Targeted Sequencing of Lung Function Loci in Chronic Obstructive Pulmonary Disease Cases and Controls

Assessing the contribution of rare genetic variants to phenotypes of chronic obstructive pulmonary disease using whole-genome sequence data

RATIONALE

Genetic variation has a substantial contribution to chronic obstructive pulmonary disease (COPD) and lung function measurements. Heritability estimates using genome-wide genotyping data can be biased if analyses do not appropriately account for the nonuniform distribution of genetic effects across the allele frequency and linkage disequilibrium (LD) spectrum. In addition, the contribution of rare variants has been unclear.

OBJECTIVES

We sought to assess the heritability of COPD and lung function using whole-genome sequence data from the Trans-Omics for Precision Medicine program.

METHODS

Using the genome-based restricted maximum likelihood method, we partitioned the genome into bins based on minor allele frequency and LD scores and estimated heritability of COPD, FEV1% predicted and FEV1/FVC ratio in 11 051 European ancestry and 5853 African-American participants.

MEASUREMENTS AND MAIN RESULTS

In European ancestry participants, the estimated heritability of COPD, FEV1% predicted and FEV1/FVC ratio were 35.5%, 55.6% and 32.5%, of which 18.8%, 19.7%, 17.8% were from common variants, and 16.6%, 35.8%, and 14.6% were from rare variants. These estimates had wide confidence intervals, with common variants and some sets of rare variants showing a statistically significant contribution (P-value < 0.05). In African-Americans, common variant heritability was similar to European ancestry participants, but lower sample size precluded calculation of rare variant heritability.

CONCLUSIONS

Our study provides updated and unbiased estimates of heritability for COPD and lung function, and suggests an important contribution of rare variants. Larger studies of more diverse ancestry will improve accuracy of these estimates.

Integrative weighted molecular network construction from transcriptomics and genome wide association data to identify shared genetic biomarkers for COPD and lung cancer

Chronic obstructive pulmonary disease (COPD) is a multifactorial progressive airflow obstruction in the lungs, accounting for high morbidity and mortality across the world. This study aims to identify potential COPD blood-based biomarkers by analyzing the dysregulated gene expression patterns in blood and lung tissues with the help of robust computational approaches. The microarray gene expression datasets from blood (136 COPD and 6 controls) and lung tissues (16 COPD and 19 controls) were analyzed to detect shared differentially expressed genes (DEGs). Then these DEGs were used to construct COPD protein network-clusters and functionally enrich them against gene ontology annotation terms. The hub genes in the COPD network clusters were then queried in GWAS catalog and in several cancer expression databases to explore their pathogenic roles in lung cancers. The comparison of blood and lung tissue datasets revealed 63 shared DEGs. Of these DEGs, 12 COPD hub gene-network clusters (SREK1, TMEM67, IRAK2, MECOM, ASB4, C1QTNF2, CDC42BPA, DPF3, DET1, CCDC74B, KHK, and DDX3Y) connected to dysregulations of protein degradation, inflammatory cytokine production, airway remodeling, and immune cell activity were prioritized with the help of protein interactome and functional enrichment analysis. Interestingly, IRAK2 and MECOM hub genes from these COPD network clusters are known for their involvement in different pulmonary diseases. Additional COPD hub genes like SREK1, TMEM67, CDC42BPA, DPF3, and ASB4 were identified as prognostic markers in lung cancer, which is reported in 1% of COPD patients. This study identified 12 gene network- clusters as potential blood based genetic biomarkers for COPD diagnosis and prognosis.

