Recent Advances in Molecular Genetic Linkage Maps of Cultivated Peanut

High-density genetic map using whole-genome resequencing for fine mapping and candidate gene discovery for disease resistance in peanut

Whole-genome resequencing (WGRS) of mapping populations has facilitated development of high-density genetic maps essential for fine mapping and candidate gene discovery for traits of interest in crop species. Leaf spots, including early leaf spot (ELS) and late leaf spot (LLS), and Tomato spotted wilt virus (TSWV) are devastating diseases in peanut causing significant yield loss. We generated WGRS data on a recombinant inbred line population, developed a SNP-based high-density genetic map, and conducted fine mapping, candidate gene discovery and marker validation for ELS, LLS and TSWV. The first sequence-based high-density map was constructed with 8869 SNPs assigned to 20 linkage groups, representing 20 chromosomes, for the 'T' population (Tifrunner × GT-C20) with a map length of 3120 cM and an average distance of 1.45 cM. The quantitative trait locus (QTL) analysis using high-density genetic map and multiple season phenotyping data identified 35 main-effect QTLs with phenotypic variation explained (PVE) from 6.32% to 47.63%. Among major-effect QTLs mapped, there were two QTLs for ELS on B05 with 47.42% PVE and B03 with 47.38% PVE, two QTLs for LLS on A05 with 47.63% and B03 with 34.03% PVE and one QTL for TSWV on B09 with 40.71% PVE. The epistasis and environment interaction analyses identified significant environmental effects on these traits. The identified QTL regions had disease resistance genes including R-genes and transcription factors. KASP markers were developed for major QTLs and validated in the population and are ready for further deployment in genomics-assisted breeding in peanut.

Relationship between drought and preharvest aflatoxin contamination in groundnut (Arachis hypogaea L.)

Groundnut is a commercial oilseed crop that is prone to infection by Aspergillus flavus or Aspergillus parasiticus. Drought impairs the defence mechanism of the plant and favours the production of aflatoxin by the fungus. Aflatoxin is a carcinogen and its presence in food and feed causes significant economic loss. The answer to the question, ‘how drought tolerance and aflatoxin resistance are related?’ is not clear. In this review paper, the relationship of drought and preharvest aflatoxin contamination (AC), the relationship of drought tolerance traits and AC, and the approaches to enhance resistance to AC are discussed using up-to-date literature. Factors leading to AC are drought, high geocarposphere temperature, kernel/pod damage, and reduced phytoalexin synthesis by the plant. If the fungus colonises a kernel with reduced water activity, the plant cannot synthesise phytoalexin and then, the fungus synthesises aflatoxin. Breeding for resistance to AC is complicated because aflatoxin concentration is costly to measure, highly variable, and influenced by the environment. Since drought tolerant cultivars have resistance to AC, traits of drought tolerance have been used as indirect selection tools for reduced AC. The genetics of aflatoxin resistance mechanisms have not been made clear as the environment influences the host-pathogen relationship. Host-pathogen interactions under the influence of environment should be studied at molecular level to identify plant resistant factors using the tools of genomics, proteomics, and metabolomics in order to develop cultivars with durable resistance. Many candidate genes involved in host-pathogen interactions have been identified due to improvements in fungal expressed sequence tags, microarrays, and genome sequencing techniques. Moreover, research projects are underway on identifying genes coding for antifungal compounds, resistance associated proteins and quantitative trait loci associated with aflatoxin resistance. This review is expected to help those who wish to work on reducing AC in groundnuts.

Establishment and evaluation of a peanut association panel and analysis of key nutritional traits

Breeding programs aim to improve the yield and quality of peanut (Arachis hypogaea L.); using association mapping to identify genetic markers linked to these quantitative traits could facilitate selection efficiency. A peanut association panel was established consisting of 268 lines with extensive phenotypic and genetic variation, meeting the requirements for association analysis. These lines were grown over 3 years and the key agronomic traits, including protein and oil content were examined. Population structure (Q) analysis showed two subpopulations and clustering analysis was consistent with Q-based membership assignment and closely related to botanical type. Relative Kinship (K) indicated that most of the panel members have no or weak familial relatedness, with 52.78% of lines showing K = 0. Linkage disequilibrium (LD) analysis showed a high level of LD occurs in the panel. Model comparisons indicated false positives can be effectively controlled by taking Q and K into consideration and more false positives were generated by K than Q. A preliminary association analysis using a Q + K model found markers significantly associated with oil, protein, oleic acid, and linoleic acid, and identified a set of alleles with positive and negative effects. These results show that this panel is suitable for association analysis, providing a resource for marker-assisted selection for peanut improvement.

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.

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.

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.

Molecular marker development from transcript sequences and germplasm evaluation for cultivated peanut (Arachis hypogaea L.)

Molecular markers are important tools for genotyping in genetic studies and molecular breeding. The SSR and SNP are two commonly used marker systems developed from genomic or transcript sequences. The objectives of this study were to: (1) assemble and annotate the publicly available ESTs in Arachis and the in-house short reads, (2) develop and validate SSR and SNP markers, and (3) investigate the genetic diversity and population structure of the peanut breeding lines and the U.S. peanut mini core collection using developed SSR markers. An NCBI EST dataset with 252,951 sequences and an in-house 454 RNAseq dataset with 288,701 sequences were assembled separately after trimming. Transcript sequence comparison and phylogenetic analysis suggested that peanut is closer to cowpea and scarlet bean than to soybean, common bean and Medicago. From these two datasets, 6455 novel SSRs and 11,902 SNPs were identified. Of the discovered SSRs, 380 representing various SSR types were selected for PCR validation. The amplification rate was 89.2 %. Twenty-two (6.5 %) SSRs were polymorphic between at least one pair of four genotypes. Sanger sequencing of PCR products targeting 110 SNPs revealed 13 true SNPs between tetraploid genotypes and 193 homoeologous SNPs within genotypes. Eight out of the 22 polymorphic SSR markers were selected to evaluate the genetic diversity of Florida peanut breeding lines and the U.S. peanut mini core collection. This marker set demonstrated high discrimination power by displaying an average polymorphism information content value of 0.783, a combined probability of identity of 10(-11), and a combined power of exclusion of 0.99991. The structure analysis revealed four sub-populations among the peanut accessions and lines evaluated. The results of this study enriched the peanut genomic resources, provided over 6000 novel SSR markers and the credentials for true peanut SNP marker development, and demonstrated the power of newly developed SSR markers in genotyping peanut germplasm and breeding materials.

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.