Integrative gene set enrichment analysis utilizing isoform-specific expression

Towards the Genetic Architecture of Complex Gene Expression Traits: Challenges and Prospects for eQTL Mapping in Humans

The discovery of expression quantitative trait loci (eQTLs) and their target genes (eGenes) has not only compensated for the limitations of genome-wide association studies for complex phenotypes but has also provided a basis for predicting gene expression. Efforts have been made to develop analytical methods in statistical genetics, a key discipline in eQTL analysis. In particular, mixed model– and deep learning–based analytical methods have been extremely beneficial in mapping eQTLs and predicting gene expression. Nevertheless, we still face many challenges associated with eQTL discovery. Here, we discuss two key aspects of these challenges: 1, the complexity of eTraits with various factors such as polygenicity and epistasis and 2, the voluminous work required for various types of eQTL profiles. The properties and prospects of statistical methods, including the mixed model method, Bayesian inference, the deep learning method, and the integration method, are presented as future directions for eQTL discovery. This review will help expedite the design and use of efficient methods for eQTL discovery and eTrait prediction.

Competing endogenous RNA network analysis explores the key lncRNAs, miRNAs, and mRNAs in type 1 diabetes

BACKGROUND

Type 1 diabetes (T1D, named insulin-dependent diabetes) has a relatively rapid onset and significantly decreases life expectancy. This study is conducted to reveal the long non-coding RNA (lncRNA)-microRNA (miRNA)-mRNA regulatory axises implicated in T1D.

METHODS

The gene expression profile under GSE55100 (GPL570 and GPL8786 datasets; including 12 T1D samples and 10 normal samples for each dataset) was extracted from Gene Expression Omnibus database. Using limma package, the differentially expressed mRNAs (DE-mRNAs), miRNAs (DE-miRNAs), and lncRNAs (DE-lncRNAs) between T1D and normal samples were analyzed. For the DE-mRNAs, the functional terms were enriched by DAVID tool, and the significant pathways were enriched using gene set enrichment analysis. The interactions among DE-lncRNAs, DE-miRNAs and DE-mRNAs were predicted using mirwalk and starbase. The lncRNA-miRNA-mRNA interaction network analysis was visualized by Cytoscape. The key genes in the interaction network were verified by quantitatively real-time PCR.

RESULTS

In comparison to normal samples, 236 DE-mRNAs, 184 DE-lncRNAs, and 45 DE-miRNAs in T1D samples were identified. For the 236 DE-mRNAs, 16 Gene Ontology (GO)_biological process (BP) terms, four GO_cellular component (CC) terms, and 57 significant pathways were enriched. A network involving 36 DE-mRNAs, 8 DE- lncRNAs, and 15 DE-miRNAs was built, such as TRG-AS1-miR-23b/miR-423-PPM1L and GAS5-miR-320a/miR-23b/miR-423-SERPINA1 regulatory axises. Quantitatively real-time PCR successfully validated the expression levels of TRG-AS1- miR-23b -PPM1L and GAS5-miR-320a- SERPINA1.

CONCLUSION

TRG-AS1-miR-23b-PPM1L and GAS5-miR-320a-SERPINA1 regulatory axises might impact the pathogenesis of T1D.

Integrating eQTL data with GWAS summary statistics in pathway-based analysis with application to schizophrenia

Many genetic variants affect complex traits through gene expression, which can be exploited to boost statistical power and enhance interpretation in genome-wide association studies (GWASs) as demonstrated by the transcriptome-wide association study (TWAS) approach. Furthermore, due to polygenic inheritance, a complex trait is often affected by multiple genes with similar functions as annotated in gene pathways. Here, we extend TWAS from gene-based analysis to pathway-based analysis: we integrate public pathway collections, expression quantitative trait locus (eQTL) data and GWAS summary association statistics (or GWAS individual-level data) to identify gene pathways associated with complex traits. The basic idea is to weight the SNPs of the genes in a pathway based on their estimated cis-effects on gene expression, then adaptively test for association of the pathway with a GWAS trait by effectively aggregating possibly weak association signals across the genes in the pathway. The P values can be calculated analytically and thus fast. We applied our proposed test with the KEGG and GO pathways to two schizophrenia (SCZ) GWAS summary association data sets, denoted by SCZ1 and SCZ2 with about 20,000 and 150,000 subjects, respectively. Most of the significant pathways identified by analyzing the SCZ1 data were reproduced by the SCZ2 data. Importantly, we identified 15 novel pathways associated with SCZ, such as GABA receptor complex (GO:1902710), which could not be uncovered by the standard single SNP-based analysis or gene-based TWAS. The newly identified pathways may help us gain insights into the biological mechanism underlying SCZ. Our results showcase the power of incorporating gene expression information and gene functional annotations into pathway-based association testing for GWAS.

The clinical significance of COL5A2 in patients with bladder cancer: A retrospective analysis of bladder cancer gene expression data

The present study was aimed to investigate the relationship between the expression of collagen type V alpha 2 chain (COL5A2) and clinical outcomes of patients with bladder cancer.Chi-square test and log-rank-based survival analysis were performed to assess the correlation of COL5A2 expression with clinical characteristics and survivals of patients with bladder cancer using GSE13507. Gene set enrichment analysis was conducted to study the relevant mechanisms.Bladder cancer patients in COL5A2 low expression group were associated with better invasiveness (P < .0001), tumor grade (P = .001), T staging (P < .0001), N staging (P = .002), cancer specific survival (P < .0001), overall survival (P < .0001), and a trend of better M staging (P = .053) than those in COL5A2 high expression group.COL5A2 might affect the progression of bladder cancer through "Coagulation," "Hypoxia," "Apical junction," "Ultraviolet response," "Epithelial mesenchymal transition," "Angiogenesis," "KRAS (KRAS proto-oncogene, GTPase) signaling,""Complement,""IL2-STAT5-signaling," "Inflammatory response," "IL6-JAK-STAT3-signaling," "Myogenesis," "TNF α signaling," "Apoptosis," and "Hedgehog-signaling."Our results demonstrated that COL5A2 was correlated with poor clinical outcomes and survivals of patients with bladder cancer, suggesting that it could be regarded as a biomarker of bladder cancer.