Genotyping-by-sequencing approaches to characterize crop genomes: choosing the right tool for the right application

Genotyping for Species Identification and Diversity Assessment Using Double-Digest Restriction Site-Associated DNA Sequencing (ddRAD-Seq)

Genotyping-by-sequencing (GBS) is a powerful approach for studying the genetic diversity of legume species. By using restriction enzymes or other methods to generate a reduced representation of the genome for sequencing, GBS can provide genome-wide single nucleotide polymorphisms (SNP) for diversity analysis at high throughput and low cost. Here we describe a novel double-digest restriction site-associated DNA sequencing (ddRAD-seq) approach. We also describe the downstream bioinformatic analysis of the sequencing data, including alignment to a reference genome, de novo assembly, SNP calling, phylogenetic analysis, and structure analysis.

Genome-Wide SNP discovery and genomic characterization in avocado (Persea americana Mill.)

Modern crop breeding is based on the use of genetically and phenotypically diverse plant material and, consequently, a proper understanding of population structure and genetic diversity is essential for the effective development of breeding programs. An example is avocado, a woody perennial fruit crop native to Mesoamerica with an increasing popularity worldwide. Despite its commercial success, there are important gaps in the molecular tools available to support on-going avocado breeding programs. In order to fill this gap, in this study, an avocado 'Hass' draft assembly was developed and used as reference to study 71 avocado accessions which represent the three traditionally recognized avocado horticultural races or subspecies (Mexican, Guatemalan and West Indian). An average of 5.72 M reads per individual and a total of 7,108 single nucleotide polymorphism (SNP) markers were produced for the 71 accessions analyzed. These molecular markers were used in a study of genetic diversity and population structure. The results broadly separate the accessions studied according to their botanical race in four main groups: Mexican, Guatemalan, West Indian and an additional group of Guatemalan × Mexican hybrids. The high number of SNP markers developed in this study will be a useful genomic resource for the avocado community.

Genetic diversity and population structure of the Mediterranean sesame core collection with use of genome-wide SNPs developed by double digest RAD-Seq

The Mediterranean sesame core collection contains agro-morphologically superior sesame accessions from geographically diverse regions in four continents. In the present investigation, the genetic diversity and population structure of this collection was analyzed with 5292 high-quality SNPs discovered by double-digest restriction site associated DNA (ddRAD) sequencing, a cost-effective and flexible next-generation sequencing method. The genetic distance between pairs of accessions varied from 0.023 to 0.524. The gene diversity was higher in accessions from Asia than from America, Africa, and Europe. The highest genetic differentiation was observed between accessions collected from America and Europe. Structure analysis showed the presence of three subpopulations among the sesame accessions, and only six accessions were placed in an admixture group. Phylogenetic tree and principal coordinate analysis clustered the accessions based on their countries of origin. However, no clear division was evident among the sesame accessions with regard to their continental locations. This result was supported by an AMOVA analysis, which revealed a genetic variation among continental groups of 5.53% of the total variation. The large number of SNPs clearly indicated that the Mediterranean sesame core collection is a highly diverse genetic resource. The collection can be exploited by breeders to select appropriate accessions that will provide high genetic gain in sesame improvement programs. The high-quality SNP data generated here should also be used in genome-wide association studies to explore qualitative trait loci and SNPs related to economically and agronomically important traits in sesame.

Long-read sequencing in deciphering human genetics to a greater depth

Through four decades' development, DNA sequencing has inched into the era of single-molecule sequencing (SMS), or the third-generation sequencing (TGS), as represented by two distinct technical approaches developed independently by Pacific Bioscience (PacBio) and Oxford Nanopore Technologies (ONT). Historically, each generation of sequencing technologies was marked by innovative technological achievements and novel applications. Long reads (LRs) are considered as the most advantageous feature of SMS shared by both PacBio and ONT to distinguish SMS from next-generation sequencing (NGS, or the second-generation sequencing) and Sanger sequencing (the first-generation sequencing). Long reads overcome the limitations of NGS and drastically improves the quality of genome assembly. Besides, ONT also contributes several unique features including ultra-long reads (ULRs) with read length above 300 kb and some close to 1 million bp, direct RNA sequencing and superior portability as made possible by pocket-sized MinION sequencer. Here, we review the history of DNA sequencing technologies and associated applications, with a special focus on the advantages as well as the limitations of ULR sequencing in genome assembly.

Core set construction and association analysis of Pinus massoniana from Guangdong province in southern China using SLAF-seq

Germplasm resource collection and utilization are important in forestry species breeding. High-through sequencing technologies have been playing increasing roles in forestry breeding. In this study, specific-locus amplified fragment sequencing (SLAF-seq) was employed to analyze 149 masson pine (Pinus massoniana) accessions collected from Guangdong in China. A large number of 471,660 SNPs in the total collection were identified from 599,164 polymorphic SLAF tags. Population structure analysis showed that 149 masson pines could not be obviously divided into subpopulations. Two core sets, containing 29 masson pine accessions for increasing resin and wood yield respectively, were obtained from the total collection. Phenotypic analyses of five traits showed abundant variations, 25 suggestive and 9 significant SNPs were associated with the resin-yielding capacity (RYC') and volume of wood (VW) using EMMAX and FaST-LMM; 22 suggestive and 11 significant SNPs were associated with RYC' and VW using mrMLM and FASTmrMLM. Moreover, a large number of associated SNPs were detected in trait HT, DBH, RW and RYC using mrMLM, FASTmrMLM, FASTmrEMMA and ISIS EM-BLASSO. The core germplasm sets would be a valuable resource for masson pine improvement and breeding. In addition, the associated SNP markers would be meaningful for masson pine resource selection.

Optimizing ddRADseq in Non-Model Species: A Case Study in Eucalyptus dunnii Maiden

Restriction site-associated DNA sequencing (RADseq) and its derived protocols, such as double digest RADseq (ddRADseq), offer a flexible and highly cost-effective strategy for efficient plant genome sampling. This has become one of the most popular genotyping approaches for breeding, conservation, and evolution studies in model and non-model plant species. However, universal protocols do not always adapt well to non-model species. Herein, this study reports the development of an optimized and detailed ddRADseq protocol in Eucalyptus dunnii, a non-model species, which combines different aspects of published methodologies. The initial protocol was established using only two samples by selecting the best combination of enzymes and through optimal size selection and simplifying lab procedures. Both single nucleotide polymorphisms (SNPs) and simple sequence repeats (SSRs) were determined with high accuracy after applying stringent bioinformatics settings and quality filters, with and without a reference genome. To scale it up to 24 samples, we added barcoded adapters. We also applied automatic size selection, and therefore obtained an optimal number of loci, the expected SNP locus density, and genome-wide distribution. Reliability and cross-sequencing platform compatibility were verified through dissimilarity coefficients of 0.05 between replicates. To our knowledge, this optimized ddRADseq protocol will allow users to go from the DNA sample to genotyping data in a highly accessible and reproducible way.

