Pawan Khera

Single Nucleotide Polymorphism-based Genetic Diversity in the Reference Set of Peanut (Arachis spp.) by Developing and Applying Cost-Effective Kompetitive Allele Specific Polymerase Chain Reaction Genotyping Assays

Kompetitive allele‐specific polymerase chain reaction (KASP) assays have emerged as cost‐effective marker assays especially for molecular breeding applications. Therefore, a set of 96 informative single nucleotide polymorphisms (SNPs) was used to develop KASP assays in groundnut or peanut (Arachis spp.). Developed assays were designated as groundnut KASP assay markers (GKAMs) and screened on 94 genotypes (validation set) that included parental lines of 27 mapping populations, seven synthetic autotetraploid and amphidiploid lines, and 19 wild species accessions. As a result, 90 GKAMs could be validated and 73 GKAMs showed polymorphism in the validation set. Validated GKAMs were screened on 280 diverse genotypes of the reference set for estimating diversity features and elucidating genetic relationships. Cluster analysis of marker allelic data grouped accessions according to their genome type, subspecies, and botanical variety. The subspecies Arachis hypogaea L. subsp. fastigiata Waldron and A. hypogaea subsp. hypogaea formed distinct cluster; however, some overlaps were found indicating their frequent intercrossing during the course of evolution. The wild species, having diploid genomes, were grouped into a single cluster. The average polymorphism information content value for polymorphic GKAMs was 0.32 in the validation set and 0.31 in the reference set. These validated and highly informative GKAMs may be useful for genetics and breeding applications in Arachis species.

Identification of QTLs associated with oil content and mapping FAD2 genes and their relative contribution to oil quality in peanut (Arachis hypogaea L.)

BackgroundPeanut is one of the major source for human consumption worldwide and its seed contain approximately 50% oil. Improvement of oil content and quality traits (high oleic and low linoleic acid) in peanut could be accelerated by exploiting linked markers through molecular breeding. The objective of this study was to identify QTLs associated with oil content, and estimate relative contribution of FAD2 genes (ahFAD2A and ahFAD2B) to oil quality traits in two recombinant inbred line (RIL) populations.ResultsImproved genetic linkage maps were developed for S-population (SunOleic 97R × NC94022) with 206 (1780.6 cM) and T-population (Tifrunner × GT-C20) with 378 (2487.4 cM) marker loci. A total of 6 and 9 QTLs controlling oil content were identified in the S- and T-population, respectively. The contribution of each QTL towards oil content variation ranged from 3.07 to 10.23% in the S-population and from 3.93 to 14.07% in the T-population. The mapping positions for ahFAD2A (A sub-genome) and ahFAD2B (B sub-genome) genes were assigned on a09 and b09 linkage groups. The ahFAD2B gene (26.54%, 25.59% and 41.02% PVE) had higher phenotypic effect on oleic acid (C18:1), linoleic acid (C18:2), and oleic/linoleic acid ratio (O/L ratio) than ahFAD2A gene (8.08%, 6.86% and 3.78% PVE). The FAD2 genes had no effect on oil content. This study identified a total of 78 main-effect QTLs (M-QTLs) with up to 42.33% phenotypic variation (PVE) and 10 epistatic QTLs (E-QTLs) up to 3.31% PVE for oil content and quality traits.ConclusionsA total of 78 main-effect QTLs (M-QTLs) and 10 E-QTLs have been detected for oil content and oil quality traits. One major QTL (more than 10% PVE) was identified in both the populations for oil content with source alleles from NC94022 and GT-C20 parental genotypes. FAD2 genes showed high effect for oleic acid (C18:1), linoleic acid (C18:2), and O/L ratio while no effect on total oil content. The information on phenotypic effect of FAD2 genes for oleic acid, linoleic acid and O/L ratio, and oil content will be applied in breeding selection.

