Genotyping-by-sequencing Datasets Research Articles

Developing best practices for genotyping-by-sequencing analysis in the construction of linkage maps.

Genotyping-by-sequencing (GBS) provides affordable methods for genotyping hundreds of individuals using millions of markers. However, this challenges bioinformatic procedures that must overcome possible artifacts such as the bias generated by polymerase chain reaction duplicates and sequencing errors. Genotyping errors lead to data that deviate from what is expected from regular meiosis. This, in turn, leads to difficulties in grouping and ordering markers, resulting in inflated and incorrect linkage maps. Therefore, genotyping errors can be easily detected by linkage map quality evaluations. We developed and used the Reads2Map workflow to build linkage maps with simulated and empirical GBS data of diploid outcrossing populations. The workflows run GATK, Stacks, TASSEL, and Freebayes for single-nucleotide polymorphism calling and updog, polyRAD, and SuperMASSA for genotype calling, as well as OneMap and GUSMap to build linkage maps. Using simulated data, we observed which genotype call software fails in identifying common errors in GBS sequencing data and proposed specific filters to better handle them. We tested whether it is possible to overcome errors in a linkage map using genotype probabilities from each software or global error rates to estimate genetic distances with an updated version of OneMap. We also evaluated the impact of segregation distortion, contaminant samples, and haplotype-based multiallelic markers in the final linkage maps. Through our evaluations, we observed that some of the approaches produce different results depending on the dataset (dataset dependent) and others produce consistent advantageous results among them (dataset independent). We set as default in the Reads2Map workflows the approaches that showed to be dataset independent for GBS datasets according to our results. This reduces the number of required tests to identify optimal pipelines and parameters for other empirical datasets. Using Reads2Map, users can select the pipeline and parameters that best fit their data context. The Reads2MapApp shiny app provides a graphical representation of the results to facilitate their interpretation.

Merging genotyping-by-sequencing data from two ex situ collections provides insights on the pea evolutionary history.

Pea (Pisum sativum L. subsp. sativum) is one of the oldest domesticated species and a widely cultivated legume. In this study, we combined next generation sequencing (NGS) data referring to two genotyping-by-sequencing (GBS) libraries, each one prepared from a different Pisum germplasm collection. The selection of single nucleotide polymorphism (SNP) loci called in both germplasm collections caused some loss of information; however, this did not prevent the obtainment of one of the largest datasets ever used to explore pea biodiversity, consisting of 652 accessions and 22 127 markers. The analysis of population structure reflected genetic variation based on geographic patterns and allowed the definition of a model for the expansion of pea cultivation from the domestication centre to other regions of the world. In genetically distinct populations, the average decay of linkage disequilibrium (LD) ranged from a few bases to hundreds of kilobases, thus indicating different evolutionary histories leading to their diversification. Genome-wide scans resulted in the identification of putative selective sweeps associated with domestication and breeding, including genes known to regulate shoot branching, cotyledon colour and resistance to lodging, and the correct mapping of two Mendelian genes. In addition to providing information of major interest for fundamental and applied research on pea, our work describes the first successful example of integration of different GBS datasets generated from ex situ collections – a process of potential interest for a variety of purposes, including conservation genetics, genome-wide association studies, and breeding.

A needle in a seedstack: an improved method for detection of rare alleles in bulk seed testing through KASP.

Amaranthus palmeri is an aggressive and prolific weed species with major impact on agricultural yield and is a prohibited noxious weed across the Midwest. Morphological identification of A. palmeri from other Amaranthus species is extremely difficult in seeds, which has led to genetic testing for seed identification in commercial seed lots. We created an inexpensive and reliable genetic test based on novel, species-specific, single nucleotide polymorphisms (SNPs) from GBS (Genotyping by Sequencing) data. We report three SNP-based genetic tests for identifying A. palmeri alone or in a mixed pool of Amaranthus spp. Sensitivity ranged from 99.8 to 100%, specificity from 99.59 to 100%. Accuracy for all three tests is > 99.7%. All three are capable of reliably detecting one A. palmeri seed in a pool of 200 Amaranthus spp. seeds. The test was validated across 20 populations of A. palmeri, along with eight other Amaranthus species, the largest and most genetically diverse panel of Amaranthus samples to date. Our work represents a marked improvement over existing commercial assays resulting in an identification assay that is (i) accurate, (ii) robust, (iii) easy to interpret and (iv) applicable to both leaf tissue and pools of up to 200 seeds. Included is a data transformation method for calling of closely grouped competitive fluorescence assays. We also present a comprehensive GBS dataset from the largest geographic panel of Amaranthus populations sequenced. Our approach serves as a model for developing markers for other difficult to identify species. © 2021 Society of Chemical Industry.