Genotyping by low-coverage whole-genome sequencing in intercross pedigrees from outbred founders: a cost-efficient approach

BACKGROUND

Experimental intercrosses between outbred founder populations are powerful resources for mapping loci that contribute to complex traits i.e. quantitative trait loci (QTL). Here, we present an approach and its accompanying software for high-resolution reconstruction of founder mosaic genotypes in the intercross offspring from such populations using whole-genome high-coverage sequence data on founder individuals (~ 30×) and very low-coverage sequence data on intercross individuals (< 0.5×). Sets of founder-line informative markers were selected for each full-sib family and used to infer the founder mosaic genotypes of the intercross individuals. The application of this approach and the quality of the estimated genome-wide genotypes are illustrated in a large F₂ pedigree between two divergently selected lines of chickens.

RESULTS

We describe how we obtained whole-genome genotype data for hundreds of individuals in a cost- and time-efficient manner by using a Tn5-based library preparation protocol and an imputation algorithm that was optimized for this application. In total, 7.6 million markers segregated in this pedigree and, within each full-sib family, between 10.0 and 13.7% of these were fully informative, i.e. fixed for alternative alleles in the founders from the divergent lines, and were used for reconstruction of the offspring mosaic genotypes. The genotypes that were estimated based on the low-coverage sequence data were highly consistent (> 95% agreement) with those obtained using individual single nucleotide polymorphism (SNP) genotyping. The estimated resolution of the inferred recombination breakpoints was relatively high, with 50% of them being defined on regions shorter than 10 kb.

CONCLUSIONS

A method and software for inferring founder mosaic genotypes in intercross offspring from low-coverage whole-genome sequencing in pedigrees from heterozygous founders are described. They provide high-quality, high-resolution genotypes in a time- and cost-efficient manner. The software is freely available at https://github.com/CarlborgGenomics/Stripes .

Single Primer Enrichment Technology (SPET) for High-Throughput Genotyping in Tomato and Eggplant Germplasm

Single primer enrichment technology (SPET) is a new, robust, and customizable solution for targeted genotyping. Unlike genotyping by sequencing (GBS), and like DNA chips, SPET is a targeted genotyping technology, relying on the sequencing of a region flanking a primer. Its reliance on single primers, rather than on primer pairs, greatly simplifies panel design, and allows higher levels of multiplexing than PCR-based genotyping. Thanks to the sequencing of the regions surrounding the target SNP, SPET allows the discovery of thousands of closely linked, novel SNPs. In order to assess the potential of SPET for high-throughput genotyping in plants, a panel comprising 5k target SNPs, designed both on coding regions and introns/UTRs, was developed for tomato and eggplant. Genotyping of two panels composed of 400 tomato and 422 eggplant accessions, comprising both domesticated material and wild relatives, generated a total of 12,002 and 30,731 high confidence SNPs, respectively, which comprised both target and novel SNPs in an approximate ratio of 1:1.6, and 1:5.5 in tomato and eggplant, respectively. The vast majority of the markers was transferrable to related species that diverged up to 3.4 million years ago (Solanum pennellii for tomato and S. macrocarpon for eggplant). Maximum Likelihood phylogenetic trees and PCA outputs obtained from the whole dataset highlighted genetic relationships among accessions and species which were congruent with what was previously reported in literature. Better discrimination among domesticated accessions was achieved by using the target SNPs, while better discrimination among wild species was achieved using the whole SNP dataset. Our results reveal that SPET genotyping is a robust, high-throughput technology for genetic fingerprinting, with a high degree of cross-transferability between crops and their cultivated and wild relatives, and allows identification of duplicates and mislabeled accessions in genebanks.

Screening populations for copy number variation using genotyping-by-sequencing: a proof of concept using soybean fast neutron mutants

BACKGROUND

The effective use of mutant populations for reverse genetic screens relies on the population-wide characterization of the induced mutations. Genome- and population-wide characterization of the mutations found in fast neutron populations has been hindered, however, by the wide range of mutations generated and the lack of affordable technologies to detect DNA sequence changes. In this study, we therefore aimed to test whether genotyping-by-sequencing (GBS) technology could be used to characterize copy number variation (CNV) induced by fast neutrons in a soybean mutant population.

RESULTS

We called CNVs from GBS data in 79 soybean mutants and assessed the sensitivity and precision of this approach by validating our results against array comparative genomic hybridization (aCGH) data for 19 of these mutants as well as targeted PCR and ddPCR assays for a representative subset of the smallest events detected by GBS. Our GBS pipeline detected 55 of the 96 events found by aCGH, with approximate detection thresholds of 60 kb, 500 kb and 1 Mb for homozygous deletions, hemizygous deletions and duplications, respectively. Among the whole set of 79 mutants, the GBS data revealed 105 homozygous deletions, 32 hemizygous deletions and 19 duplications. This included several extremely large events, exhibiting maximum sizes of ~ 11.2 Mb for a homozygous deletion, ~ 11.6 Mb for a hemizygous deletion, and ~ 50 Mb for a duplication.

CONCLUSIONS

This study provides a proof of concept that GBS can be used as an affordable high-throughput method for assessing CNVs in fast neutron mutants. The modularity of this GBS approach allows combining as many different libraries or sequencing runs as is necessary for reaching the goals of a particular study. This method should enable the low-cost genome-wide characterization of hundreds to thousands of individuals in fast neutron mutant populations or any population with large genomic deletions and duplications.

Gene Networks Underlying Cannabinoid and Terpenoid Accumulation in Cannabis

Glandular trichomes are specialized anatomical structures that accumulate secretions with important biological roles in plant-environment interactions. These secretions also have commercial uses in the flavor, fragrance, and pharmaceutical industries. The capitate-stalked glandular trichomes of Cannabis sativa (cannabis), situated on the surfaces of the bracts of the female flowers, are the primary site for the biosynthesis and storage of resins rich in cannabinoids and terpenoids. In this study, we profiled nine commercial cannabis strains with purportedly different attributes, such as taste, color, smell, and genetic origin. Glandular trichomes were isolated from each of these strains, and cell type-specific transcriptome data sets were acquired. Cannabinoids and terpenoids were quantified in flower buds. Statistical analyses indicated that these data sets enable the high-resolution differentiation of strains by providing complementary information. Integrative analyses revealed a coexpression network of genes involved in the biosynthesis of both cannabinoids and terpenoids from imported precursors. Terpene synthase genes involved in the biosynthesis of the major monoterpenes and sesquiterpenes routinely assayed by cannabis testing laboratories were identified and functionally evaluated. In addition to cloning variants of previously characterized genes, specifically CsTPS14CT [(-)-limonene synthase] and CsTPS15CT (β-myrcene synthase), we functionally evaluated genes that encode enzymes with activities not previously described in cannabis, namely CsTPS18VF and CsTPS19BL (nerolidol/linalool synthases), CsTPS16CC (germacrene B synthase), and CsTPS20CT (hedycaryol synthase). This study lays the groundwork for developing a better understanding of the complex chemistry and biochemistry underlying resin accumulation across commercial cannabis strains.

