Genetic Diversity, Population Structure, and Linkage Disequilibrium of an Association-Mapping Panel Revealed by Genome-Wide SNP Markers in Sesame

Genome-wide SNP discovery and genotyping delineates potential QTLs underlying major yield-attributing traits in buckwheat

Buckwheat (Fagopyrum spp.) is an important nutritional and nutraceutical-rich pseudo-cereal crop. Despite its obvious potential as a functional food, buckwheat has not been fully harnessed due to its low yield, self-incompatibility, increased seed cracking, limited seed set, lodging, and frost susceptibility. The inadequate availability of genomics resources in buckwheat is one of the major reasons for this. In the present study, genome-wide association mapping (GWAS) was conducted to identify loci associated with various morphological and yield-related traits in buckwheat. High throughput genotyping by sequencing led to the identification of 34,978 single nucleotide polymorphisms that were distributed across eight chromosomes. Population structure analysis grouped the genotypes into three sub-populations. The genotypes were also characterized for various qualitative and quantitative traits at two diverse locations, the analysis of which revealed a significant difference in the mean values. The association analysis revealed a total of 71 significant marker-trait associations across eight chromosomes. The candidate genes were identified near 100 Kb of quantitative trait loci (QTLs), providing insights into several metabolic and biosynthetic pathways. The integration of phenology and GWAS in the present study is useful to uncover the consistent genomic regions, related markers associated with various yield-related traits, and potential candidate genes having implications for being utilized in molecular breeding for the improvement of economically important traits in buckwheat. Moreover, the identified QTLs will assist in tracking the desirable alleles of target genes within the buckwheat breeding populations/germplasm.

Genome-wide association study as a powerful tool for dissecting competitive traits in legumes

Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity and quality of protein and other nutritional compounds in view of climate change conditions. The global need for plant-based proteins has increased the demand for seeds with a high protein content that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as a standard approach in agricultural genetics for examining such intricate characters. Recent development in machine learning methods shows promising applications for dimensionality reduction, which is a major challenge in GWAS. With the advancement in biotechnology, sequencing, and bioinformatics tools, estimation of linkage disequilibrium (LD) based associations between a genome-wide collection of single-nucleotide polymorphisms (SNPs) and desired phenotypic traits has become accessible. The markers from GWAS could be utilized for genomic selection (GS) to predict superior lines by calculating genomic estimated breeding values (GEBVs). For prediction accuracy, an assortment of statistical models could be utilized, such as ridge regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased predictor (gBLUP), Bayesian, and random forest (RF). Both naturally diverse germplasm panels and family-based breeding populations can be used for association mapping based on the nature of the breeding system (inbred or outbred) in the plant species. MAGIC, MCILs, RIAILs, NAM, and ROAM are being used for association mapping in several crops. Several modifications of NAM, such as doubled haploid NAM (DH-NAM), backcross NAM (BC-NAM), and advanced backcross NAM (AB-NAM), have also been used in crops like rice, wheat, maize, barley mustard, etc. for reliable marker-trait associations (MTAs), phenotyping accuracy is equally important as genotyping. Highthroughput genotyping, phenomics, and computational techniques have advanced during the past few years, making it possible to explore such enormous datasets. Each population has unique virtues and flaws at the genomics and phenomics levels, which will be covered in more detail in this review study. The current investigation includes utilizing elite breeding lines as association mapping population, optimizing the choice of GWAS selection, population size, and hurdles in phenotyping, and statistical methods which will analyze competitive traits in legume breeding.

Omics technologies towards sesame improvement: a review

Genetic improvement of sesame (Sesamum indicum L.), one of the most important oilseed crops providing edible oil, proteins, minerals, and vitamins, is important to ensure a balanced diet for the growing world population. Increasing yield, seed protein, oil, minerals, and vitamins is urgently needed to meet the global demand. The production and productivity of sesame is very low due to various biotic and abiotic stresses. Therefore, various efforts have been made to combat these constraints and increase the production and productivity of sesame through conventional breeding. However, less attention has been paid to the genetic improvement of the crop through modern biotechnological methods, leaving it lagging behind other oilseed crops. Recently, however, the scenario has changed as sesame research has entered the era of "omics" and has made significant progress. Therefore, the purpose of this paper is to provide an overview of the progress made by omics research in improving sesame. This review presents a number of efforts that have been made over past decade using omics technologies to improve various traits of sesame, including seed composition, yield, and biotic and abiotic resistant varieties. It summarizes the advances in genetic improvement of sesame using omics technologies, such as germplasm development (web-based functional databases and germplasm resources), gene discovery (molecular markers and genetic linkage map construction), proteomics, transcriptomics, and metabolomics that have been carried out in the last decade. In conclusion, this review highlights future directions that may be important for omics-assisted breeding in sesame genetic improvement.