Genome-Wide SNP Identification and Association Mapping for Seed Mineral Concentration in Mung Bean (Vigna radiata L.).

Mung bean (Vigna radiata L.) quality is dependent on seed chemical composition, which in turn determines the benefits of its consumption for human health and nutrition. While mung bean is rich in a range of nutritional components, such as protein, carbohydrates and vitamins, it remains less well studied than other legume crops in terms of micronutrients. In addition, mung bean genomics and genetic resources are relatively sparse. The objectives of this research were three-fold, namely: to develop a genome-wide marker system for mung bean based on genotyping by sequencing (GBS), to evaluate diversity of mung beans available to breeders in the United States and finally, to perform a genome-wide association study (GWAS) for nutrient concentrations based on a seven mineral analysis using inductively coupled plasma (ICP) spectroscopy. All parts of our research were performed with 95 cultivated mung bean genotypes chosen from the USDA core collection representing accessions from 13 countries. Overall, we identified a total of 6,486 high quality single nucleotide polymorphisms (SNPs) from the GBS dataset and found 43 marker × trait associations (MTAs) with calcium, iron, potassium, manganese, phosphorous, sulfur or zinc concentrations in mung bean grain produced in either of two consecutive years’ field experiments. The MTAs were scattered across 35 genomic regions explaining on average 22% of the variation for each seed nutrient in each year. Most of the gene regions provided valuable candidate loci to use in future breeding of new varieties of mung bean and further the understanding of genetic control of nutritional properties in the crop. Other SNPs identified in this study will serve as important resources to enable marker-assisted selection (MAS) for nutritional improvement in mung bean and to analyze cultivars of mung bean.

Evaluation of genetic diversity, agronomic traits, and anthracnose resistance in the NPGS Sudan Sorghum Core collection

BackgroundThe United States Department of Agriculture (USDA) National Plant Germplasm System (NPGS) sorghum core collection contains 3011 accessions randomly selected from 77 countries. Genomic and phenotypic characterization of this core collection is necessary to encourage and facilitate its utilization in breeding programs and to improve conservation efforts. In this study, we examined the genome sequences of 318 accessions belonging to the NPGS Sudan sorghum core set, and characterized their agronomic traits and anthracnose resistance response.ResultsWe identified 183,144 single nucleotide polymorphisms (SNPs) located within or in proximity of 25,124 annotated genes using the genotyping-by-sequencing (GBS) approach. The core collection was genetically highly diverse, with an average pairwise genetic distance of 0.76 among accessions. Population structure and cluster analysis revealed five ancestral populations within the Sudan core set, with moderate to high level of genetic differentiation. In total, 171 accessions (54%) were assigned to one of these populations, which covered 96% of the total genomic variation. Genome scan based on Tajima’s D values revealed two populations under balancing selection. Phenotypic analysis showed differences in agronomic traits among the populations, suggesting that these populations belong to different ecogeographical regions. A total of 55 accessions were resistant to anthracnose; these accessions could represent multiple resistance sources. Genome-wide association study based on fixed and random model Circulating Probability (farmCPU) identified genomic regions associated with plant height, flowering time, panicle length and diameter, and anthracnose resistance response. Integrated analysis of the Sudan core set and sorghum association panel indicated that a large portion of the genetic variation in the Sudan core set might be present in breeding programs but remains unexploited within some clusters of accessions.ConclusionsThe NPGS Sudan core collection comprises genetically and phenotypically diverse germplasm with multiple anthracnose resistance sources. Population genomic analysis could be used to improve screening efforts and identify the most valuable germplasm for breeding programs. The new GBS data set generated in this study represents a novel genomic resource for plant breeders interested in mining the genetic diversity of the NPGS sorghum collection.

