The quest for rare variants: pooled multiplexed next generation sequencing in plants

Genotyping of SNPs in bread wheat at reduced cost from pooled experiments and imputation

KEY MESSAGE

Pooling and imputation are computational methods that can be combined for achieving cost-effective and accurate high-density genotyping of both common and rare variants, as demonstrated in a MAGIC wheat population. The plant breeding industry has shown growing interest in using the genotype data of relevant markers for performing selection of new competitive varieties. The selection usually benefits from large amounts of marker data, and it is therefore crucial to dispose of data collection methods that are both cost-effective and reliable. Computational methods such as genotype imputation have been proposed earlier in several plant science studies for addressing the cost challenge. Genotype imputation methods have though been used more frequently and investigated more extensively in human genetics research. The various algorithms that exist have shown lower accuracy at inferring the genotype of genetic variants occurring at low frequency, while these rare variants can have great significance and impact in the genetic studies that underlie selection. In contrast, pooling is a technique that can efficiently identify low-frequency items in a population, and it has been successfully used for detecting the samples that carry rare variants in a population. In this study, we propose to combine pooling and imputation and demonstrate this by simulating a hypothetical microarray for genotyping a population of recombinant inbred lines in a cost-effective and accurate manner, even for rare variants. We show that with an adequate imputation model, it is feasible to accurately predict the individual genotypes at lower cost than sample-wise genotyping and time-effectively. Moreover, we provide code resources for reproducing the results presented in this study in the form of a containerized workflow.

Weighted Selection Probability to Prioritize Susceptible Rare Variants in Multi-Phenotype Association Studies with Application to a Soybean Genetic Data Set

Rare variant association studies with multiple traits or diseases have drawn a lot of attention since association signals of rare variants can be boosted if more than one phenotype outcome is associated with the same rare variants. Most of the existing statistical methods to identify rare variants associated with multiple phenotypes are based on a group test, where a pre-specified genetic region is tested one at a time. However, these methods are not designed to locate susceptible rare variants within the genetic region. In this article, we propose new statistical methods to prioritize rare variants within a genetic region when a group test for the genetic region identifies a statistical association with multiple phenotypes. It computes the weighted selection probability (WSP) of individual rare variants and ranks them from largest to smallest according to their WSP. In simulation studies, we demonstrated that the proposed method outperforms other statistical methods in terms of true positive selection, when multiple phenotypes are correlated with each other. We also applied it to our soybean single nucleotide polymorphism (SNP) data with 13 highly correlated amino acids, where we identified some potentially susceptible rare variants in chromosome 19.

Long-Amplicon Single-Molecule Sequencing Reveals Novel, Trait-Associated Variants of VERNALIZATION1 Homoeologs in Hexaploid Wheat

The gene VERNALIZATION1 (VRN1) is a key controller of vernalization requirement in wheat. The genome of hexaploid wheat (Triticum aestivum) harbors three homoeologous VRN1 loci on chromosomes 5A, 5B, and 5D. Structural sequence variants including small and large deletions and insertions and single nucleotide polymorphisms (SNPs) in the three homoeologous VRN1 genes not only play an important role in the control of vernalization requirement, but also have been reported to be associated with other yield related traits of wheat. Here we used single-molecule sequencing of barcoded long-amplicons to assay the full-length sequences (∼13 kbp plus 700 bp from the promoter sequence) of the three homoeologous VRN1 genes in a panel of 192 predominantly European winter wheat cultivars. Long read sequences revealed previously undetected duplications, insertions and single-nucleotide polymorphisms in the three homoeologous VRN1 genes. All the polymorphisms were confirmed by Sanger sequencing. Sequence analysis showed the predominance of the winter alleles vrn-A1, vrn-B1, and vrn-D1 across the investigated cultivars. Associations of SNPs and structural variations within the three VRN1 genes with 20 economically relevant traits including yield, nodal root-angle index and quality related traits were evaluated at the levels of alleles, haplotypes, and copy number variants. Cultivars carrying structural variants within VRN1 genes showed lower grain yield, protein yield and biomass compared to those with intact genes. Cultivars carrying a single vrn-A1 copy and a unique haplotype with a high number of SNPs were found to have elevated grain yield, kernels per spike and kernels per m2 along with lower grain sedimentation values. In addition, we detected a novel SNP polymorphism within the G-quadruplex region of the promoter of vrn-A1 that was associated with deeper roots in winter wheat. Our findings show that multiplex, single-molecule long-amplicon sequencing is a useful tool for detecting variants in target genes within large plant populations, and can be used to simultaneously assay sequence variants among target multiple gene homoeologs in polyploid crops. Numerous novel VRN1 haplotypes and alleles were identified that showed significantly associations to economically important traits. These polymorphisms were converted into PCR or KASP assays for use in marker-assisted breeding.

Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes

The performance of genomic prediction (GP) on genetically correlated traits can be improved through an interdependence multi-trait model under a multi-environment context. In this study, a panel of 237 soft facultative wheat (Triticum aestivum L.) lines was evaluated to compare single- and multi-trait models for predicting grain yield (GY), harvest index (HI), spike fertility (SF), and thousand grain weight (TGW). The panel was phenotyped in two locations and two years in Florida under drought and moderately drought stress conditions, while the genotyping was performed using 27,957 genotyping-by-sequencing (GBS) single nucleotide polymorphism (SNP) makers. Five predictive models including Multi-environment Genomic Best Linear Unbiased Predictor (MGBLUP), Bayesian Multi-trait Multi-environment (BMTME), Bayesian Multi-output Regressor Stacking (BMORS), Single-trait Multi-environment Deep Learning (SMDL), and Multi-trait Multi-environment Deep Learning (MMDL) were compared. Across environments, the multi-trait statistical model (BMTME) was superior to the multi-trait DL model for prediction accuracy in most scenarios, but the DL models were comparable to the statistical models for response to selection. The multi-trait model also showed 5 to 22% more genetic gain compared to the single-trait model across environment reflected by the response to selection. Overall, these results suggest that multi-trait genomic prediction can be an efficient strategy for economically important yield component related traits in soft wheat.

Identification of phytic acid mutants in oilseed rape (Brassica napus) by large-scale screening of mutant populations through amplicon sequencing

Brassica napus (oilseed rape) is an important oil crop in temperate regions, which originated from hybridization of Brassica oleracea and Brassica rapa. Owing to its polyploidy, the functional study of single genes is cumbersome. Phytic acid is considered as an antinutritive compound, and we aimed to knock out the underlying synthesis and transporter genes to identify low phytic acid mutants. We implemented a high-throughput next-generation sequencing screening protocol for an ethylmethane sulfonate population of 7680 plants in six gene families (BnMIPS, BnMIK, Bn2-PGK, BnIPK1, BnIPK2, and BnMRP5) with two paralogues for each gene. A total of 1487 mutations were revealed, and the vast majority (96%) were confirmed by Sanger sequencing. Furthermore, the characterization of double mutants of Bn.2-PGK2 showed a significant reduction of phytic acid contents. We propose to use three-dimensional pooling combined with amplicon stacking and next-generation sequencing to identify mutations in polyploid oilseed rape in a fast and cost-effective manner for complex metabolic pathways. Furthermore, the mutants identified in Bn2-PGK2 might be a very valuable resource for industrial production of oilseed rape protein for human consumption.

Multiplex PCR Targeted Amplicon Sequencing (MTA-Seq): Simple, Flexible, and Versatile SNP Genotyping by Highly Multiplexed PCR Amplicon Sequencing

