Author response: Genetic association and causal inference converge on hyperglycaemia as a modifiable factor to improve lung function Article Swipe

Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Measures of lung function are heritable, and thus, we sought to utilise genetics to propose drug-repurposing candidates that could improve respiratory outcomes. Lung function measures were found to be genetically correlated with seven druggable biochemical traits, with further evidence of a causal relationship between increased fasting glucose and diminished lung function. Moreover, we developed polygenic scores for lung function specifically within pathways with known drug targets and investigated their relationship with pulmonary phenotypes and gene expression in independent cohorts to prioritise individuals who may benefit from particular drug-repurposing opportunities. A transcriptome-wide association study (TWAS) of lung function was then performed which identified several drug–gene interactions with predicted lung function increasing modes of action. Drugs that regulate blood glucose were uncovered through both polygenic scoring and TWAS methodologies. In summary, we provided genetic justification for a number of novel drug-repurposing opportunities that could improve lung function. eLife digest Chronic respiratory disorders like asthma affect around 600 million people worldwide. Although these illnesses are widespread, they can have several different underlying causes, making them difficult to treat. Drugs that work well on one type of respiratory disorder may be completely ineffective on another. Understanding the biological and environmental factors that cause these illnesses will allow them to be treated more effectively by tailoring therapies to each patient. Reduced lung function is a factor in respiratory disorders and it can have many genetic causes. Studying the genes of patients with reduced lung function can reveal the genes involved, some of which may already be targets of existing drugs for other illnesses. So, could a patient’s genetics be used to repurpose existing drugs to treat their respiratory disorders? Reay et al. combined three methods to link genetics and biological processes to the causes of reduced lung function. The results reveal several factors that could lead to new treatments. In one example, reduced lung function showed a link to genes associated with high blood sugar. As such, treatments used in diabetes might help improve lung function in some patients. Reay et al. also developed a scoring system that could predict the efficacy of a treatment based on a patient’s genetics. The study suggests that COVID-19 infection could be affected by blood sugar levels too. Chronic respiratory disorders are a critical issue worldwide and have proven difficult to treat, but these results suggest a way to identify new therapies and target them to the right patients. The findings also support a connection between lung function and blood sugar levels. This implies that perhaps existing diabetes treatments – including diet and lifestyle changes aimed at reducing or limiting blood sugar – could be repurposed to treat respiratory disorders in some patients. The next step will be to perform clinical trials to test whether these therapies are in fact effective. Introduction Optimal lung (pulmonary) function is vital for the ongoing maintenance of homeostasis, with reduced pulmonary function associated with a marked increase in the risk of mortality (Vasquez et al., 2017; Young et al., 2007). This is particularly critical due to the considerable number of disorders for which diminished pulmonary function is a clinical hallmark. For instance, chronic obstructive pulmonary disease (COPD), characterised by an irreversible limitation of airflow, is one of the leading causes of death worldwide (Quaderi and Hurst, 2018). Pulmonary manifestations are also common amongst disorders not directly classified as respiratory conditions, including diabetes (Pitocco et al., 2012; Walter et al., 2003), congenital heart disease (Alonso-Gonzalez et al., 2013), and inflammatory bowel disease (Ji et al., 2016; Yilmaz et al., 2010). Bacterial and viral infection, such as Streptococcus pneumoniae, Mycobacterium tuberculosis, influenza, and coronaviruses, also cause severe declines in respiratory function. In order to better manage the spectrum of respiratory disorders, there is a desperate need for new interventions, including those that can be targeted to an individual’s heterogeneous risk factors. While the development pathway for new compounds is difficult, there are likely to be opportunities for precision repurposing of existing drugs to enhance lung function and improve patient outcomes. Spirometry measures of pulmonary function have been shown to display significant heritability both in twin designs and genome-wide association studies (GWAS) (Palmer et al., 2001; Ingebrigtsen et al., 2011; Shrine et al., 2019). Genomics may reveal clinically relevant insights into the biology underlying lung function, and thus, could be leveraged for drug repurposing. We sought to interrogate