Trait associations in the pangenome of pigeon pea (Cajanus cajan).

Pigeon pea (Cajanus cajan) is an important orphan crop mainly grown by smallholder farmers in India and Africa. Here, we present the first pigeon pea pangenome based on 89 accessions mainly from India and the Philippines, showing that there is significant genetic diversity in Philippine individuals that is not present in Indian individuals. Annotation of variable genes suggests that they are associated with self-fertilization and response to disease. We identified 225 SNPs associated with nine agronomically important traits over three locations and two different time points, with SNPs associated with genes for transcription factors and kinases. These results will lead the way to an improved pigeon pea breeding programme.

Identification and characterization of more than 4 million intervarietal SNPs across the group 7 chromosomes of bread wheat.

Despite being a major international crop, our understanding of the wheat genome is relatively poor due to its large size and complexity. To gain a greater understanding of wheat genome diversity, we have identified single nucleotide polymorphisms between 16 Australian bread wheat varieties. Whole-genome shotgun Illumina paired read sequence data were mapped to the draft assemblies of chromosomes 7A, 7B and 7D to identify more than 4 million intervarietal SNPs. SNP density varied between the three genomes, with much greater density observed on the A and B genomes than the D genome. This variation may be a result of substantial gene flow from the tetraploid Triticum turgidum, which possesses A and B genomes, during early co-cultivation of tetraploid and hexaploid wheat. In addition, we examined SNP density variation along the chromosome syntenic builds and identified genes in low-density regions which may have been selected during domestication and breeding. This study highlights the impact of evolution and breeding on the bread wheat genome and provides a substantial resource for trait association and crop improvement. All SNP data are publically available on a generic genome browser GBrowse at www.wheatgenome.info.

High-resolution skim genotyping by sequencing reveals the distribution of crossovers and gene conversions in Cicer arietinum and Brassica napus.

KEY MESSAGE: We characterise the distribution of crossover and non-crossover recombination in Brassica napus and Cicer arietinum using a low-coverage genotyping by sequencing pipeline SkimGBS. The growth of next-generation DNA sequencing technologies has led to a rapid increase in sequence-based genotyping for applications including diversity assessment, genome structure validation and gene-trait association. We have established a skim-based genotyping by sequencing method for crop plants and applied this approach to genotype-segregating populations of Brassica napus and Cicer arietinum. Comparison of progeny genotypes with those of the parental individuals allowed the identification of crossover and non-crossover (gene conversion) events. Our results identify the positions of recombination events with high resolution, permitting the mapping and frequency assessment of recombination in segregating populations.

A chromosomal genomics approach to assess and validate the desi and kabuli draft chickpea genome assemblies.

With the expansion of next-generation sequencing technology and advanced bioinformatics, there has been a rapid growth of genome sequencing projects. However, while this technology enables the rapid and cost-effective assembly of draft genomes, the quality of these assemblies usually falls short of gold standard genome assemblies produced using the more traditional BAC by BAC and Sanger sequencing approaches. Assembly validation is often performed by the physical anchoring of genetically mapped markers, but this is prone to errors and the resolution is usually low, especially towards centromeric regions where recombination is limited. New approaches are required to validate reference genome assemblies. The ability to isolate individual chromosomes combined with next-generation sequencing permits the validation of genome assemblies at the chromosome level. We demonstrate this approach by the assessment of the recently published chickpea kabuli and desi genomes. While previous genetic analysis suggests that these genomes should be very similar, a comparison of their chromosome sizes and published assemblies highlights significant differences. Our chromosomal genomics analysis highlights short defined regions that appear to have been misassembled in the kabuli genome and identifies large-scale misassembly in the draft desi genome. The integration of chromosomal genomics tools within genome sequencing projects has the potential to significantly improve the construction and validation of genome assemblies. The approach could be applied both for new genome assemblies as well as published assemblies, and complements currently applied genome assembly strategies.

Genome assembly, comparative genomics, and identification of genes/pathways underlying plant growth-promoting traits of an actinobacterial strain, Amycolatopsis sp. (BCA-696).