Molecular breeding for introgression of fatty acid desaturase mutant alleles (ahFAD2A and ahFAD2B) enhances oil quality in high and low oil containing peanut genotypes

High oleate peanuts have two marketable benefits, health benefits to consumers and extended shelf life of peanut products. Two mutant alleles present on linkage group a09 (ahFAD2A) and b09 (ahFAD2B) control composition of three major fatty acids, oleic, linoleic and palmitic acids which together determine peanut oil quality. In conventional breeding, selection for fatty acid composition is delayed to advanced generations. However by using DNA markers, breeders can reject large number of plants in early generations and therefore can optimize time and resources. Here, two approaches of molecular breeding namely marker-assisted backcrossing (MABC) and marker-assisted selection (MAS) were employed to transfer two FAD2 mutant alleles from SunOleic 95R into the genetic background of ICGV 06110, ICGV 06142 and ICGV 06420. In summary, 82 MABC and 387 MAS derived introgression lines (ILs) were developed using DNA markers with elevated oleic acid varying from 62 to 83%. Oleic acid increased by 0.5-1.1 folds, with concomitant reduction of linoleic acid by 0.4-1.0 folds and palmitic acid by 0.1-0.6 folds among ILs compared to recurrent parents. Finally, high oleate ILs, 27 with high oil (53-58%), and 28 ILs with low oil content (42-50%) were selected that may be released for cultivation upon further evaluation.

Genomewide Association Studies for 50 Agronomic Traits in Peanut Using the ‘Reference Set’ Comprising 300 Genotypes from 48 Countries of the Semi-Arid Tropics of the World

Peanut is an important and nutritious agricultural commodity and a livelihood of many small-holder farmers in the semi-arid tropics (SAT) of world which are facing serious production threats. Integration of genomics tools with on-going genetic improvement approaches is expected to facilitate accelerated development of improved cultivars. Therefore, high-resolution genotyping and multiple season phenotyping data for 50 important agronomic, disease and quality traits were generated on the ‘reference set’ of peanut. This study reports comprehensive analyses of allelic diversity, population structure, linkage disequilibrium (LD) decay and marker-trait association (MTA) in peanut. Distinctness of all the genotypes can be established by using either an unique allele detected by a single SSR or a combination of unique alleles by two or more than two SSR markers. As expected, DArT features (2.0 alleles/locus, 0.125 PIC) showed lower allele frequency and polymorphic information content (PIC) than SSRs (22.21 alleles /locus, 0.715 PIC). Both marker types clearly differentiated the genotypes of diploids from tetraploids. Multi-allelic SSRs identified three sub-groups (K = 3) while the LD simulation trend line based on squared-allele frequency correlations (r2) predicted LD decay of 15–20 cM in peanut genome. Detailed analysis identified a total of 524 highly significant MTAs (pvalue >2.1×10–6) with wide phenotypic variance (PV) range (5.81–90.09%) for 36 traits. These MTAs after validation may be deployed in improving biotic resistance, oil/ seed/ nutritional quality, drought tolerance related traits, and yield/ yield components.

Genetic Mapping of QTLs Controlling Fatty Acids Provided Insights into the Genetic Control of Fatty Acid Synthesis Pathway in Peanut (Arachis hypogaea L.)

