Genome Analysis Revives a Forgotten Hybrid Crop Edo-dokoro in the Genus Dioscorea

忘れられた作物「えどどころ」の起原 --ゲノム解析が明らかにする青森県三八上北地域に残る栽培イモの歴史--. 京都大学プレスリリース. 2022-08-10.A rhizomatous Dioscorea crop “Edo-dokoro” was described in old records of Japan, but its botanical identify has not been characterized. We found that Edo-dokoro is still produced by four farmers in Tohoku-machi of Aomori Prefecture, Japan. Rhizomes of Edo-dokoro are a delicacy to the local people and are sold in the markets. Morphological characters of Edo-dokoro suggest its hybrid origin between the two species, D. tokoro and D. tenuipes. Genome analysis revealed that Edo-dokoro is likely originated by hybridization of a male D. tokoro to a female D. tenuipes, followed by a backcross with a male plant of D. tokoro. Edo-dokoro is a typical minor crop possibly maintained for more than 300 years but now almost forgotten from the public. We hypothesize that there are many such uncharacterized genetic heritages passed over generations by small scale farmers that await serious scientific investigation for future use and improvement by using modern genomics information

High-performance pipeline for MutMap and QTL-seq

[Summary] Bulked segregant analysis implemented in MutMap and QTL-seq is a powerful and efficient method to identify loci contributing to important phenotypic traits. However, the previous pipelines were not user-friendly to install and run. Here, we describe new pipelines for MutMap and QTL-seq. These updated pipelines are approximately 5–8 times faster than the previous pipeline, are easier for novice users to use, and can be easily installed through bioconda with all dependencies. [Availability] The new pipelines of MutMap and QTL-seq are written in Python and can be installed via bioconda. The source code and manuals are available online (MutMap: https://github.com/YuSugihara/MutMap, QTL-seq: https://github.com/YuSugihara/QTL-seq)

Identification of candidate flowering and sex genes in white Guinea yam (D. rotundata Poir.) by SuperSAGE transcriptome profiling

Open Access JournalDioecy (distinct male and female individuals) combined with scarce to non-flowering are common features of cultivated yam (Dioscorea spp.). However, the molecular mechanisms underlying flowering and sex determination in Dioscorea are unknown. We conducted SuperSAGE transcriptome profiling of male, female and monoecious individuals to identify flowering and sex-related genes in white Guinea yam (D. rotundata). SuperSAGE analysis generated a total of 20,236 unique tags, of which 13,901 were represented by a minimum of 10 tags. Of these, 88 tags were significantly differentially expressed in male, female and monoecious plants. Of the 88 differentially expressed SuperSAGE tags, 18 corresponded to genes previously implicated in flower development and sex determination in multiple plant species. We validated the SuperSAGE data with quantitative real-time PCR (qRT-PCR)-based analysis of the expression of four candidate genes. Our findings suggest that mechanisms of flowering and sex determination are likely conserved in Dioscorea. We further investigated the flowering patterns of 1938 D. rotundata accessions representing diverse geographical origins over two years, revealing that over 85% of the accessions are either male or non-flowering, and that less than 15% are female, while monoecious plants are rare. Intensity of flowering appeared to be a function of sex, with male plants flowering more abundantly than female ones. Candidate genes identified in this study can be targeted with the aim to induce regular flowering in poor to non-flowering cultivars. Findings of the study provide important inputs for further studies aiming to overcome the challenge of flowering in yams and to improve the efficiency of yam breeding

Genome analyses reveal the hybrid origin of the staple crop white Guinea yam (Dioscorea rotundata)

西アフリカの主食作物ギニアヤムの起源を解明 --ギニアヤムはサバンナと熱帯雨林に生育する野生種の雑種起源--. 京都大学プレスリリース. 2020-12-11.White Guinea yam (Dioscorea rotundata) is an important staple tuber crop in West Africa. However, its origin remains unclear. In this study, we resequenced 336 accessions of white Guinea yam and compared them with the sequences of wild Dioscorea species using an improved reference genome sequence of D. rotundata. In contrast to a previous study suggesting that D. rotundata originated from a subgroup of Dioscorea praehensilis, our results suggest a hybrid origin of white Guinea yam from crosses between the wild rainforest species D. praehensilis and the savannah-adapted species Dioscorea abyssinica. We identified a greater genomic contribution from D. abyssinica in the sex chromosome of Guinea yam and extensive introgression around the SWEETIE gene. Our findings point to a complex domestication scenario for Guinea yam and highlight the importance of wild species as gene donors for improving this crop through molecular breeding