Ecological genomics of adaptation to unpredictability in experimental rotifer populations

Elucidating the genetic basis of phenotypic variation in response to different environments is key to understanding how populations evolve. Facultatively sexual rotifers can develop adaptive responses to fluctuating environments. In a previous evolution experiment, diapause-related traits changed rapidly in response to two selective regimes (predictable vs unpredictable) in laboratory populations of the rotifer Brachionus plicatilis. Here, we investigate the genomic basis of adaptation to environmental unpredictability in these experimental populations. We identified and genotyped genome-wide polymorphisms in 169 clones from both selective regimes after seven cycles of selection using genotyping by sequencing (GBS). Additionally, we used GBS data from the 270 field clones from which the laboratory populations were established. This GBS dataset was used to identify candidate SNPs under selection. A total of 76 SNPs showed divergent selection, three of which are candidates for being under selection in the particular unpredictable fluctuation pattern studied. Most of the remaining SNPs showed strong signals of adaptation to laboratory conditions. Furthermore, a genotype-phenotype association approach revealed five SNPs associated with two key life-history traits in the adaptation to unpredictability. Our results contribute to elucidating the genomic basis for adaptation to unpredictable environments and lay the groundwork for future evolution studies in rotifers.

LD-annot: A Bioinformatics Tool to Automatically Provide Candidate SNPs With Annotations for Genetically Linked Genes.

A multitude of model and non-model species studies have now taken full advantage of powerful high-throughput genotyping advances such as SNP arrays and genotyping-by-sequencing (GBS) technology to investigate the genetic basis of trait variation. However, due to incomplete genome coverage by these technologies, the identified SNPs are likely in linkage disequilibrium (LD) with the causal polymorphisms, rather than be causal themselves. In addition, researchers could benefit from annotations for the identified candidate SNPs and, simultaneously, for all neighboring genes in genetic linkage. In such case, LD extent estimation surrounding the candidate SNPs is required to determine the regions encompassing genes of interest. We describe here an automated pipeline, “LD-annot,” designed to delineate specific regions of interest for a given experiment and candidate polymorphisms on the basis of LD extent, and furthermore, provide annotations for all genes within such regions. LD-annot uses standard file formats, bioinformatics tools, and languages to provide identifiers, coordinates, and annotations for genes in genetic linkage with each candidate polymorphism. Although the focus lies upon SNP arrays and GBS data as they are being routinely deployed, this pipeline can be applied to a variety of datasets as long as genotypic data are available for a high number of polymorphisms and formatted into a vcf file. A checkpoint procedure in the pipeline allows to test several threshold values for linkage without having to rerun the entire pipeline, thus saving the user computational time and resources. We applied this new pipeline to four different sample sets: two breeding populations GBS datasets, one within-pedigree SNP set coming from whole genome sequencing (WGS), and a very large multi-varieties SNP dataset obtained from WGS, representing variable sample sizes, and numbers of polymorphisms. LD-annot performed within minutes, even when very high numbers of polymorphisms are investigated and thus will efficiently assist research efforts aimed at identifying biologically meaningful genetic polymorphisms underlying phenotypic variation. LD-annot tool is available under a GPL license from https://github.com/ArnaudDroitLab/LD-annot.

A Systematic Gene-Centric Approach to Define Haplotypes and Identify Alleles on the Basis of Dense Single Nucleotide Polymorphism Datasets.

