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. 2025 May 29;20(5):e0320304. doi: 10.1371/journal.pone.0320304

Bidirectional genetic links between chronic obstructive pulmonary disease and frailty: Genome-wide association study insights

Fuhui Yan 1, Tong Wu 2, Qiang Meng 3, Feng Qu 3,*
Editor: Hoh Boon-Peng4
PMCID: PMC12122021  PMID: 40440631

Abstract

Background

Recent research underscores a potential correlation between chronic obstructive pulmonary disease (COPD) and frailty, suggesting a shared genetic foundation. However, specific genetic factors and mechanisms underlying this association remain unclear. This study aimed to explore genetic connections between COPD and frailty using genome-wide association studies to enhance our understanding and improve clinical management and prevention strategies for these conditions.

Method

We utilised summary statistics for genome-wide association studies to examine the genetic correlations between COPD and frailty using linkage disequilibrium score regression. Local genetic correlations were evaluated using the ρ-heritability estimates from summary statistics method. Using the established two-sample Mendelian randomization approach, causal relationships have been identified. Shared genetic variants were quantified using a bivariate causal mixture model. Shared loci and single nucleotide polymorphisms were identified by conjoint false discovery rate (conjFDR). Gene enrichment and transcriptome-wide association studies (TWAS) were conducted to explore potential transcriptomic associations across tissues.

Results

We observed a significant genetic correlation between COPD and frailty (Rg =  0.4324, P =  6.09 ×  10 − 26). MiXeR estimated 3,200-shared causal variants. Additionally, we discovered 16 shared loci linked to 91 genes, offering novel insights into gene expression across diverse tissues. The TWAS revealed 25 shared genes, representing a significant advance in understanding the genetic overlap between COPD and frailty. Furthermore, out of the 25 SNPs identified through TWAS, 4 overlapped with the lead SNPs, specifically [HLA-DRB1, PBX3, SLC22A5/OCTN2, SLMAP].

Conclusions

Our study shows a common genetic foundation for COPD and frailty, identifying multiple shared loci and offering insights into their underlying causal connections. These findings enhance our understanding of the biological mechanisms linking these conditions and may guide future research and treatment strategies for related diseases.

Introduction

Chronic obstructive pulmonary disease (COPD), now the third leading cause of global mortality [1], imposes a significant economic burden. In 2017, chronic respiratory diseases afflicted 549 million individuals, with COPD constituting approximately 55% of these cases [2]. This increase in COPD in recent decades is primarily attributed to population aging and prolonged risk factor exposure, and this trend is expected to persist [3]. Frailty, a prevalent geriatric syndrome, is characterised by diminished physiological reserves, increased susceptibility to injury, and decreased stressor resistance, potentially leading to clinical events triggered by minor external stimuli, with a prevalence among the elderly ranging from 11 to 60.6% [4,5]. Frailty occurrence is closely linked to factors such as inflammation, nutrition, genetics, and endocrine influences [6] involving multiple organ system damage. The co-occurrence of COPD and frailty has gained significant attention lately, with mounting evidence indicating a link between the two [7,8]. Patients with COPD face twice the risk of frailty as their age-matched counterparts without it [9]. Frailty affects chronic lung diseases, and, reciprocally, chronic lung diseases increase frailty risk. Patients with chronic lung conditions demonstrate a greater predisposition to frailty, demonstrating frailty signs years earlier than healthy community-dwelling populations [8]. Certain biological features of chronic lung diseases reflect potential frailty-associated mechanisms, including chronic systemic inflammation, malnutrition, decreased activity, fatigue, muscle weakness, and slower gait speed (with the latter three being phenotypic criteria of frailty) [8], suggesting that these conditions share common pathophysiological roots with pathological, epidemiological, and clinical correlations [8,10] While significant research has highlighted the connections between COPD and frailty, the biological mechanisms underlying their potential comorbid relationship remain elusive.