Coval: Improving Alignment Quality and Variant Calling Accuracy for Next-Generation Sequencing Data

<div><p>Accurate identification of DNA polymorphisms using next-generation sequencing technology is challenging because of a high rate of sequencing error and incorrect mapping of reads to reference genomes. Currently available short read aligners and DNA variant callers suffer from these problems. We developed the Coval software to improve the quality of short read alignments. Coval is designed to minimize the incidence of spurious alignment of short reads, by filtering mismatched reads that remained in alignments after local realignment and error correction of mismatched reads. The error correction is executed based on the base quality and allele frequency at the non-reference positions for an individual or pooled sample. We demonstrated the utility of Coval by applying it to simulated genomes and experimentally obtained short-read data of rice, nematode, and mouse. Moreover, we found an unexpectedly large number of incorrectly mapped reads in ‘targeted’ alignments, where the whole genome sequencing reads had been aligned to a local genomic segment, and showed that Coval effectively eliminated such spurious alignments. We conclude that Coval significantly improves the quality of short-read sequence alignments, thereby increasing the calling accuracy of currently available tools for SNP and indel identification. Coval is available at <a href="http://sourceforge.net/projects/coval105/" target="_blank">http://sourceforge.net/projects/coval105/</a>.</p></div

Improvement of SNP/indel calling accuracy by Coval-Refine in targeted alignment.

<p>The whole chromosomes (All chr), chromosome 10 (Chr10), a 1 Mb fragment of chromosome 10 (Chr10-1M: positions 1000001 to 2000000 of Chr10) from the simulated rice genome were aligned with 75-bp paired-end reads sequenced from the whole rice genome using BWA. The alignments were filtered (+, bars in dark- and middle-red and in dark- and middle-blue) or not filtered (–, bars in light red and in light blue) with Coval-Refine in the basic mode. Two different filtering conditions of Coval-Refine for mismatch reads were applied; one is the default option for removing reads with three or more mismatches (middle-red and middle-blue bars), the other removing the second paired-end mate read when the first mate is filtered and removing a read pair that contained more than two total mismatches (dark red and dark blue bars). The mean coverage of read depth before and after (indicated with parentheses) the Coval-Refine treatment is indicated under the reference chromosome name. Homozygous SNPs and indels were called as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075402#pone-0075402-g001" target="_blank">Figure 1</a>. TPR and FPR for the called SNPs are shown with red and blue bars, respectively.</p

Calling accuracy of SNPs from alignment data containing multiple samples.

<p>The experimentally obtained rice reads (60, 30, and 15 millions) were mixed with the simulated 75 bp paired-end reads (60, 90, and 105 millions) generated by dwgsim with the rice simulated genome as template, respectively, yielding 120 millions of reads. The read mixtures were aligned to the rice simulated genome, resulting in alignments with average read depth of 24×, and each read set (sample) in the read mixtures was discriminated from the other read set using the RG tag. The SNPs were called using Coval-Call with a maximum of 80 reads covering the called positions, a minimum allele frequency at the called position of 0.2 (for 50% homozygous sample), 0.1 (for 50% heterozygous and 25% homozygous samples), or 0.05 (for 25% heterozygous and 12.5% homozygous samples), a minimum of three reads (for 50% homozygous sample) or two reads (for the others) supporting the called allele.</p>a<p>Percentage of the experimentally obtained rice read sample in the read mixture.</p>b<p>Heterozygosity of the experimentally obtained rice read sample (Homo: 0% heterozygosity, Hetero: 50% heterozygosity).</p

Improvement by Coval-Refine of SNP/indel calling accuracy of variant calling tools for mouse alignment data.

<p>(A) SNP calling accuracy with or without Coval-Refine. (B) Indel calling accuracy with or without Coval-Refine. A simulated mouse genome was aligned with real mouse read data using BWA. The alignments were filtered (+, striped bars) or not filtered (–, plain bars) with Coval-Refine. Homozygous SNPs and indels were called with the indicated variant callers under the same conditions as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075402#pone-0075402-g001" target="_blank">Figure 1</a>.</p