Genetic diversity of gliadin-coding alleles in bread wheat (Triticum aestivum L.) from Northern Kazakhstan

Background

Spring bread wheat (Triticum aestivum L.) represents the main cereal crop in Northern Kazakhstan. The quality of wheat grain and flour strongly depends on the structure of gluten, comprised of gliadin and glutenin proteins. Electrophoresis spectra of gliadins are not altered by environmental conditions or plant growth, are easily reproducible and very useful for wheat germplasm identification in addition to DNA markers. Genetic polymorphism of two Gli loci encoding gliadins can be used for selection of preferable genotypes of wheat with high grain quality.

Methods

Polyacrylamide gel electrophoresis was used to analyse genetic diversity of gliadins in a germplasm collection of spring bread wheat from Northern Kazakhstan.

Results

The highest frequencies of gliadin alleles were found as follows, in Gli1: -A1f (39.3%), -B1e (71.9%), and -D1a (41.0%); and in Gli-2: -A2q (17.8%), -B2t (13.5%), and -D2q (20.4%). The combination of these alleles in a single genotype may be associated with higher quality of grain as well as better adaptation to the dry environment of Northern Kazakhstan; preferable for wheat breeding in locations with similar conditions.

Genome-wide SNP discovery for development of high-density genetic map and QTL mapping of ascochyta blight resistance in chickpea (Cicer arietinum L.)

A high-density linkage map of chickpea using 3430 SNPs was constructed and used to identify QTLs and candidate genes for ascochyta blight resistance in chickpea. Chickpea cultivation in temperate conditions is highly vulnerable to ascochyta blight infection. Cultivation of resistant cultivars in combination with fungicide application within an informed disease management package is the most effective method to control ascochyta blight in chickpeas. Identifying new sources of resistance is critical for continued improvement in ascochyta blight resistance in chickpea. The objective of this study was to identify genetic loci and candidate genes controlling the resistance to ascochyta blight in recombinant inbred lines derived from crossing cultivars Amit and ICCV 96029. The RILs were genotyped using the genotyping-by-sequencing procedure and Illumina^® GoldenGate array. The RILs were evaluated in the field over three site-years and in three independent greenhouse experiments. A genetic map with eight linkage groups was constructed using 3430 SNPs. Eight QTLs for resistance were identified on chromosomes 2, 3, 4, 5 and 6. The QTLs individually explained 7-40% of the phenotypic variations. The QTLs on chromosomes 2 and 6 were associated with the resistance at vegetative stage only. The QTLs on chromosomes 2 and 4 that were previously reported to be conserved across diverse genetic backgrounds and against different isolates of Ascochyta rabiei were confirmed in this study. Candidate genes were identified within the QTL regions. Their co-localization with the underlying QTLs was confirmed by genetic mapping. The candidate gene-based SNP markers would lead to more efficient marker-assisted selection for ascochyta blight resistance and would provide a framework for fine mapping and subsequent cloning of the genes associated with the resistance.

Genetic Mapping and Molecular Characterization of a Broad-spectrum Phytophthora sojae Resistance Gene in Chinese Soybean

Phytophthora root rot (PRR) causes serious annual soybean yield losses worldwide. The most effective method to prevent PRR involves growing cultivars that possess genes conferring resistance to Phytophthora sojae (Rps). In this study, QTL-sequencing combined with genetic mapping was used to identify RpsX in soybean cultivar Xiu94-11 resistance to all P. sojae isolates tested, exhibiting broad-spectrum PRR resistance. Subsequent analysis revealed RpsX was located in the 242-kb genomic region spanning the RpsQ locus. However, a phylogenetic investigation indicated Xiu94-11 carrying RpsX is distantly related to the cultivars containing RpsQ, implying RpsX and RpsQ have different origins. An examination of candidate genes revealed RpsX and RpsQ share common nonsynonymous SNP and a 144-bp insertion in the Glyma.03g027200 sequence encoding a leucine-rich repeat (LRR) region. Glyma.03g027200 was considered to be the likely candidate gene of RpsQ and RpsX. Sequence analyses confirmed that the 144-bp insertion caused by an unequal exchange resulted in two additional LRR-encoding fragments in the candidate gene. A marker developed based on the 144-bp insertion was used to analyze the genetic population and germplasm, and proved to be useful for identifying the RpsX and RpsQ alleles. This study implies that the number of LRR units in the LRR domain may be important for PRR resistance in soybean.

CropSNPdb: a database of SNP array data for Brassica crops and hexaploid bread wheat

Advances in sequencing technology have led to a rapid rise in the genomic data available for plants, driving new insights into the evolution, domestication and improvement of crops. Single nucleotide polymorphisms (SNPs) are a major component of crop genomic diversity, and are invaluable as genetic markers in research and breeding programs. High-throughput SNP arrays, or 'SNP chips', can generate reproducible sets of informative SNP markers and have been broadly adopted. Although there are many public repositories for sequencing data, which are routinely uploaded, there are no formal repositories for crop SNP array data. To make SNP array data more easily accessible, we have developed CropSNPdb (http://snpdb.appliedbioinformatics.com.au), a database for SNP array data produced by the Illumina Infinium™ hexaploid bread wheat (Triticum aestivum) 90K and Brassica 60K arrays. We currently host SNPs from datasets covering 526 Brassica lines and 309 bread wheat lines, and provide search, download and upload utilities for users. CropSNPdb provides a useful repository for these data, which can be applied for a range of genomics and molecular crop-breeding activities.

Accelerating crop genetic gains with genomic selection

Genomic prediction based on additive genetic effects can accelerate genetic gain. There are opportunities for further improvement by including non-additive effects that access untapped sources of genetic diversity. Several studies have reported a worrying gap between the projected global future demand for plant-based products and the current annual rates of production increase, indicating that enhancing the rate of genetic gain might be critical for future food security. Therefore, new breeding technologies and strategies are required to significantly boost genetic improvement of future crop cultivars. Genomic selection (GS) has delivered considerable genetic gain in animal breeding and is becoming an essential component of many modern plant breeding programmes as well. In this paper, we review the lessons learned from implementing GS in livestock and the impact of GS on crop breeding, and discuss important features for the success of GS under different breeding scenarios. We highlight major challenges associated with GS including rapid genotyping, phenotyping, genotype-by-environment interaction and non-additivity and give examples for opportunities to overcome these issues. Finally, the potential of combining GS with other modern technologies in order to maximise the rate of crop genetic improvement is discussed, including the potential of increasing prediction accuracy by integration of crop growth models in GS frameworks.