The draft genome sequence of an agriculturally important actinobacterial species Amycolatopsis sp. BCA-696 was developed and characterized in this study. Amycolatopsis BCA-696 is known for its biocontrol properties against charcoal rot and also for plant growth-promotion (PGP) in several crop species. The next-generation sequencing (NGS)-based draft genome of Amycolatopsis sp. BCA-696 comprised of ~ 9.05 Mb linear chromosome with 68.75% GC content. In total, 8716 protein-coding sequences and 61 RNA-coding sequences were predicted in the genome. This newly developed genome sequence has been also characterized for biosynthetic gene clusters (BGCs) and biosynthetic pathways. Furthermore, we have also reported that the Amycolatopsis sp. BCA-696 produces the glycopeptide antibiotic vancomycin that inhibits the growth of pathogenic gram-positive bacteria. A comparative analysis of the BCA-696 genome with publicly available closely related genomes of 14 strains of Amycolatopsis has also been conducted. The comparative analysis has identified a total of 4733 core and 466 unique orthologous genes present in the BCA-696 genome The unique genes present in BCA-696 was enriched with antibiotic biosynthesis and resistance functions. Genome assembly of the BCA-696 has also provided genes involved in key pathways related to PGP and biocontrol traits such as siderophores, chitinase, and cellulase production.

Genome-wide association study and expression of candidate genes for Fe and Zn concentration in sorghum grains.

Sorghum germplasm showed grain Fe and Zn genetic variability, but a few varieties were biofortified with these minerals. This work contributes to narrowing this gap. Fe and Zn concentrations along with 55,068 high-quality GBS SNP data from 140 sorghum accessions were used in this study. Both micronutrients exhibited good variability with respective ranges of 22.09-52.55 ppm and 17.92-43.16 ppm. Significant marker-trait associations were identified on chromosomes 1, 3, and 5. Two major effect SNPs (S01_72265728 and S05_58213541) explained 35% and 32% of Fe and Zn phenotypic variance, respectively. The SNP S01_72265728 was identified in the cytochrome P450 gene and showed a positive effect on Fe accumulation in the kernel, while S05_58213541 was intergenic near Sobic.005G134800 (zinc-binding ribosomal protein) and showed negative effect on Zn. Tissue-specific in silico expression analysis resulted in higher levels of Sobic.003G350800 gene product in several tissues such as leaf, root, flower, panicle, and stem. Sobic.005G188300 and Sobic.001G463800 were expressed moderately at grain maturity and anthesis in leaf, root, panicle, and seed tissues. The candidate genes expressed in leaves, stems, and grains will be targeted to improve grain and stover quality. The haplotypes identified will be useful in forward genetics breeding.

Double-digest restriction-associated DNA sequencing-based genotyping and its applications in sesame germplasm management.

Sesame (Sesamum indicum L.) is an ancient oilseed crop belonging to the family Pedaliaceae and a globally cultivated crop for its use as oil and food. In this study, 2496 sesame accessions, being conserved at the National Genebank of ICAR-National Bureau of Plant Genetic Resources (NBPGR), were genotyped using genomics-assisted double-digest restriction-associated DNA sequencing (ddRAD-seq) approach. A total of 64,910 filtered single-nucleotide polymorphisms (SNPs) were utilized to assess the genome-scale diversity. Applications of this genome-scale information (reduced representation using restriction enzymes) are demonstrated through the development of a molecular core collection (CC) representing maximal SNP diversity. This information is also applied in developing a mid-density panel (MDP) comprising 2515 hyper-variable SNPs, representing almost equally the genic and non-genic regions. The sesame CC comprising 384 accessions, a representative set of accessions with maximal diversity, was identified using multiple criteria such as k-mer (subsequence of length "k" in a sequence read) diversity, observed heterozygosity, CoreHunter3, GenoCore, and genetic differentiation. The coreset constituted around 15% of the total accessions studied, and this small subset had captured >60% SNP diversity of the entire population. In the coreset, the admixture analysis shows reduced genetic complexity, increased nucleotide diversity (π), and is geographically distributed without any repetitiveness in the CC germplasm. Within the CC, India-originated accessions exhibit higher diversity (as expected based on the center of diversity concept), than those accessions that were procured from various other countries. The identified CC set and the MDP will be a valuable resource for genomics-assisted accelerated sesame improvement program.

