement of landraces to modern inbred breeds. Their findings suggest stronger selection during domestication compared to 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 1 , 2 , 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 3 . 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 4 , 5 . 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.

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.

Significance Meiotic recombination is known to vary over 1,000-fold in many eukaryotic organisms, including maize. This regional genomic variation has enormous consequences for plant breeders, who rely on meiotic cross-overs to fine-map quantitative traits and introgress favorable alleles. Deleterious mutations are also predicted to accumulate preferentially within low-recombination regions, particularly within historically outcrossing species, such as maize. Here, we show that meiotic recombination is predictable across diverse crosses based on several genomic features of the reference genome. We demonstrate that the extant patterns of recombination are historically stable and tied to variation in the number of deleterious mutations. The ability of plant breeders to exploit recombination to purge segregating deleterious alleles will determine the efficacy of future crop improvement. Among the fundamental evolutionary forces, recombination arguably has the largest impact on the practical work of plant breeders. Varying over 1,000-fold across the maize genome, the local meiotic recombination rate limits the resolving power of quantitative trait mapping and the precision of favorable allele introgression. The consequences of low recombination also theoretically extend to the species-wide scale by decreasing the power of selection relative to genetic drift, and thereby hindering the purging of deleterious mutations. In this study, we used genotyping-by-sequencing (GBS) to identify 136,000 recombination breakpoints at high resolution within US and Chinese maize nested association mapping populations. We find that the pattern of cross-overs is highly predictable on the broad scale, following the distribution of gene density and CpG methylation. Several large inversions also suppress recombination in distinct regions of several families. We also identify recombination hotspots ranging in size from 1 kb to 30 kb. We find these hotspots to be historically stable and, compared with similar regions with low recombination, to have strongly differentiated patterns of DNA methylation and GC content. We also provide evidence for the historical action of GC-biased gene conversion in recombination hotspots. Finally, using genomic evolutionary rate profiling (GERP) to identify putative deleterious polymorphisms, we find evidence for reduced genetic load in hotspot regions, a phenomenon that may have considerable practical importance for breeding programs worldwide.

In addition to single-nucleotide polymorphisms, structural variation is abundant in many plant genomes. The structural variation across a species can be represented by a ‘pan-genome’, which is essential to fully understand the genetic control of phenotypes. However, the pan-genome’s complexity hinders its accurate assembly via sequence alignment. Here we demonstrate an approach to facilitate pan-genome construction in maize. By performing 18 trillion association tests we map 26 million tags generated by reduced representation sequencing of 14,129 maize inbred lines. Using machine-learning models we select 4.4 million accurately mapped tags as sequence anchors, 1.1 million of which are presence/absence variations. Structural variations exhibit enriched association with phenotypic traits, indicating that it is a significant source of adaptive variation in maize. The ability to efficiently map ultrahigh-density pan-genome sequence anchors enables fine characterization of structural variation and will advance both genetic research and breeding in many crops. Structural variations in crop genomes are thought to be responsible for significant differences in phenotype and they can be well-represented by a ‘pan-genome’. Here, Lu et al. develop an approach to genetically map pan-genome sequence anchors using 14,129 inbred lines of maize, showing structural variation is a significant source of adaptive variation.

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.

Transferable DNA markers are essential for breeding and genetics. Grapevine ( Vitis ) breeders utilize disease resistance alleles from congeneric species ~20 million years divergent, but existing Vitis marker platforms have cross-species transfer rates as low as 2%. Here, we apply a marker strategy targeting the inferred Vitis core genome. Incorporating seven linked-read de novo assemblies and three existing assemblies, the Vitis collinear core genome is estimated to converge at 39.8 Mb (8.67% of the genome). Adding shotgun genome sequences from 40 accessions enables identification of conserved core PCR primer binding sites flanking polymorphic haplotypes with high information content. From these target regions, we develop 2,000 rhAmpSeq markers as a PCR multiplex and validate the panel in four biparental populations spanning the diversity of the Vitis genus, showing transferability increases to 91.9%. This marker development strategy should be widely applicable for genetic studies in many taxa, particularly those ~20 million years divergent. Trait introgression requires universal markers, but cross-species transferability of current SNP markers can be as low as 2%. Here, the authors use an AmpSeq haplotype strategy targeting the collinear core genome for marker development and show transferability increases to 91.4% in the Vitis genus.

Predicting extinction risk from climate change requires understanding adaptive variation and local adaptation across species’ ranges. We combine experimental and -omics approaches with climate change modeling to identify molecular mechanisms of local adaptation to heat stress in brook trout, a coldwater species experiencing extirpations due to warming temperatures. We identify genomic variation corresponding with thermal conditions across the native range, suggesting local adaptation, and experimentally identify variants linked with gene expression responses to thermal stress. Using climate projections, we find that southern brook trout populations are the most vulnerable to extirpation from climate warming and mid-range populations are the most promising candidates for receiving assisted gene flow to improve climate resilience. Together, this work highlights the importance of genomic information in managing populations threatened by climate change. Brook trout are at risk from climate change, making it crucial to understand how they adapt to warming temperatures. This study identifies genomic variation linked with response to heat stress, revealing population differences in vulnerability and the potential for assisted gene flow to improve climate resilience.

The development of next-generation sequencing (NGS) enabled a shift from array-based genotyping to directly sequencing genomic libraries for high-throughput genotyping. Even though whole-genome sequencing was initially too costly for routine analysis in large populations such as breeding or genetic studies, continued advancements in genome sequencing and bioinformatics have provided the opportunity to capitalize on whole-genome information. As new sequencing platforms can routinely provide high-quality sequencing data for sufficient genome coverage to genotype various breeding populations, a limitation comes in the time and cost of library construction when multiplexing a large number of samples. Here we describe a high-throughput whole-genome skim-sequencing (skim-seq) approach that can be utilized for a broad range of genotyping and genomic characterization. Using optimized low-volume Illumina Nextera chemistry, we developed a skim-seq method and combined up to 960 samples in one multiplex library using dual index barcoding. With the dual-index barcoding, the number of samples for multiplexing can be adjusted depending on the amount of data required, and could be extended to 3,072 samples or more. Panels of doubled haploid wheat lines ( Triticum aestivum , CDC Stanley x CDC Landmark), wheat-barley ( T . aestivum x Hordeum vulgare ) and wheat-wheatgrass ( Triticum durum x Thinopyrum intermedium ) introgression lines as well as known monosomic wheat stocks were genotyped using the skim-seq approach. Bioinformatics pipelines were developed for various applications where sequencing coverage ranged from 1 × down to 0.01 × per sample. Using reference genomes, we detected chromosome dosage, identified aneuploidy, and karyotyped introgression lines from the skim-seq data. Leveraging the recent advancements in genome sequencing, skim-seq provides an effective and low-cost tool for routine genotyping and genetic analysis, which can track and identify introgressions and genomic regions of interest in genetics research and applied breeding programs.