A gene-centric approach for haplotype definition was developed and implemented in R. The tool allows for allelic characterization at given loci in germplasm collections. Allelic status at four maturity genes is predicted on the basis of marker genotyping data. Assessing the allelic diversity within a germplasm collection and identifying individuals carrying favorable alleles is challenging. Advances in high-throughput technologies allow the genotyping of many individuals for thousands of markers but bridging the gap between single nucleotide polymorphisms (SNPs) and relevant alleles remains difficult. We developed a systematic approach that defines haplotypes from large SNP catalogs that aims to identify haplotypes that can be equated to alleles at given genes. Unlike haplotype visualization tools, our approach selects SNP markers that flank a gene and define haplotypes that correspond to this gene's alleles. We tested this approach on four known soybean [Glycine max (L.) Merr.] maturity genes (E1, GmGia, GmPhyA3, and GmPhyA2) in a collection of 67 lines and two genotypic datasets [a SNP array and genotyping-by-sequencing (GBS)]. For E1, GmGia, and GmPhyA3, we identified SNP haplotypes such that the allele found at these genes could be accurately predicted from the haplotype in 97.3% of the cases. For these genes, of the 12 known alleles in the collection, 10 and 8 could be correctly predicted from the haplotypes found with the SNP array and GBS datasets, with success rates of 98 and 97% for all allele-line combinations, respectively. The approach proved equally successful for data derived from a SNP array and GBS. However, in the case of GmPhyA2, a lack of markers in the genomic region prevented the identification of alleles, regardless of the dataset. We demonstrate the feasibility and reproducibility of our approach and identify limits to its applicability.

A Comparison of Mainstream Genotyping Platforms for the Evaluation and Use of Barley Genetic Resources.

We compared the performance of two commonly used genotyping platforms, genotyping-by-sequencing (GBS) and single nucleotide polymorphism-arrays (SNP), to investigate the extent and pattern of genetic variation within a collection of 1,000 diverse barley genotypes selected from the German Federal ex situ GenBank hosted at IPK Gatersleben. Each platform revealed equivalent numbers of robust bi-allelic SNPs (39,733 and 37,930 SNPs for the 50K SNP-array and GBS datasets respectively). A small overlap of 464 SNPs was common to both platforms, indicating that the methodologies we used selectively access informative polymorphism in different portions of the barley genome. Approximately half of the GBS dataset was comprised of SNPs with minor allele frequencies (MAFs) below 1%, illustrating the power of GBS to detect rare alleles in diverse germplasm collections. While desired for certain applications, the highly robust calling of alleles at the same SNPs across multiple populations is an advantage of the SNP-array, allowing direct comparisons of data from related or unrelated studies. Overall MAFs and diversity statistics (π) were higher for the SNP-array data, potentially reflecting the conscious removal of markers with a low MAF in the ascertainment population. A comparison of similarity matrices revealed a positive correlation between both approaches, supporting the validity of using either for entire GenBank characterization. To explore the potential of each dataset for focused genetic analyses we explored the outcomes of their use in genome-wide association scans for row type, growth habit and non-adhering hull, and discriminant analysis of principal components for the drivers of sub-population differentiation. Interpretation of the results from both types of analysis yielded broadly similar conclusions indicating that choice of platform used for such analyses should be determined by the research question being asked, group preferences and their capabilities to extract and interpret the different types of output data easily and quickly. Access to the requisite infrastructure for running, processing, analyzing, querying, storing, and displaying either datatype is an additional consideration. Our investigations reveal that for barley the cost per genotyping assay is less for SNP-arrays than GBS, which translates to a cost per informative datapoint being significantly lower for the SNP-array.