Peanut, a high-oil crop with about 50% oil content, is either crushed for oil or used as edible products. Fatty acid composition determines the oil quality which has high relevance to consumer health, flavor, and shelf life of commercial products. In addition to the major fatty acids, oleic acid (C18:1) and linoleic acid (C18:2) accounting for about 80% of peanut oil, the six other fatty acids namely palmitic acid (C16:0), stearic acid (C18:0), arachidic acid (C20:0), gadoleic acid (C20:1), behenic acid (C22:0), and lignoceric acid (C24:0) are accounted for the rest 20%. To determine the genetic basis and to improve further understanding on effect of FAD2 genes on these fatty acids, two recombinant inbred line (RIL) populations namely S-population (high oleic line ‘SunOleic 97R’ × low oleic line ‘NC94022’) and T-population (normal oleic line ‘Tifrunner’ × low oleic line ‘GT-C20’) were developed. Genetic maps with 206 and 378 marker loci for the S- and the T-population, respectively were used for quantitative trait locus (QTL) analysis. As a result, a total of 164 main-effect (M-QTLs) and 27 epistatic (E-QTLs) QTLs associated with the minor fatty acids were identified with 0.16% to 40.56% phenotypic variation explained (PVE). Thirty four major QTLs (>10% of PVE) mapped on five linkage groups and 28 clusters containing more than three QTLs were also identified. These results suggest that the major QTLs with large additive effects would play an important role in controlling composition of these minor fatty acids in addition to the oleic and linoleic acids in peanut oil. The interrelationship among these fatty acids should be considered while breeding for improved peanut genotypes with good oil quality and desired fatty acid composition.

Mapping Quantitative Trait Loci of Resistance to Tomato Spotted Wilt Virus and Leaf Spots in a Recombinant Inbred Line Population of Peanut (Arachis hypogaea L.) from SunOleic 97R and NC94022

Peanut is vulnerable to a range of diseases, such as Tomato spotted wilt virus (TSWV) and leaf spots which will cause significant yield loss. The most sustainable, economical and eco-friendly solution for managing peanut diseases is development of improved cultivars with high level of resistance. We developed a recombinant inbred line population from the cross between SunOleic 97R and NC94022, named as the S-population. An improved genetic linkage map was developed for the S-population with 248 marker loci and a marker density of 5.7 cM/loci. This genetic map was also compared with the physical map of diploid progenitors of tetraploid peanut, resulting in an overall co-linearity of about 60% with the average co-linearity of 68% for the A sub-genome and 47% for the B sub-genome. The analysis using the improved genetic map and multi-season (2010–2013) phenotypic data resulted in the identification of 48 quantitative trait loci (QTLs) with phenotypic variance explained (PVE) from 3.88 to 29.14%. Of the 48 QTLs, six QTLs were identified for resistance to TSWV, 22 QTLs for early leaf spot (ELS) and 20 QTLs for late leaf spot (LLS), which included four, six, and six major QTLs (PVE larger than 10%) for each disease, respectively. A total of six major genomic regions (MGR) were found to have QTLs controlling more than one disease resistance. The identified QTLs and resistance gene-rich MGRs will facilitate further discovery of resistance genes and development of molecular markers for these important diseases.

Genetic Dissection of Novel QTLs for Resistance to Leaf Spots and Tomato Spotted Wilt Virus in Peanut (Arachis hypogaea L.)

Peanut is an important crop, economically and nutritiously, but high production cost is a serious challenge to peanut farmers as exemplified by chemical spray to control foliar diseases such as leaf spots and thrips, the vectors of tomato spotted wilt virus (TSWV). The objective of this research was to map the quantitative trait loci (QTLs) for resistance to leaf spots and TSWV in one recombinant inbred line (RIL) mapping population of “Tifrunner × GT-C20” for identification of linked markers for marker-assisted breeding. Here, we report the improved genetic linkage map with 418 marker loci with a marker density of 5.3 cM/loci and QTLs associated with multi-year (2010–2013) field phenotypes of foliar disease traits, including early leaf spot (ELS), late leaf spot (LLS), and TSWV. A total of 42 QTLs were identified with phenotypic variation explained (PVE) from 6.36 to 15.6%. There were nine QTLs for resistance to ELS, 22 QTLs for LLS, and 11 QTLs for TSWV, including six, five, and one major QTLs with PVE higher than 10% for resistance to each disease, respectively. Of the total 42 QTLs, 34 were mapped on the A sub-genome and eight mapped on the B sub-genome suggesting that the A sub-genome harbors more resistance genes than the B sub-genome. This genetic linkage map was also compared with two diploid peanut physical maps, and the overall co-linearity was 48.4% with an average co-linearity of 51.7% for the A sub-genome and 46.4% for the B sub-genome. The identified QTLs associated markers and potential candidate genes will be studied further for possible application in molecular breeding in peanut genetic improvement for disease resistance.