Next-generation sequencing (NGS) technologies have enabled genome re-sequencing for exploring genome-wide polymorphisms among individuals, as well as targeted re-sequencing for the rapid and simultaneous detection of polymorphisms in genes associated with various biological functions. Therefore, a simple and robust method for targeted re-sequencing should facilitate genotyping in a wide range of biological fields. In this study, we developed a simple, custom, targeted re-sequencing method, designated "multiplex PCR targeted amplicon sequencing (MTA-seq)," and applied it to the genotyping of the model grass Brachypodium distachyon. To assess the practical usability of MTA-seq, we applied it to the genotyping of genome-wide single-nucleotide polymorphisms (SNPs) identified in natural accessions (Bd1-1, Bd3-1, Bd21-3, Bd30-1, Koz-1, Koz-3, and Koz-4) by comparing the re-sequencing data with that of reference accession Bd21. Examination of SNP-genotyping accuracy in 443 amplicons from eight parental accessions and an F₁ progeny derived by crossing of Bd21 and Bd3-1 revealed that ~95% of the SNPs were correctly called. The assessment suggested that the method provided an efficient framework for accurate and robust SNP genotyping. The method described here enables easy design of custom target SNP-marker panels in various organisms, facilitating a wide range of high-throughput genetic applications, such as genetic mapping, population analysis, molecular breeding, and genomic diagnostics.

Genomic Selection in the Era of Next Generation Sequencing for Complex Traits in Plant Breeding

Genomic selection (GS) is a promising approach exploiting molecular genetic markers to design novel breeding programs and to develop new markers-based models for genetic evaluation. In plant breeding, it provides opportunities to increase genetic gain of complex traits per unit time and cost. The cost-benefit balance was an important consideration for GS to work in crop plants. Availability of genome-wide high-throughput, cost-effective and flexible markers, having low ascertainment bias, suitable for large population size as well for both model and non-model crop species with or without the reference genome sequence was the most important factor for its successful and effective implementation in crop species. These factors were the major limitations to earlier marker systems viz., SSR and array-based, and was unimaginable before the availability of next-generation sequencing (NGS) technologies which have provided novel SNP genotyping platforms especially the genotyping by sequencing. These marker technologies have changed the entire scenario of marker applications and made the use of GS a routine work for crop improvement in both model and non-model crop species. The NGS-based genotyping have increased genomic-estimated breeding value prediction accuracies over other established marker platform in cereals and other crop species, and made the dream of GS true in crop breeding. But to harness the true benefits from GS, these marker technologies will be combined with high-throughput phenotyping for achieving the valuable genetic gain from complex traits. Moreover, the continuous decline in sequencing cost will make the WGS feasible and cost effective for GS in near future. Till that time matures the targeted sequencing seems to be more cost-effective option for large scale marker discovery and GS, particularly in case of large and un-decoded genomes.

Highly efficient de novo mutant identification in a Sorghum bicolor TILLING population using the ComSeq approach

Screening large populations for carriers of known or de novo rare single nucleotide polymorphisms (SNPs) is required both in Targeting induced local lesions in genomes (TILLING) experiments in plants and in screening of human populations. We previously suggested an approach that combines the mathematical field of compressed sensing with next-generation sequencing to allow such large-scale screening. Based on pooled measurements, this method identifies multiple carriers of heterozygous or homozygous rare alleles while using only a small fraction of resources. Its rigorous mathematical foundations allow scalable and robust detection, and provide error correction and resilience to experimental noise. Here we present a large-scale experimental demonstration of our computational approach, in which we targeted a TILLING population of 1024 Sorghum bicolor lines to detect carriers of de novo SNPs whose frequency was less than 0.1%, using only 48 pools. Subsequent validation confirmed that all detected lines were indeed carriers of the predicted mutations. This novel approach provides a highly cost-effective and robust tool for biologists and breeders to allow identification of novel alleles and subsequent functional analysis.

New resources for genetic studies in Populus nigra: genome-wide SNP discovery and development of a 12k Infinium array

Whole genome resequencing of 51 Populus nigra (L.) individuals from across Western Europe was performed using Illumina platforms. A total number of 1 878 727 SNPs distributed along the P. nigra reference sequence were identified. The SNP calling accuracy was validated with Sanger sequencing. SNPs were selected within 14 previously identified QTL regions, 2916 expressional candidate genes related to rust resistance, wood properties, water-use efficiency and bud phenology and 1732 genes randomly spread across the genome. Over 10 000 SNPs were selected for the construction of a 12k Infinium Bead-Chip array dedicated to association mapping. The SNP genotyping assay was performed with 888 P. nigra individuals. The genotyping success rate was 91%. Our high success rate was due to the discovery panel design and the stringent parameters applied for SNP calling and selection. In the same set of P. nigra genotypes, linkage disequilibrium throughout the genome decayed on average within 5-7 kb to half of its maximum value. As an application test, ADMIXTURE analysis was performed with a selection of 600 SNPs spread throughout the genome and 706 individuals collected along 12 river basins. The admixture pattern was consistent with genetic diversity revealed by neutral markers and the geographical distribution of the populations. These newly developed SNP resources and genotyping array provide a valuable tool for population genetic studies and identification of QTLs through natural-population based genetic association studies in P. nigra.