Twin studies indicate a potential genetic influence on increased frailty risk. Heritability estimates for the syndromic and cumulative deficit frailty index are 25% and 30%, respectively, revealing significant genetic (0.57) and environmental (0.44) correlations between the indices. Monozygotic twins exhibit higher intragroup correlations for both frailty measures than dizygotic twins [11]. Furthermore, 45% of the interindividual variability in the frailty index is genetic, and 52% is attributed to environmental factors. Similarly, consistent evidence has emphasised a genetic influence on COPD onset [12]. Multiple genome-wide association studies (GWAS) have revealed an increasing number of single nucleotide polymorphisms (SNPs) and susceptibility loci as causative genes for COPD and frailty [12–14]. COPD and frailty have been demonstrated as polygenic diseases, wherein their genetic foundations are influenced by several common variants with subtle effects alongside a few rare variants that exert pronounced effects.

Existing randomised controlled trials (RCTs) and numerous cross-sectional studies have elucidated the correlations between COPD and frailty. However, the genetic causality or potential mechanistic links remain unclear, warranting further investigation. 6 Traditional RCTs, although regarded as the gold standard for directly establishing causality, encounter challenges in uncovering the shared genetic basis of common pathophysiologies and complex comorbidities of the conditions. Mendelian randomisation (MR), a method that uses genetic variants as instruments to identify causal relationships between risk factors and disease outcomes, has been employed to uncover the causal link between frailty and COPD. However, MR has certain limitations, such as susceptibility to pleiotropy, potential confounding, and weak instrument bias. Moreover, the potential shared pathological mechanisms between frailty and COPD deserve further attention and investigation. Quantile-quantile (Q-Q) plots—a graphical tool—enable the evaluation of GWAS data quality and reliability of genetic research findings through visualisation of the genetic overlap between two traits. The conjoint false discovery rate (conjFDR) method enhances the identification of shared genetic loci by integrating data from multiple traits, thus reducing false positives and improving robustness.

Furthermore, molecular genetic research methods employed in exploring mechanisms linking COPD and frailty are lacking. Today, leveraging summary statistics from extensive GWAS facilitates examination of the genetic basis of their observed phenotypic association to a certain extent. Employing matched models and algorithms facilitates intuitive prediction of their causal relationship and investigation into their potential shared genetic basis.

This research aims to uncover the genetic links between chronic obstructive pulmonary disease (COPD) and frailty by integrating various advanced algorithms and models, and to elucidate their shared genetic mechanisms. Utilizing the conjFDR method, we identified and confirmed 16 loci with pleiotropic effects across these traits, including 7 newly discovered ones. This advancement will aid future efforts in screening and treating comorbid conditions. Through functional annotation and gene set enrichment analyses, we delineated molecular pathways and human phenotypes related to these loci. Overall, these discoveries enhance our comprehension of the genetic framework of COPD and frailty, offering new perspectives on their comorbid mechanisms and identifying potential susceptibility factors for the frailty-COPD comorbidity.

Methods

Study populations

We integrated datasets from European ancestry individuals, drawing from sources including FinnGen (https://www.finngen.fi/en) [15], UK Biobank, and TwinGene. Data on COPD was collected from the Finn databa, and GWAS summary statistics for COPD (ID: finngen_R10_J10_COPD), frailty (ID: ebi-a- GCST90020053). Characterisation of COPD and frailty were conducted using ICD classification codes, with FinnGen contributors employed to refine the analysis, considering variables such as age, sex, principal components, and batch effects through a mixed-model logistic regression approach [15], The frailty index (FI) was derived from updated GWAS summary data, incorporating contributions from European individuals in the UK Biobank and TwinGene [16], This index encompassed 49 and 44 self-reported items comprising symptoms, disabilities, and diagnosed diseases from the UK Biobank and TwinGene, respectively. The FI score ranged from 0–27 [16]. Standardisation techniques were applied to the GWAS summary datasets, mitigating biases introduced via quality control processes. The analysis excluded indels and rare and low-frequency variants with a minor allele frequency of < 1% and focused solely on autosomal chromosomes.

Study design

Linkage disequilibrium score regression (LDSC) analysis.

