A cross-tissue transcriptome-wide association study identifies novel susceptibility genes for lung cancer in Chinese populations

Identification of genetically predicted DNA methylation markers associated with non-small cell lung cancer risk among 34,964 cases and 448,579 controls

BACKGROUND

Although the associations between genetic variations and lung cancer risk have been explored, the epigenetic consequences of DNA methylation in lung cancer development are largely unknown. Here, the genetically predicted DNA methylation markers associated with non-small cell lung cancer (NSCLC) risk by a two-stage case-control design were investigated.

METHODS

The genetic prediction models for methylation levels based on genetic and methylation data of 1595 subjects from the Framingham Heart Study were established. The prediction models were applied to a fixed-effect meta-analysis of screening data sets with 27,120 NSCLC cases and 27,355 controls to identify the methylation markers, which were then replicated in independent data sets with 7844 lung cancer cases and 421,224 controls. Also performed was a multi-omics functional annotation for the identified CpGs by integrating genomics, epigenomics, and transcriptomics and investigation of the potential regulation pathways.

RESULTS

Of the 29,894 CpG sites passing the quality control, 39 CpGs associated with NSCLC risk (Bonferroni-corrected p ≤ 1.67 × 10-6 ) were originally identified. Of these, 16 CpGs remained significant in the validation stage (Bonferroni-corrected p ≤ 1.28 × 10-3 ), including four novel CpGs. Multi-omics functional annotation showed nine of 16 CpGs were potentially functional biomarkers for NSCLC risk. Thirty-five genes within a 1-Mb window of 12 CpGs that might be involved in regulatory pathways of NSCLC risk were identified.

CONCLUSIONS

Sixteen promising DNA methylation markers associated with NSCLC were identified. Changes of the methylation level at these CpGs might influence the development of NSCLC by regulating the expression of genes nearby.

PLAIN LANGUAGE SUMMARY

The epigenetic consequences of DNA methylation in lung cancer development are still largely unknown. This study used summary data of large-scale genome-wide association studies to investigate the associations between genetically predicted levels of methylation biomarkers and non-small cell lung cancer risk at the first time. This study looked at how well larotrectinib worked in adult patients with sarcomas caused by TRK fusion proteins. These findings will provide a unique insight into the epigenetic susceptibility mechanisms of lung cancer.

Integrating genomics and proteomics data to identify candidate plasma biomarkers for lung cancer risk among European descendants

BACKGROUND

Plasma proteins are potential biomarkers for complex diseases. We aimed to identify plasma protein biomarkers for lung cancer.

METHODS

We investigated genetically predicted plasma levels of 1130 proteins in association with lung cancer risk among 29,266 cases and 56,450 controls of European descent. For proteins significantly associated with lung cancer risk, we evaluated associations of genetically predicted expression of their coding genes with the risk of lung cancer.

RESULTS

Nine proteins were identified with genetically predicted plasma levels significantly associated with overall lung cancer risk at a false discovery rate (FDR) of <0.05. Proteins C2, MICA, AIF1, and CTSH were associated with increased lung cancer risk, while proteins SFTPB, HLA-DQA2, MICB, NRP1, and GMFG were associated with decreased lung cancer risk. Stratified analyses by histological types revealed the cross-subtype consistency of these nine associations and identified an additional protein, ICAM5, significantly associated with lung adenocarcinoma risk (FDR < 0.05). Coding genes of NRP1 and ICAM5 proteins are located at two loci that have never been reported by previous GWAS. Genetically predicted blood levels of genes C2, AIF1, and CTSH were associated with lung cancer risk, in directions consistent with those shown in protein-level analyses.

CONCLUSION

Identification of novel plasma protein biomarkers provided new insights into the biology of lung cancer.

Context-aware single-cell multiome approach identified cell-type specific lung cancer susceptibility genes

Genome-wide association studies (GWAS) identified over fifty loci associated with lung cancer risk. However, the genetic mechanisms and target genes underlying these loci are largely unknown, as most risk-associated-variants might regulate gene expression in a context-specific manner. Here, we generated a barcode-shared transcriptome and chromatin accessibility map of 117,911 human lung cells from age/sex-matched ever- and never-smokers to profile context-specific gene regulation. Accessible chromatin peak detection identified cell-type-specific candidate cis-regulatory elements (cCREs) from each lung cell type. Colocalization of lung cancer candidate causal variants (CCVs) with these cCREs prioritized the variants for 68% of the GWAS loci, a subset of which was also supported by transcription factor abundance and footprinting. cCRE colocalization and single-cell based trait relevance score nominated epithelial and immune cells as the main cell groups contributing to lung cancer susceptibility. Notably, cCREs of rare proliferating epithelial cell types, such as AT2-proliferating (0.13%) and basal cells (1.8%), overlapped with CCVs, including those in TERT. A multi-level cCRE-gene linking system identified candidate susceptibility genes from 57% of lung cancer loci, including those not detected in tissue- or cell-line-based approaches. cCRE-gene linkage uncovered that adjacent genes expressed in different cell types are correlated with distinct subsets of coinherited CCVs, including JAML and MPZL3 at the 11q23.3 locus. Our data revealed the cell types and contexts where the lung cancer susceptibility genes are functional.