Gene flow signature in the S-allele region of cultivated buckwheat

BackgroundBuckwheat (Fagopyrum esculentum Moench.) is an annual crop that originated in southern China. The nutritious seeds are used in cooking much like cereal grains. Buckwheat is an outcrossing species with heteromorphic self-incompatibility due to its dimorphic (i.e., short- and long-styled) flowers and intra-morph infertility. The floral morphology and intra-morph incompatibility are both determined by a single S locus. Plants with short-styled flowers are heterozygous (S/s) and plants with long-styled flowers are homozygous recessive (s/s) at this locus, and the S/S genotype is not found. Recently, we built a draft genome assembly of buckwheat and identified the 5.4-Mb-long S-allele region harbored by short-styled plants.In this study, the first report on the genome-wide diversity of buckwheat, we used a genotyping-by-sequencing (GBS) dataset to evaluate the genome-wide nucleotide diversity within cultivated buckwheat landraces worldwide. We also investigated the utility of the S-allele region for phylogenetic analysis of buckwheat.ResultsBuckwheat showed high nucleotide diversity (0.0065), comparable to that of other outcrossing plants, based on a genome-wide simple nucleotide polymorphism (SNP) analysis. Phylogenetic analyses based on genome-wide SNPs showed that cultivated buckwheat comprises two groups, Asian and European, and revealed lower nucleotide diversity in the European group (0.0055) and low differentiation between the Asian and European groups.The nucleotide diversity (0.0039) estimated from SNPs in the S-allele region is lower than that in genome-wide SNPs. Phylogenetic analysis based on this region detected three diverged groups, S-1, S-2, and S-3.ConclusionThe SNPs detected using the GBS dataset were effective for elucidating the evolutionary history of buckwheat, and led to the following conclusions: (1) the low nucleotide diversity of the entire genome in the European group and low differentiation between the Asian and European groups suggested genetic bottlenecks associated with dispersion from Asia to Europe, and/or recent intensified cultivation and selection in Europe; and (2) the high diversification in the S-allele region was indicative of gene flows from wild to cultivated buckwheat, suggesting that cultivated buckwheat may have multiple origins.

Rarity does not limit genetic variation or preclude subpopulation structure in the geographically restricted desert forb Astragalus lentiginosus var. piscinensis.

Characteristics of rare taxa include small population sizes and limited geographical ranges. The genetic consequences of rarity are poorly understood for most taxa. A small geographical range could result in reduced opportunity for isolation by distance or environment, thereby limiting genetic structure and variation, but few studies explore genetic structure at small spatial scales with sufficient resolution to test this hypothesis. Moreover, few comparative genetic studies exist among infrataxa differing in rarity. Here, we compare genetic variation among varieties of Astragalus lentiginosus differing in range size. Additionally, we ask if genetic structure exists in A. lentiginosus var. piscinensis, a rare taxon consisting of several thousand individuals that persist on ~8 km2 of alkaline soil. We compared genetic variation among 11 varieties of A. lentiginosus differing in range size using a genotyping by sequencing (GBS) approach, which generated 11,475 single nucleotide polymorphisms (SNPs). We characterized genetic structure among subpopulations of A. lentiginosus var. piscinensis using a second GBS data set of 7274 SNPs and explored associations between genetic structure and environmental variation. We found no association between genetic variation and range size among varieties of A. lentiginosus. Additionally, despite the extremely small range of A. lentiginosus var. piscinensis, we report a well-defined genetic structure among subpopulations associated with microhabitat variation in soil composition. Our results suggest that fine scale genetic structure may exist within other rare Astragalus taxa and that rarity does not preclude the maintenance of genetic diversity in this genus.

Genetic sex assignment in wild populations using genotyping-by-sequencing data: A statistical threshold approach.

Establishing the sex of individuals in wild systems can be challenging and often requires genetic testing. Genotyping-by-sequencing (GBS) and other reduced-representation DNA sequencing (RRS) protocols (e.g., RADseq, ddRAD) have enabled the analysis of genetic data on an unprecedented scale. Here, we present a novel approach for the discovery and statistical validation of sex-specific loci in GBS data sets. We used GBS to genotype 166 New Zealand fur seals (NZFS, Arctocephalus forsteri) of known sex. We retained monomorphic loci as potential sex-specific markers in the locus discovery phase. We then used (i) a sex-specific locus threshold (SSLT) to identify significantly male-specific loci within our data set; and (ii) a significant sex-assignment threshold (SSAT) to confidently assign sex in silico the presence or absence of significantly male-specific loci to individuals in our data set treated as unknowns (98.9% accuracy for females; 95.8% for males, estimated via cross-validation). Furthermore, we assigned sex to 86 individuals of true unknown sex using our SSAT and assessed the effect of SSLT adjustments on these assignments. From 90 verified sex-specific loci, we developed a panel of three sex-specific PCR primers that we used to ascertain sex independently of our GBS data, which we show amplify reliably in at least two other pinniped species. Using monomorphic loci normally discarded from large SNP data sets is an effective way to identify robust sex-linked markers for nonmodel species. Our novel pipeline can be used to identify and statistically validate monomorphic and polymorphic sex-specific markers across a range of species and RRS data sets.

