Structural variation in complex genome: detection, integration and function

Chromosome-level genome assembly of Dongxiang wild rice (Oryza rufipogon) provides insights into resistance to disease and freezing

Dongxiang wild rice (DXWR, Oryza rufipogon Griff.) belongs to common wild rice O. rufipogon, which is the well-known ancestral progenitor of cultivated rice, possessing important gene resources for rice breeding. However, the distribution of DXWR is decreasing rapidly, and no reference genome has been published to date. In this study, we constructed a chromosome-level reference genome of DXWR by Oxford Nanopore Technology (ONT) and High-through chromosome conformation capture (Hi-C). A total of 58.41 Gb clean data from ONT were de novo assembled into 231 contigs with the total length of 413.46 Mb and N50 length of 5.18 Mb. These contigs were clustered and ordered into 12 pseudo-chromosomes covering about 97.39% assembly with Hi-C data, with a scaffold N50 length of 33.47 Mb. Moreover, 54.10% of the genome sequences were identified as repeat sequences. 33,862 (94.21%) genes were functionally annotated from a total of predicted 35,942 protein-coding sequences. Compared with other species of Oryza genus, the genes related to disease and cold resistance in DXWR had undergone a large-scale expansion, which may be one of the reasons for the stronger disease resistance and cold resistance of DXWR. Comparative transcriptome analysis also determined a list of differentially expressed genes under normal and cold treatment, which supported DXWR as a cold-tolerant variety. The collinearity between DXWR and cultivated rice was high, but there were still some significant structural variations, including a specific inversion on chromosome 11, which may be related to the differentiation of DXWR. The high-quality chromosome-level reference genome of DXWR assembled in this study will become a valuable resource for rice molecular breeding and genetic research in the future.

X-CNV: genome-wide prediction of the pathogenicity of copy number variations

Background

Gene copy number variations (CNVs) contribute to genetic diversity and disease prevalence across populations. Substantial efforts have been made to decipher the relationship between CNVs and pathogenesis but with limited success.

Results

We have developed a novel computational framework X-CNV (www.unimd.org/XCNV), to predict the pathogenicity of CNVs by integrating more than 30 informative features such as allele frequency (AF), CNV length, CNV type, and some deleterious scores. Notably, over 14 million CNVs across various ethnic groups, covering nearly 93% of the human genome, were unified to calculate the AF. X-CNV, which yielded area under curve (AUC) values of 0.96 and 0.94 in training and validation sets, was demonstrated to outperform other available tools in terms of CNV pathogenicity prediction. A meta-voting prediction (MVP) score was developed to quantitively measure the pathogenic effect, which is based on the probabilistic value generated from the XGBoost algorithm. The proposed MVP score demonstrated a high discriminative power in determining pathogenetic CNVs for inherited traits/diseases in different ethnic groups.

Conclusions

The ability of the X-CNV framework to quantitatively prioritize functional, deleterious, and disease-causing CNV on a genome-wide basis outperformed current CNV-annotation tools and will have broad utility in population genetics, disease-association studies, and diagnostic screening.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00945-4.

Mutational Characteristics of Causative Genes in Chinese Hereditary Spherocytosis Patients: a Report on Fourteen Cases and a Review of the Literature

Background: Hereditary spherocytosis (HS), characterized by the presence of spherocytic red cells in peripheral blood, hemolysis, splenomegaly, jaundice, and gallstones, is a common form of inherited hemolytic anemia (HA). To date, five causative genes associated with HS have been identified, including ANK1, SPTB, SPTA1, SLC4A1, and EPB42.

Methods: Clinically suspected patients with HS or undiagnosed HA from 14 Chinese families were enrolled in this study. We presented the patients’ clinical features and identified the causative gene variants in these patients using whole exome sequencing (WES), with 10 novel and four reported mutations in the ANK1 and SPTB genes (seven mutations in ANK1 and seven in SPTB), individually. Then, we reviewed all available literature on Chinese HS patients from 2000 to 2020 in PubMed and Chinese Journals with genetic results and clinical information, to delineate gene mutation spectrum and potential correlation with phenotypes.