the genomic architecture of three spirometry indices to propose drug-repurposing candidates which could be used to improve lung function: forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), and their ratio (FEV1/FVC). Firstly, we assessed each lung function trait for evidence of genetic correlation with biochemical traits that could be pharmacologically modulated, followed by models to investigate whether there was evidence of causation. The previously developed pharmagenic enrichment score (PES) framework was then implemented to identify druggable pathways enriched with lung function-associated variation and calculate pathway-specific polygenic scores (PGS) to prioritise individuals who may benefit from a repurposed compound which interacts with the pathway (Reay et al., 2020). A transcriptome-wide association study (TWAS) of FEV1 and FVC was also undertaken to reveal genes which could be targeted by existing drugs that may increase pulmonary function. Finally, we considered the repurposing candidates proposed by these strategies in the context of three respiratory viruses (severe acute respiratory syndrome coronavirus 2 [SARS-CoV2], influenza [H1N1], and human adenovirus [HAdV]), specifically analysing the interactions between viral and human proteins. An overview schematic of this study is detailed in Figure 1 and Figure 1—figure supplement 1. Figure 1 with 1 supplement see all Download asset Open asset Overview of strategies for genetically informed drug repurposing to improve lung function. The left flow chart outlines our workflow for using causal inference to identify drug targets, while the right flow chart shows the workflow for functionally partitioning the heritable component into drug targets. In both cases, we utilise or integrate genome-wide association studies (GWAS) data for lung function (including three spirometry phenotypes: forced expiratory volume in 1 s [FEV1], forced vital capacity [FVC], and their ratio [FEV1/FVC]) and quantitative biochemical traits (e.g. hormones and metabolites) which can be pharmacologically modulated. Using this data, we established genetic correlation between lung function and the biochemical traits using linkage disequilibrium score regression (LDSC). We then constructed a latent causal variable (LCV) model to investigate evidence of causality for significantly correlated biochemical–lung function trait pairs. To further support causal inference between significant pairs, we implemented Mendelian randomisation. Where a causal relationship between a modifiable biochemical trait and lung function is established, we can infer a novel treatment. The right flow chart shows the workflow for utilising heritable components for drug repurposing. Specifically, polygenic scores for lung function were calculated using lung function GWAS single nucleotide polymorphisms (SNPs) within biological pathways that can be targeted by approved drugs, rather than a genome-wide score. Individuals with low genetically predicted lung function by a pharmagenic enrichment score (PES) (low PES) relative to a reference population may benefit from a compound which modulates said pathway. To further support putative genetically predicted targets for drug repositioning a transcriptome-wide association study of lung function was performed. Druggable genes for which genetically predicted expression was correlated with a spirometry measure. Genes with positive genetic covariance between imputed expression and lung function (i.e. increased expression associated with increased lung function) could be modulated by an agonist compound, whilst genes for which decreased predicted expression is associated with improved lung function could be targeted by an antagonist compound. Results Measures of lung function were genetically correlated with clinically significant metabolites and hormones We assessed genetic correlation between three pulmonary function measurements (FEV1, FVC, and FEV1/FVC) and 172 GWAS summary statistics of European ancestry using bivariate linkage disequilibrium score regression (LDSC) (Bulik-Sullivan et al., 2015; Zheng et al., 2017). A number of clinically significant traits displayed significant genetic correlation with FEV1, FVC, and/or FEV1/FVC after correcting for the number of tests performed (p<2.9×10−4, Figure 2a, Supplementary file 1a–c). FVC had the largest number of genetic correlations which surpassed Bonferroni correction (N = 35), followed by FEV1 and FEV1/FVC for which 25 and 8 traits survived multiple testing correction, respectively. The trait most significantly correlated with both FEV1 and FVC was waist circumference – FEV1: rg = −0.19, SE = 0.02, p=5.71×10−20; FVC: rg = −0.24, SE = 0.02, p=9.54×10−33 – whilst asthma demonstrated the most significant correlation with FEV1/FVC (rg = −0.35, SE = 0.05, p=3.49×10−12). Figure 2 with 1 supplement see all Download asset Open asset Genome-wide investigation of biochemical traits related to lung function. (a) Heatmap of genetic correlations (rg) between three spirometry measures (forced expiratory volume in 1 s [FEV1], forced vital capacity [FVC], and their ratio [FEV1/FVC]) and a number of European ancestry genome-wide association studies. Genetic correlation estimates were plotted if the trait was significantly correlated with at least one of the lung function traits after Bonferroni correction. Hierarchical clustering was applied to the rows and utilised Pearson’s correlation distance. (b) Latent causal variable models between correlated biochemical traits (selected by linkage disequilibrium score regression) that are potentially drug targets (metabolite or hormone traits) and each measure of lung function. The posterior mean genetic causality proportion (GCP) is plotted, with the error bars representing the upper and lower limits defined by its posterior mean standard error. A positive GCP estimate significantly different than zero indicates partial genetic causality of the biochemical trait on the spirometry measure. Interestingly, there was evidence of genetic correlation between measures of lung function and circulating levels of both metabolites and hormones. This is notable as these molecules can be pharmacologically modulated, potentially informing novel therapeutic strategies and drug-repurposing opportunities to improve lung function. Significant genetic correlations were observed with four metabolites (fasting glucose, high-density lipoprotein [HDL], triglycerides, and urate) and two hormones (fasting insulin and leptin) for at least one measure of lung function (Table 1). Table 1 Significant genetic correlations between lung function measures and metabolite and hormone GWAS. Lung function traitBiochemical traitGenetic correlation (rg)*p-ValueFEV1Fasting insulin−0.23 (0.04)6.61 × 10−8Leptin (BMI unadjusted)−0.25 (0.05)3.74 × 10−7Leptin (BMI adjusted)−0.24 (0.05)9.13 × 10−7Urate−0.12 (0.03)9.46 × 10−6Fasting glucose−0.13 (0.03)1 × 10−4FVCFasting insulin−0.31 (0.04)6.98 × 10−14Leptin (BMI unadjusted)−0.33 (0.05)2.85 × 10−12Leptin (BMI adjusted)−0.27 (0.05)1.21 × 10−8HDL cholesterol0.14 (0.03)9.97 × 10−7Urate−0.12 (0.02)9.54 × 10−7Triglycerides−0.11 (0.03)1.53 × 10−5Fasting glucose−0.12 (0.03)1 × 10−4FEV1/FVCHDL cholesterol−0.11 (0.03)2 × 10−4 *Genetic correlations which survived multiple testing correction for each lung function trait individually are reported with their respective standard error. Evidence of a causal relationship between fasting glucose and lung function supports antihyperglycaemic compounds as drug-repurposing candidates. FEV1: forced expiratory volume in 1 s; FVC: forced vital capacity; FEV1/FVC: ratio of FEV1 to FVC; HDL: high-density lipoprotein; BMI: body mass index; GWAS: genome-wide association studies. The genetic correlations observed between lung function measures and metabolite/hormone traits may be clinically actionable; however, a significant estimate of genetic correlation does not imply causality (O'Connor and Price, 2018). In response, we constructed a latent causal variable (LCV) model to estimate mean posterior genetic causality proportion (GCP^) for each metabolite or hormone trait and the lung function measure with which it is genetically correlated (Figure 2b, Supplementary file 1d). The LCV method assumes that a latent variable mediates the genetic correlation between two traits and tests whether this latent variable displays stronger correlation with either of the traits. We used the recommended threshold for partial genetic causality of |GCP| > 0.6 as this has been demonstrated in simulations to appropriately guard against false positives (O'Connor and Price, 2018). There was strong evidence of partial genetic causality of fasting glucose on FVC: GCP^ = 0.77, SE = 0.15, P H0:GCP = 0 = 1.32 × 10−56. Importantly, the posterior mean GCP estimate for the relationship between fasting glucose and FVC remained strong (|GCP| > 0.6) using a fasting glucose GWAS additionally adjusted for body mass index (BMI): GCP^ = 0.63, SE = 0.22, p=1.67×10−56. The LCV model constructed between fasting glucose and FEV1 did not surpass the threshold of |GCP| > 0.6 we use to designate partial genetic causality; however, it was directionally consistent with the fasting glucose to FVC estimate and closely approaches this threshold, FEV1: GCP^ = 0.57, SE = 0.18, P H0:GCP = 0 = 7.18 × 10−12. A strong posterior GCP estimate was observed for urate and FVC (GCP^=0.73), although the relatively low heritability z score as calculated by the LCV framework (z < 7) may lead to a biased estimate. As a result, the relationship between urate and FVC should be treated with caution and further study would be needed to replicate this finding in a urate GWAS with a more precise heritability estimate. The estimate between HDL cholesterol and FEV1/FVC (GCP^=0.59, SE = 0.26, P H0:GCP = 0 = 4.12 × 10−7) was also close to the GCP threshold but we do not denote this as strong evidence of genetic causality given the 0.6 threshold was not exceeded. There was no strong evidence of genetic causality between any of the remaining LDSC-prioritised hormone or metabolite traits and FEV1, FVC, or FEV1/FVC. As it was the most significant LCV model, the causal effect of fasting glucose on FEV1 and FVC was further investigated utilising a