Genotyping-by-Sequencing in a Species Complex of Australian Hummock Grasses (Triodia): Methodological Insights and Phylogenetic Resolution.

Next-generation sequencing is becoming increasingly accessible to researchers asking biosystematic questions, but current best practice in both choosing a specific approach and effectively analysing the resulting data set is still being explored. We present a case study for the use of genotyping-by-sequencing (GBS) to resolve relationships in a species complex of Australian arid and semi-arid grasses (Triodia R.Br.), highlighting our solutions to methodological challenges in the use of GBS data. We merged overlapping paired-end reads then optimised locus assembly in the program PyRAD to generate GBS data sets for phylogenetic and distance-based analyses. In addition to traditional concatenation analyses in RAxML, we also demonstrate the novel use of summary species tree analyses (taking gene trees as input) with GBS loci. We found that while species tree analyses were relatively robust to variation in PyRAD assembly parameters, our RAxML analyses resulted in well-supported but conflicting topologies under different assembly settings. Despite this conflict, multiple clades in the complex were consistently supported as distinct across analyses. Our GBS data assembly and analyses improve the resolution of taxa and phylogenetic relationships in the Triodia basedowii complex compared to our previous study based on Sanger sequencing of nuclear (ITS/ETS) and chloroplast (rps16-trnK spacer) markers. The genomic results also partly support previous evidence for hybridization between species in the complex. Our methodological insights for analysing GBS data will assist researchers using similar data to resolve phylogenetic relationships within species complexes.

Comparing Genotyping‐by‐Sequencing and Single Nucleotide Polymorphism Chip Genotyping for Quantitative Trait Loci Mapping in Wheat

ABSTRACTArray‐ or chip‐based single nucleotide polymorphism (SNP) markers are widely used in genomic studies because of their abundance in a genome and lower cost per data point than older marker technologies. Genotyping‐by‐sequencing (GBS), a relatively newer approach of genotyping, suggests equal appeal because of its lesser cost per data point and the avoidance of ascertainment bias during genotyping. In this study, we compared the results from quantitative trait loci (QTL) mapping, marker distribution on linkage maps, genome size, recombination sites covered by the markers, and cost per polymorphic marker, as well as the methodology and workflow between the Illumina Infinium 9000 SNP‐chip genotyping with GBS. Results indicate that while GBS offers similar genome coverage at almost one‐fourth the cost of SNP chip, the SNP‐chip method is less demanding of computational skills and resources. Eight and nine QTL were detected in the GBS and SNP‐chip datasets, respectively, with one QTL common between the systems. Additionally, imputation accuracy of the GBS dataset was examined by introducing missing values randomly and imputing the missing alleles using a probabilistic principal components algorithm. Imputation results suggest recovery of the missing alleles with reasonable accuracy in datasets with low (up to 40%) amount of missing data is possible and can provide acceptable accuracy in gene mapping. Overall, the comparative results indicate that both approaches provide good genome coverage and similar mapping results. The choice of the genotyping platform is decided by the nature of the study and available resources.

Scanning and Filling: Ultra-Dense SNP Genotyping Combining Genotyping-By-Sequencing, SNP Array and Whole-Genome Resequencing Data.