Whole genome sequences in pulse crops: a global community resource to expedite translational genomics and knowledge-based crop improvement

Unprecedented developments in legume genomics over the last decade have resulted in the acquisition of a wide range of modern genomic resources to underpin genetic improvement of grain legumes. The genome enabled insights direct investigators in various ways that primarily include unearthing novel structural variations, retrieving the lost genetic diversity, introducing novel/exotic alleles from wider gene pools, finely resolving the complex quantitative traits and so forth. To this end, ready availability of cost-efficient and high-density genotyping assays allows genome wide prediction to be increasingly recognized as the key selection criterion in crop breeding. Further, the high-dimensional measurements of agronomically significant phenotypes obtained by using new-generation screening techniques will empower reference based resequencing as well as allele mining and trait mapping methods to comprehensively associate genome diversity with the phenome scale variation. Besides stimulating the forward genetic systems, accessibility to precisely delineated genomic segments reveals novel candidates for reverse genetic techniques like targeted genome editing. The shifting paradigm in plant genomics in turn necessitates optimization of crop breeding strategies to enable the most efficient integration of advanced omics knowledge and tools. We anticipate that the crop improvement schemes will be bolstered remarkably with rational deployment of these genome-guided approaches, ultimately resulting in expanded plant breeding capacities and improved crop performance.

Using Populus as a lignocellulosic feedstock for bioethanol

Populus species along with species from the sister genus Salix will provide valuable feedstock resources for advanced second-generation biofuels. Their inherent fast growth characteristics can particularly be exploited for short rotation management, a time and energy saving cultivation alternative for lignocellulosic feedstock supply. Salicaceae possess inherent cell wall characteristics with favorable cellulose to lignin ratios for utilization as bioethanol crop. We review economically important traits relevant for intensively managed biofuel crop plantations, genomic and phenotypic resources available for Populus, breeding strategies for forest trees dedicated to bioenergy provision, and bioprocesses and downstream applications related to opportunities using Salicaceae as a renewable resource. Challenges need to be resolved for every single step of the conversion process chain, i.e., starting from tree domestication for improved performance as a bioenergy crop, bioconversion process, policy development for land use changes associated with advanced biofuels, and harvest and supply logistics associated with industrial-scale biorefinery plants using Populus as feedstock. Significant hurdles towards cost and energy efficiency, environmental friendliness, and yield maximization with regards to biomass pretreatment, saccharification, and fermentation of celluloses and the sustainability of biorefineries as a whole still need to be overcome.

Moving toward a comprehensive map of central plant metabolism

Decades of intensive study have led to the discovery of the main pathways involved in central metabolism but only some of the pathways and regulatory networks in which they are embedded. In this review, we discuss techniques used to assemble these pathways into a systems biology framework that can enable accurate modeling of the response of central metabolism to changes, including ways to perturb metabolic systems and assemble the resulting data into a meaningful network. Critically, these networks are of such size and complexity that it is possible to derive them only if data from different groups can be comprehensively and meaningfully combined. We conclude that it is essential to establish common standards for the description of experimental conditions and data collection and to store this information in databases to which the whole community can contribute.