Genome-wide discovery of di-nucleotide SSR markers based on whole genome re-sequencing data of Cicer arietinum L. and Cicer reticulatum Ladiz

Simple sequence repeats (SSRs) are valuable genetic markers due to their co-dominant inheritance, multi-allelic and reproducible nature. They have been largely used for exploiting genetic architecture of plant germplasms, phylogenetic analysis, and mapping studies. Among the SSRs, di-nucleotide repeats are the most frequent of the simple repeats distributed throughout the plant genomes. In present study, we aimed to discover and develop di-nucleotide SSR markers by using the whole genome re-sequencing (WGRS) data from Cicer arietinum L. and C. reticulatum Ladiz. A total of 35,329 InDels were obtained in C. arietinum, whereas 44,331 InDels in C. reticulatum. 3387 InDels with 2 bp length were detected in C. arietinum, there were 4704 in C. reticulatum. Among 8091 InDels, 58 di-nucleotide regions that were polymorphic between two species were selected and used for validation. We tested primers for evaluation of genetic diversity in 30 chickpea genotypes including C. arietinum, C. reticulatum, C. echinospermum P.H. Davis, C. anatolicum Alef., C. canariense A. Santos & G.P. Lewis, C. microphyllum Benth., C. multijugum Maesen, C. oxyodon Boiss. & Hohen. and C. songaricum Steph ex DC. A total of 244 alleles were obtained for 58 SSR markers giving an average of 2.36 alleles per locus. The observed heterozygosity was 0.08 while the expected heterozygosity was 0.345. Polymorphism information content was found to be 0.73 across all loci. Phylogenetic tree and principal coordinate analysis clearly divided the accessions into four groups. The SSR markers were also evaluated in 30 genotypes of a RIL population obtained from an interspecific cross between C. arietinum and C. reticulatum. Chi-square (χ²) test revealed an expected 1:1 segregation ratio in the population. These results demonstrated the success of SSR identification and marker development for chickpea with the use of WGRS data. The newly developed 58 SSR markers are expected to be useful for chickpea breeders.

Genome-Wide Association Study of Phenolic Content and Antioxidant Properties in Eggplant Germplasm

The phenolic compounds in eggplant offer potential natural antioxidants for improved health. A large number of samples were examined in order to find eggplant germplasm with a high potential for health promotion. A genome-wide association study (GWAS) was conducted to identify single nucleotide polymorphisms (SNPs) associated with variations in total phenolic content (TPC) and antioxidant activity in eggplants, including ABTS (2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)) scavenging activity and ferric reducing antioxidant power (FRAP). TPC values varied from 14.19 to 842.90 mg gallic acid equivalent (GAE)/100 g of dry weight of eggplant fruit powder. TPC showed a strong positive correlation with both FRAP and ABTS (r = 0.89 *** and 0.77 ***, respectively). The GWAS identified 20 SNPs that were significantly associated out of 29,183 SNPs. Out of the 20 significant SNPs, 11 showed associations with TPC, 4 with ABTS activity, and 5 with FRAP. Among the SNPs associated with TPC, one SNP was found on each of Chromosomes 3, 4, 7, and 12. In contrast, Chromosome 5 comprised two SNPs associated to TPC. Furthermore, the gene encoding IRX12 laccase-4 on Chromosome 10 was found to contain five SNPs associated with TPC. Four significantly linked SNPs on Chromosomes 1 (1 SNP), 4 (2 SNPs), and 10 (1 SNP) were found to be related to ABTS activity. The identified SNPs will be further examined as markers for selecting desirable eggplant varieties and exploring the links between candidate genes, phenolic content, and antioxidant activity. The findings of this study could assist in further study and the development of eggplants with improved health advantages through targeted breeding.