Genotyping-by-sequencing (GBS) represents a highly cost-effective high-throughput genotyping approach. By nature, however, GBS is subject to generating sizeable amounts of missing data and these will need to be imputed for many downstream analyses. The extent to which such missing data can be tolerated in calling SNPs has not been explored widely. In this work, we first explore the use of imputation to fill in missing genotypes in GBS datasets. Importantly, we use whole genome resequencing data to assess the accuracy of the imputed data. Using a panel of 301 soybean accessions, we show that over 62,000 SNPs could be called when tolerating up to 80% missing data, a five-fold increase over the number called when tolerating up to 20% missing data. At all levels of missing data examined (between 20% and 80%), the resulting SNP datasets were of uniformly high accuracy (96–98%). We then used imputation to combine complementary SNP datasets derived from GBS and a SNP array (SoySNP50K). We thus produced an enhanced dataset of >100,000 SNPs and the genotypes at the previously untyped loci were again imputed with a high level of accuracy (95%). Of the >4,000,000 SNPs identified through resequencing 23 accessions (among the 301 used in the GBS analysis), 1.4 million tag SNPs were used as a reference to impute this large set of SNPs on the entire panel of 301 accessions. These previously untyped loci could be imputed with around 90% accuracy. Finally, we used the 100K SNP dataset (GBS + SoySNP50K) to perform a GWAS on seed oil content within this collection of soybean accessions. Both the number of significant marker-trait associations and the peak significance levels were improved considerably using this enhanced catalog of SNPs relative to a smaller catalog resulting from GBS alone at ≤20% missing data. Our results demonstrate that imputation can be used to fill in both missing genotypes and untyped loci with very high accuracy and that this leads to more powerful genetic analyses.

Development and GBS-genotyping of introgression lines (ILs) using two wild species of rice, O. meridionalis and O. rufipogon, in a common recurrent parent, O.sativa cv. Curinga.

Two populations of interspecific introgression lines (ILs) in a common recurrent parent were developed for use in pre-breeding and QTL mapping. The ILs were derived from crosses between cv Curinga, a tropical japonica upland cultivar, and two different wild donors, Oryza meridionalis Ng. accession (W2112) and Oryza rufipogon Griff. accession (IRGC 105491). The lines were genotyped using genotyping-by-sequencing (GBS) and SSRs. The 32 Curinga/O. meridionalis ILs contain 76.73 % of the donor genome in individual introgressed segments, and each line has an average of 94.9 % recurrent parent genome. The 48 Curinga/O. rufipogon ILs collectively contain 97.6 % of the donor genome with an average of 89.9 % recurrent parent genome per line. To confirm that these populations were segregating for traits of interest, they were phenotyped for pericarp color in the greenhouse and for four agronomic traits—days to flowering, plant height, number of tillers, and number of panicles—in an upland field environment. Seeds from these IL libraries and the accompanying GBS datasets are publicly available and represent valuable genetic resources for exploring the genetics and breeding potential of rice wild relatives.Electronic supplementary materialThe online version of this article (doi:10.1007/s11032-015-0276-7) contains supplementary material, which is available to authorized users.

Genotyping-by-sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding.

Marker-assisted selection (MAS) refers to the use of molecular markers to assist phenotypic selections in crop improvement. Several types of molecular markers, such as single nucleotide polymorphism (SNP), have been identified and effectively used in plant breeding. The application of next-generation sequencing (NGS) technologies has led to remarkable advances in whole genome sequencing, which provides ultra-throughput sequences to revolutionize plant genotyping and breeding. To further broaden NGS usages to large crop genomes such as maize and wheat, genotyping-by-sequencing (GBS) has been developed and applied in sequencing multiplexed samples that combine molecular marker discovery and genotyping. GBS is a novel application of NGS protocols for discovering and genotyping SNPs in crop genomes and populations. The GBS approach includes the digestion of genomic DNA with restriction enzymes followed by the ligation of barcode adapter, PCR amplification and sequencing of the amplified DNA pool on a single lane of flow cells. Bioinformatic pipelines are needed to analyze and interpret GBS datasets. As an ultimate MAS tool and a cost-effective technique, GBS has been successfully used in implementing genome-wide association study (GWAS), genomic diversity study, genetic linkage analysis, molecular marker discovery and genomic selection under a large scale of plant breeding programs.