Heritability Analyses Uncover Shared Genetic Effects of Lung Function and Change over Time

Genetic influence on lung functions has been identified in previous studies; however, the relative longitudinal effects of genetic factors and their interactions with smoking on lung function remain unclear. Here, we identified the longitudinal effects of genetic variants on lung function by determining single nucleotide polymorphism (SNP) heritability and genetic correlations, and by analyzing interactions with smoking. Subject-specific means and annual change rates were calculated for eight spirometric measures obtained from 6622 Korean adults aged 40−69 years every two years for 14 years, and their heritabilities were estimated separately. Statistically significant (p < 0.05) heritability for the subject-specific means of all spirometric measures (8~32%) and change rates of forced expiratory volume in 1 s to forced vital capacity ratio (FEV1/FVC; 16%) and post-bronchodilator FEV1/FVC (17%) were detected. Significant genetic correlations of the change rate with the subject-specific mean were observed for FEV1/FVC (ρg = 0.64) and post-bronchodilator FEV1/FVC (ρg = 0.47). Furthermore, post-bronchodilator FEV1/FVC showed significant heritability of SNP-by-smoking interaction (hGXS2 = 0.4) for the annual change rate. The GWAS also detected genome-wide significant SNPs for FEV1 (rs4793538), FEV1/FVC (rs2704589, rs62201158, and rs9391733), and post-bronchodilator FEV1/FVC (rs2445936). We found statistically significant evidence of heritability role on the change in lung function, and this was shared with the effects on cross-sectional measurements. We also found some evidence of interaction with smoking for the change of lung function.

A large-scale genome-wide association analysis of lung function in the Chinese population identifies novel loci and highlights shared genetic aetiology with obesity

BACKGROUND

Lung function is a heritable complex phenotype with obesity being one of its important risk factors. However, knowledge of their shared genetic basis is limited. Most genome-wide association studies (GWASs) for lung function have been based on European populations, limiting the generalisability across populations. Large-scale lung function GWASs in other populations are lacking.

METHODS

We included 100 285 subjects from the China Kadoorie Biobank (CKB). To identify novel loci for lung function, single-trait GWAS analyses were performed on forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC) and FEV1/FVC in the CKB. We then performed genome-wide cross-trait analysis between lung function and obesity traits (body mass index (BMI), BMI-adjusted waist-to-hip ratio and BMI-adjusted waist circumference) to investigate the shared genetic effects in the CKB. Finally, polygenic risk scores (PRSs) of lung function were developed in the CKB and their interaction with BMI's association on lung function were examined. We also conducted cross-trait analysis in parallel with the CKB using up to 457 756 subjects from the UK Biobank (UKB) for replication and investigation of ancestry-specific effects.

RESULTS

We identified nine genome-wide significant novel loci for FEV1, six for FVC and three for FEV1/FVC in the CKB. FEV1 and FVC showed significant negative genetic correlation with obesity traits in both the CKB and UKB. Genetic loci shared between lung function and obesity traits highlighted important biological pathways, including cell proliferation, embryo, skeletal and tissue development, and regulation of gene expression. Mendelian randomisation analysis suggested significant negative causal effects of BMI on FEV1 and on FVC in both the CKB and UKB. Lung function PRSs significantly modified the effect of change in BMI on change in lung function during an average follow-up of 8 years.

CONCLUSION

This large-scale GWAS of lung function identified novel loci and shared genetic aetiology between lung function and obesity. Change in BMI might affect change in lung function differently according to a subject's polygenic background. These findings may open new avenues for the development of molecular-targeted therapies for obesity and lung function improvement.

A genome wide association study for lung function in the Korean population using an exome array

BACKGROUND/AIMS

Lung function is an objective indicator of diagnosis and prognosis of respiratory diseases. Many common genetic variants have been associated with lung function in multiple ethnic populations. We looked for coding variants associated with forced expiratory volume in 1 second (FEV1) and FEV1/forced vital capacity (FVC) in the Korean general population.

METHODS

We carried out exome array analysis and lung function measurements of the FEV1 and FEV1/FVC in 7,524 individuals of the Korean population. We evaluated single variants with minor allele frequency greater than 0.5%. We performed look-ups for candidate coding variants associations in the UK Biobank, SpiroMeta, and CHARGE consortia.

