Marker‐Assisted Backcrossing to Introgress Resistance to Fusarium Wilt Race 1 and Ascochyta Blight in C 214, an Elite Cultivar of Chickpea

Fusarium wilt (FW) and Ascochyta blight (AB) are two major constraints to chickpea (Cicer arietinum L.) production. Therefore, two parallel marker-assisted backcrossing (MABC) programs by targeting foc1 locus and two quantitative trait loci (QTL) regions, ABQTL-I and ABQTL-II, were undertaken to introgress resistance to FW and AB, respectively, in C 214, an elite cultivar of chickpea. In the case of FW, foreground selection (FGS) was conducted with six markers (TR19, TA194, TAA60, GA16, TA110, and TS82) linked to foc1 in the cross C 214 × WR 315 (FW-resistant). On the other hand, eight markers (TA194, TR58, TS82, GA16, SCY17, TA130, TA2, and GAA47) linked with ABQTL-I and ABQTL-II were used in the case of AB by deploying C 214 × ILC 3279 (AB-resistant) cross. Background selection (BGS) in both crosses was employed with evenly distributed 40 (C 214 × WR 315) to 43 (C 214 × ILC 3279) SSR markers in the chickpea genome to select plant(s) with higher recurrent parent genome (RPG) recovery. By using three backcrosses and three rounds of selfing, 22 BC3F4 lines were generated for C 214 × WR 315 cross and 14 MABC lines for C 214 × ILC 3279 cross. Phenotyping of these lines has identified three resistant lines (with 92.7–95.2% RPG) to race 1 of FW, and seven resistant lines (with 81.7–85.40% RPG) to AB that may be tested for yield and other agronomic traits under multilocation trials for possible release and cultivation

Cytogenetics to functional genomics: six decades journey of Professor P.K. Gupta

We had the fortune of starting our scientific/research careers in the Molecular Biology and Crop Biotechnology Laboratory of Professor P.K. Gupta at Ch. Charan Singh University, Meerut, UP, India. Here, we describe the most important scientific contributions of our beloved mentor in the area of cytotaxonomy, cytogenetics, mutation breeding, quantitative genetics, molecular biology, crop biotechnology and plant genomics, on his 85th birthday. Important contributions made in the development and use of genomics resources including the development and use of different kinds of molecular markers, genetic and physical mapping, quantitative trait locus (QTL) interval mapping, genome-wide association mapping and molecular breeding including marker-assisted selection have been briefly summarized. Efforts have been also made to give readers a glimpse of important contributions in the study of cytology/apomixis of grasses, cytotaxonomic studies in asteraceae/fabaceae, nuclear/repetitive DNA content in Lolium, interspecific/intergeneric relationships involving the genus Hordeum and re-examining taxonomy of the tribe Triticeae

Efficient Breeding of Crop Plants

In crop breeding programs, the rate of genetic gain which is achieved using the traditional breeding is insufficient to meet the increased demand of food for the rapidly expanding global population. The main constraint with the conventional breeding is the time which is required in developing crosses, followed by selection and testing of the experimental cultivars. Although, using this technique, lot of progress has been made in increasing the productivity, the time has come to think beyond this and integrate the recent advances in the area of genomics, phenomics and computational biology into the conventional breeding program for increasing its efficiency. While doing this emphasis on proper characterization and use of plant genetic resources, defining the breeding objectives and use of recent advances in holistic way are also essential. Therefore, in this chapter, we first highlight the importance of plant breeding followed by significance of the plant genetic resources in the breeding program, need of ideotype breeding and the breeding objectives for important traits including resistance against various biotic and abiotic stresses. We then discuss the limitations of conventional breeding and advantages of genomics-assisted breeding. While doing this, we also discuss various molecular breeding tools and genomic resources as well as different approaches for efficient breeding including marker-assisted selection, marker-assisted recurrent selection and genomic selection. This is followed by importance of other non-conventional approaches including the recent one on gene editing, speed breeding and role of integrated data management and bioinformatics in the breeding programs. We also discuss the significance of phenomics and phenotyping platforms in the crop breeding as well as role of computational techniques like artificial intelligence and machine learning in analysing the huge data which is being generated in the breeding programs. Finally, we conclude with a brief note on the emerging challenges in breeding which need to be addressed and the thrust areas of research for the future