Next-Generation Sequencing Accelerates Crop Gene Discovery

The identification and isolation of genes underlying quantitative trait loci (QTLs) associated with agronomic traits in crops have been recently accelerated thanks to next-generation sequencing (NGS)-based technologies combined with plant genetics. With NGS, various revisited genetic approaches, which benefited from higher marker density, have been elaborated. These approaches improved resolution in QTL position and assisted in determining functional causative variations in genes. Examples of QTLs/genes associated with agronomic traits in crops and identified using different strategies based on whole-genome sequencing (WGS)/whole-genome resequencing (WGR) or RNA-seq are presented and discussed in this review. More specifically, we summarize and illustrate how NGS boosted bulk-segregant analysis (BSA), expression profiling, and the construction of polymorphism databases to facilitate the detection of QTLs and causative genes.

From markers to genome-based breeding in wheat

Recent technological advances in wheat genomics provide new opportunities to uncover genetic variation in traits of breeding interest and enable genome-based breeding to deliver wheat cultivars for the projected food requirements for 2050. There has been tremendous progress in development of whole-genome sequencing resources in wheat and its progenitor species during the last 5 years. High-throughput genotyping is now possible in wheat not only for routine gene introgression but also for high-density genome-wide genotyping. This is a major transition phase to enable genome-based breeding to achieve progressive genetic gains to parallel to projected wheat production demands. These advances have intrigued wheat researchers to practice less pursued analytical approaches which were not practiced due to the short history of genome sequence availability. Such approaches have been successful in gene discovery and breeding applications in other crops and animals for which genome sequences have been available for much longer. These strategies include, (i) environmental genome-wide association studies in wheat genetic resources stored in genbanks to identify genes for local adaptation by using agroclimatic traits as phenotypes, (ii) haplotype-based analyses to improve the statistical power and resolution of genomic selection and gene mapping experiments, (iii) new breeding strategies for genome-based prediction of heterosis patterns in wheat, and (iv) ultimate use of genomics information to develop more efficient and robust genome-wide genotyping platforms to precisely predict higher yield potential and stability with greater precision. Genome-based breeding has potential to achieve the ultimate objective of ensuring sustainable wheat production through developing high yielding, climate-resilient wheat cultivars with high nutritional quality.

Computational aspects underlying genome to phenome analysis in plants

Recent advances in genomics technologies have greatly accelerated the progress in both fundamental plant science and applied breeding research. Concurrently, high-throughput plant phenotyping is becoming widely adopted in the plant community, promising to alleviate the phenotypic bottleneck. While these technological breakthroughs are significantly accelerating quantitative trait locus (QTL) and causal gene identification, challenges to enable even more sophisticated analyses remain. In particular, care needs to be taken to standardize, describe and conduct experiments robustly while relying on plant physiology expertise. In this article, we review the state of the art regarding genome assembly and the future potential of pangenomics in plant research. We also describe the necessity of standardizing and describing phenotypic studies using the Minimum Information About a Plant Phenotyping Experiment (MIAPPE) standard to enable the reuse and integration of phenotypic data. In addition, we show how deep phenotypic data might yield novel trait-trait correlations and review how to link phenotypic data to genomic data. Finally, we provide perspectives on the golden future of machine learning and their potential in linking phenotypes to genomic features.

Genomic Selection for Ascochyta Blight Resistance in Pea

Genomic selection (GS) is a breeding tool, which is rapidly gaining popularity for plant breeding, particularly for traits that are difficult to measure. One such trait is ascochyta blight resistance in pea (Pisum sativum L.), which is difficult to assay because it is strongly influenced by the environment and depends on the natural occurrence of multiple pathogens. Here we report a study of the efficacy of GS for predicting ascochyta blight resistance in pea, as represented by ascochyta blight disease score (ASC), and using nucleotide polymorphism data acquired through genotyping-by-sequencing. The effects on prediction accuracy of different GS models and different thresholds for missing genotypic data (which modified the number of single nucleotide polymorphisms used in the analysis) were compared using cross-validation. Additionally, the inclusion of marker × environment interactions in a genomic best linear unbiased prediction (GBLUP) model was evaluated. Finally, different ways of combining trait data from two field trials using bivariate, spatial, and single-stage analyses were compared to results obtained using a mean value. The best prediction accuracy achieved for ASC was 0.56, obtained using GBLUP analysis with a mean value for ASC and data quality threshold of 70% (i.e., missing SNP data in <30% of lines). GBLUP and Bayesian Reproducing kernel Hilbert spaces regression (RKHS) performed slightly better than the other models trialed, whereas different missing data thresholds made minimal differences to prediction accuracy. The prediction accuracies of individual, randomly selected, testing/training partitions were highly variable, highlighting the effect that the choice of training population has on prediction accuracy. The inclusion of marker × environment interactions did not increase the prediction accuracy for lines which had not been phenotyped, but did improve the results of prediction across environments. GS is potentially useful for pea breeding programs pursuing ascochyta blight resistance, both for predicting breeding values for lines that have not been phenotyped, and for providing enhanced estimated breeding values for lines for which trait data is available.

Evaluation and Recommendations for Routine Genotyping Using Skim Whole Genome Re-sequencing in Canola

Whole genome sequencing offers genome wide, unbiased markers, and inexpensive library preparation. With the cost of sequencing decreasing rapidly, many plant genomes of modest size are amenable to skim whole genome resequencing (skim WGR). The use of skim WGR in diverse sample sets without the use of imputation was evaluated in silico in 149 canola samples representative of global diversity. Fastq files with an average of 10x coverage of the reference genome were used to generate skim samples representing 0.25x, 0.5x, 1x, 2x, 3x, 4x, and 5x sequencing coverage. Applying a pre-defined list of SNPs versus de novo SNP discovery was evaluated. As skim WGR is expected to result in some degree of insufficient allele sampling, all skim coverage levels were filtered at a range of minimum read depths from a relaxed minimum read depth of 2 to a stringent read depth of 5, resulting in 28 list-based SNP sets. As a broad recommendation, genotyping pre-defined SNPs between 1x and 2x coverage with relatively stringent depth filtering is appropriate for a diverse sample set of canola due to a balance between marker number, sufficient accuracy, and sequencing cost, but depends on the intended application. This was experimentally examined in two sample sets with different genetic backgrounds: 1x coverage of 1,590 individuals from 84 Australian spring type four-parent crosses aimed at maximizing diversity as well as one commercial F1 hybrid, and 2x coverage of 379 doubled haploids (DHs) derived from a subset of the four-parent crosses. To determine optimal coverage in a simpler genetic background, the DH sample sequence coverage was further down sampled in silico. The flexible and cost-effective nature of the protocol makes it highly applicable across a range of species and purposes.