Analysis of Genetic Diversity and Population Structure of Cowpea (Vigna unguiculata (L.) Walp) Genotypes Using Single Nucleotide Polymorphism Markers

Cowpea (Vigna unguiculata (L.) Walp) is an important legume crop with immense potential for nutritional and food security, income generation, and livestock feed in Sub-Saharan Africa. The crop is highly tolerant to heat and drought stresses which makes it an extremely important crop for improving resilience in crop production in the face of climate change. This study was carried out to assess the genetic diversity and population structure of 90 cowpea accessions using single nucleotide polymorphism (SNP) markers. Out of 11,940 SNPs used, 5864 SNPs were polymorphic and maintained for genome diversity analysis. Polymorphic information content (PIC) values ranged from 0.22 to 0.32 with a mean value of 0.27. The model-based Bayesian STRUCTURE analysis classified 90 cowpea accessions into four subpopulations at K = 4, while the distance-based cluster analysis grouped the accessions into three distinct clusters. The analysis of molecular variance (AMOVA) revealed that 59% and 69% of the total molecular variation was attributed to among individual variation for model-based and distance-based populations, respectively, and 18% was attributed to within individual variations. Furthermore, the low heterozygosity among cowpea accessions and the high inbreeding coefficient observed in this study suggests that the accessions reached an acceptable level of homozygosity. This study would serve as a reference for future selection and breeding programs of cowpea with desirable traits and systematic conservation of these plant genetic resources.

Genome-Wide Association Study for Agro-Morphological Traits in Eggplant Core Collection

Eggplant is one of the most economically and nutritionally important vegetables worldwide. The study of the association of phenotypic traits with genetic factors is vital for the rapid and efficient identification and selection of eggplant genetic resources for breeding purposes with desired traits. The eggplant resources (587) collected from different countries, including Korea, were used for establishing the core collection. A total of 288 accessions were selected from 587 Solanum accessions based on 52 single nucleotide polymorphisms (SNPs) markers together with 17 morphological traits. This core collection was further used to analyze the genetic associations of eggplant morphological variations. A large variation was found among the evaluated eggplant accessions for some agro-morphological traits. Stem prickles and leaf prickles showed a significant positive correlation (r = 0.83***), followed by days to flowering and days to maturity (r = 0.64***). A total of 114,981 SNPs were filtered and used for phylogenetic tree analysis, population structure analysis, and genome-wide association study (GWAS). Among the agro-morphological traits, significantly associated SNPs were found for six traits. A total of 377 significantly associated SNPs with six agro-morphological traits were identified. These six traits and the number of SNPs were: days to maturity (51), flower size (121), fruit width (20), harvest fruit color (42), leaf prickles (38), and stem prickles (105). The largest fraction of significant SNPs (11.94%) was obtained on chromosome Ch01, followed by Ch07 and Ch06 with 11.67% and 10.08%, respectively. This study will help to develop markers linked to the most important agro-morphological traits of eggplant genetic resources and support the selection of desirable traits for eggplant breeding programs.

Genetic Diversity, Population Structure and Linkage Disequilibrium Analyses in Tropical Maize Using Genotyping by Sequencing

Several maize breeding programs in India have developed numerous inbred lines but the lines have not been characterized using high-density molecular markers. Here, we studied the molecular diversity, population structure, and linkage disequilibrium (LD) patterns in a panel of 314 tropical normal corn, two sweet corn, and six popcorn inbred lines developed by 17 research centers in India, and 62 normal corn from the International Maize and Wheat Improvement Center (CIMMYT). The 384 inbred lines were genotyped with 60,227 polymorphic single nucleotide polymorphisms (SNPs). Most of the pair-wise relative kinship coefficients (58.5%) were equal or close to 0, which suggests the lack of redundancy in the genomic composition in the majority of inbred lines. Genetic distance among most pairs of lines (98.3%) varied from 0.20 to 0.34 as compared with just 1.7% of the pairs of lines that differed by <0.20, which suggests greater genetic variation even among sister lines. The overall average of 17% heterogeneity was observed in the panel indicated the need for further inbreeding in the high heterogeneous genotypes. The mean nucleotide diversity and frequency of polymorphic sites observed in the panel were 0.28 and 0.02, respectively. The model-based population structure, principal component analysis, and phylogenetic analysis revealed three to six groups with no clear patterns of clustering by centers-wise breeding lines, types of corn, kernel characteristics, maturity, plant height, and ear placement. However, genotypes were grouped partially based on their source germplasm from where they derived.