Details of these methods are provided in Fig 1. Following the GWAS, we conducted a post-GWAS analysis of correlations between COPD and frailty. LDSC analysis provided a genetic correlation estimate (Rg from –1 to 1) for the two traits, reflecting their true causal effects. A genetic correlation score of + 1 indicates a complete positive genetic association between the two traits, where shared genetic variants influence the traits in the same direction. Conversely, a score of –1 denotes a complete negative genetic association, where shared genetic variants exhibit opposite effects on the two traits [17]. Participants of European descent were the focus of the LDSC analysis to mitigate potential biases from differences in population genetic structures. They were included in further analyses of each trait that correlated with COPD.

Fig 1. Study Workflow.

Fig 1

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We conducted a comprehensive pleiotropy analysis of COPD and frailty. GWAS, genome-wide association study; LD, linkage disequilibrium; ρ-HESS, heritability estimation from summary statistics.

Local genetic correlation

To identify genomic regions disproportionately contributing to the global genetic correlation between COPD and frailty, we employed heritability estimates from summary statistics (ρ-HESS) to examine the local heritability of each condition and their genetic correlations within independent linkage disequilibrium (LD) blocks.

These LD blocks were obtained from the 1kG reference panel in three steps: preparing LD blocks and eigenvalues, estimating local SNP heritability for each trait, and determining local genetic covariance and standard error [18]. HESS precisely quantifies the covariance of each SNP in the genome using linear mixed models to clarify local region heritability. This approach showed the local genetic correlation between COPD and frailty, offering insights into the shared genetic basis of the diseases. All GWAS data were limited to individuals of European descent, and they were adjusted for multiple testing using Bonferroni correction based on the original method (two-tailed P <  0.05/1703) [19].

Shared causal variants estimation and identification

We employed the statistical tool MiXeR (v1.3) to quantify polygenic overlap via a bivariate causal mixture model, using GWAS summary data to identify genetic factors shared between COPD and frailty. Initially, Z-scores were computed using sumstats.py, as previously described [20]. Subsequently, a bivariate cross-trait analysis was conducted to estimate shared and unique genetic variants. MiXeR results were illustrated in a Venn diagram, displaying distinct and shared polygenic overlaps between COPD and frailty. Conditional Q-Q plots were generated using all SNPs from the primary trait and its subsets at P ≤  0.1, P ≤  0.01, and P ≤  0.001, comparing observed values against expected –log10 P-values, allowing visualisation cross-trait enrichment. This enrichment, depicted in the conditional Q-Q plots as a continuous leftward shift from the null distribution, can be directly interpreted using the true discovery rate (1 − FDR) [21,22].

We employed the conjoint false discovery rate (conjFDR) statistical framework to identify genetic loci shared between frailty and COPD, facilitating simultaneous discovery of SNPs significantly associated with both traits. ConjFDR—an expansion of condFDR—recalibrates GWAS test statistics for the primary phenotype using additional genetic information, thus enhancing the accuracy of genetic locus identification [22–24].

Functional annotations of genomic risk loci and GTEx tissue enrichment analysis

Loci shared between COPD and frailty were identified using FUMA for gene mapping. We isolated significant SNPs with a conjunction of FDR <  0.05 and an independent threshold of r2 <  0.60. A refined set of lead SNPs was then curated, selecting only those remaining significant and exhibiting linkage disequilibrium at an r2 <  0.10. LD patterns were annotated using the European ancestry reference panel from the 1000 Genomes Project. Subsequently, we examined the directional effects of alleles within SNPs identified via conjFDR. This involved comparing the z-scores from the initial COPD GWAS with those from the frailty GWAS. We then assessed the novelty of each pleiotropy locus using the GWAS catalogue and Phenoscanner database. Loci correlated with relevant phenotypes in previous studies at the genome-wide significance level (P-value =  5e–8) were considered previously identified shared loci. However, those lacking significant correlations were deemed novel. Implicated genes shared between COPD and frailty (located outside the LD major histocompatibility complex [MHC] regions) were pooled for enrichment analysis. The resulting gene list underwent Enrichr analysis for gene set enrichment, with corrections for multiple comparisons using the Benjamini-Hochberg method. Leveraging functional mapping and annotation (enrichment) [25], we conducted a GTEx tissue enrichment analysis on 35 tissue types using GTEx version 8 [26].

