Shared genetic aetiology of respiratory diseases: a genome-wide multitraits association analysis

Abstract

Objective: This study aims to explore the common genetic basis between respiratory diseases and to identify shared molecular and biological mechanisms.

Methods: This genome-wide pleiotropic association study uses multiple statistical methods to systematically analyse the shared genetic basis between five respiratory diseases (asthma, chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, lung cancer and snoring) using the largest publicly available genome wide association studies summary statistics. The missions of this study are to evaluate global and local genetic correlations, to identify pleiotropic loci, to elucidate biological pathways at the multiomics level and to explore causal relationships between respiratory diseases. Data were collected from 27 November 2022 to 30 March 2023 and analysed from 14 April 2023 to 13 July 2023.

Main outcomes and measures: The primary outcomes are shared genetic loci, pleiotropic genes, biological pathways and estimates of genetic correlations and causal effects.

Results: Significant genetic correlations were found for 10 paired traits in 5 respiratory diseases. Cross-Phenotype Association identified 12 400 significant potential pleiotropic single-nucleotide polymorphism at 156 independent pleiotropic loci. In addition, multitrait colocalisation analysis identified 15 colocalised loci and a subset of colocalised traits. Gene-based analyses identified 432 potential pleiotropic genes and were further validated at the transcriptome and protein levels. Both pathway enrichment and single-cell enrichment analyses supported the role of the immune system in respiratory diseases. Additionally, five pairs of respiratory diseases have a causal relationship.

Conclusions and relevance: This study reveals the common genetic basis and pleiotropic genes among respiratory diseases. It provides strong evidence for further therapeutic strategies and risk prediction for the phenomenon of respiratory disease comorbidity.

Keywords: Asthma; COPD ÀÜ Mechanisms; Interstitial Fibrosis; Lung Cancer; Sleep apnoea.

Figure 1

Overall study design. We conducted a comprehensive cross-trait analysis of five respiratory diseases from different perspectives. COPD, chronic obstructive pulmonary disease; CPASSOC, Cross Phenotype Association; FUMA, functional mapping and annotation; GWAS, genome-wide association studies; GO, Gene Ontology; HDL, high-definition likelihood; HyPrColoc, hypothesis prioritisation in multitrait colocalisation; IPF, idiopathic pulmonary fibrosis; KEGG, Kyoto Encyclopaedia of Genes and Genomes; LDSC, linkage disequilibrium score regression; LAVA, local analysis of variant association; MAGMA; multimarker analysis of GenoMic annotation; MR, Mendelian randomisation; PWAS, proteome-wide association study; SNP, single-nucleotide polymorphisms; TWAS, transcriptome-wide association study.

Figure 2

Genetic correlation among five respiratory diseases. (A) Global genetic correlations among five respiratory diseases were explored with LDSC and HDL methods. **p adjusted <0.05. (B) Frequency distribution of localised genetic correlations for five respiratory diseases determined by the LAVA method. (C) High consistency of the LDSC and HDL methods for investigating global genetic correlations. (D) Counts of trait pairs with local genetic associations in specific regions of the chromosome. COPD, chronic obstructive pulmonary disease; HDL, high-definition likelihood; IPF, idiopathic pulmonary fibrosis; LC, lung cancer; LDSC, linkage disequilibrium score regression.

Figure 3

Manhattan plot of pleiotropic loci Manhattan plot of pleiotropic loci analysed by the CPASSOC method, with the x-axis denoting chromosomal location and the y-axis denoting the −log10 p value. The horizontal line indicates the genome-wide significance threshold of p=5×10⁻⁸. 156 pleiotropic loci were identified at the genome-wide significance level, of which, 15 were colocalised loci (black dots represent index SNPs of pleiotropic loci and red dots represent index SNPs of colocalised loci). CPASSOC, Cross-Phenotype Association; SNP, single-nucleotide polymorphism.

Figure 4

Biological functional and tissue and single cell-specific enrichment of candidate pleiotropic genes. (A) Top five pathways most significantly enriched for GO and KEGG gene sets. (B) Tissue-specific enrichment analysis using the PCGA (based on GTEx and PanglaoDB) identified top five significantly enriched tissues and single cell (p adjusted <0.05). BP, biological process; CC, cellular component; KEGG, Kyoto Encyclopaedia of Genes and Genomes; GO, Gene Ontology; GTEx, Genotype-Tissue Expression; KEGG, Kyoto Encyclopaedia of Genes and Genomes; MF, molecular function.

Figure 5

A bidirectional causal effect estimated with random effects IVW method. Error bars represent the 95% CI of the corresponding MR estimates. P adjusted, p value after corrected using false discovery rate; P.pleiotropy, the resultant pleiotropy remained significant after sensitivity analysis. COPD, chronic obstructive pulmonary disease; IPF, idiopathic pulmonary fibrosis; IVW, inverse variance weighted; LC, lung cancer.