Identification of SNP and SSR Markers in Finger Millet Using Next Generation Sequencing Technologies

Finger millet is an important cereal crop in eastern Africa and southern India with excellent grain storage quality and unique ability to thrive in extreme environmental conditions. Since negligible attention has been paid to improving this crop to date, the current study used Next Generation Sequencing (NGS) technologies to develop both Simple Sequence Repeat (SSR) and Single Nucleotide Polymorphism (SNP) markers. Genomic DNA from cultivated finger millet genotypes KNE755 and KNE796 was sequenced using both Roche 454 and Illumina technologies. Non-organelle sequencing reads were assembled into 207 Mbp representing approximately 13% of the finger millet genome. We identified 10,327 SSRs and 23,285 non-homeologous SNPs and tested 101 of each for polymorphism across a diverse set of wild and cultivated finger millet germplasm. For the 49 polymorphic SSRs, the mean polymorphism information content (PIC) was 0.42, ranging from 0.16 to 0.77. We also validated 92 SNP markers, 80 of which were polymorphic with a mean PIC of 0.29 across 30 wild and 59 cultivated accessions. Seventy-six of the 80 SNPs were polymorphic across 30 wild germplasm with a mean PIC of 0.30 while only 22 of the SNP markers showed polymorphism among the 59 cultivated accessions with an average PIC value of 0.15. Genetic diversity analysis using the polymorphic SNP markers revealed two major clusters; one of wild and another of cultivated accessions. Detailed STRUCTURE analysis confirmed this grouping pattern and further revealed 2 sub-populations within wild E. coracana subsp. africana. Both STRUCTURE and genetic diversity analysis assisted with the correct identification of the new germplasm collections. These polymorphic SSR and SNP markers are a significant addition to the existing 82 published SSRs, especially with regard to the previously reported low polymorphism levels in finger millet. Our results also reveal an unexploited finger millet genetic resource that can be included in the regional breeding programs in order to efficiently optimize productivity

Morphological and Molecular Characterization of Toxigenic Aspergillus flavus from Groundnut Kernels in Kenya

Pathogenesis of Aspergillus flavus on important agricultural products is a key concern on human health due to the synthesis and secretion of the hazardous secondary metabolite, aflatoxin. This study identified and further characterized aflatoxigenic A. flavus from groundnuts sampled from sundry shops in Kenya using integrated morphological and molecular approaches. The groundnuts were plated on potato dextrose agar for isolation and morphological observation of A. flavus based on macroscopic and microscopic features. Molecular characterization was done through amplification and comparison of the partial sequence of the ITS1-5.8S-ITS2 region. The expression analysis of aflR, aflS, aflD, aflP, and aflQ genes in the aflatoxin biosynthesis pathways was conducted to confirm the positive identification of A. flavus. The gene expression also aided to delineate toxigenic isolates of A. flavus from atoxigenic ones. Morphologically, 18 isolates suspected to be A. flavus were identified. Out of these, 14 isolates successfully amplified the 500 bp ITS region of A. flavus or Aspergillus oryzae, while 4 isolates were not amplified. All the remaining 14 isolates expressed at least one of the aflatoxigenic genes but only 5 had all the genes expressed. Partial sequencing revealed that isolates 5, 11, 12, 13, and 15 had 99.2%, 97.6%, 98.4%, 97.5%, and 100% homology, respectively, to the A. flavus isolate LUOHE, ITS-5.8S-ITS2, obtained from the NCBI database. The five isolates were accurate identification of atoxigenic A. flavus. Precise identification of toxigenic strains of A. flavus will be useful in establishing control strategies of the fungus in food products

UGbS-Flex, a novel bioinformatics pipeline for imputation-free SNP discovery in polyploids without a reference genome: finger millet as a case study

Abstract Background Research on orphan crops is often hindered by a lack of genomic resources. With the advent of affordable sequencing technologies, genotyping an entire genome or, for large-genome species, a representative fraction of the genome has become feasible for any crop. Nevertheless, most genotyping-by-sequencing (GBS) methods are geared towards obtaining large numbers of markers at low sequence depth, which excludes their application in heterozygous individuals. Furthermore, bioinformatics pipelines often lack the flexibility to deal with paired-end reads or to be applied in polyploid species. Results UGbS-Flex combines publicly available software with in-house python and perl scripts to efficiently call SNPs from genotyping-by-sequencing reads irrespective of the species’ ploidy level, breeding system and availability of a reference genome. Noteworthy features of the UGbS-Flex pipeline are an ability to use paired-end reads as input, an effective approach to cluster reads across samples with enhanced outputs, and maximization of SNP calling. We demonstrate use of the pipeline for the identification of several thousand high-confidence SNPs with high representation across samples in an F3-derived F2 population in the allotetraploid finger millet. Robust high-density genetic maps were constructed using the time-tested mapping program MAPMAKER which we upgraded to run efficiently and in a semi-automated manner in a Windows Command Prompt Environment. We exploited comparative GBS with one of the diploid ancestors of finger millet to assign linkage groups to subgenomes and demonstrate the presence of chromosomal rearrangements. Conclusions The paper combines GBS protocol modifications, a novel flexible GBS analysis pipeline, UGbS-Flex, recommendations to maximize SNP identification, updated genetic mapping software, and the first high-density maps of finger millet. The modules used in the UGbS-Flex pipeline and for genetic mapping were applied to finger millet, an allotetraploid selfing species without a reference genome, as a case study. The UGbS-Flex modules, which can be run independently, are easily transferable to species with other breeding systems or ploidy levels

A summary of sequencing reads generated for each genotype and the resulting assemblies.

<p>A summary of sequencing reads generated for each genotype and the resulting assemblies.</p

A list of polymorphic SNP markers and their characteristics after validation across 89 <i>Eleusine</i> accessions.

<p>A list of polymorphic SNP markers and their characteristics after validation across 89 <i>Eleusine</i> accessions.</p

Molecular function categories.

<p>The distribution of SNP-containing putative genes that were assigned GO terms in PANTHER (<a href="http://pantherdb.org/" target="_blank">http://pantherdb.org/</a>). Catalytic activity category was over-represented.</p

Population structure of 30 “wild” <i>Eleusine</i> accessions.

<p>Output of STRUCTURE analysis done using 76 polymorphic SNP markers across 30 “wild” accessions only. Four sub-populations are shown in red, green, blue and orange. The only genotype appearing under red (MS16) has been confirmed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159437#pone.0159437.g002" target="_blank">Fig 2</a> to be cultivated. The 3 genotypes highlighted in green are <i>E</i>. <i>kigeziensis</i> (Lesk10, EDL16 and MD48) while blue and orange represent subsp. <i>africana</i> subpopulations 2 and 1 respectively. The orange sub-population is composed of the same samples clustering in red under <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159437#pone.0159437.g003" target="_blank">Fig 3</a>. The corresponding accession codes are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159437#pone.0159437.t001" target="_blank">Table 1</a>.</p

Characteristics of polymorphic SSRs after validation across 10 genotypes.

<p>Characteristics of polymorphic SSRs after validation across 10 genotypes.</p

A summary of identified SSR and SNP markers and their frequency across genotypes KNE796 and KNE755.

<p>A summary of identified SSR and SNP markers and their frequency across genotypes KNE796 and KNE755.</p