Genome-Wide SNP Markers for Genotypic and Phenotypic Differentiation of Melon (Cucumis melo L.) Varieties Using Genotyping-by-Sequencing

Melon (Cucumis melo L.) is an economically important horticultural crop with abundant morphological and genetic variability. Complex genetic variations exist even among melon varieties and remain unclear to date. Therefore, unraveling the genetic variability among the three different melon varieties, muskmelon (C. melo subsp. melo), makuwa (C. melo L. var. makuwa), and cantaloupes (C. melo subsp. melo var. cantalupensis), could provide a basis for evolutionary research. In this study, we attempted a systematic approach with genotyping-by-sequencing (GBS)-derived single nucleotide polymorphisms (SNPs) to reveal the genetic structure and diversity, haplotype differences, and marker-based varieties differentiation. A total of 6406 GBS-derived SNPs were selected for the diversity analysis, in which the muskmelon varieties showed higher heterozygote SNPs. Linkage disequilibrium (LD) decay varied significantly among the three melon varieties, in which more rapid LD decay was observed in muskmelon (r² = 0.25) varieties. The Bayesian phylogenetic tree provided the intraspecific relationships among the three melon varieties that formed, as expected, individual clusters exhibiting the greatest genetic distance based on the posterior probability. The haplotype analysis also supported the phylogeny result by generating three major networks for 48 haplotypes. Further investigation for varieties discrimination allowed us to detect a total of 52 SNP markers that discriminated muskmelon from makuwa varieties, of which two SNPs were converted into cleaved amplified polymorphic sequence markers for practical use. In addition to these markers, the genome-wide association study identified two SNPs located in the genes on chromosome 6, which were significantly associated with the phenotypic traits of melon seed. This study demonstrated that a systematic approach using GBS-derived SNPs could serve to efficiently classify and manage the melon varieties in the genebank.

Genome-wide association study and its applications in the non-model crop Sesamum indicum

BACKGROUND

Sesame is a rare example of non-model and minor crop for which numerous genetic loci and candidate genes underlying features of interest have been disclosed at relatively high resolution. These progresses have been achieved thanks to the applications of the genome-wide association study (GWAS) approach. GWAS has benefited from the availability of high-quality genomes, re-sequencing data from thousands of genotypes, extensive transcriptome sequencing, development of haplotype map and web-based functional databases in sesame.

RESULTS

In this paper, we reviewed the GWAS methods, the underlying statistical models and the applications for genetic discovery of important traits in sesame. A novel online database SiGeDiD ( http://sigedid.ucad.sn/ ) has been developed to provide access to all genetic and genomic discoveries through GWAS in sesame. We also tested for the first time, applications of various new GWAS multi-locus models in sesame.

CONCLUSIONS

Collectively, this work portrays steps and provides guidelines for efficient GWAS implementation in sesame, a non-model crop.

Genome-wide association study of seed coat color in sesame (Sesamum indicum L.)

Sesame (Sesamum indicum L.) is an important and ancient oilseed crop. Sesame seed coat color is related to biochemical functions involved in protein and oil metabolism, and antioxidant content. Because of its complication, the genetic basis of sesame seed coat color remains poorly understood. To elucidate the factors affecting the genetic architecture of seed coat color, 366 sesame germplasm lines were evaluated for seed coat color in 12 environments. The genome-wide association studies (GWAS) for three seed coat color space values, best linear unbiased prediction (BLUP) values from a multi-environment trial analysis and principal component scores (PCs) of three seed coat color space values were conducted. GWAS for three seed coat color space values identified a total of 224 significant single nucleotide polymorphisms (SNPs, P < 2.34×10-7), with phenotypic variation explained (PVE) ranging from 1.01% to 22.10%, and 35 significant SNPs were detected in more than 6 environments. Based on BLUP values, 119 significant SNPs were identified, with PVE ranging from 8.83 to 31.98%. Comparing the results of the GWAS using phenotypic data from different environments and the BLUP values, all significant SNPs detected in more than 6 environments were also detected using the BLUP values. GWAS for PCs identified 197 significant SNPs, and 30 were detected in more than 6 environments. GWAS results for PCs were consistent with those for three color space values. Out of 224 significant SNPs, 22 were located in the confidence intervals of previous reported quantitative trait loci (QTLs). Finally, 92 candidate genes were identified in the vicinity of the 4 SNPs that were most significantly associated with sesame seed coat color. The results in this paper will provide new insights into the genetic basis of sesame seed coat color, and should be useful for molecular breeding in sesame.