Analysis of genetic diversity and population structure of peanut cultivars and breeding lines from China, India and the US using simple sequence repeat markers

Cultivated peanut is grown worldwide as rich-source of oil and protein. A broad genetic base is needed for cultivar improvement. The objectives of this study were to develop highly informative simple sequence repeat (SSR) markers and to assess the genetic diversity and population structure of peanut cultivars and breeding lines from different breeding programs in China, India and the US. A total of 111 SSR markers were selected for this study, resulting in a total of 472 alleles. The mean values of gene diversity and polymorphic information content (PIC) were 0.480 and 0.429, respectively. Country-wise analysis revealed that alleles per locus in three countries were similar. The mean gene diversity in the US, China and India was 0.363, 0.489 and 0.47 with an average PIC of 0.323, 0.43 and 0.412, respectively. Genetic analysis using the STRUCTURE divided these peanut lines into two populations (P1, P2), which was consistent with the dendrogram based on genetic distance (G1, G2) and the clustering of principal component analysis. The groupings were related to peanut market types and the geographic origin with a few admixtures. The results could be used by breeding programs to assess the genetic diversity of breeding materials to broaden the genetic base and for molecular genetics studies.

Annotation of Trait Loci on Integrated Genetic Maps of Arachis Species

Peanut or groundnut (Arachis hypogaea L.) is second, behind soybean, in the world’s legume oilseed market. In 2012, global production was 41.2metric tons from an area of 24.7million hectares (FAOSTAT, 2014). Yield of peanut under stressed environments is an ultimate goal of improvement for enhanced production as it is usually susceptible to a range of abiotic and biotic stresses, such as drought, tomato spotted wilt virus (TSWV), early leaf spot (ELS) and late leaf spot (LLS), nematodes, rust, and aflatoxin contamination (Guo et al., 2012a). However, cultivated peanut is an allotetraploid (2n=4x=40) with a large genome, which greatly complicates interpretation of genomic data compared with the diploid wild relatives (2n=2x=20) (Guo et al., 2013). It is difficult to transfer alleles from wild species to cultivated peanuts (Simpson, 1991). For the last ten years, extensive efforts in the area of peanut genomics have resulted in a large number of genetic and genomic resources such as mapping populations, expressed sequence tags (ESTs), a wide range of molecular markers, transcriptome and proteomics (Guo et al., 2013; Katam et al., 2014; Varshney et al., 2013). These genetic and genomic resources have been successfully used to construct genetic maps, to identify quantitative trait loci (QTLs) of traits of Authors personal copy 164 Peanuts Peanuts, First Edition, 2016, 163-207 interest, and to conduct marker-assisted selection and association mapping for peanut improvement (Pandey et al., 2014a).

Mitochondrial SSRs and their utility in distinguishing wild species, CMS lines and maintainer lines in pigeonpea (Cajanus cajan L.)

Analysis of the pigeonpea mitochondrial genome sequence identified 25 SSRs. Mononucleotide SSR motifs were the most abundant repeats followed by dinucleotide and trinucleotide repeats. Primer pairs could be designed for 24 SSRs, 23 of which were polymorphic amongst the 22 genotypes consisting of cytoplasmic male sterile (CMS or A) line, maintainer or B line and wild Cajanus species representing six different CMS systems viz., A1, A2, A4, A5, A6 and A8. These markers amplified a total of 107 alleles ranging from 2 to 10 with an average of 4.65 alleles per locus. The polymorphic information content for these markers ranged from 0.09 to 0.84 with an average of 0.52 per marker. Hence, the present study adds a novel set of 24 mitochondrial SSR markers to the markers repository in pigeonpea, which would be useful to distinguish the genotypes based on mitochondrial genome types in evolutionary and phylogenetic studies.