Genome-wide association analyses for lung function and chronic obstructive pulmonary disease identify new loci and potential druggable targets

Chronic obstructive pulmonary disease (COPD) is characterized by reduced lung function and is the third leading cause of death globally. Through genome-wide association discovery in 48,943 individuals, selected from extremes of the lung function distribution in UK Biobank, and follow-up in 95,375 individuals, we increased the yield of independent signals for lung function from 54 to 97. A genetic risk score was associated with COPD susceptibility (odds ratio per 1 s.d. of the risk score (∼6 alleles) (95% confidence interval) = 1.24 (1.20-1.27), P = 5.05 × 10‾⁴⁹), and we observed a 3.7-fold difference in COPD risk between individuals in the highest and lowest genetic risk score deciles in UK Biobank. The 97 signals show enrichment in genes for development, elastic fibers and epigenetic regulation pathways. We highlight targets for drugs and compounds in development for COPD and asthma (genes in the inositol phosphate metabolism pathway and CHRM3) and describe targets for potential drug repositioning from other clinical indications.This work was funded by a Medical Research Council (MRC) strategic award to M.D.T., I.P.H., D.S. and L.V.W. (MC_PC_12010). This research has been conducted using the UK Biobank Resource under application 648. This article presents independent research funded partially by the National Institute for Health Research (NIHR). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the UK Department of Health. This research used the ALICE and SPECTRE High-Performance Computing Facilities at the University of Leicester. Additional acknowledgments and funding details can be found in the Supplementary Note

Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk

Lung-function impairment underlies chronic obstructive pulmonary disease (COPD) and predicts mortality. In the largest multi-ancestry genome-wide association meta-analysis of lung function to date, comprising 580,869 participants, we identified 1,020 independent association signals implicating 559 genes supported by ≥2 criteria from a systematic variant-to-gene mapping framework. These genes were enriched in 29 pathways. Individual variants showed heterogeneity across ancestries, age and smoking groups, and collectively as a genetic risk score showed strong association with COPD across ancestry groups. We undertook phenome-wide association studies for selected associated variants as well as trait and pathway-specific genetic risk scores to infer possible consequences of intervening in pathways underlying lung function. We highlight new putative causal variants, genes, proteins and pathways, including those targeted by existing drugs. These findings bring us closer to understanding the mechanisms underlying lung function and COPD, and should inform functional genomics experiments and potentially future COPD therapies

Evaluating heart rate variability with 10 second multichannel electrocardiograms in a large population-based sample

Introduction Heart rate variability (HRV), defined as the variability of consecutive heart beats, is an important biomarker for dysregulations of the autonomic nervous system (ANS) and is associated with the development, course, and outcome of a variety of mental and physical health problems. While guidelines recommend using 5 min electrocardiograms (ECG), recent studies showed that 10 s might be sufficient for deriving vagal-mediated HRV. However, the validity and applicability of this approach for risk prediction in epidemiological studies is currently unclear to be used. Methods This study evaluates vagal-mediated HRV with ultra-short HRV (usHRV) based on 10 s multichannel ECG recordings of N = 4,245 and N = 2,392 participants of the Study of Health in Pomerania (SHIP) from two waves of the SHIP-TREND cohort, additionally divided into a healthy and health-impaired subgroup. Association of usHRV with HRV derived from long-term ECG recordings (polysomnography: 5 min before falling asleep [N = 1,041]; orthostatic testing: 5 min of rest before probing an orthostatic reaction [N = 1,676]) and their validity with respect to demographic variables and depressive symptoms were investigated. Results High correlations (r = .52–.75) were revealed between usHRV and HRV. While controlling for covariates, usHRV was the strongest predictor for HRV. Furthermore, the associations of usHRV and HRV with age, sex, obesity, and depressive symptoms were similar. Conclusion This study provides evidence that usHRV derived from 10 s ECG might function as a proxy of vagal-mediated HRV with similar characteristics. This allows the investigation of ANS dysregulation with ECGs that are routinely performed in epidemiological studies to identify protective and risk factors for various mental and physical health problems

Body surface scan anthropometrics are related to cardiorespiratory fitness in the general population

