Dadakhalandar Doddamani

Draft genome of the peanut A-genome progenitor (Arachis duranensis) provides insights into geocarpy, oil biosynthesis, and allergens

Significance We present a draft genome of the peanut A-genome progenitor, Arachis duranensis, providing details on total genes present in the genome. Genome analysis suggests that the peanut lineage was affected by at least three polyploidizations since the origin of eudicots. Resequencing of synthetic Arachis tetraploids reveals extensive gene conversion since their formation by human hands. The A. duranensis genome provides a major source of candidate genes for fructification, oil biosynthesis, and allergens, expanding knowledge of understudied areas of plant biology and human health impacts of plants. This study also provides millions of structural variations that can be used as genetic markers for the development of improved peanut varieties through genomics-assisted breeding. Peanut or groundnut (Arachis hypogaea L.), a legume of South American origin, has high seed oil content (45–56%) and is a staple crop in semiarid tropical and subtropical regions, partially because of drought tolerance conferred by its geocarpic reproductive strategy. We present a draft genome of the peanut A-genome progenitor, Arachis duranensis, and 50,324 protein-coding gene models. Patterns of gene duplication suggest the peanut lineage has been affected by at least three polyploidizations since the origin of eudicots. Resequencing of synthetic Arachis tetraploids reveals extensive gene conversion in only three seed-to-seed generations since their formation by human hands, indicating that this process begins virtually immediately following polyploid formation. Expansion of some specific gene families suggests roles in the unusual subterranean fructification of Arachis. For example, the S1Fa-like transcription factor family has 126 Arachis members, in contrast to no more than five members in other examined plant species, and is more highly expressed in roots and etiolated seedlings than green leaves. The A. duranensis genome provides a major source of candidate genes for fructification, oil biosynthesis, and allergens, expanding knowledge of understudied areas of plant biology and human health impacts of plants, informing peanut genetic improvement and aiding deeper sequencing of Arachis diversity.

Prioritization of candidate genes in “ QTL-hotspot ” region for drought tolerance in chickpea ( Cicer arietinum L.)

A combination of two approaches, namely QTL analysis and gene enrichment analysis were used to identify candidate genes in the “QTL-hotspot” region for drought tolerance present on the Ca4 pseudomolecule in chickpea. In the first approach, a high-density bin map was developed using 53,223 single nucleotide polymorphisms (SNPs) identified in the recombinant inbred line (RIL) population of ICC 4958 (drought tolerant) and ICC 1882 (drought sensitive) cross. QTL analysis using recombination bins as markers along with the phenotyping data for 17 drought tolerance related traits obtained over 1–5 seasons and 1–5 locations split the “QTL-hotspot” region into two subregions namely “QTL-hotspot_a” (15 genes) and “QTL-hotspot_b” (11 genes). In the second approach, gene enrichment analysis using significant marker trait associations based on SNPs from the Ca4 pseudomolecule with the above mentioned phenotyping data, and the candidate genes from the refined “QTL-hotspot” region showed enrichment for 23 genes. Twelve genes were found common in both approaches. Functional validation using quantitative real-time PCR (qRT-PCR) indicated four promising candidate genes having functional implications on the effect of “QTL-hotspot” for drought tolerance in chickpea.

Pearl millet genome sequence provides a resource to improve agronomic traits in arid environments

Pearl millet [Cenchrus americanus (L.) Morrone] is a staple food for more than 90 million farmers in arid and semi-arid regions of sub-Saharan Africa, India and South Asia. We report the ∼1.79 Gb draft whole genome sequence of reference genotype Tift 23D2B1-P1-P5, which contains an estimated 38,579 genes. We highlight the substantial enrichment for wax biosynthesis genes, which may contribute to heat and drought tolerance in this crop. We resequenced and analyzed 994 pearl millet lines, enabling insights into population structure, genetic diversity and domestication. We use these resequencing data to establish marker trait associations for genomic selection, to define heterotic pools, and to predict hybrid performance. We believe that these resources should empower researchers and breeders to improve this important staple crop.