RESULTS

We identified coding variants in the SMIM29 (C6orf1) (p = 1.2 × 10-5) and HMGA1 locus on chromosome 6p21, the GIT2 (p = 6.5 × 10-5) locus on chromosome 12q24, and the ARHGEF40 (p = 9.9 × 10-5) locus on chromosome 14q11 as having a significant association with lung function (FEV1). We also confirmed a previously reported association with lung function and chronic obstructive pulmonary disease in the FAM13A (p = 4.54 × 10-6) locus on chromosome 4q22, in TNXB (p = 1.30 × 10-6) and in AGER (p = 1.09 × 10-8) locus on chromosome 6p21.

CONCLUSION

Our exome array analysis identified that several protein coding variants were associated with lung function in the Korean population. Common coding variants in SMIM29 (C6orf1), HMGA1, GIT2, FAM13A, TNXB, AGER and low-frequency variant in ARHGEF40 potentially affect lung function, which warrant further study.

Omics studies for comprehensive understanding of immunoglobulin A nephropathy: state-of-the-art and future directions

Immunoglobulin A nephropathy (IgAN) is the most common worldwide primary glomerulonephritis with a strong autoimmune component. The disease shows variability in both clinical phenotypes and endpoints and can be potentially subdivided into more homogeneous subtypes through the identification of specific molecular biomarkers. This review focuses on the role of omics in driving the identification of potential molecular subtypes of the disease through the integration of multilevel data from genomics, transcriptomics, epigenomics, proteomics and metabolomics. First, the identification of molecular biomarkers, including mapping of the full spectrum of common and rare IgAN risk alleles, could permit a more precise stratification of IgAN patients. Second, the analysis of transcriptomic patterns and their modulation by epigenetic factors like microRNAs has the potential to increase our understanding in the pathogenic mechanisms of the disease. Third, the specificity of urinary proteomic and metabolomic signatures and the understanding of their functional relevance may contribute to the development of new non-invasive biomarkers for a better molecular characterization of the renal damage and its follow-up. All these approaches can give information for targeted therapeutic decisions and will support novel clinical decision making. In conclusion, we offer a framework of omic studies and outline barriers and potential solutions that should be used for improving the diagnosis and treatment of the disease. The ongoing decade is exploiting novel high-throughput molecular technologies and computational analyses for improving the diagnosis (precision nephrology) and treatment (personalized therapy) of the IgAN subtypes.

Whole-Genome Sequencing in Severe Chronic Obstructive Pulmonary Disease

Genome-wide association studies have identified common variants associated with chronic obstructive pulmonary disease (COPD). Whole-genome sequencing (WGS) offers comprehensive coverage of the entire genome, as compared with genotyping arrays or exome sequencing. We hypothesized that WGS in subjects with severe COPD and smoking control subjects with normal pulmonary function would allow us to identify novel genetic determinants of COPD. We sequenced 821 patients with severe COPD and 973 control subjects from the COPDGene and Boston Early-Onset COPD studies, including both non-Hispanic white and African American individuals. We performed single-variant and grouped-variant analyses, and in addition, we assessed the overlap of variants between sequencing- and array-based imputation. Our most significantly associated variant was in a known region near HHIP (combined P = 1.6 × 10^-9); additional variants approaching genome-wide significance included previously described regions in CHRNA5, TNS1, and SERPINA6/SERPINA1 (the latter in African American individuals). None of our associations were clearly driven by rare variants, and we found minimal evidence of replication of genes identified by previously reported smaller sequencing studies. With WGS, we identified more than 20 million new variants, not seen with imputation, including more than 10,000 of potential importance in previously identified COPD genome-wide association study regions. WGS in severe COPD identifies a large number of potentially important functional variants, with the strongest associations being in known COPD risk loci, including HHIP and SERPINA1. Larger sample sizes will be needed to identify associated variants in novel regions of the genome.