Genetic dissection of Striga hermonthica (Del.) Benth. resistance via genome-wide association and genomic prediction in tropical maize germplasm

KEY MESSAGE

Genome-wide association revealed that resistance to Striga hermonthica is influenced by multiple genomic regions with moderate effects. It is possible to increase genetic gains from selection for Striga resistance using genomic prediction. Striga hermonthica (Del.) Benth., commonly known as the purple witchweed or giant witchweed, is a serious problem for maize-dependent smallholder farmers in sub-Saharan Africa. Breeding for Striga resistance in maize is complicated due to limited genetic variation, complexity of resistance and challenges with phenotyping. This study was conducted to (i) evaluate a set of diverse tropical maize lines for their responses to Striga under artificial infestation in three environments in Kenya; (ii) detect quantitative trait loci associated with Striga resistance through genome-wide association study (GWAS); and (iii) evaluate the effectiveness of genomic prediction (GP) of Striga-related traits. An association mapping panel of 380 inbred lines was evaluated in three environments under artificial Striga infestation in replicated trials and genotyped with 278,810 single-nucleotide polymorphism (SNP) markers. Genotypic and genotype x environment variations were significant for measured traits associated with Striga resistance. Heritability estimates were moderate (0.42) to high (0.92) for measured traits. GWAS revealed 57 SNPs significantly associated with Striga resistance indicator traits and grain yield (GY) under artificial Striga infestation with low to moderate effect. A set of 32 candidate genes physically near the significant SNPs with roles in plant defense against biotic stresses were identified. GP with different cross-validations revealed that prediction of performance of lines in new environments is better than prediction of performance of new lines for all traits. Predictions across environments revealed high accuracy for all the traits, while inclusion of GWAS-detected SNPs led to slight increase in the accuracy. The item-based collaborative filtering approach that incorporates related traits evaluated in different environments to predict GY and Striga-related traits outperformed GP for Striga resistance indicator traits. The results demonstrated the polygenic nature of resistance to S. hermonthica, and that implementation of GP in Striga resistance breeding could potentially aid in increasing genetic gain for this important trait.

Genome-wide association studies for agronomical traits in winter rice accessions of Assam

The 297 winter rice accessions of Assam, North East India were genotyped by sequencing (GBS). The 50,985 high-quality SNPs were filtered and assigned to 12 rice chromosomes. The population structure analysis revealed three major subgroups SG1, SG2, and SG3 consisting of 30, 8, and 143 accessions respectively. The remaining 116 accessions were grouped as admixture population. Phenotypic data were recorded on13 agronomical traits for genome-wide association studies (GWAS). The 60 significant marker-trait associations (MTAs) were identified for 11 agronomical traits, which explained 0 to 15% of phenotypic variance (PV). A QTL 'hot spot' was detected near the centromeric region on chromosome 6. The identified QTLs may be validated and utilized in 'genomics assisted breeding' for improvement of existing rice cultivars of Assam and North East India.

Comparative analysis and implications of the chloroplast genomes of three thistles (Carduus L., Asteraceae)

BACKGROUND

Carduus, commonly known as plumeless thistles, is a genus in the Asteraceae family that exhibits both medicinal value and invasive tendencies. However, the genomic data of Carduus (i.e., complete chloroplast genomes) have not been sequenced.

METHODS

We sequenced and assembled the chloroplast genome (cpDNA) sequences of three Carduus species using the Illumina Miseq sequencing system and Geneious Prime. Phylogenetic relationships between Carduus and related taxa were reconstructed using Maximum Likelihood and Bayesian Inference analyses. In addition, we used a single nucleotide polymorphism (SNP) in the protein coding region of the matK gene to develop molecular markers to distinguish C. crispus from C. acanthoides and C. tenuiflorus.