gmRAD: an integrated SNP calling pipeline for genetic mapping with RADseq across a hybrid population

Restriction site-associated DNA sequencing (RADseq) is a powerful technology that has been extensively applied in population genetics, phylogenetics and genetic mapping. Although many software packages are available for ecological and evolutionary studies, a few effective tools are available for extracting genotype data with RADseq for genetic mapping, a prerequisite for quantitative trait locus mapping, comparative genomics and genome scaffold assembly. Here, we present an integrated pipeline called gmRAD for generating single nucleotide polymorphism (SNP) genotypes from RADseq data, de novo, across a genetic mapping population derived by crossing two parents. As an analytical strategy, the software takes five steps to implement the whole algorithms, including clustering the first (forward) reads of each parent, building two parental references, generating parental SNP catalogs, calling SNP genotypes across all individuals and filtering the genotype data for genetic linkage mapping. All the steps can be completed with a simple command line, but they can be also performed optionally if prerequisite files are available. To validate its application, we also performed a real data analysis with RADseq data from an F1 hybrid population derived by crossing Populus deltoides and Populus simonii. The software gmRAD is freely available at https://github.com/tongchf/gmRAD.

Effective BAC clone anchoring with genotyping-by-sequencing and Diversity Arrays Technology in a large genome cereal rye

Identification of bacterial artificial chromosome (BAC) clones containing specific sequences is a prerequisite for many applications, such as physical map anchoring or gene cloning. Existing BAC library screening strategies are either low-throughput or require a considerable initial input of resources for platform establishment. We describe a high-throughput, reliable, and cost-effective BAC library screening approach deploying genotyping platforms which are independent from the availability of sequence information: a genotyping-by-sequencing (GBS) method DArTSeq and the microarray-based Diversity Arrays Technology (DArT). The performance of these methods was tested in a very large and complex rye genome. The DArTseq approach delivered superior results: a several fold higher efficiency of addressing genetic markers to BAC clones and anchoring of BAC clones to genetic map and also a higher reliability. Considering the sequence independence of the platform, the DArTseq-based library screening can be proposed as an attractive method to speed up genomics research in resource poor species.

Haplotype-Based Genome-Wide Prediction Models Exploit Local Epistatic Interactions Among Markers

Genome-wide prediction approaches represent versatile tools for the analysis and prediction of complex traits. Mostly they rely on marker-based information, but scenarios have been reported in which models capitalizing on closely-linked markers that were combined into haplotypes outperformed marker-based models. Detailed comparisons were undertaken to reveal under which circumstances haplotype-based genome-wide prediction models are superior to marker-based models. Specifically, it was of interest to analyze whether and how haplotype-based models may take local epistatic effects between markers into account. Assuming that populations consisted of fully homozygous individuals, a marker-based model in which local epistatic effects inside haplotype blocks were exploited (LEGBLUP) was linearly transformable into a haplotype-based model (HGBLUP). This theoretical derivation formally revealed that haplotype-based genome-wide prediction models capitalize on local epistatic effects among markers. Simulation studies corroborated this finding. Due to its computational efficiency the HGBLUP model promises to be an interesting tool for studies in which ultra-high-density SNP data sets are studied. Applying the HGBLUP model to empirical data sets revealed higher prediction accuracies than for marker-based models for both traits studied using a mouse panel. In contrast, only a small subset of the traits analyzed in crop populations showed such a benefit. Cases in which higher prediction accuracies are observed for HGBLUP than for marker-based models are expected to be of immediate relevance for breeders, due to the tight linkage a beneficial haplotype will be preserved for many generations. In this respect the inheritance of local epistatic effects very much resembles the one of additive effects.

Next-generation sequencing to identify candidate genes and develop diagnostic markers for a novel Phytophthora resistance gene, RpsHC18, in soybean

KEY MESSAGE

A novel Phytophthora sojae resistance gene RpsHC18 was identified and finely mapped on soybean chromosome 3. Two NBS-LRR candidate genes were identified and two diagnostic markers of RpsHC18 were developed. Phytophthora root rot caused by Phytophthora sojae is a destructive disease of soybean. The most effective disease-control strategy is to deploy resistant cultivars carrying Phytophthora-resistant Rps genes. The soybean cultivar Huachun 18 has a broad and distinct resistance spectrum to 12 P. sojae isolates. Quantitative trait loci sequencing (QTL-seq), based on the whole-genome resequencing (WGRS) of two extreme resistant and susceptible phenotype bulks from an F2:3 population, was performed, and one 767-kb genomic region with ΔSNP-index ≥ 0.9 on chromosome 3 was identified as the RpsHC18 candidate region in Huachun 18. The candidate region was reduced to a 146-kb region by fine mapping. Nonsynonymous SNP and haplotype analyses were carried out in the 146-kb region among ten soybean genotypes using WGRS. Four specific nonsynonymous SNPs were identified in two nucleotide-binding sites-leucine-rich repeat (NBS-LRR) genes, RpsHC18-NBL1 and RpsHC18-NBL2, which were considered to be the candidate genes. Finally, one specific SNP marker in each candidate gene was successfully developed using a tetra-primer ARMS-PCR assay, and the two markers were verified to be specific for RpsHC18 and to effectively distinguish other known Rps genes. In this study, we applied an integrated genomic-based strategy combining WGRS with traditional genetic mapping to identify RpsHC18 candidate genes and develop diagnostic markers. These results suggest that next-generation sequencing is a precise, rapid and cost-effective way to identify candidate genes and develop diagnostic markers, and it can accelerate Rps gene cloning and marker-assisted selection for breeding of P. sojae-resistant soybean cultivars.

Linkage and association mapping reveals the genetic basis of brown fibre (Gossypium hirsutum)

Brown fibre cotton is an environmental-friendly resource that plays a key role in the textile industry. However, the fibre quality and yield of natural brown cotton are poor, and fundamental research on brown cotton is relatively scarce. To understand the genetic basis of brown fibre cotton, we constructed linkage and association populations to systematically examine brown fibre accessions. We fine-mapped the brown fibre region, Lc1 , and dissected it into 2 loci, qBF-A07-1 and qBF-A07-2. The qBF-A07-1 locus mediates the initiation of brown fibre production, whereas the shade of the brown fibre is affected by the interaction between qBF-A07-1 and qBF-A07-2. Gh_A07G2341 and Gh_A07G0100 were identified as candidate genes for qBF-A07-1 and qBF-A07-2, respectively. Haploid analysis of the signals significantly associated with these two loci showed that most tetraploid modern brown cotton accessions exhibit the introgression signature of Gossypium barbadense. We identified 10 quantitative trait loci (QTLs) for fibre yield and 19 QTLs for fibre quality through a genome-wide association study (GWAS) and found that qBF-A07-2 negatively affects fibre yield and quality through an epistatic interaction with qBF-A07-1. This study sheds light on the genetics of fibre colour and lint-related traits in brown fibre cotton, which will guide the elite cultivars breeding of brown fibre cotton.

