Development of genic-SSR markers by deep transcriptome sequencing in pigeonpea [Cajanus cajan (L.) Millspaugh]

<p>Abstract</p> <p>Background</p> <p>Pigeonpea [<it>Cajanus cajan </it>(L.) Millspaugh], one of the most important food legumes of semi-arid tropical and subtropical regions, has limited genomic resources, particularly expressed sequence based (genic) markers. We report a comprehensive set of validated genic simple sequence repeat (SSR) markers using deep transcriptome sequencing, and its application in genetic diversity analysis and mapping.</p> <p>Results</p> <p>In this study, 43,324 transcriptome shotgun assembly unigene contigs were assembled from 1.696 million 454 GS-FLX sequence reads of separate pooled cDNA libraries prepared from leaf, root, stem and immature seed of two pigeonpea varieties, Asha and UPAS 120. A total of 3,771 genic-SSR loci, excluding homopolymeric and compound repeats, were identified; of which 2,877 PCR primer pairs were designed for marker development. Dinucleotide was the most common repeat motif with a frequency of 60.41%, followed by tri- (34.52%), hexa- (2.62%), tetra- (1.67%) and pentanucleotide (0.76%) repeat motifs. Primers were synthesized and tested for 772 of these loci with repeat lengths of ≥18 bp. Of these, 550 markers were validated for consistent amplification in eight diverse pigeonpea varieties; 71 were found to be polymorphic on agarose gel electrophoresis. Genetic diversity analysis was done on 22 pigeonpea varieties and eight wild species using 20 highly polymorphic genic-SSR markers. The number of alleles at these loci ranged from 4-10 and the polymorphism information content values ranged from 0.46 to 0.72. Neighbor-joining dendrogram showed distinct separation of the different groups of pigeonpea cultivars and wild species. Deep transcriptome sequencing of the two parental lines helped <it>in silico </it>identification of polymorphic genic-SSR loci to facilitate the rapid development of an intra-species reference genetic map, a subset of which was validated for expected allelic segregation in the reference mapping population.</p> <p>Conclusion</p> <p>We developed 550 validated genic-SSR markers in pigeonpea using deep transcriptome sequencing. From these, 20 highly polymorphic markers were used to evaluate the genetic relationship among species of the genus <it>Cajanus</it>. A comprehensive set of genic-SSR markers was developed as an important genomic resource for diversity analysis and genetic mapping in pigeonpea.</p

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

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

Genetic Diversity and Population Structure in Landraces and Improved Rice Varieties from India

A set of 50 rice genotypes comprising landraces, local selections, and improved varieties were characterized using simple sequence repeat (SSR) and inter simple sequence repeat (ISSR) markers to study genetic diversity and population structure. Following unweighted pair group method with arithmetic mean based clustering using binary data of polymorphic markers, the genotypes were grouped into 5 clusters and 11 sub-clusters, whereas population structure analysis separated 50 rice genotypes into 5 sub-populations. Grouping of rice genotypes showed better resemblance with the pedigree information of the genotypes. Both genetic diversity and population structure analysis separated majority of the improved varieties from landraces and local selections. Some of the SSR markers amplified unique alleles which were specific to a particular genotype and could distinguish them from the rest. The results indicate that these rice genotypes exhibit a higher genetic diversity and can be very useful in rice improvement program

QTL analysis for grain weight in common wheat

Quantitative trait loci (QTL) analysis for grain weight (GW = 1000 grain weight) in common wheat was conducted using a set of 100 recombinant inbred lines (RILs) derived from a cross 'Rye Selection 111 (high GW) × Chinese Spring (low GW)'. The RILs and their two parental genotypes were evaluated for GW in six different environments (three locations × two years). Genotyping of RILs was carried out using 449 (30 SSRs, 299 AFLP and 120 SAMPL) polymorphic markers. Using the genotyping data of RILs, framework linkage maps were prepared for three chromosomes (1A, 2B, 7A), which were earlier identified by us to carry important/major genes for GW following monosomic analysis. QTL analysis for GW was conducted following genome-wide single marker regression analysis (SMA) and composite interval mapping (CIM) using molecular maps for the three chromosomes. Following SMA, 12 markers showed associations with GW, individual markers explaining 6.57% to 10.76% PV (phenotypic variation) for GW in individual environments. The high grain weight parent, Rye Selection111, which is an agronomically superior genotype, contributed favourable alleles for GW at six of the 12 marker loci identified through SMA. The CIM identified two stable and definitive QTLs, one each on chromosome arms 2BS and 7AS, which were also identified through SMA, and a third suggestive QTL on 1AS. These QTLs explained 9.06% to 19.85% PV for GW in different environments. The QTL for GW on 7AS is co-located with a QTL for heading date suggesting the occurrence of a QTL having a positive pleiotropic effect on the two traits. Some of the markers identified during the present study may prove useful for marker-assisted selection, while breeding for high GW in common wheat

Genome-wide QTL analysis for pre-harvest sprouting tolerance in bread wheat

A framework linkage map comprising 214 molecular marker (SSR, AFLP, SAMPL) loci was prepared using an intervarietal recombinant inbred line (RIL) mapping population of bread wheat. The RIL population that was developed from the cross SPR8198 (red-grained and PHS tolerant genotype) × HD2329 (white-grained and PHS susceptible genotype) following single seed descent segregated for pre-harvest sprouting (PHS). The RIL population and parental genotypes were evaluated in six different environments and the data on PHS were collected. Using the linkage map and PHS data, genome-wide single-locus and two-locus QTL analyses were conducted for PHS tolerance (PHST). Single-locus analysis following composite interval mapping (CIM) detected a total of seven QTL, located on specific arms of five different chromosome (1AS, 2AL, 2DL, 3AL and 3BL). These seven QTL included two major QTL one each on 2AL and 3AL. Two of these seven QTL were also detected following two-locus analysis, which resolved a total of four main-effect QTL (M-QTL), and 12 epistatic QTL (E-QTL), the latter involved in 7 QTL × QTL interactions. Interestingly, none of these M-QTL and E-QTL detected by two-locus analysis was involved in Q × E and Q × Q × E interactions, supporting the results of ANOVA, where genotype × environment interaction were non-significant. The QTL for PHS detected in the present study may be efficiently utilized for marker-aided selection for enhancing PHST in bread wheat

A SNP‐based genetic dissection of versatile traits in bread wheat (Triticum aestivum L.)