Results: A total of 158 variants (including 144 in previous reports and 14 in this study) indicated that ANK1 (46%) and SPTB (42%) were the most frequently mutated genes in Chinese HS patients, followed by SLC4A1 (11%) and SPTA1 (1%), while no mutations in EPB42 was reported. Most of the mutations in ANK1 and SPTB were nonsense (26/73 in ANK1 and 32/66 in SPTB) and frameshift (20/73 in ANK1 and 15/66 in SPTB), while missense mutations (14/18) accounted for the majority in SLC4A1. The higher mutation frequency of ANK1 was found in its exon 8, 9, 26, and 28. The majority of mutations in SPTB were located in its exon 13, 15, and 18–30, whereas mutations in SLC4A1 were scattered throughout the entire region of the gene.

Conclusion: Our study expanded the mutation spectrum of ANK1 and SPTB. Furthermore, we clarified the mutational characteristics of causative genes by reviewing all available literature on Chinese patients with HS.

AtMAD: Arabidopsis thaliana multi-omics association database

Integration analysis of multi-omics data provides a comprehensive landscape for understanding biological systems and mechanisms. The abundance of high-quality multi-omics data (genomics, transcriptomics, methylomics and phenomics) for the model organism Arabidopsis thaliana enables scientists to study the genetic mechanism of many biological processes. However, no resource is available to provide comprehensive and systematic multi-omics associations for Arabidopsis. Here, we developed an Arabidopsis thaliana Multi-omics Association Database (AtMAD, http://www.megabionet.org/atmad), a public repository for large-scale measurements of associations between genome, transcriptome, methylome, pathway and phenotype in Arabidopsis, designed for facilitating identification of eQTL, emQTL, Pathway-mQTL, Phenotype-pathway, GWAS, TWAS and EWAS. Candidate variants/methylations/genes were identified in AtMAD for specific phenotypes or biological processes, many of them are supported by experimental evidence. Based on the multi-omics association strategy, we have identified 11 796 cis-eQTLs and 10 119 trans-eQTLs. Among them, 68 837 environment-eQTL associations and 149 622 GWAS-eQTL associations were identified and stored in AtMAD. For expression–methylation quantitative trait loci (emQTL), we identified 265 776 emQTLs and 122 344 pathway-mQTLs. For TWAS and EWAS, we obtained 62 754 significant phenotype-gene associations and 3 993 379 significant phenotype-methylation associations, respectively. Overall, the multi-omics associated network in AtMAD will provide new insights into exploring biological mechanisms of plants at multi-omics levels.

Pan-Genome of Wild and Cultivated Soybeans

Soybean is one of the most important vegetable oil and protein feed crops. To capture the entire genomic diversity, it is needed to construct a complete high-quality pan-genome from diverse soybean accessions. In this study, we performed individual de novo genome assemblies for 26 representative soybeans that were selected from 2,898 deeply sequenced accessions. Using these assembled genomes together with three previously reported genomes, we constructed a graph-based genome and performed pan-genome analysis, which identified numerous genetic variations that cannot be detected by direct mapping of short sequence reads onto a single reference genome. The structural variations from the 2,898 accessions that were genotyped based on the graph-based genome and the RNA sequencing (RNA-seq) data from the representative 26 accessions helped to link genetic variations to candidate genes that are responsible for important traits. This pan-genome resource will promote evolutionary and functional genomics studies in soybean.

The Past, Present, and Future of Maize Improvement: Domestication, Genomics, and Functional Genomic Routes toward Crop Enhancement

After being domesticated from teosinte, cultivated maize (Zea mays ssp. mays) spread worldwide and now is one of the most important staple crops. Due to its tremendous phenotypic and genotypic diversity, maize also becomes to be one of the most widely used model plant species for fundamental research, with many important discoveries reported by maize researchers. Here, we provide an overview of the history of maize domestication and key genes controlling major domestication-related traits, review the currently available resources for functional genomics studies in maize, and discuss the functions of most of the maize genes that have been positionally cloned and can be used for crop improvement. Finally, we provide some perspectives on future directions regarding functional genomics research and the breeding of maize and other crops.