Development and Applications of a High Throughput Genotyping Tool for Polyploid Crops: Single Nucleotide Polymorphism (SNP) Array

Polypoid species play significant roles in agriculture and food production. Many crop species are polyploid, such as potato, wheat, strawberry, and sugarcane. Genotyping has been a daunting task for genetic studies of polyploid crops, which lags far behind the diploid crop species. Single nucleotide polymorphism (SNP) array is considered to be one of, high-throughput, relatively cost-efficient and automated genotyping approaches. However, there are significant challenges for SNP identification in complex, polyploid genomes, which has seriously slowed SNP discovery and array development in polyploid species. Ploidy is a significant factor impacting SNP qualities and validation rates of SNP markers in SNP arrays, which has been proven to be a very important tool for genetic studies and molecular breeding. In this review, we (1) discussed the pros and cons of SNP array in general for high throughput genotyping, (2) presented the challenges of and solutions to SNP calling in polyploid species, (3) summarized the SNP selection criteria and considerations of SNP array design for polyploid species, (4) illustrated SNP array applications in several different polyploid crop species, then (5) discussed challenges, available software, and their accuracy comparisons for genotype calling based on SNP array data in polyploids, and finally (6) provided a series of SNP array design and genotype calling recommendations. This review presents a complete overview of SNP array development and applications in polypoid crops, which will benefit the research in molecular breeding and genetics of crops with complex genomes.

Ensembl variation resources

The major goal of sequencing humans and many other species is to understand the link between genomic variation, phenotype and disease. There are numerous valuable and well-established variation resources, but collating and making sense of non-homogeneous, often large-scale data sets from disparate sources remains a challenge. Without a systematic catalogue of these data and appropriate query and annotation tools, understanding the genome sequence of an individual and assessing their disease risk is impossible. In Ensembl, we substantially solve this problem: we develop methods to facilitate data integration and broad access; aggregate information in a consistent manner and make it available a variety of standard formats, both visually and programmatically; build analysis pipelines to compare variants to comprehensive genomic annotation sets; and make all tools and data publicly available.

Development of basic technologies for improvement of breeding and cultivation of Japanese gentian

Japanese gentians are the most important ornamental flowers in Iwate Prefecture and their breeding and cultivation have been actively conducted for half a century. With its cool climate and large hilly and mountainous area, more than 60% of gentian production in Japan occurs in Iwate Prefecture. Recent advances in gentian breeding and cultivation have facilitated the efficient breeding of new cultivars; disease control and improved cultivation conditions have led to the stable production of Japanese gentians. Molecular biology techniques have been developed and applied in gentian breeding, including the diagnosis of viral diseases and analysis of physiological disorders to improve gentian production. This review summarizes such recent approaches that will assist in the development of new cultivars and support cultivation. More recently, new plant breeding techniques, including several new biotechnological methods such as genome editing and viral vectors, have also been developed in gentian. We, therefore, present examples of their application to gentians and discuss their advantages in future studies of gentians.

A comparison of genotyping-by-sequencing analysis methods on low-coverage crop datasets shows advantages of a new workflow, GB-eaSy

BACKGROUND

Genotyping-by-sequencing (GBS), a method to identify genetic variants and quickly genotype samples, reduces genome complexity by using restriction enzymes to divide the genome into fragments whose ends are sequenced on short-read sequencing platforms. While cost-effective, this method produces extensive missing data and requires complex bioinformatics analysis. GBS is most commonly used on crop plant genomes, and because crop plants have highly variable ploidy and repeat content, the performance of GBS analysis software can vary by target organism. Here we focus our analysis on soybean, a polyploid crop with a highly duplicated genome, relatively little public GBS data and few dedicated tools.

RESULTS

We compared the performance of five GBS pipelines using low-coverage Illumina sequence data from three soybean populations. To address issues identified with existing methods, we developed GB-eaSy, a GBS bioinformatics workflow that incorporates widely used genomics tools, parallelization and automation to increase the accuracy and accessibility of GBS data analysis. Compared to other GBS pipelines, GB-eaSy rapidly and accurately identified the greatest number of SNPs, with SNP calls closely concordant with whole-genome sequencing of selected lines. Across all five GBS analysis platforms, SNP calls showed unexpectedly low convergence but generally high accuracy, indicating that the workflows arrived at largely complementary sets of valid SNP calls on the low-coverage data analyzed.

CONCLUSIONS

We show that GB-eaSy is approximately as good as, or better than, other leading software solutions in the accuracy, yield and missing data fraction of variant calling, as tested on low-coverage genomic data from soybean. It also performs well relative to other solutions in terms of the run time and disk space required. In addition, GB-eaSy is built from existing open-source, modular software packages that are regularly updated and commonly used, making it straightforward to install and maintain. While GB-eaSy outperformed other individual methods on the datasets analyzed, our findings suggest that a comprehensive approach integrating the results from multiple GBS bioinformatics pipelines may be the optimal strategy to obtain the largest, most highly accurate SNP yield possible from low-coverage polyploid sequence data.

Genome-wide identification and validation of new reference genes for transcript normalization in developmental and post-harvested fruits of Actinidia chinensis

The appropriate reference genes are important and essential for reliable results of transcript normalization in real-time qRT-PCR. In the current study, we identified 1203 stably expressed genes from 35,286 genes' expression profiles in developmental fruits of Actinidia chinensis. We manually selected six candidate genes and assessed their expression levels, using two sets of fruit samples of A. chinensis: flesh fruits at four developmental stages and post-harvested fruits. The expression stability of these six genes was assessed by three independent algorithms: geNorm, NormFinder, and BestKeeper. Statistical results indicated these six genes can serve as internal control in both developmental and post-harvested fruits. Among these genes, UBQ_CONJ_E2 (Ubiquitin-conjugating enzyme E2 36) and TUB_FCB (Tubulin folding cofactor B) were the two best reference genes identified in this study. The identification and validation of these reference genes can be helpful for elucidating the studies of fruit development and post-harvested fruits' storage in A. chinensis and other fruit crops of Actinidiaceae.