The assessment of cardiorespiratory fitness (CRF) is an important tool for prognosis evaluation of cardiovascular events. The gold standard to measure CRF is cardiopulmonary exercise testing (CPET) to determine peak oxygen uptake (VO2peak). However, CPET is not only time consuming but also expensive and is therefore not widely applicable in daily practice. The aim of our study was to analyze, whether and which anthropometric markers derived from a 3D body scanner were related to VO2peak in a general population-based study. We analyzed data (SHIP-START-3) from 3D body scanner and CPET of 1035 subjects (529 women; 51.1%, age range 36–93). A total of 164 anthropometric markers were detected with the 3D body scanner VITUS Smart XXL using the software AnthroScan Professional. Anthropometric measurements were standardized and associated with CRF by sex-stratified linear regression models adjusted for age and height. Anthropometric markers were ranked according to the  − log- p values derived from these regression models. In men a greater left and right thigh-knee-ratio, a longer forearm-fingertip length, a greater left thigh circumference and greater left upper arm circumference were most strongly associated with a higher VO2peak. In women a greater left and right thigh circumference, left calf circumference, thigh thickness and right calf circumference were most strongly associated with a higher VO2peak. The detected VO2peak-related anthropometric markers could be helpful in assessing CRF in clinical routine. Commonly used anthropometric markers, e.g. waist and hip circumference, were not among the markers associated with VO2peak

Author Correction:New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries

Correction to: Nature Genetics https://doi.org/10.1038/s41588-018-0321-7, published online 25 February 2019

Author Correction:New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries (Nature Genetics, (2019), 51, 3, (481-493), 10.1038/s41588-018-0321-7)

Correction to: Nature Geneticshttps://doi.org/10.1038/s41588-018-0321-7, published online 25 February 2019. In the version of the article initially published, unconsented individuals were erroneously included in SPIROMICS consortium results. The analysis has now been repeated with the unconsented individuals removed. The change in the results does not affect the conclusions in the paper. The corrections required to the paper are as follows: In the third paragraph of the “Association with FEV1/FVC and COPD in multiple ancestries” section: “(n = 6,979 cases and 3,915 controls)”, should be “(n = 6,964 cases and 3,904 controls)” and “P = 2.87 × 10–75” should be “P = 2.21 × 10–75”. In the fourth paragraph of the “Association with FEV1/FVC and COPD in multiple ancestries” section: “4.73 (95% CI: [3.79, 5.90]), P = 3.00 × 10−43”, should be “4.71 (95% CI: [3.77, 5.87]), P = 7.24 × 10−43”. In the Fig. 3b table, the SPIROMICS row: “1.54, 1.38, 1.72, 4.47 × 10–14, 988, 537”, should be “1.55, 1.39, 1.74, 6.80 × 10–14, 973, 526”; and the Meta-analysis row: “1.55, 1.48, 1.62, 1.48 × 10–75, 6,979, 3,915”, should be “1.55, 1.48, 1.62, 2.21 × 10–75, 6,964, 3,904”. In the final paragraph of the Discussion: “The 279-variant GRS we constructed was associated with a 4.73-fold increased relative risk…”, should be “The 279-variant GRS we constructed was associated with a 4.71-fold increased relative risk…” In the fifth paragraph of the “Effect of genetic risk score on COPD susceptibility in multiple ancestries” section in the Methods: “SPIROMICS (988 cases, 537 controls)”, should be “SPIROMICS (973 cases, 526 controls)”. In the third paragraph of the “Association with FEV1/FVC and COPD in multiple ancestries” section: “(n = 6,979 cases and 3,915 controls)”, should be “(n = 6,964 cases and 3,904 controls)” and “P = 2.87 × 10–75” should be “P = 2.21 × 10–75”. In the fourth paragraph of the “Association with FEV1/FVC and COPD in multiple ancestries” section: “4.73 (95% CI: [3.79, 5.90]), P = 3.00 × 10−43”, should be “4.71 (95% CI: [3.77, 5.87]), P = 7.24 × 10−43”. In the Fig. 3b table, the SPIROMICS row: “1.54, 1.38, 1.72, 4.47 × 10–14, 988, 537”, should be “1.55, 1.39, 1.74, 6.80 × 10–14, 973, 526”; and the Meta-analysis row: “1.55, 1.48, 1.62, 1.48 × 10–75, 6,979, 3,915”, should be “1.55, 1.48, 1.62, 2.21 × 10–75, 6,964, 3,904”. In the final paragraph of the Discussion: “The 279-variant GRS we constructed was associated with a 4.73-fold increased relative risk…”, should be “The 279-variant GRS we constructed was associated with a 4.71-fold increased relative risk…” In the fifth paragraph of the “Effect of genetic risk score on COPD susceptibility in multiple ancestries” section in the Methods: “SPIROMICS (988 cases, 537 controls)”, should be “SPIROMICS (973 cases, 526 controls)”. The correction is due to 26 unconsented SPIROMICS samples being originally included in the analysis. The analyses that previously included these samples have been rerun with data from these 26 samples removed. Supplementary Information accompanies the online version of this amendment and includes: Updated Supplementary Text and Figures wherein we have changed: On page 23 (description of SPIROMICS cohort) the number of COPD cases has been changed from 988 to 973 and controls from 537 to 526. Supplementary Figure 9 – the forest plots have been updated for the new results for association with 279 variants after reanalysis of SPIROMICS. Supplementary Table 20 – the demographics for SPIROMICS have been updated. Supplementary Table 21 – the results rows for the SPIROMICS and “Meta-analysis of 5 European-ancestry study groups” have been updated. Supplementary Table 22 – The “Meta-analysis of 5 European cohorts” columns have been updated after SPIROMICS reanalysis. Updated Supplementary Tables wherein we have changed: Supplementary Table 29 – columns X–Z (“Meta-analysis of 5 external European-ancestry COPD cohorts (Cases = 6,964; Controls = 3,904)”) after reanalysis of SPIROMICS data. Updated Supplementary Text and Figures wherein we have changed: On page 23 (description of SPIROMICS cohort) the number of COPD cases has been changed from 988 to 973 and controls from 537 to 526. Supplementary Figure 9 – the forest plots have been updated for the new results for association with 279 variants after reanalysis of SPIROMICS. Supplementary Table 20 – the demographics for SPIROMICS have been updated. Supplementary Table 21 – the results rows for the SPIROMICS and “Meta-analysis of 5 European-ancestry study groups” have been updated. Supplementary Table 22 – The “Meta-analysis of 5 European cohorts” columns have been updated after SPIROMICS reanalysis. Updated Supplementary Tables wherein we have changed: Supplementary Table 29 – columns X–Z (“Meta-analysis of 5 external European-ancestry COPD cohorts (Cases = 6,964; Controls = 3,904)”) after reanalysis of SPIROMICS data

