A comprehensive whole genome database of ethnic minority populations.

China, is characterized by its remarkable ethnical diversity, which necessitates whole genome variation data from multiple populations as crucial tools for advancing population genetics and precision medical research. However, there has been a scarcity of research concentrating on the whole genome of ethnic minority groups. To fill this gap, we developed the Guizhou Multi-ethnic Genome Database (GMGD). It comprises whole genome sequencing data from 476 healthy unrelated individuals spanning 11 ethnic minorities groups in Guizhou Province, Southwest China, including Bouyei, Dong, Miao, Yi, Bai, Gelo, Zhuang, Tujia, Yao, Hui, and Sui. The GMGD database comprises more than 16.33 million variants in GRCh38 and 16.20 million variants in GRCh37. Among these, approximately 11.9% (1,956,322) of the variants in GRCh38 and 18.5% (3,009,431) of the variants in GRCh37 are entirely new and do not exist in the dbSNP database. These novel variants shed light on the genetic diversity landscape across these populations, providing valuable insights with an average coverage of 5.5 ×. This makes GMGD the largest genome-wide database encompassing the most diverse ethnic groups to date. The GMGD interactive interface facilitates researchers with multi-dimensional mutation search methods and displays population frequency differences among global populations. Furthermore, GMGD is equipped with a genotype-imputation function, enabling enhanced capabilities for low-depth genomic research or targeted region capture studies. GMGD offers unique insights into the genomic variation landscape of different ethnic groups, which are freely accessible at https://db.cngb.org/pop/gmgd/ .

Establish six-gene prognostic model for glioblastoma based on multi-omics data of TCGA database.

Glioblastoma (GBM) is the most common primary intracranial tumor with extremely high malignancy and poor prognosis. In order to identify the GBM prognostic biomarkers and establish a prognostic model, we analyzed the expression profile data of GBM in The Cancer Genome Atlas (TCGA) database as the experimental group. First, we identified the differentially expressed genes of different survival periods among the GBM patients. The GISTIC software and Kaplan Meier (KM) survival curve were used to analyze the copy number variation of GBM to identify the survival-associated amplified gene (SAG). We selected the intersection genes of up-regulated ones in short survival group and SAG, performed univariate Cox regression and iterative Lasso regression with them to identify the important candidate genes and establish a prognostic model. Based on the model, the prognostic score was calculated. The patients were divided into high-risk and low-risk groups according to the median prognostic score. Meanwhile ROC curve was used to evaluate the validity of the model, applying the KM survival analysis of the high-risk and low-risk groups. Multivariate Cox regression analysis was used to determine the independence of the prognostic score. All the data were verified with three external datasets: GEO GSE16011, CGGA, and Rembrandt. The results showed that differential expression analysis of different survival periods of GBM identified 426 up-regulated genes and 65 down-regulated genes in the TCGA GBM dataset. The intersection of up-regulated genes in short survival group and SAG yielded 47 genes. After the screening, the six-gene combination (EN2,PPBP,LRRC61,SEL1L3,CPA4,DDIT4L) prognostic model was finally determined. The area under ROC curve of the model in TCGA experimental group and three external validation group were all greater than 0.6, even reaching 0.912. KM analysis showed that the prognosis of the high-risk and low-risk groups was significant different (P<0.05). In the multivariate Cox regression analysis, the six-gene prognostic score was an independent factor influencing the prognosis of GBM patients (P<0.05). In summary, this study established a prognostic model of six-gene (EN2,PPBP,LRRC61,SEL1L3,CPA4,DDIT4L) for GBM. This six-gene model has good predictive ability and could be used as an independent prognostic marker for GBM patients.

EWASdb: epigenome-wide association study database.

DNA methylation, the most intensively studied epigenetic modification, plays an important role in understanding the molecular basis of diseases. Furthermore, epigenome-wide association study (EWAS) provides a systematic approach to identify epigenetic variants underlying common diseases/phenotypes. However, there is no comprehensive database to archive the results of EWASs. To fill this gap, we developed the EWASdb, which is a part of 'The EWAS Project', to store the epigenetic association results of DNA methylation from EWASs. In its current version (v 1.0, up to July 2018), the EWASdb has curated 1319 EWASs associated with 302 diseases/phenotypes. There are three types of EWAS results curated in this database: (i) EWAS for single marker; (ii) EWAS for KEGG pathway and (iii) EWAS for GO (Gene Ontology) category. As the first comprehensive EWAS database, EWASdb has been searched or downloaded by researchers from 43 countries to date. We believe that EWASdb will become a valuable resource and significantly contribute to the epigenetic research of diseases/phenotypes and have potential clinical applications. EWASdb is freely available at http://www.ewas.org.cn/ewasdb or http://www.bioapp.org/ewasdb.

Comparison of the general co-expression landscapes between human and mouse.

The murine model serves as an important experimental system in biomedical science because of its high degree of similarities at the sequence level with human. Recent studies have compared the transcriptional landscapes between human and mouse, but the general co-expression landscapes have not been characterized. Here, we calculated the general co-expression coefficients and constructed the general co-expression maps for human and mouse. The differences and similarities of the general co-expression maps between the two species were compared in detail. The results showed low similarities in the human and mouse, with only about 36.54% of the co-expression relationships conserved between the two species. These results indicate that researchers should pay attention to these differences when performing research using the expression data of human and mouse. To facilitate use of this information, we also developed the human-mouse general co-expression difference database (coexpressMAP) to search differences in co-expression between human and mouse. This database is freely available at http://www.bioapp.org/coexpressMAP.

The framework for population epigenetic study.

At present, understanding of DNA methylation at the population level is still limited. Here, we first extended the classical framework of population genetics, such as single nucleotide polymorphism allele frequency, linkage disequilibrium (LD), LD block and haplotype, to epigenetics. Then, as an example, we compared the DNA methylation disequilibrium (MD) maps between HapMap CEU (Caucasian residents of European ancestry from Utah) population and YRI (Yoruba people from Ibadan) population (lymphoblastoid cell lines). We analyzed the differences and similarities between CEU and YRI from the following aspects: SMP (single methylation polymorphism) allele frequency, SMP allele association, MD, MD block and methylation haplotype (meplotype) frequency. The results showed that CEU and YRI had similar distribution of SMP allele frequency, and shared many MD block region. We believe that the framework of population genetics can be used in the population epigenetics. The population epigenetic framework also has potential prospects in the study of complex diseases, such as epigenome-wide association study.

EWAS: epigenome-wide association studies software 1.0 - identifying the association between combinations of methylation levels and diseases.

Similar to the SNP (single nucleotide polymorphism) data, there is non-random association of the DNA methylation level (we call it methylation disequilibrium, MD) between neighboring methylation loci. For the case-control study of complex diseases, it is important to identify the association between methylation levels combination types (we call it methylecomtype) and diseases/phenotypes. We extended the classical framework of SNP haplotype-based association study in population genetics to DNA methylation level data, and developed a software EWAS to identify the disease-related methylecomtypes. EWAS can provide the following basic functions: (1) calculating the DNA methylation disequilibrium coefficient between two CpG loci; (2) identifying the MD blocks across the whole genome; (3) carrying out case-control association study of methylecomtypes and identifying the disease-related methylecomtypes. For a DNA methylation level data set including 689 samples (354 cases and 335 controls) and 473864 CpG loci, it takes only about 25 min to complete the full scan. EWAS v1.0 can rapidly identify the association between combinations of methylation levels (methylecomtypes) and diseases. EWAS v1.0 is freely available at: http://www.ewas.org.cn or http://www.bioapp.org/ewas.