Genotyping-by-Sequencing Derived High-Density Linkage Map and its Application to QTL Mapping of Flag Leaf Traits in Bread Wheat

Winter wheat parents ‘Harry’ (drought tolerant) and ‘Wesley’ (drought susceptible) were used to develop a recombinant inbred population with future goals of identifying genomic regions associated with drought tolerance. To precisely map genomic regions, high-density linkage maps are a prerequisite. In this study genotyping-by- sequencing (GBS) was used to construct the high-density linkage map. The map contained 3,641 markers distributed on 21 chromosomes and spanned 1,959 cM with an average distance of 1.8 cM between markers. The constructed linkage map revealed strong collinearity in marker order across 21 chromosomes with POPSEQ-v2.0, which was based on a high-density linkage map. The reliability of the linkage map for QTL mapping was demonstrated by co-localizing the genes to previously mapped genomic regions for two highly heritable traits, chaff color, and leaf cuticular wax. Applicability of linkage map for QTL mapping of three quantitative traits, flag leaf length, width, and area, identified 21 QTLs in four environments, and QTL expression varied across the environments. Two major stable QTLs, one each for flag leaf length (Qfll.hww-7A) and flag leaf width (Qflw.hww-5A) were identified. The map constructed will facilitate QTL and fine mapping of quantitative traits, map-based cloning, comparative mapping, and in marker-assisted wheat breeding endeavors.

Surveying the Maize community for their diversity and pedigree visualization needs to prioritize tool development and curation

The Maize Genetics and Genomics Database (MaizeGDB) team prepared a survey to identify breeders’ needs for visualizing pedigrees, diversity data and haplotypes in order to prioritize tool development and curation efforts at MaizeGDB. The survey was distributed to the maize research community on behalf of the Maize Genetics Executive Committee in Summer 2015. The survey garnered 48 responses from maize researchers, of which more than half were self-identified as breeders. The survey showed that the maize researchers considered their top priorities for visualization as: (i) displaying single nucleotide polymorphisms in a given region for a given list of lines, (ii) showing haplotypes for a given list of lines and (iii) presenting pedigree relationships visually. The survey also asked which populations would be most useful to display. The following two populations were on top of the list: (i) 3000 publicly available maize inbred lines used in Romay et al. (Comprehensive genotyping of the USA national maize inbred seed bank. Genome Biol, 2013;14:R55) and (ii) maize lines with expired Plant Variety Protection Act (ex-PVP) certificates. Driven by this strong stakeholder input, MaizeGDB staff are currently working in four areas to improve its interface and web-based tools: (i) presenting immediate progenies of currently available stocks at the MaizeGDB Stock pages, (ii) displaying the most recent ex-PVP lines described in the Germplasm Resources Information Network (GRIN) on the MaizeGDB Stock pages, (iii) developing network views of pedigree relationships and (iv) visualizing genotypes from SNP-based diversity datasets. These survey results can help other biological databases to direct their efforts according to user preferences as they serve similar types of data sets for their communities.

Database URL

https://www.maizegdb.org.

Current Status and Challenges in Identifying Disease Resistance Genes in Brassica napus

Brassica napus is an economically important crop across different continents including temperate and subtropical regions in Europe, Canada, South Asia, China and Australia. Its widespread cultivation also brings setbacks as it plays host to fungal, oomycete and chytrid pathogens that can lead to serious yield loss. For sustainable crop production, identification of resistance (R) genes in B. napus has become of critical importance. In this review, we discuss four key pathogens affecting Brassica crops: Clubroot (Plasmodiophora brassicae), Blackleg (Leptosphaeria maculans and L. biglobosa), Sclerotinia Stem Rot (Sclerotinia sclerotiorum), and Downy Mildew (Hyaloperonospora parasitica). We first review current studies covering prevalence of these pathogens on Brassica crops and highlight the R genes and QTL that have been identified from Brassica species against these pathogens. Insights into the relationships between the pathogen and its Brassica host, the unique host resistance mechanisms and how these affect resistance outcomes is also presented. We discuss challenges in identification and deployment of R genes in B. napus in relation to highly specific genetic interactions between host subpopulations and pathogen pathotypes and emphasize the need for common or shared techniques and research materials or tighter collaboration between researchers to reconcile the inconsistencies in the research outcomes. Using current genomics tools, we provide examples of how characterization and cloning of R genes in B. napus can be carried out more effectively. Lastly, we put forward strategies to breed resistant cultivars through introgressions supported by genomic approaches and suggest prospects that can be implemented in the future for a better, pathogen-resistant B. napus.

Assessing and Exploiting Functional Diversity in Germplasm Pools to Enhance Abiotic Stress Adaptation and Yield in Cereals and Food Legumes

There is a need to accelerate crop improvement by introducing alleles conferring host plant resistance, abiotic stress adaptation, and high yield potential. Elite cultivars, landraces and wild relatives harbor useful genetic variation that needs to be more easily utilized in plant breeding. We review genome-wide approaches for assessing and identifying alleles associated with desirable agronomic traits in diverse germplasm pools of cereals and legumes. Major quantitative trait loci and single nucleotide polymorphisms (SNPs) associated with desirable agronomic traits have been deployed to enhance crop productivity and resilience. These include alleles associated with variation conferring enhanced photoperiod and flowering traits. Genetic variants in the florigen pathway can provide both environmental flexibility and improved yields. SNPs associated with length of growing season and tolerance to abiotic stresses (precipitation, high temperature) are valuable resources for accelerating breeding for drought-prone environments. Both genomic selection and genome editing can also harness allelic diversity and increase productivity by improving multiple traits, including phenology, plant architecture, yield potential and adaptation to abiotic stresses. Discovering rare alleles and useful haplotypes also provides opportunities to enhance abiotic stress adaptation, while epigenetic variation has potential to enhance abiotic stress adaptation and productivity in crops. By reviewing current knowledge on specific traits and their genetic basis, we highlight recent developments in the understanding of crop functional diversity and identify potential candidate genes for future use. The storage and integration of genetic, genomic and phenotypic information will play an important role in ensuring broad and rapid application of novel genetic discoveries by the plant breeding community. Exploiting alleles for yield-related traits would allow improvement of selection efficiency and overall genetic gain of multigenic traits. An integrated approach involving multiple stakeholders specializing in management and utilization of genetic resources, crop breeding, molecular biology and genomics, agronomy, stress tolerance, and reproductive/seed biology will help to address the global challenge of ensuring food security in the face of growing resource demands and climate change induced stresses.

Use of genotyping-by-sequencing to determine the genetic structure in the medicinal plant chamomile, and to identify flowering time and alpha-bisabolol associated SNP-loci by genome-wide association mapping

Background

Chamomile (Matricaria recutita L.) has a long history of use in herbal medicine with various applications, and the flower heads contain numerous secondary metabolites which are medicinally active.

In the major crop plants, next generation sequencing (NGS) approaches are intensely applied to exploit genetic resources, to develop genomic resources and to enhance breeding. Here, genotyping-by-sequencing (GBS) has been used in the non-model medicinal plant chamomile to evaluate the genetic structure of the cultivated varieties/populations, and to perform genome wide association study (GWAS) focusing on genes with large effect on flowering time and the medicinally important alpha-bisabolol content.