The Progression in Developing Genomic Resources for Crop Improvement.

Sequencing technologies have rapidly evolved over the past two decades, and new technologies are being continually developed and commercialized. The emerging sequencing technologies target generating more data with fewer inputs and at lower costs. This has also translated to an increase in the number and type of corresponding applications in genomics besides enhanced computational capacities (both hardware and software). Alongside the evolving DNA sequencing landscape, bioinformatics research teams have also evolved to accommodate the increasingly demanding techniques used to combine and interpret data, leading to many researchers moving from the lab to the computer. The rich history of DNA sequencing has paved the way for new insights and the development of new analysis methods. Understanding and learning from past technologies can help with the progress of future applications. This review focuses on the evolution of sequencing technologies, their significant enabling role in generating plant genome assemblies and downstream applications, and the parallel development of bioinformatics tools and skills, filling the gap in data analysis techniques.

Exploring the sorghum race level diversity utilizing 272 sorghum accessions genomic resources.

Due to evolutionary divergence, sorghum race populations exhibit significant genetic and morphological variation. A k-mer-based sorghum race sequence comparison identified the conserved k-mers of all 272 accessions from sorghum and the race-specific genetic signatures identified the gene variability in 10,321 genes (PAVs). To understand sorghum race structure, diversity and domestication, a deep learning-based variant calling approach was employed in a set of genotypic data derived from a diverse panel of 272 sorghum accessions. The data resulted in 1.7 million high-quality genome-wide SNPs and identified selective signature (both positive and negative) regions through a genome-wide scan with different (iHS and XP-EHH) statistical methods. We discovered 2,370 genes associated with selection signatures including 179 selective sweep regions distributed over 10 chromosomes. Co-localization of these regions undergoing selective pressure with previously reported QTLs and genes revealed that the signatures of selection could be related to the domestication of important agronomic traits such as biomass and plant height. The developed k-mer signatures will be useful in the future to identify the sorghum race and for trait and SNP markers for assisting in plant breeding programs.

A pilot-scale comparison between single and double-digest RAD markers generated using GBS strategy in sesame (Sesamum indicum L.).

To reduce the genome sequence representation, restriction site-associated DNA sequencing (RAD-seq) protocols is being widely used either with single-digest or double-digest methods. In this study, we genotyped the sesame population (48 sample size) in a pilot scale to compare single and double-digest RAD-seq (sd and ddRAD-seq) methods. We analysed the resulting short-read data generated from both protocols and assessed their performance impacting the downstream analysis using various parameters. The distinct k-mer count and gene presence absence variation (PAV) showed a significant difference between the sesame samples studied. Additionally, the variant calling from both datasets (sdRAD-seq and ddRAD-seq) exhibits a significant difference between them. The combined variants from both datasets helped in identifying the most diverse samples and possible sub-groups in the sesame population. The most diverse samples identified from each analysis (k-mer, gene PAV, SNP count, Heterozygosity, NJ and PCA) can possibly be representative samples holding major diversity of the small sesame population used in this study. The best possible strategies with suggested inputs for modifications to utilize the RAD-seq strategy efficiently on a large dataset containing thousands of samples to be subjected to molecular analysis like diversity, population structure and core development studies were discussed.

Coverage-based consensus calling (CbCC) of short sequence reads and comparison of CbCC results to identify SNPs in chickpea (Cicer arietinum; Fabaceae), a crop species without a reference genome.