QTL analysis for some quantitative traits in bread wheat*

Quantitative trait loci (QTL) analysis was conducted in bread wheat for 14 important traits utilizing data from four different mapping populations involving different approaches of QTL analysis. Analysis for grain protein content (GPC) suggested that the major part of genetic variation for this trait is due to environmental interactions. In contrast, pre-harvest sprouting tolerance (PHST) was controlled mainly by main effect QTL (M-QTL) with very little genetic variation due to environmental interactions; a major QTL for PHST was detected on chromosome arm 3AL. For grain weight, one QTL each was detected on chromosome arms 1AS, 2BS and 7AS. QTL for 4 growth related traits taken together detected by different methods ranged from 37 to 40; nine QTL that were detected by single-locus as well as two-locus analyses were all M-QTL. Similarly, single-locus and two-locus QTL analyses for seven yield and yield contributing traits in two populations respectively allowed detection of 25 and 50 QTL by composite interval mapping (CIM), 16 and 25 QTL by multiple-trait composite interval mapping (MCIM) and 38 and 37 QTL by two-locus analyses. These studies should prove useful in QTL cloning and wheat improvement through marker aided selection

Cropping breeding for sustainable agriculture...

There has been significant improvement in production and productivity of important cereal crops globally as a consequence of the “Green Revolution ” and other initiatives [1]. However, today the stage has reached that the available traditional methods of crop improvement are not sufficient to provide enough and staple food grains to the constantly growing world population [2]. This situation is projected to be worse by the year 2050, especially in context of climate change [3]. In other words, the conventional plant breeding practices may not be able to achieve the sustainability in today’s agriculture. It is under such circumstances that advances in plant genomics research are opening up a new era in plant breeding , where the linkage of genes to specific traits will lead to more efficient and predictable breeding programs in future. Several initiatives have been started towards use of genomics technologies in number of crop plants to ensure the sustainable..

G4008.47: Developing genomic resources for pigeonpea using next generation sequencing technologies

Summary Pigeonpea (Cajanus cajan L.), an important legume crop in Indian subcontinent, ranks sixth in area and production. However, the productivity of pigeonpea crop in semi-arid regions is less than 750 kg/ha due to exposure of the crop with several diseases. Biotechnological tools especially molecular markers have been proven very useful for improving the breeding efficiency in several major crop species, however, few genomic resources and low level of genetic diversity in pigeonpea germplasm is another bottleneck to varietal improvement. To achieve the goal of generating large expressed sequence tag (EST) resources, Roche/FLX 454 sequencing was carried out on a normalized cDNA pool prepared from 31 tissues produced 494,353 short transcript reads (STRs). 150.8 million Illumina sequence tags were generated from 10 pigeonpea genotypes. For identification of SNPs, tags for two genotypes of a given mapping population were aligned with 127,754 TAs (the pigeonpea transcriptome assembly). The number of SNPs in an individual cross ranged from 704 to 6,263. In total, 12,141 SNPs were identified across these genotypes. In terms of developing the marker platforms, CAPS markers could not be validated at a good rated. As a result, a total of 1,834 SNPs were attempted for KASPar assays and successful assays were developed for 1,616 SNPs and tested on a set of 94 genotypes. In addition, four genetic maps were developed based on SSR markers and by using these maps, in addition to two earlier maps, a consensus genetic linkage map was developed that includes a total of 339 SSR marker loci. Activities wise progress made in this project have been given as follows: Activity 1: Develop pigeonpea EST resources using 454-FLX Based on its phenology and the utility in breeding programs Pusa Ageti (ICP 28) was chosen for developing these genomic/transcriptomic resources. Deep sequencing was undertaken on cDNAs pools of 31 different developmental stages. Roche/FLX454 sequencing of this normalized cDNA pool generated 494,353 short transcript reads (STRs). Publicly available ESTs and newly generated datasets were analyzed separately and in combination. In order to develop a transcriptome reference in pigeonpea, 505,170 Roche/454 STRs and Sanger ESTs were assembled in combination to yield a total of 127,754 pigeonpea transcript assemblies (CcTAs). Activity 2: SNP development through Solexa-based transcriptome sequencing from 10 pigeonpea genotypes For identification of SNPs, tags for two genotypes of a given mapping population were aligned with 127,754 TAs and variants were identified using the Alpheus program of NCGR. The number of SNPs in an individual cross ranged from 704 (BSMR 736 × TAT 10) to 6,263 (ICPL 87119 × ICPL 87091). In total, 12,141 SNPs were identified; however, only six SNPs were found in common across three populations (ICPL 20096 × ICPL 332, ICP 7035 × TTB7 and BSMR 736 × TAT 10) (Dubey et al. 2011). Activity 3: Genetically map within existing mapping populations SNPs Due to non availability of suitable restriction sites, CAPS assays could be designed for only 116 SNPs. A very low level i.e., only 10 SNPs out of 116 SNPs showed expected results. As a result, these efforts were abandoned. For developing the Illumina GoldenGate assays, ADT scores were calculated and submitted to Illumina pipleline. However because of inordinate delay in receiving reagents, it was decided not to undertake development of GoldenGate assays. As a result, a total of 1,834 SNPs were attempted for KASPar assays and successful assays were developed for 1,616 SNPs and tested on a set of 94 genotypes. In addition, four genetic maps were developed based on SSR markers and by using these maps, in addition to two earlier maps, a consensus genetic linkage map was developed that includes a total of 339 SSR marker loci. Activity 4: Develop a consensus map for pigeonpea Genotyping data generated for four intra-specific mapping populations (ICPB 2049 × ICPL 99050, ICPA 2039 × ICPR 2447, ICPA 2043 × ICPR 3467 and ICPA 2043 × ICPR 2671) together with earlier published two genetic linkage maps (ICP 8863 × ICPL 20097 and TTB 7 × ICP 7035) were used for developing the consensus genetic map in cultivated pigeonpea. Segregation data for 348 markers obtained on 6 different mapping populations was used for merging multiple genetic maps. All the common markers collectively led to the synthesis of a consensus map comprising 339 loci profiled on 11 LGs and covering a map distance of 1,058.98 cM (Bohra et al. communicated). References Dubey A, et al., Varshney RK (2011) Defining the transcriptome assembly and its use for genome dynamics and transcriptome profiling studies in pigeonpea (Cajanus cajan L.). DNA Research. doi: 10.1093/dnares/dsr007 Bohra A, et al., Varshney RK (2011) An intra-specific consensus genetic map of pigeonpea [Cajanus cajan (L.) Millspaugh] derived from six mapping populations. BMC Genomics, communicate