Mendelian randomisation (MR) approach. MR differs from an LCV model as it exploits genome-wide significant variants as genetic instrumental variables (IVs) to calculate a causal estimate of an exposure (fasting glucose) on an outcome (lung function). Given genetic correlation may bias MR due to pleiotropy, we implement MR here as a validation of the LCV results as it uses different set of statistical parameters and assumptions; however, the estimates derived from MR should be viewed cautiously in light genetic correlation which exists between fasting glucose and lung function (O'Connor and Price, 2018). We selected 32 genome-wide significant variants associated with glucose in approximate linkage equilibrium as IVs (p<5×10−8, r2 < 0.001) to ensure that variants were both rigorously associated with the exposure and independent from one another. A 1 mmol/L increase in fasting glucose was associated with a −0.088 (95% confidence interval [CI]: −0.17, −0.01) standard deviation decline in FVC using an inverse variance weighted (IVW) estimator with multiplicative random effects. Similarly, elevated fasting glucose was also shown to have a negative effect on FEV1: βIVW = −0.096 (95% CI: –0.18, –0.01). The IVW estimate for fasting glucose was only nominally significant for both FVC and FEV1 (p=0.033 and 0.023, respectively), with relatively wide confidence intervals to approach zero, and thus, the estimate should be treated with appropriate caution. We implemented a number of sensitivity analyses to test the rigour of our causal estimate of the effect of fasting glucose on lung function (Figure 2—figure supplement 1, Supplementary file 1e–g). Firstly, we obtained an analogous, and statistically significant, causal estimate using the weighted median method (FVC: βWeighted median = −0.09 [95% CI: –0.16, –0.04], p=1.87×10−3, FEV1: βWeighted median = −0.07 [95% CI: –0.13, –0.01], p=0.025). The weighted median method relaxes the assumption that all IVs must be valid, as described elsewhere (Bowden et al., 2016). An MR–Egger model was then constructed, which includes a non-zero intercept term which can be used as a measure of unbalanced pleiotropy (Bowden et al., 2015). The causal estimate using MR–Egger was in the same direction for FEV1 and FVC; however, it was non-significant (FVC: βMR Egger = –0.13 [95% CI: −0.30, 0.04], p=0.148, FEV1: βMR Egger = –0.12 [95% CI: −0.30, 0.06], p=0.21). Importantly, the MR–Egger intercept was not significantly different from zero in the FEV1 or FVC model, indicating no evidence of unbalanced pleiotropy. This was supported by a non-significant global test of pleiotropy implemented as part of the MR-Pleiotropy Residual Sum and Outlier (MR PRESSO) framework (Supplementary file 1f; Verbanck et al., 2018). Furthermore, we evaluated whether there was any evidence of reverse causation, that is, FEV1 and FVC exerting a causal effect on fasting glucose using the MR Steiger directionality test, with our observed direction of causation from glucose to lung function supported. Finally, we successively recalculated the IVW causal estimate for the effect of fasting glucose on FEV1 and FVC by removing one IV at a time in a ‘leave-one-out’ analysis (Supplementary file 1g; Burgess et al., 2017). An analogous causal estimate was derived regardless of which IV was removed; however, there were five IVs (FEV1 model = two outlier single nucleotide polymorphisms (SNPs) , FVC model = four outlier SNPs [two outlier SNPs shared]) for which the estimate was marginally non-significant after exclusion (maximum p=0.11, IVW with multiplicative random effects). We then used a phenome-wide association approach to demonstrate that these five SNPs were (i) annotated to genes with important roles in glycaemic homeostasis and (ii) were almost exclusively associated with glycaemic traits or diabetes (Supplementary file 1g–l). As a result, we concluded that these IVs did not likely represent horizontal pleiotropy, which would bias the causal estimate, but instead were biologically salient IVs with large effects (Supplementary file 1g). Whilst smoking status (ever vs. never smoked) was a covariate in the lung function GWAS, we sought to assess whether the relationship between blood glucose and lung function could be driven by residual effects of smoking. There was a significant genetic correlation between the number of cigarettes smoked per day and fasting glucose (rg = 0.16, SE = 0.043), although this was not observed with the ‘ever vs. never smoked’ phenotype (rg = 0.007, SE = 0.039). However, an LCV model constructed for fasting glucose and cigarettes smoked per day did not indicate evidence of genetic causality in contrast to the glucose/lung function models: GCP^ = −0.47, SE = 0.33, P H0:GCP = 0 = 0.25. The MR IVs for glucose were further checked for association with either ‘ever vs. never smoked’ and ‘cigarettes per day’, with none of the IVs demonstrating any association with either smoking phenotype at a genome-wide (p<5×10−8) or suggestive (p<1×10−5) significance threshold (Supplementary file 1m, n). Moreover, we investigated the possibility that our results may be impacted by