Marker-assisted introgression of a QTL region to improve rust resistance in three elite and popular varieties of peanut (Arachis hypogaea L.).

KEY MESSAGE: Successful introgression of a major QTL for rust resistance, through marker-assisted backcrossing, in three popular Indian peanut cultivars generated several promising introgression lines with enhanced rust resistance and higher yield. Leaf rust, caused by Puccinia arachidis Speg, is one of the major devastating diseases in peanut (Arachis hypogaea L.). One QTL region on linkage group AhXV explaining upto 82.62 % phenotypic variation for rust resistance was validated and introgressed from cultivar 'GPBD 4' into three rust susceptible varieties ('ICGV 91114', 'JL 24' and 'TAG 24') through marker-assisted backcrossing (MABC). The MABC approach employed a total of four markers including one dominant (IPAHM103) and three co-dominant (GM2079, GM1536, GM2301) markers present in the QTL region. After 2-3 backcrosses and selfing, 200 introgression lines (ILs) were developed from all the three crosses. Field evaluation identified 81 ILs with improved rust resistance. Those ILs had significantly increased pod yields (56-96 %) in infested environments compared to the susceptible parents. Screening of selected 43 promising ILs with 13 markers present on linkage group AhXV showed introgression of the target QTL region from the resistant parent in 11 ILs. Multi-location field evaluation of these ILs should lead to the release of improved varieties. The linked markers may be used in improving rust resistance in peanut breeding programmes.

Improved Genetic Map Identified Major QTLs for Drought Tolerance- and Iron Deficiency Tolerance-Related Traits in Groundnut.

A deep understanding of the genetic control of drought tolerance and iron deficiency tolerance is essential to hasten the process of developing improved varieties with higher tolerance through genomics-assisted breeding. In this context, an improved genetic map with 1205 loci was developed spanning 2598.3 cM with an average 2.2 cM distance between loci in the recombinant inbred line (TAG 24 × ICGV 86031) population using high-density 58K single nucleotide polymorphism (SNP) "Axiom_Arachis" array. Quantitative trait locus (QTL) analysis was performed using extensive phenotyping data generated for 20 drought tolerance- and two iron deficiency tolerance-related traits from eight seasons (2004-2015) at two locations in India, one in Niger, and one in Senegal. The genome-wide QTL discovery analysis identified 19 major main-effect QTLs with 10.0-33.9% phenotypic variation explained (PVE) for drought tolerance- and iron deficiency tolerance- related traits. Major main-effect QTLs were detected for haulm weight (20.1% PVE), SCMR (soil plant analytical development (SPAD) chlorophyll meter reading, 22.4% PVE), and visual chlorosis rate (33.9% PVE). Several important candidate genes encoding glycosyl hydrolases; malate dehydrogenases; microtubule-associated proteins; and transcription factors such as MADS-box, basic helix-loop-helix (bHLH), NAM, ATAF, and CUC (NAC), and myeloblastosis (MYB) were identified underlying these QTL regions. The putative function of these genes indicated their possible involvement in plant growth, development of seed and pod, and photosynthesis under drought or iron deficiency conditions in groundnut. These genomic regions and candidate genes, after validation, may be useful to develop molecular markers for deploying genomics-assisted breeding for enhancing groundnut yield under drought stress and iron-deficient soil conditions.

Molecular Mapping of Oil Content and Fatty Acids Using Dense Genetic Maps in Groundnut (Arachis hypogaea L.).

Enhancing seed oil content with desirable fatty acid composition is one of the most important objectives of groundnut breeding programs globally. Genomics-assisted breeding facilitates combining multiple traits faster, however, requires linked markers. In this context, we have developed two different F2 mapping populations, one for oil content (OC-population, ICGV 07368 × ICGV 06420) and another for fatty acid composition (FA-population, ICGV 06420 × SunOleic 95R). These two populations were phenotyped for respective traits and genotyped using Diversity Array Technology (DArT) and DArTseq genotyping platforms. Two genetic maps were developed with 854 (OC-population) and 1,435 (FA-population) marker loci with total map distance of 3,526 and 1,869 cM, respectively. Quantitative trait locus (QTL) analysis using genotyping and phenotyping data identified eight QTLs for oil content including two major QTLs, qOc-A10 and qOc-A02, with 22.11 and 10.37% phenotypic variance explained (PVE), respectively. For seven different fatty acids, a total of 21 QTLs with 7.6-78.6% PVE were identified and 20 of these QTLs were of major effect. Two mutant alleles, ahFAD2B and ahFAD2A, also had 18.44 and 10.78% PVE for palmitic acid, in addition to oleic (33.8 and 17.4% PVE) and linoleic (41.0 and 19.5% PVE) acids. Furthermore, four QTL clusters harboring more than three QTLs for fatty acids were identified on the three LGs. The QTLs identified in this study could be further dissected for candidate gene discovery and development of diagnostic markers for breeding improved groundnut varieties with high oil content and desirable oil quality.