RESULTS

The cpDNA sequences of C. crispus, C. acanthoides, and C. tenuiflorus ranged from 152,342 bp to 152,617 bp in length. Comparative genomic analysis revealed high conservation in terms of gene content (including 80 protein-coding, 30 tRNA, and four rRNA genes) and gene order within the three focal species and members of subfamily Carduoideae. Despite their high similarity, the three species differed with respect to the number and content of repeats in the chloroplast genome. Additionally, eight hotspot regions, including psbI-trnS_GCU, trnE_UUC-rpoB, trnR_UCU-trnG_UCC, psbC-trnS_UGA, trnT_UGU-trnL_UAA, psbT-psbN, petD-rpoA, and rpl16-rps3, were identified in the study species. Phylogenetic analyses inferred from 78 protein-coding and non-coding regions indicated that Carduus is polyphyletic, suggesting the need for additional studies to reconstruct relationships between thistles and related taxa. Based on a SNP in matK, we successfully developed a molecular marker and protocol for distinguishing C. crispus from the other two focal species. Our study provides preliminary chloroplast genome data for further studies on plastid genome evolution, phylogeny, and development of species-level markers in Carduus.

Genome-Wide Discovery of InDel Markers in Sesame (Sesamum indicum L.) Using ddRADSeq

The development and validation of different types of molecular markers is crucial to conducting marker-assisted sesame breeding. Insertion-deletion (InDel) markers are highly polymorphic and suitable for low-cost gel-based genotyping. From this perspective, this study aimed to discover and develop InDel markers through bioinformatic analysis of double digest restriction site-associated DNA sequencing (ddRADSeq) data from 95 accessions belonging to the Mediterranean sesame core collection. Bioinformatic analysis indicated the presence of 7477 InDel positions genome wide. Deletions accounted for 61% of the InDels and short deletions (1-2 bp) were the most abundant type (94.9%). On average, InDels of at least 2 bp in length had a frequency of 2.99 InDels/Mb. The 86 InDel sites having length ≥8 bp were detected in genome-wide analysis. These regions can be used for the development of InDel markers considering low-cost genotyping with agarose gels. In order to validate these InDels, a total of 38 InDel regions were selected and primers were successfully amplified. About 13% of these InDels were in the coding sequences (CDSs) and in the 3'- and 5'- untranslated regions (UTRs). Furthermore, the efficiencies of these 16 InDel markers were assessed on 32 sesame accessions. The polymorphic information content (PIC) of these 16 markers ranged from 0.06 to 0.62 (average: 0.33). These results demonstrated the success of InDel identification and marker development for sesame with the use of ddRADSeq data. These agarose-resolvable InDel markers are expected to be useful for sesame breeders.

Genetic diversity analysis of a flax (Linum usitatissimum L.) global collection

Background

A sustainable breeding program requires a minimum level of germplasm diversity to provide varied options for the selection of new breeding lines. To maximize genetic gain of the North Dakota State University (NDSU) flax breeding program, we aimed to increase the genetic diversity of its parental stocks by incorporating diverse genotypes. For this purpose, we analyzed the genetic diversity, linkage disequilibrium, and population sub-structure of 350 globally-distributed flax genotypes with 6200 SNP markers.

Results

All the genotypes tested clustered into seven sub-populations (P1 to P7) based on the admixture model and the output of neighbor-joining (NJ) tree analysis and principal coordinate analysis were in line with that of structure analysis. The largest sub-population separation arose from a cluster of NDSU/American genotypes with Turkish and Asian genotypes. All sub-populations showed moderate genetic diversity (average H = 0.22 and I = 0.34). The pairwise F_st comparison revealed a great degree of divergence (F_st > 0.25) between most of the combinations. A whole collection mantel test showed significant positive correlation (r = 0.30 and p < 0.01) between genetic and geographic distances, whereas it was non-significant for all sub-populations except P4 and P5 (r = 0.251, 0.349 respectively and p < 0.05). In the entire collection, the mean linkage disequilibrium was 0.03 and it decayed to its half maximum within < 21 kb distance.

Conclusions

To maximize genetic gain, hybridization between NDSU stock (P5) and Asian individuals (P6) are potentially the best option as genetic differentiation between them is highest (F_st > 0.50). In contrast, low genetic differentiation between P5 and P2 may enhance the accumulation of favorable alleles for oil and fiber upon crossing to develop dual purpose varieties. As each sub-population consists of many genotypes, a Neighbor-Joining tree and kinship matrix assist to identify distantly related genotypes. These results also inform genotyping decisions for future association mapping studies to ensure the identification of a sufficient number of molecular markers to tag all linkage blocks.