Genomics and molecular breeding in lesser explored pulse crops: current trends and future opportunities

Pulses are multipurpose crops for providing income, employment and food security in the underprivileged regions, notably the FAO-defined low-income food-deficit countries. Owing to their intrinsic ability to endure environmental adversities and the least input/management requirements, these crops remain central to subsistence farming. Given their pivotal role in rain-fed agriculture, substantial research has been invested to boost the productivity of these pulse crops. To this end, genomic tools and technologies have appeared as the compelling supplement to the conventional breeding. However, the progress in minor pulse crops including dry beans (Vigna spp.), lupins, lablab, lathyrus and vetches has remained unsatisfactory, hence these crops are often labeled as low profile or lesser researched. Nevertheless, recent scientific and technological breakthroughs particularly the next generation sequencing (NGS) are radically transforming the scenario of genomics and molecular breeding in these minor crops. NGS techniques have allowed de novo assembly of whole genomes in these orphan crops. Moreover, the availability of a reference genome sequence would promote re-sequencing of diverse genotypes to unlock allelic diversity at a genome-wide scale. In parallel, NGS has offered high-resolution genetic maps or more precisely, a robust genetic framework to implement whole-genome strategies for crop improvement. As has already been demonstrated in lupin, sequencing-based genotyping of the representative sample provided access to a number of functionally-relevant markers that could be deployed straight away in crop breeding programs. This article attempts to outline the recent progress made in genomics of these lesser explored pulse crops, and examines the prospects of genomics assisted integrated breeding to enhance and stabilize crop yields.

InsertionMapper: a pipeline tool for the identification of targeted sequences from multidimensional high throughput sequencing data

Background

The advent of next-generation high-throughput technologies has revolutionized whole genome sequencing, yet some experiments require sequencing only of targeted regions of the genome from a very large number of samples. These regions can be amplified by PCR and sequenced by next-generation methods using a multidimensional pooling strategy. However, there is at present no available generalized tool for the computational analysis of target-enriched NGS data from multidimensional pools.

Results

Here we present InsertionMapper, a pipeline tool for the identification of targeted sequences from multidimensional high throughput sequencing data. InsertionMapper consists of four independently working modules: Data Preprocessing, Database Modeling, Dimension Deconvolution and Element Mapping. We illustrate InsertionMapper with an example from our project 'New reverse genetics resources for maize’, which aims to sequence-index a collection of 15,000 independent insertion sites of the transposon Ds in maize. Identified sequences are validated by PCR assays. This pipeline tool is applicable to similar scenarios requiring analysis of the tremendous output of short reads produced in NGS sequencing experiments of targeted genome sequences.

Conclusions

InsertionMapper is proven efficacious for the identification of target-enriched sequences from multidimensional high throughput sequencing data. With adjustable parameters and experiment configurations, this tool can save great computational effort to biologists interested in identifying their sequences of interest within the huge output of modern DNA sequencers. InsertionMapper is freely accessible at https://sourceforge.net/p/insertionmapper and http://bo.csam.montclair.edu/du/insertionmapper.

Fine de novo sequencing of a fungal genome using only SOLiD short read data: verification on Aspergillus oryzae RIB40

The development of next-generation sequencing (NGS) technologies has dramatically increased the throughput, speed, and efficiency of genome sequencing. The short read data generated from NGS platforms, such as SOLiD and Illumina, are quite useful for mapping analysis. However, the SOLiD read data with lengths of <60 bp have been considered to be too short for de novo genome sequencing. Here, to investigate whether de novo sequencing of fungal genomes is possible using only SOLiD short read sequence data, we performed de novo assembly of the Aspergillus oryzae RIB40 genome using only SOLiD read data of 50 bp generated from mate-paired libraries with 2.8- or 1.9-kb insert sizes. The assembled scaffolds showed an N50 value of 1.6 Mb, a 22-fold increase than those obtained using only SOLiD short read in other published reports. In addition, almost 99% of the reference genome was accurately aligned by the assembled scaffold fragments in long lengths. The sequences of secondary metabolite biosynthetic genes and clusters, whose products are of considerable interest in fungal studies due to their potential medicinal, agricultural, and cosmetic properties, were also highly reconstructed in the assembled scaffolds. Based on these findings, we concluded that de novo genome sequencing using only SOLiD short reads is feasible and practical for molecular biological study of fungi. We also investigated the effect of filtering low quality data, library insert size, and k-mer size on the assembly performance, and recommend for the assembly use of mild filtered read data where the N50 was not so degraded and the library has an insert size of ∼2.0 kb, and k-mer size 33.