collider bias given the lung function GWAS we utilised was phenotypically covaried for smoking status. We leveraged a smaller UK Biobank GWAS of FEV1 and FVC from the Neale Lab that did not adjust for smoking (N = 272,338). The posterior mean GCP and IVW estimates were in the same direction and relatively analogous for both spirometry measures to that observed using the larger GWAS covaried for smoking status, with no apparent evidence that the negative relationship between glucose and lung function was influenced by smoking as a collider variable. In summary, these data suggested that there is an effect of fasting glucose on lung function beyond what is directly attributable to a residual impact of smoking. Implementation of the pharmagenic enrichment score for genetically informed drug repurposing in respiratory distress We aimed to further expand drug-repurposing opportunities for lung function using the PES approach (Reay et al., 2020). Briefly, PES aims to implement genetically informed drug repurposing with PGS calculated using genetic variants specifically within druggable pathways (Figure 3a). In the context of this study, individuals with a depleted PES for lung function genetically predicted lung function) to pathways with known drug targets may specifically benefit from drugs which these Firstly, we performed association of FEV1 and FVC using a of from the at least one gene which is modulated by an approved = The FEV1/FVC phenotype is directly in this given that it is used as a rather than as a quantitative and thus, we on repurposing candidates for FEV1 and FVC were annotated to genes using genomic with both and and Figure with 2 see all Download asset Open asset The pharmagenic enrichment score (PES) framework to identify and implement drug-repurposing candidates for lung function. (a) Overview of the PES polygenic scores of lung function measures are constructed using variants specifically within druggable Individuals with a depleted that is, lower genetically predicted spirometry measures using variants in the may benefit from a drug which modulates the pathway in (b) The number of and drugs with targets in at least one PES per 1 1 is a different with its on the The association between a polygenic score (PGS) of forced vital capacity and an FVC PES which was nominally significant but did not multiple testing correction after for genome-wide The relationship between the and residual FVC in an independent is plotted, with confidence intervals of the regression by Significant correlations between the expression of genes in a PES and three lung function PES and pathways in The relationship between PES and gene expression is as a the is the by standard and the is the with more Genes which are associated after multiple testing correction for the number of genes in the pathway are < or < The an nominally significant association association using the FEV1 and FVC GWAS was undertaken at each with both and a was significant at multiple the most significantly associated was The only one druggable enriched with variants after multiple testing correction < by the – = SE = = (Supplementary file There were no with known drug targets using which survived multiple testing correction < for association with the to more variation uncovered more druggable (Supplementary file Specifically, there were seven and which survived correction for FEV1 and FVC, < 0.05, Table Table 2 with known drug targets enriched with lung function-associated common variation after the of multiple testing correction < pathway × × by the × pathway × in × in × × × × × × of × in × × × is the most significant association all the and FVC: forced vital capacity; FEV1: forced expiratory volume in 1 should be that there were two pathways related to however, as these were from different and had a different number of we considered them A number of biological processes were by these such as in factor in and function For each PES we performed drug to identify approved compounds predicted to the enriched pathway. Firstly, we investigated and with a statistically significant of target genes in each of these < Drugs which target (i) multiple and (ii) more genes than by were to be particularly relevant for a biological pathway. There were such from the PES candidates which survived multiple testing correction enriched with the targets of an compound in of and Supplementary file – notable drugs the antihyperglycaemic compounds (e.g. and (e.g. and and and compound was annotated with its the most common amongst these compounds was and = followed by system = and and = Figure of these compounds was to by a in to effects and evidence as detailed in Supplementary file (Figure supplement 1). drug–gene was undertaken for remaining PES an approved compound with statistically drug–gene interactions with at least two of evidence from (Supplementary file In order to test the of FEV1 and FVC PES we utilised an independent from the = Firstly, we constructed a genome-wide PGS for FEV1 and FVC at different (Supplementary file 3a). The FEV1 genetic score of the variance in FEV1 in the whilst the FVC PGS of variance in of the seven P