PREMISE OF THE STUDY: Next-generation sequencing (NGS) technologies are frequently used for resequencing and mining of single nucleotide polymorphisms (SNPs) by comparison to a reference genome. In crop species such as chickpea (Cicer arietinum) that lack a reference genome sequence, NGS-based SNP discovery is a challenge. Therefore, unlike probability-based statistical approaches for consensus calling and by comparison with a reference sequence, a coverage-based consensus calling (CbCC) approach was applied and two genotypes were compared for SNP identification. METHODS: A CbCC approach is used in this study with four commonly used short read alignment tools (Maq, Bowtie, Novoalign, and SOAP2) and 15.7 and 22.1 million Illumina reads for chickpea genotypes ICC4958 and ICC1882, together with the chickpea trancriptome assembly (CaTA). KEY RESULTS: A nonredundant set of 4543 SNPs was identified between two chickpea genotypes. Experimental validation of 224 randomly selected SNPs showed superiority of Maq among individual tools, as 50.0% of SNPs predicted by Maq were true SNPs. For combinations of two tools, greatest accuracy (55.7%) was reported for Maq and Bowtie, with a combination of Bowtie, Maq, and Novoalign identifying 61.5% true SNPs. SNP prediction accuracy generally increased with increasing reads depth. CONCLUSIONS: This study provides a benchmark comparison of tools as well as read depths for four commonly used tools for NGS SNP discovery in a crop species without a reference genome sequence. In addition, a large number of SNPs have been identified in chickpea that would be useful for molecular breeding.

Construction of Practical Haplotype Graph (PHG) with the Whole-Genome Sequence Data.

With the emerging sequencing technologies and cost reduction, the sequence data generation has accelerated from a single individual to multiple (thousands of) individuals of a species. The terabytes of sequence data generated from thousands of individuals include the majority of the redundant sequence which depends on the level of sequence similarity within the population of individuals. Managing large datasets and creating the unique catalogue sequence from such a large population is challenging to analyze, store, and retrieve the information. In this chapter, we discuss the practical haplotype graph (PHG) which addresses the above said challenges and also able to retrieve required information such as variants and sequences more efficiently, which enable researchers to manage and assess large genomic data.

Genomic prediction of preliminary yield trials in chickpea: Effect of functional annotation of SNPs and environment.

Achieving yield potential in chickpea (Cicer arietinum L.) is limited by many constraints that include biotic and abiotic stresses. Combining next-generation sequencing technology with advanced statistical modeling has the potential to increase genetic gain efficiently. Whole genome resequencing data was obtained from 315 advanced chickpea breeding lines from the Australian chickpea breeding program resulting in more than 298,000 single nucleotide polymorphisms (SNPs) discovered. Analysis of population structure revealed a distinct group of breeding lines with many alleles that are absent from recently released Australian cultivars. Genome-wide association studies (GWAS) using these Australian breeding lines identified 20 SNPs significantly associated with grain yield in multiple field environments. A reduced level of nucleotide diversity and extended linkage disequilibrium suggested that some regions in these chickpea genomes may have been through selective breeding for yield or other traits. A large introgression segment that introduced from C. echinospermum for phytophthora root rot resistance was identified on chromosome 6, yet it also has unintended consequences of reducing yield due to linkage drag. We further investigated the effect of genotype by environment interaction on genomic prediction of yield. We found that the training set had better prediction accuracy when phenotyped under conditions relevant to the targeted environments. We also investigated the effect of SNP functional annotation on prediction accuracy using different subsets of SNPs based on their genomic locations: regulatory regions, exome, and alternative splice sites. Compared with the whole SNP dataset, a subset of SNPs did not significantly decrease prediction accuracy for grain yield despite consisting of a smaller number of SNPs.

Sorghum Pan-Genome Explores the Functional Utility for Genomic-Assisted Breeding to Accelerate the Genetic Gain.