Genome-wide dissection of AP2/ERF and HSP90 gene families in five legumes and expression profiles in chickpea and pigeonpea

Summary APETALA2/ethylene response factor (AP2/ERF) and heat‐shock protein 90 (HSP90) are two significant classes of transcription factor and molecular chaperone proteins which are known to be implicated under abiotic and biotic stresses. Comprehensive survey identified a total of 147 AP2/ERF genes in chickpea, 176 in pigeonpea, 131 in Medicago, 179 in common bean and 140 in Lotus, whereas the number of HSP90 genes ranged from 5 to 7 in five legumes. Sequence alignment and phylogenetic analyses distinguished AP2, ERF, DREB, RAV and soloist proteins, while HSP90 proteins segregated on the basis of their cellular localization. Deeper insights into the gene structure allowed ERF proteins to be classified into AP2s based on DNA‐binding domains, intron arrangements and phylogenetic grouping. RNA‐seq and quantitative real‐time PCR (qRT‐PCR) analyses in heat‐stressed chickpea as well as Fusarium wilt (FW)‐ and sterility mosaic disease (SMD)‐stressed pigeonpea provided insights into the modus operandi of AP2/ERF and HSP90 genes. This study identified potential candidate genes in response to heat stress in chickpea while for FW and SMD stresses in pigeonpea. For instance, two DREB genes (Ca_02170 and Ca_16631) and three HSP90 genes (Ca_23016, Ca_09743 and Ca_25602) in chickpea can be targeted as potential candidate genes. Similarly, in pigeonpea, a HSP90 gene, C.cajan_27949, was highly responsive to SMD in the resistant genotype ICPL 20096, can be recommended for further functional validation. Also, two DREB genes, C.cajan_41905 and C.cajan_41951, were identified as leads for further investigation in response to FW stress in pigeonpea.

Recent breeding programs enhanced genetic diversity in both desi and kabuli varieties of chickpea ( Cicer arietinum L.)

In order to understand the impact of breeding on genetic diversity and gain insights into temporal trends in diversity in chickpea, a set of 100 chickpea varieties released in 14 countries between 1948 and 2012 were re-sequenced. For analysis, the re-sequencing data for 29 varieties available from an earlier study was also included. Copy number variations and presence absence variations identified in the present study have potential to drive phenotypic variations for trait improvement. Re-sequencing of a large number of varieties has provided opportunities to inspect the genetic and genomic changes reflecting the history of breeding, which we consider as breeding signatures and the selected loci may provide targets for crop improvement. Our study also reports enhanced diversity in both desi and kabuli varieties as a result of recent chickpea breeding efforts. The current study will aid the explicit efforts to breed for local adaptation in the context of anticipated climate changes.

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.

CicArMiSatDB: the chickpea microsatellite database

BackgroundChickpea (Cicer arietinum) is a widely grown legume crop in tropical, sub-tropical and temperate regions. Molecular breeding approaches seem to be essential for enhancing crop productivity in chickpea. Until recently, limited numbers of molecular markers were available in the case of chickpea for use in molecular breeding. However, the recent advances in genomics facilitated the development of large scale markers especially SSRs (simple sequence repeats), the markers of choice in any breeding program. Availability of genome sequence very recently opens new avenues for accelerating molecular breeding approaches for chickpea improvement.DescriptionIn order to assist genetic studies and breeding applications, we have developed a user friendly relational database named the Chickpea Microsatellite Database (CicArMiSatDB http://cicarmisatdb.icrisat.org). This database provides detailed information on SSRs along with their features in the genome. SSRs have been classified and made accessible through an easy-to-use web interface.ConclusionsThis database is expected to help chickpea community in particular and legume community in general, to select SSRs of particular type or from a specific region in the genome to advance both basic genomics research as well as applied aspects of crop improvement.