Results

GBS analysis allowed the identification of 6495 high-quality SNP-markers in our panel of 91 M. recutita plants from 33 origins (2–4 genotypes each) and 4 M. discoidea plants as outgroup, grown in the greenhouse in Gatersleben, Germany. M. recutita proved to be clearly distinct from the outgroup, as was demonstrated by different cluster and principal coordinate analyses using the SNP-markers. Chamomile genotypes from the same origin were mostly genetically similar. Model-based cluster analysis revealed one large group of tetraploid genotypes with low genetic differentiation including 39 plants from 14 origins. Tetraploids tended to display lower genetic diversity than diploids, probably reflecting their origin by artificial polyploidisation from only a limited set of genetic backgrounds.

Analyses of flowering time demonstrated that diploids generally flowered earlier than tetraploids, and the analysis of alpha-bisabolol identified several tetraploid genotypes with a high content. GWAS identified highly significant (P < 0.01) SNPs for flowering time (9) and alpha-bisabolol (71). One sequence harbouring SNPs associated with flowering time was described to play a role in self-pollination in Arabidopsis thaliana, whereas four sequences harbouring SNPs associated with alpha-bisabolol were identified to be involved in plant biotic and abiotic stress response in various plants species.

Conclusions

The first genomic resource for future applications to enhance breeding in chamomile was created, andanalyses of diversity will facilitate the exploitation of these genetic resources. The GWAS data pave the way for future research towards the genetics underlying important traits in chamomile, the identification of marker-trait associations, and development of reliable markers for practical breeding.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3991-0) contains supplementary material, which is available to authorized users.

Crop Breeding Chips and Genotyping Platforms: Progress, Challenges, and Perspectives

There is a rapidly rising trend in the development and application of molecular marker assays for gene mapping and discovery in field crops and trees. Thus far, more than 50 SNP arrays and 15 different types of genotyping-by-sequencing (GBS) platforms have been developed in over 25 crop species and perennial trees. However, much less effort has been made on developing ultra-high-throughput and cost-effective genotyping platforms for applied breeding programs. In this review, we discuss the scientific bottlenecks in existing SNP arrays and GBS technologies and the strategies to develop targeted platforms for crop molecular breeding. We propose that future practical breeding platforms should adopt automated genotyping technologies, either array or sequencing based, target functional polymorphisms underpinning economic traits, and provide desirable prediction accuracy for quantitative traits, with universal applications under wide genetic backgrounds in crops. The development of such platforms faces serious challenges at both the technological level due to cost ineffectiveness, and the knowledge level due to large genotype-phenotype gaps in crop plants. It is expected that such genotyping platforms will be achieved in the next ten years in major crops in consideration of (a) rapid development in gene discovery of important traits, (b) deepened understanding of quantitative traits through new analytical models and population designs, (c) integration of multi-layer -omics data leading to identification of genes and pathways responsible for important breeding traits, and (d) improvement in cost effectiveness of large-scale genotyping. Crop breeding chips and genotyping platforms will provide unprecedented opportunities to accelerate the development of cultivars with desired yield potential, quality, and enhanced adaptation to mitigate the effects of climate change.

Optimized double-digest genotyping by sequencing (ddGBS) method with high-density SNP markers and high genotyping accuracy for chickens

High-density single nucleotide polymorphism (SNP) markers are crucial to improve the resolution and accuracy of genome-wide association study (GWAS) and genomic selection (GS). Numerous approaches, including whole genome sequencing, genome sampling sequencing, and SNP chips are able to discover or genotype markers at different densities and costs. Achieving an optimal balance between sequencing resolution and budgets, especially in large-scale population genetics research, constitutes a major challenge. Here, we performed improved double-enzyme digestion genotyping by sequencing (ddGBS) on chicken. We evaluated eight double-enzyme digestion combinations, and EcoR I- Mse I was chosen as the optimal combination for the chicken genome. We firstly proposed that two parameters, optimal read-count point (ORP) and saturated read-count point (SRP), could be utilized to determine the optimal sequencing volume. A total of 291,772 high-density SNPs from 824 animals were identified. By validation using the SNP chip, we found that the consistency between ddGBS data and the SNP chip is over 99%. The approach that we developed in chickens, which is high-quality, high-density, cost-effective (300 K, $30/sample), and time-saving (within 48 h), will have broad applications in animal breeding programs.

From Genomes to GENE-omes: Exome Sequencing Concept and Applications in Crop Improvement

Exome sequencing represents targeted capture and sequencing of 1-2% of 'high-value genomic regions' (subset of the genome) which are enriched for functional variants and harbors low level of repetitive regions. We discuss here an overview of exome sequencing, ways to approach plant exomes, and advantages and applicability of this powerful approach in deciphering functional regions of genomes. Though initially this approach was developed as an alternative to whole genome sequencing (WGS), but the multitude of benefits conferred by sequence capture via hybridization approaches created a niche for itself to solve many of biological riddles, particularly for resolving phylogenetic distances. The technique has also proved to be successful in understanding the basis of natural and induced molecular variation, marker development and developing genomic resources for complex, wild and non-model species, which are still intractable for WGS efforts. Thus, with profound applications of this powerful sequencing strategy, near future is expected to witness a collective expansion of both techniques, i.e., sequence capture via hybridization for evolutionary and ecological research and WGS approaches for its universal accessibility.

Development of a Genetic Map for Onion (Allium cepa L.) Using Reference-Free Genotyping-by-Sequencing and SNP Assays

Single nucleotide polymorphisms (SNPs) play important roles as molecular markers in plant genomics and breeding studies. Although onion (Allium cepa L.) is an important crop globally, relatively few molecular marker resources have been reported due to its large genome and high heterozygosity. Genotyping-by-sequencing (GBS) offers a greater degree of complexity reduction followed by concurrent SNP discovery and genotyping for species with complex genomes. In this study, GBS was employed for SNP mining in onion, which currently lacks a reference genome. A segregating F₂ population, derived from a cross between 'NW-001' and 'NW-002,' as well as multiple parental lines were used for GBS analysis. A total of 56.15 Gbp of raw sequence data were generated and 1,851,428 SNPs were identified from the de novo assembled contigs. Stringent filtering resulted in 10,091 high-fidelity SNP markers. Robust SNPs that satisfied the segregation ratio criteria and with even distribution in the mapping population were used to construct an onion genetic map. The final map contained eight linkage groups and spanned a genetic length of 1,383 centiMorgans (cM), with an average marker interval of 8.08 cM. These robust SNPs were further analyzed using the high-throughput Fluidigm platform for marker validation. This is the first study in onion to develop genome-wide SNPs using GBS. The resulting SNP markers and developed linkage map will be valuable tools for genetic mapping of important agronomic traits and marker-assisted selection in onion breeding programs.