Marker-trait association study for protein content in chickpea (Cicer arietinum L.)

Chickpea (Cicer arietinum L.) is the second most important cool season food legume cultivated in arid and semiarid regions of the world. The objective of the present study was to study variation for protein content in chickpea germplasm, and to find markers associated with it. A set of 187 genotypes comprising both international and exotic collections, and representing both desi and kabuli types with protein content ranging from 13.25% to 26.77% was used. Twenty-three SSR markers representing all eight linkage groups (LG) amplifying 153 loci were used for the analysis. Population structure analysis identified three subpopulations, and corresponding Q values of principal components were used to take care of population structure in the analysis which was performed using general linear and mixed linear models. Marker-trait association (MTA) analysis identified nine significant associations representing four QTLs in the entire population. Subpopulation analyses identified ten significant MTAs representing five QTLs, four of which were common with that of the entire population. Two most significant QTLs linked with markers TR26.205 and CaM1068.195 were present on LG3 and LG5. Gene ontology search identified 29 candidate genes in the region of significant MTAs on LG3. The present study will be helpful in concentrating on LG3 and LG5 for identification of closely linked markers for protein content in chickpea and for their use in molecular breeding programme for nutritional quality improvement

Novel SSR markers from BAC-End Sequences, DArT Arrays and a comprehensive genetic map with 1,291 marker loci for Chickpea (Cicer arietinum L.)

Chickpea (Cicer arietinum L.) is the third most important cool season food legume, cultivated in arid and semi-arid regions of the world. The goal of this study was to develop novel molecular markers such as microsatellite or simple sequence repeat (SSR) markers from bacterial artificial chromosome (BAC)-end sequences (BESs) and diversity arrays technology (DArT) markers, and to construct a high-density genetic map based on recombinant inbred line (RIL) population ICC 4958 (C. arietinum)×PI 489777 (C. reticulatum). A BAC-library comprising 55,680 clones was constructed and 46,270 BESs were generated. Mining of these BESs provided 6,845 SSRs, and primer pairs were designed for 1,344 SSRs. In parallel, DArT arrays with ca. 15,000 clones were developed, and 5,397 clones were found polymorphic among 94 genotypes tested. Screening of newly developed BES-SSR markers and DArT arrays on the parental genotypes of the RIL mapping population showed polymorphism with 253 BES-SSR markers and 675 DArT markers. Segregation data obtained for these polymorphic markers and 494 markers data compiled from published reports or collaborators were used for constructing the genetic map. As a result, a comprehensive genetic map comprising 1,291 markers on eight linkage groups (LGs) spanning a total of 845.56 cM distance was developed (http://cmap.icrisat.ac.in/cmap/sm/cp/thudi/). The number of markers per linkage group ranged from 68 (LG 8) to 218 (LG 3) with an average inter-marker distance of 0.65 cM. While the developed resource of molecular markers will be useful for genetic diversity, genetic mapping and molecular breeding applications, the comprehensive genetic map with integrated BES-SSR markers will facilitate its anchoring to the physical map (under construction) to accelerate map-based cloning of genes in chickpea and comparative genome evolution studies in legumes