NGS-QCbox and Raspberry for Parallel, Automated and Rapid Quality Control Analysis of Large-Scale Next Generation Sequencing (Illumina) Data

Rapid popularity and adaptation of next generation sequencing (NGS) approaches have generated huge volumes of data. High throughput platforms like Illumina HiSeq produce terabytes of raw data that requires quick processing. Quality control of the data is an important component prior to the downstream analyses. To address these issues, we have developed a quality control pipeline, NGS-QCbox that scales up to process hundreds or thousands of samples. Raspberry is an in-house tool, developed in C language utilizing HTSlib (v1.2.1) (http://htslib.org), for computing read/base level statistics. It can be used as stand-alone application and can process both compressed and uncompressed FASTQ format files. NGS-QCbox integrates Raspberry with other open-source tools for alignment (Bowtie2), SNP calling (SAMtools) and other utilities (bedtools) towards analyzing raw NGS data at higher efficiency and in high-throughput manner. The pipeline implements batch processing of jobs using Bpipe (https://github.com/ssadedin/bpipe) in parallel and internally, a fine grained task parallelization utilizing OpenMP. It reports read and base statistics along with genome coverage and variants in a user friendly format. The pipeline developed presents a simple menu driven interface and can be used in either quick or complete mode. In addition, the pipeline in quick mode outperforms in speed against other similar existing QC pipeline/tools. The NGS-QCbox pipeline, Raspberry tool and associated scripts are made available at the URL https://github.com/CEG-ICRISAT/NGS-QCbox and https://github.com/CEG-ICRISAT/Raspberry for rapid quality control analysis of large-scale next generation sequencing (Illumina) data.

Development and evaluation of high‐density Axiom®CicerSNP Array for high‐resolution genetic mapping and breeding applications in chickpea

Summary To accelerate genomics research and molecular breeding applications in chickpea, a high‐throughput SNP genotyping platform ‘Axiom® CicerSNP Array’ has been designed, developed and validated. Screening of whole‐genome resequencing data from 429 chickpea lines identified 4.9 million SNPs, from which a subset of 70 463 high‐quality nonredundant SNPs was selected using different stringent filter criteria. This was further narrowed down to 61 174 SNPs based on p‐convert score ≥0.3, of which 50 590 SNPs could be tiled on array. Among these tiled SNPs, a total of 11 245 SNPs (22.23%) were from the coding regions of 3673 different genes. The developed Axiom® CicerSNP Array was used for genotyping two recombinant inbred line populations, namely ICCRIL03 (ICC 4958 × ICC 1882) and ICCRIL04 (ICC 283 × ICC 8261). Genotyping data reflected high success and polymorphic rate, with 15 140 (29.93%; ICCRIL03) and 20 018 (39.57%; ICCRIL04) polymorphic SNPs. High‐density genetic maps comprising 13 679 SNPs spanning 1033.67 cM and 7769 SNPs spanning 1076.35 cM were developed for ICCRIL03 and ICCRIL04 populations, respectively. QTL analysis using multilocation, multiseason phenotyping data on these RILs identified 70 (ICCRIL03) and 120 (ICCRIL04) main‐effect QTLs on genetic map. Higher precision and potential of this array is expected to advance chickpea genetics and breeding applications.

Expression of drought responsive genes in pigeonpea and in silico comparison with soybean cDNA library

Pigeonpea, a drought tolerant, semi-arid pulse crop has been investigated for the expression of differentially expressed genes (DEGs) under drought stress. The cDNA library of soybean leaf tissue retrieved from the Unigene database of the NCBI, were compared for in silico expression using IDEG6 web statistical tool. A list of 52 non-redundant DEGs consisting of 11 up-regulated and 41 down-regulated was obtained. Among these, more photosynthesis and light harvesting proteins were down-regulated in drought stress conditions. Pathways were assigned based on KEGG database, revealing 32 genes involved in 17 metabolic pathways. Homologous sequences of six up-regulated genes namely, ADF3, APB, ASR, DLP, LTP1, and UGE5 were then used for quantitative reverse transcription PCR (qRT-PCR) in pigeonpea. The qRT-PCR result revealed the significant up-regulation of dehydrin-like protein (DLP) (5.02 log2 fold) and down-regulation of acid phosphatase class B family protein (APB) (9.43 log2 fold) and non-specific lipid transfer protein 1-like (LTP1) (18.81 log2 fold) in pigeonpea water-stressed leaf sample compared to well-watered leaf samples. No significant difference was observed in the stressed root compared to the stressed pigeonpea leaf sample except that APB showed an up-regulation of 11.35 log2 fold change.