Genome-wide association study of Striga resistance in early maturing white tropical maize inbred lines

BACKGROUND

Striga hermonthica (Benth.) parasitism militates against increased maize production and productivity in savannas of sub-Saharan Africa (SSA). Identification of Striga resistance genes is important in developing genotypes with durable resistance. So far, there is only one report on the existence of QTL for Striga resistance on chromosome 6 of maize. The objective of this study was to identify genomic regions significantly associated with grain yield and other agronomic traits under artificial Striga field infestation. A panel of 132 early-maturing maize inbreds were phenotyped for key agronomic traits under Striga-infested and Striga-free conditions. The inbred lines were also genotyped using 47,440 DArTseq markers from which 7224 markers were retained for population structure analysis and genome-wide association study (GWAS).

RESULTS

The inbred lines were grouped into two major clusters based on structure analysis as well as the neighbor-joining hierarchical clustering. A total of 24 SNPs significantly associated with grain yield, Striga damage at 8 and 10 weeks after planting (WAP), ears per plant and ear aspect under Striga infestation were detected. Under Striga-free conditions, 11 SNPs significantly associated with grain yield, number of ears per plant and ear aspect were identified. Three markers physically located close to the putative genes GRMZM2G164743 (bin 10.05), GRMZM2G060216 (bin 3.06) and GRMZM2G103085 (bin 5.07) were detected, linked to grain yield, Striga damage at 8 and 10 WAP and number of ears per plant under Striga infestation, explaining 9 to 42% of the phenotypic variance. Furthermore, the S9_154,978,426 locus on chromosome 9 was found at 2.61 Mb close to the ZmCCD1 gene known to be associated with the reduction of strigolactone production in the maize roots.

CONCLUSIONS

Presented in this study is the first report of the identification of significant loci on chromosomes 9 and 10 of maize that are closely linked to ZmCCD1 and amt5 genes, respectively and may be related to plant defense mechanisms against Striga parasitism. After validation, the identified loci could be targets for breeders for marker-assisted selection (MAS) to accelerate genetic enhancement of maize for Striga resistance in the tropics, particularly in SSA, where the parasitic weed is endemic.

Genome-Wide Association Mapping for Agronomic and Seed Quality Traits of Field Pea (Pisum sativum L.)

Genome-wide association study (GWAS) was conducted to identify loci associated with agronomic (days to flowering, days to maturity, plant height, seed yield and seed weight), seed morphology (shape and dimpling), and seed quality (protein, starch, and fiber concentrations) traits of field pea (Pisum sativum L.). A collection of 135 pea accessions from 23 different breeding programs in Africa (Ethiopia), Asia (India), Australia, Europe (Belarus, Czech Republic, Denmark, France, Lithuania, Netherlands, Russia, Sweden, Ukraine and United Kingdom), and North America (Canada and USA), was used for the GWAS. The accessions were genotyped using genotyping-by-sequencing (GBS). After filtering for a minimum read depth of five, and minor allele frequency of 0.05, 16,877 high quality SNPs were selected to determine marker-trait associations (MTA). The LD decay (LD_1/2max,90) across the chromosomes varied from 20 to 80 kb. Population structure analysis grouped the accessions into nine subpopulations. The accessions were evaluated in multi-year, multi-location trials in Olomouc (Czech Republic), Fargo, North Dakota (USA), and Rosthern and Sutherland, Saskatchewan (Canada) from 2013 to 2017. Each trait was phenotyped in at least five location-years. MTAs that were consistent across multiple trials were identified. Chr5LG3_566189651 and Chr5LG3_572899434 for plant height, Chr2LG1_409403647 for lodging resistance, Chr1LG6_57305683 and Chr1LG6_366513463 for grain yield, Chr1LG6_176606388, Chr2LG1_457185, Chr3LG5_234519042 and Chr7LG7_8229439 for seed starch concentration, and Chr3LG5_194530376 for seed protein concentration were identified from different locations and years. This research identified SNP markers associated with important traits in pea that have potential for marker-assisted selection towards rapid cultivar improvement.

Development of molecular markers for invasive alien plants in Korea: a case study of a toxic weed, Cenchrus longispinus L., based on next generation sequencing data

BACKGROUND

Genomic data play an important role in plant research because of its implications in studying genomic evolution, phylogeny, and developing molecular markers. Although the information of invasive alien plants was collected, the genomic data of those species have not been intensively studied.

METHODS

We employ the next generation sequencing and PCR methods to explore the genomic data as well as to develop and test the molecular markers.