Sorghum (Sorghum bicolor L.) is a staple food crops in the arid and rainfed production ecologies. Sorghum plays a critical role in resilient farming and is projected as a smart crop to overcome the food and nutritional insecurity in the developing world. The development and characterisation of the sorghum pan-genome will provide insight into genome diversity and functionality, supporting sorghum improvement. We built a sorghum pan-genome using reference genomes as well as 354 genetically diverse sorghum accessions belonging to different races. We explored the structural and functional characteristics of the pan-genome and explain its utility in supporting genetic gain. The newly-developed pan-genome has a total of 35,719 genes, a core genome of 16,821 genes and an average of 32,795 genes in each cultivar. The variable genes are enriched with environment responsive genes and classify the sorghum accessions according to their race. We show that 53% of genes display presence-absence variation, and some of these variable genes are predicted to be functionally associated with drought adaptation traits. Using more than two million SNPs from the pan-genome, association analysis identified 398 SNPs significantly associated with important agronomic traits, of which, 92 were in genes. Drought gene expression analysis identified 1,788 genes that are functionally linked to different conditions, of which 79 were absent from the reference genome assembly. This study provides comprehensive genomic diversity resources in sorghum which can be used in genome assisted crop improvement.

Investigating Drought Tolerance in Chickpea Using Genome-Wide Association Mapping and Genomic Selection Based on Whole-Genome Resequencing Data.

Drought tolerance is a complex trait that involves numerous genes. Identifying key causal genes or linked molecular markers can facilitate the fast development of drought tolerant varieties. Using a whole-genome resequencing approach, we sequenced 132 chickpea varieties and advanced breeding lines and found more than 144,000 single nucleotide polymorphisms (SNPs). We measured 13 yield and yield-related traits in three drought-prone environments of Western Australia. The genotypic effects were significant for all traits, and many traits showed highly significant correlations, ranging from 0.83 between grain yield and biomass to -0.67 between seed weight and seed emergence rate. To identify candidate genes, the SNP and trait data were incorporated into the SUPER genome-wide association study (GWAS) model, a modified version of the linear mixed model. We found that several SNPs from auxin-related genes, including auxin efflux carrier protein (PIN3), p-glycoprotein, and nodulin MtN21/EamA-like transporter, were significantly associated with yield and yield-related traits under drought-prone environments. We identified four genetic regions containing SNPs significantly associated with several different traits, which was an indication of pleiotropic effects. We also investigated the possibility of incorporating the GWAS results into a genomic selection (GS) model, which is another approach to deal with complex traits. Compared to using all SNPs, application of the GS model using subsets of SNPs significantly associated with the traits under investigation increased the prediction accuracies of three yield and yield-related traits by more than twofold. This has important implication for implementing GS in plant breeding programs.

Genome Analysis Identified Novel Candidate Genes for Ascochyta Blight Resistance in Chickpea Using Whole Genome Re-sequencing Data.

Ascochyta blight (AB) is a fungal disease that can significantly reduce chickpea production in Australia and other regions of the world. In this study, 69 chickpea genotypes were sequenced using whole genome re-sequencing (WGRS) methods. They included 48 Australian varieties differing in their resistance ranking to AB, 16 advanced breeding lines from the Australian chickpea breeding program, four landraces, and one accession representing the wild chickpea species Cicer reticulatum. More than 800,000 single nucleotide polymorphisms (SNPs) were identified. Population structure analysis revealed relatively narrow genetic diversity amongst recently released Australian varieties and two groups of varieties separated by the level of AB resistance. Several regions of the chickpea genome were under positive selection based on Tajima's D test. Both Fst genome- scan and genome-wide association studies (GWAS) identified a 100 kb region (AB4.1) on chromosome 4 that was significantly associated with AB resistance. The AB4.1 region co-located to a large QTL interval of 7 Mb∼30 Mb identified previously in three different mapping populations which were genotyped at relatively low density with SSR or SNP markers. The AB4.1 region was validated by GWAS in an additional collection of 132 advanced breeding lines from the Australian chickpea breeding program, genotyped with approximately 144,000 SNPs. The reduced level of nucleotide diversity and long extent of linkage disequilibrium also suggested the AB4.1 region may have gone through selective sweeps probably caused by selection of the AB resistance trait in breeding. In total, 12 predicted genes were located in the AB4.1 QTL region, including those annotated as: NBS-LRR receptor-like kinase, wall-associated kinase, zinc finger protein, and serine/threonine protein kinases. One significant SNP located in the conserved catalytic domain of a NBS-LRR receptor-like kinase led to amino acid substitution. Transcriptional analysis using qPCR showed that some predicted genes were significantly induced in resistant lines after inoculation compared to non-inoculated plants. This study demonstrates the power of combining WGRS data with relatively simple traits to rapidly develop "functional makers" for marker-assisted selection and genomic selection.