Genome- and Transcriptome-Wide Association Studies Identify Susceptibility Genes and Pathways for Periodontitis

Several genes associated with periodontitis have been identified through genome-wide association studies (GWAS); however, known genes only explain a minority of the estimated heritability. We aimed to explore more susceptibility genes and the underlying mechanisms of periodontitis. Firstly, a genome-wide meta-analysis of 38,532 patients and 316,185 healthy controls was performed. Then, cross- and single-tissue transcriptome-wide association studies (TWAS) were conducted based on GWAS summary statistics and the Genotype-Tissue Expression (GTEx) project. Risk genes were evaluated to determine if they were differentially expressed in periodontitis sites compared with unaffected sites using public datasets. Finally, gene co-expression network analysis was conducted to identify the functional biology of the susceptible genes. A total of eight single nucleotide polymorphisms (SNPs) within the introns of lncRNA LINC02141 approached genome-wide significance after meta-analysis. EZH1 was identified as a novel susceptibility gene for periodontitis by TWAS and was significantly upregulated in periodontitis-affected gingival tissues. EZH1 co-expression genes were greatly enriched in the cell-substrate junction, focal adhesion and other important pathways. Our findings may offer a fundamental clue for comprehending the genetic mechanisms of periodontitis.

Novel insight into the aetiology of rheumatoid arthritis gained by a cross-tissue transcriptome-wide association study

BACKGROUND

Although genome-wide association studies (GWASs) have identified more than 100 loci associated with rheumatoid arthritis (RA) susceptibility, the causal genes and biological mechanisms remain largely unknown.

METHODS

A cross-tissue transcriptome-wide association study (TWAS) using the unified test for molecular signaturestool was performed to integrate GWAS summary statistics from 58 284 individuals (14 361 RA cases and 43 923 controls) with gene-expression matrix in the Genotype-Tissue Expression project. Subsequently, a single tissue by using FUSION software was conducted to validate the significant associations. We also compared the TWAS with different gene-based methodologies, including Summary Data Based Mendelian Randomization (SMR) and Multimarker Analysis of Genomic Annotation (MAGMA). Further in silico analyses (conditional and joint analysis, differential expression analysis and gene-set enrichment analysis) were used to deepen our understanding of genetic architecture and comorbidity aetiology of RA.

RESULTS

We identified a total of 47 significant candidate genes for RA in both cross-tissue and single-tissue test after multiple testing correction, of which 40 TWAS-identified genes were verified by SMR or MAGMA. Among them, 13 genes were situated outside of previously reported significant loci by RA GWAS. Both TWAS-based and MAGMA-based enrichment analyses illustrated the shared genetic determinants among autoimmune thyroid disease, asthma, type I diabetes mellitus and RA.

CONCLUSION

Our study unveils 13 new candidate genes whose predicted expression is associated with risk of RA, providing new insights into the underlying genetic architecture of RA.

Functional studies of lung cancer GWAS beyond association

Fourteen years after the first genome-wide association study (GWAS) of lung cancer was published, approximately forty-five genomic loci have now been significantly associated with lung cancer risk. While functional characterization was performed for several of these loci, a comprehensive summary of current molecular understanding of lung cancer risk has been lacking. Further, many novel computational and experimental tools now became available to accelerate the functional assessment of disease-associated variants, moving beyond locus-by-locus approaches. In this review, we first highlight the heterogeneity of lung cancer GWAS findings across histological subtypes, ancestries, and smoking status, which poses unique challenges to follow-up studies. We then summarize the published lung cancer post-GWAS studies for each risk-associated locus to assess the current understanding of biological mechanisms beyond the initial statistical association. We further summarize strategies for GWAS functional follow-up studies considering cutting-edge functional genomics tools and providing a catalog of available resources relevant to lung cancer. Overall, we aim to highlight the importance of integrating computational and experimental approaches to draw biological insights from the lung cancer GWAS results beyond association.

Identifying and Exploring the Candidate Susceptibility Genes of Cirrhosis Using the Multi-Tissue Transcriptome-Wide Association Study

Objective: We identify and explore the candidate susceptibility genes for cirrhosis and their underlying biological mechanism.

Methods: We downloaded the genome-wide association studies summary data of 901 cirrhosis cases and 451,363 controls and integrated them with reference models of five potential tissues from the Genotype-Tissue Expression (GTEx) Project, including whole blood, liver, pancreas, spleen, and thyroid, to identify genes whose expression is predicted to be associated with cirrhosis. Then, we downloaded gene expression data of individuals with hepatocellular carcinoma from TCGA database to conduct differential expression analysis to validate these identified genes and explored their possible role in driving cirrhosis via functional enrichment and gene set enrichment analysis (GSEA).

Results: We identified 10 significant genes (SKIV2L, JPH4, UQCC2, RP11-91I8.3, MAU2, ERAP1, PUS3, ZNF677, ARHGAP40, and SHANK3) associated with cirrhosis at a Bonferroni-corrected threshold of p < 0.01, among which two (SKIV2L and JPH4) were identified in the liver and five (SKIV2L, JPH4, MAU2, SHANK3, and UQCC2) were validated by differential expression analysis at an FDR-corrected threshold of p < 0.01. The enrichment analysis showed that the degradation process of RNA, which is enriched by 58 genes, is significantly under-enriched in liver cancer tissues (p = 0.0268).

Conclusion: We have identified several candidate genes for cirrhosis in multiple tissues and performed differential genetic analysis using the liver cancer database to verify the significant genes. We found that the genes SKIV2L and JPH4 identified in the liver are of particular concern. Finally, through enrichment analysis, we speculate that the process of mRNA transcription and RNA degradation may play a role in cirrhosis.