Advances in genetics and molecular breeding of three legume crops of semi-arid tropics using next-generation sequencing and high-throughput genotyping technologies.

Molecular markers are the most powerful genomic tools to increase the efficiency and precision of breeding practices for crop improvement. Progress in the development of genomic resources in the leading legume crops of the semi-arid tropics (SAT), namely, chickpea (Cicer arietinum), pigeonpea (Cajanus cajan) and groundnut (Arachis hypogaea), as compared to other crop species like cereals, has been very slow. With the advances in next-generation sequencing (NGS) and high-throughput (HTP) genotyping methods, there is a shift in development of genomic resources including molecular markers in these crops. For instance, 2,000 to 3,000 novel simple sequence repeats (SSR) markers have been developed each for chickpea, pigeonpea and groundnut. Based on Sanger, 454/FLX and Illumina transcript reads, transcriptome assemblies have been developed for chickpea (44,845 transcript assembly contigs, or TACs) and pigeonpea (21,434 TACs). Illumina sequencing of some parental genotypes of mapping populations has resulted in the development of 120 million reads for chickpea and 128.9 million reads for pigeonpea. Alignment of these Illumina reads with respective transcriptome assemblies have provided more than 10,000 SNPs each in chickpea and pigeonpea. A variety of SNP genotyping platforms including GoldenGate, VeraCode and Competitive Allele Specific PCR (KASPar) assays have been developed in chickpea and pigeonpea. By using above resources, the first-generation or comprehensive genetic maps have been developed in the three legume speciesmentioned above. Analysis of phenotyping data together with genotyping data has provided candidate markers for drought-tolerance-related root traits in chickpea, resistance to foliar diseases in groundnut and sterility mosaic disease (SMD) and fertility restoration in pigeonpea. Together with these traitassociated markers along with those already available, molecular breeding programmes have been initiated for enhancing drought tolerance, resistance to fusarium wilt and ascochyta blight in chickpea and resistance to foliar diseases in groundnut. These trait-associated robust markers along with other genomic resources including genetic maps and genomic resources will certainly accelerate crop improvement programmes in the SAT legumes.

Advances in Arachis genomics for peanut improvement.

Peanut genomics is very challenging due to its inherent problem of genetic architecture. Blockage of gene flow from diploid wild relatives to the tetraploid; cultivated peanut, recent polyploidization combined with self pollination, and the narrow genetic base of the primary genepool have resulted in low genetic diversity that has remained a major bottleneck for genetic improvement of peanut. Harnessing the rich source of wild relatives has been negligible due to differences in ploidy level as well as genetic drag and undesirable alleles for low yield. Lack of appropriate genomic resources has severely hampered molecular breeding activities, and this crop remains among the less-studied crops. The last five years, however, have witnessed accelerated development of genomic resources such as development of molecular markers, genetic and physical maps, generation of expressed sequenced tags (ESTs), development of mutant resources, and functional genomics platforms that facilitate the identification of QTLs and discovery of genes associated with tolerance/resistance to abiotic and biotic stresses and agronomic traits. Molecular breeding has been initiated for several traits for development of superior genotypes. The genome or at least gene space sequence is expected to be available in near future and this will further accelerate use of biotechnological approaches for peanut improvement.

An international reference consensus genetic map with 897 marker loci based on 11 mapping populations for tetraploid groundnut (Arachis hypogaea L.).

Only a few genetic maps based on recombinant inbred line (RIL) and backcross (BC) populations have been developed for tetraploid groundnut. The marker density, however, is not very satisfactory especially in the context of large genome size (2800 Mb/1C) and 20 linkage groups (LGs). Therefore, using marker segregation data for 10 RILs and one BC population from the international groundnut community, with the help of common markers across different populations, a reference consensus genetic map has been developed. This map is comprised of 897 marker loci including 895 simple sequence repeat (SSR) and 2 cleaved amplified polymorphic sequence (CAPS) loci distributed on 20 LGs (a01-a10 and b01-b10) spanning a map distance of 3, 863.6 cM with an average map density of 4.4 cM. The highest numbers of markers (70) were integrated on a01 and the least number of markers (21) on b09. The marker density, however, was lowest (6.4 cM) on a08 and highest (2.5 cM) on a01. The reference consensus map has been divided into 20 cM long 203 BINs. These BINs carry 1 (a10_02, a10_08 and a10_09) to 20 (a10_04) loci with an average of 4 marker loci per BIN. Although the polymorphism information content (PIC) value was available for 526 markers in 190 BINs, 36 and 111 BINs have at least one marker with >0.70 and >0.50 PIC values, respectively. This information will be useful for selecting highly informative and uniformly distributed markers for developing new genetic maps, background selection and diversity analysis. Most importantly, this reference consensus map will serve as a reliable reference for aligning new genetic and physical maps, performing QTL analysis in a multi-populations design, evaluating the genetic background effect on QTL expression, and serving other genetic and molecular breeding activities in groundnut.