The continuous increase in global population prompts increased wheat production. Future wheat (Triticum aestivum L.) breeding will heavily rely on dissecting molecular and genetic bases of wheat yield and related traits which is possible through the discovery of quantitative trait loci (QTLs) in constructed populations, such as recombinant inbred lines (RILs). Here, we present an evaluation of 92 RILs in a bi-parental RIL mapping population (the International Triticeae Mapping Initiative Mapping Population [ITMI/MP]) using newly generated phenotypic data in 3-year experiments (2015), older phenotypic data (1997-2009), and newly created single nucleotide polymorphism (SNP) marker data based on 92 of the original RILs to search for novel and stable QTLs. Our analyses of more than 15 unique traits observed in multiple experiments included analyses of 46 traits in three environments in the USA, 69 traits in eight environments in Germany, 149 traits in 10 environments in Russia, and 28 traits in four environments in India (292 traits in 25 environments) with 7584 SNPs (292 × 7584 = 2 214 528 data points). A total of 874 QTLs were detected with limit of detection (LOD) scores of 2.01-3.0 and 432 QTLs were detected with LOD &gt; 3.0. Moreover, 769 QTLs could be assigned to 183 clusters based on the common markers and relative proximity of related QTLs, indicating gene-rich regions throughout the A, B, and D genomes of common wheat. This upgraded genotype-phenotype information of ITMI/MP can assist breeders and geneticists who can make crosses with suitable RILs to improve or investigate traits of interest

Indian wheat genomics initiative for harnessing the potential of wheat germplasm resources for breeding disease-resistant, nutrient-dense, and climate-resilient cultivars

Wheat is one of the major staple cereal food crops in India. However, most of the wheat-growing areas experience several biotic and abiotic stresses, resulting in poor quality grains and reduced yield. To ensure food security for the growing population in India, there is a compelling need to explore the untapped genetic diversity available in gene banks for the development of stress-resistant/tolerant cultivars. The improvement of any crop lies in exploring and harnessing the genetic diversity available in its genetic resources in the form of cultivated varieties, landraces, wild relatives, and related genera. A huge collection of wheat genetic resources is conserved in various gene banks across the globe. Molecular and phenotypic characterization followed by documentation of conserved genetic resources is a prerequisite for germplasm utilization in crop improvement. The National Genebank of India has an extensive and diverse collection of wheat germplasm, comprising Indian wheat landraces, primitive cultivars, breeding lines, and collection from other countries. The conserved germplasm can contribute immensely to the development of wheat cultivars with high levels of biotic and abiotic stress tolerance. Breeding wheat varieties that can give high yields under different stress environments has not made much headway due to high genotypes and environmental interaction, non-availability of truly resistant/tolerant germplasm, and non-availability of reliable markers linked with the QTL having a significant impact on resistance/tolerance. The development of new breeding technologies like genomic selection (GS), which takes into account the G × E interaction, will facilitate crop improvement through enhanced climate resilience, by combining biotic and abiotic stress resistance/tolerance and maximizing yield potential. In this review article, we have summarized different constraints being faced by Indian wheat-breeding programs, challenges in addressing biotic and abiotic stresses, and improving quality and nutrition. Efforts have been made to highlight the wealth of Indian wheat genetic resources available in our National Genebank and their evaluation for the identification of trait-specific germplasm. Promising genotypes to develop varieties of important targeted traits and the development of different genomics resources have also been highlighted

Translational Chickpea Genomics Consortium to Accelerate Genetic Gains in Chickpea (Cicer arietinum L.)

The Translational Chickpea Genomics Consortium (TCGC) was set up to increase the production and productivity of chickpea (Cicer arietinum L.). It represents research institutes from six major chickpea growing states (Madhya Pradesh, Maharashtra, Andhra Pradesh, Telangana, Karnataka and Uttar Pradesh) of India. The TCGC team has been engaged in deploying modern genomics approaches in breeding and popularizing improved varieties in farmers&rsquo; fields across the states. Using marker-assisted backcrossing, introgression lines with enhanced drought tolerance and fusarium wilt resistance have been developed in the genetic background of 10 elite varieties of chickpea. Multi-location evaluation of 100 improved lines (70 desi and 30 kabuli) during 2016&ndash;2017 and 2018&ndash;2019 enabled the identification of top performing desi and kabuli lines. In total, 909 Farmer Participatory Varietal Selection trials were conducted in 158 villages in 16 districts of the five states, during 2017&ndash;2018, 2018&ndash;2019, and 2019&ndash;2020, involving 16 improved varieties. New molecular breeding lines developed in different genetic backgrounds are potential candidates for national trials under the ICAR-All India Coordinated Research Project on Chickpea. The comprehensive efforts of TCGC resulted in the development and adoption of high-yielding varieties that will increase chickpea productivity and the profitability of chickpea growing farmers