TAG Sequence Identification of Genomic Regions Using TAGdb.

Second-generation sequencing (SGS) technology has enabled the sequencing of genomes and identification of genes. However, large complex plant genomes remain particularly difficult for de novo assembly. Access to the vast quantity of raw sequence data may facilitate discoveries; however the volume of this data makes access difficult. This chapter discusses the Web-based tool TAGdb that enables researchers to identify paired read second-generation DNA sequence data that share identity with a submitted query sequence. The identified reads can be used for PCR amplification of genomic regions to identify genes and promoters without the need for genome assembly.

An efficient approach to BAC based assembly of complex genomes.

BACKGROUND: There has been an exponential growth in the number of genome sequencing projects since the introduction of next generation DNA sequencing technologies. Genome projects have increasingly involved assembly of whole genome data which produces inferior assemblies compared to traditional Sanger sequencing of genomic fragments cloned into bacterial artificial chromosomes (BACs). While whole genome shotgun sequencing using next generation sequencing (NGS) is relatively fast and inexpensive, this method is extremely challenging for highly complex genomes, where polyploidy or high repeat content confounds accurate assembly, or where a highly accurate 'gold' reference is required. Several attempts have been made to improve genome sequencing approaches by incorporating NGS methods, to variable success. RESULTS: We present the application of a novel BAC sequencing approach which combines indexed pools of BACs, Illumina paired read sequencing, a sequence assembler specifically designed for complex BAC assembly, and a custom bioinformatics pipeline. We demonstrate this method by sequencing and assembling BAC cloned fragments from bread wheat and sugarcane genomes. CONCLUSIONS: We demonstrate that our assembly approach is accurate, robust, cost effective and scalable, with applications for complete genome sequencing in large and complex genomes.

Bioinformatics: identification of markers from next-generation sequence data.

With the advent of sequencing technology, next-generation sequencing (NGS) technology has dramatically revolutionized plant genomics. NGS technology combined with new software tools enables the discovery, validation, and assessment of genetic markers on a large scale. Among different markers systems, simple sequence repeats (SSRs) and Single nucleotide polymorphisms (SNPs) are the markers of choice for genetics and plant breeding. SSR markers have been a choice for large-scale characterization of germplasm collections, construction of genetic maps, and QTL identification. Similarly, SNPs are the most abundant genetic variations with higher frequencies throughout the genome of plant species. This chapter discusses various tools available for genome assembly and widely focuses on SSR and SNP marker discovery.

CicArVarDB: SNP and InDel database for advancing genetics research and breeding applications in chickpea.

Molecular markers are valuable tools for breeders to help accelerate crop improvement. High throughput sequencing technologies facilitate the discovery of large-scale variations such as single nucleotide polymorphisms (SNPs) and simple sequence repeats (SSRs). Sequencing of chickpea genome along with re-sequencing of several chickpea lines has enabled the discovery of 4.4 million variations including SNPs and InDels. Here we report a repository of 1.9 million variations (SNPs and InDels) anchored on eight pseudomolecules in a custom database, referred as CicArVarDB that can be accessed at http://cicarvardb.icrisat.org/. It includes an easy interface for users to select variations around specific regions associated with quantitative trait loci, with embedded webBLAST search and JBrowse visualisation. We hope that this database will be immensely useful for the chickpea research community for both advancing genetics research as well as breeding applications for crop improvement. Database URL: http://cicarvardb.icrisat.org.