RESULTS

In this study, we characterize the chloroplast genomes (cpDNA) of Cenchrus longispinus and C. echinatus, of which the lengths are 137,144 and 137,131 bp, respectively. These two newly sequenced genomes include 78 protein-coding genes, 30 tRNA, and four rRNA. There are 56 simple single repeats and 17 forward repeats in the chloroplast genome of C. longispinus. Most of the repeats locate in non-coding regions. However, repeats can be found in infA, ndhD, ndhH, ndhK, psbC, rpl22, rpoC2, rps14, trnA-UGC, trnC-GCA, trnF-GAA, trnQ-UUG, trnS-UGA, trnS-GCU, and ycf15. The phylogenomic analysis revealed the monophyly of Cenchrus but not Panicum species in tribe Paniceae. The single nucleotide polymorphism sites in atpB, matK, and ndhD were successfully used for developing molecular markers to distinguish C. longispinus and related taxa. The simple PCR protocol for using the newly developed molecular markers was also provided.

Genetic diversity and population structure of the Mediterranean sesame core collection with use of genome-wide SNPs developed by double digest RAD-Seq

The Mediterranean sesame core collection contains agro-morphologically superior sesame accessions from geographically diverse regions in four continents. In the present investigation, the genetic diversity and population structure of this collection was analyzed with 5292 high-quality SNPs discovered by double-digest restriction site associated DNA (ddRAD) sequencing, a cost-effective and flexible next-generation sequencing method. The genetic distance between pairs of accessions varied from 0.023 to 0.524. The gene diversity was higher in accessions from Asia than from America, Africa, and Europe. The highest genetic differentiation was observed between accessions collected from America and Europe. Structure analysis showed the presence of three subpopulations among the sesame accessions, and only six accessions were placed in an admixture group. Phylogenetic tree and principal coordinate analysis clustered the accessions based on their countries of origin. However, no clear division was evident among the sesame accessions with regard to their continental locations. This result was supported by an AMOVA analysis, which revealed a genetic variation among continental groups of 5.53% of the total variation. The large number of SNPs clearly indicated that the Mediterranean sesame core collection is a highly diverse genetic resource. The collection can be exploited by breeders to select appropriate accessions that will provide high genetic gain in sesame improvement programs. The high-quality SNP data generated here should also be used in genome-wide association studies to explore qualitative trait loci and SNPs related to economically and agronomically important traits in sesame.

Whole Genome Diversity, Population Structure, and Linkage Disequilibrium Analysis of Chickpea (Cicer arietinum L.) Genotypes Using Genome-Wide DArTseq-Based SNP Markers

Characterization of genetic diversity, population structure, and linkage disequilibrium is a prerequisite for proper management of breeding programs and conservation of genetic resources. In this study, 186 chickpea genotypes, including advanced "Kabuli" breeding lines and Iranian landrace "Desi" chickpea genotypes, were genotyped using DArTseq-Based single nucleotide polymorphism (SNP) markers. Out of 3339 SNPs, 1152 markers with known chromosomal position were selected for genome diversity analysis. The number of mapped SNP markers varied from 52 (LG8) to 378 (LG4), with an average of 144 SNPs per linkage group. The chromosome size that was covered by SNPs varied from 16,236.36 kbp (LG8) to 67,923.99 kbp (LG5), while LG4 showed a higher number of SNPs, with an average of 6.56 SNPs per Mbp. Polymorphism information content (PIC) value of SNP markers ranged from 0.05 to 0.50, with an average of 0.32, while the markers on LG4, LG6, and LG8 showed higher mean PIC value than average. Unweighted neighbor joining cluster analysis and Bayesian-based model population structure grouped chickpea genotypes into four distinct clusters. Principal component analysis (PCoA) and discriminant analysis of principal component (DAPC) results were consistent with that of the cluster and population structure analysis. Linkage disequilibrium (LD) was extensive and LD decay in chickpea germplasm was relatively low. A few markers showed r2 ≥ 0.8, while 2961 pairs of markers showed complete LD (r2 = 1), and a huge LD block was observed on LG4. High genetic diversity and low kinship value between pairs of genotypes suggest the presence of a high genetic diversity among the studied chickpea genotypes. This study also demonstrates the efficiency of DArTseq-based SNP genotyping for large-scale genome analysis in chickpea. The genotypic markers provided in this study are useful for various association mapping studies when combined with phenotypic data of different traits, such as seed yield, abiotic, and biotic stresses, and therefore can be efficiently used in breeding programs to improve chickpea.