New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries

Abstract Reduced lung function predicts mortality and is key to the diagnosis of chronic obstructive pulmonary disease (COPD). In a genome-wide association study in 400,102 individuals of European ancestry, we define 279 lung function signals, 139 of which are new. In combination, these variants strongly predict COPD in independent populations. Furthermore, the combined effect of these variants showed generalizability across smokers and never smokers, and across ancestral groups. We highlight biological pathways, known and potential drug targets for COPD and, in phenome-wide association studies, autoimmune-related and other pleiotropic effects of lung function–associated variants. This new genetic evidence has potential to improve future preventive and therapeutic strategies for COPD

Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk

Funder: British Lung Foundation (BLF); doi: https://doi.org/10.13039/501100000351Funder: DH | National Institute for Health Research (NIHR); doi: https://doi.org/10.13039/501100000272Lung-function impairment underlies chronic obstructive pulmonary disease (COPD) and predicts mortality. In the largest multi-ancestry genome-wide association meta-analysis of lung function to date, comprising 588,452 participants, we identified 1,020 independent association signals implicating 559 genes supported by ≥2 criteria from a systematic variant-to-gene mapping framework. These genes were enriched in 29 pathways. Individual variants showed heterogeneity across ancestries, age and smoking groups, and collectively as a genetic risk score showed strong association with COPD across ancestry groups. We undertook phenome-wide association studies for selected associated variants as well as trait and pathway-specific genetic risk scores to infer possible consequences of intervening in pathways underlying lung function. We highlight new putative causal variants, genes, proteins and pathways, including those targeted by existing drugs. These findings bring us closer to understanding the mechanisms underlying lung function and COPD, and should inform functional genomics experiments and potentially future COPD therapies

Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk

Funder: British Lung Foundation (BLF); doi: https://doi.org/10.13039/501100000351Funder: DH | National Institute for Health Research (NIHR); doi: https://doi.org/10.13039/501100000272Lung-function impairment underlies chronic obstructive pulmonary disease (COPD) and predicts mortality. In the largest multi-ancestry genome-wide association meta-analysis of lung function to date, comprising 588,452 participants, we identified 1,020 independent association signals implicating 559 genes supported by ≥2 criteria from a systematic variant-to-gene mapping framework. These genes were enriched in 29 pathways. Individual variants showed heterogeneity across ancestries, age and smoking groups, and collectively as a genetic risk score showed strong association with COPD across ancestry groups. We undertook phenome-wide association studies for selected associated variants as well as trait and pathway-specific genetic risk scores to infer possible consequences of intervening in pathways underlying lung function. We highlight new putative causal variants, genes, proteins and pathways, including those targeted by existing drugs. These findings bring us closer to understanding the mechanisms underlying lung function and COPD, and should inform functional genomics experiments and potentially future COPD therapies