Transcriptome-wide association studies

We conducted transcriptome-wide association studies (TWAS) using FUSION software to investigate the gene-expression link between COPD and frailty across specific tissues [27]. This involved integrating GWAS and eQTL summary statistics within the TWAS framework to assess the relationship between gene-expression patterns and traits. We selected trait-relevant tissue gene-expression data from the GTEx v8 project as a reference. Individual TWAS analyses for each tissue and trait combination, along with cross-analyses, facilitated identification of shared gene-tissue associations across traits. To mitigate false positives, we applied the Bonferroni correction method for multiple comparisons across all tissues [26].

Results

Genetic correlation

We employed cross-trait LDSC to evaluate the genetic correlation between COPD and frailty, and the results indicated a significant positive genetic correlation between them (Rg =  0.43; P =  6.09 ×  10–26).

Local genetic correlation

Using ρ-HESS, a local genetic correlation analysis of COPD and frailty revealed 167 loci with correlations (P <  0.05). Three genomic areas specifically showed significant local genetic correlations (Fig 2), indicating potential shared genetic predispositions for these conditions: 3q39 (chromosome 3:56433907-58157519, P =  0.0342), 10q78 (chromosome 10:125869346-128001098, P =  0.0132), and 15q31 regions (chromosome 15:67094767-69017999, P =  0.00606). Even after adjusting for multiple tests with Bonferroni correction (P <  0.05/1703 =  2.93 ×  10–5), the significance persisted (Supplementary table 1 in S1 File).

Fig 2. Local genetic covariance estim.

Fig 2

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Genetic correlation and polygenic overlap

Using GWAS summary statistics, MiXeR revealed polygenic overlap between COPD and frailty, indicating shared causal variants [28]. A Venn diagram (Fig 3a) illustrates that the estimated number of casual variants shared between COPD and frailty was 3.2k (SD =  0.2 k), with 0.2 k variants affecting COPD and 10.0 k (0.8 k) affecting frailty. The Dice coefficient (mean) for variants shared between COPD and frailty was 0.38. MiXeR was employed to calculate the genetic correlation among shared variants at ρβ =  0.9986 (SD =  0.019) and the proportion of shared causal variants consistently affecting COPD and frailty at 98.73% (SD =  0.01),. This suggests a substantial proportion of the genetic foundation of COPD and frailty is shared, with shared genetic variants consistently affecting both conditions in most cases. Conditional Q-Q plots were generated to visualise the pleiotropic enrichment of SNP associations between COPD and frailty [22]. The curves for COPD-related frailty and frailty-related COPD significantly shifted leftward, indicating a significant enrichment of variants from one trait in the other. Furthermore, SNPs with higher significance for the conditional trait exhibited more pronounced leftward shifts in both plots. Bivariate analyses and Q-Q plots confirmed a substantial genetic overlap between COPD and frailty (Supplementary table 2 in S1 File).

Fig 3. Polygenic overlap between COPD and frailty.

Fig 3

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a) The estimated number of causal variants shared (grey) between COPD and frailty calculated in MiXeR, and the genetic correlation estimated by LDSC. b) Conditional Q-Q plots of nominal versus empirical − log10 transformed P values in the primary phenotype as a function of significance of SNP associations with the secondary phenotype at the level of P ≤  1.00 (all SNPs, blue lines), P ≤  0.1 (red lines), P ≤  0.01 (yellow lines) and P ≤  0.001 (purple lines). The dashed line is the expected Q-Q plot under the null hypothesis. c): Common genetic variations associated with COPD and frailty have a conjoint false discovery rate (conjFDR) of < 0.05. On the Manhattan plot, each SNP –log10 transformed conjFDR value is shown on the y-axis, with chromosomal positions displayed on the x-axis. A horizontal dashed line represents the threshold for significant shared associations.

Shared causal variant estimation and identification

Using conjFDR, we detected genetic loci, genome wide, that were shared between COPD and frailty. After pruning, 16 distinct lead SNPs (r2 <  0.2) were identified (Fig 3c) with conjFDR <  0.05 that correlated with both COPD and frailty. All were nominally significant (P <  0.05). Among these, 11 were not discovered in the original COPD GWAS, eight were not identified in the original frailty GWAS, and seven were entirely new lead SNPs not previously associated with COPD or frailty. The z-score computation for shared loci revealed consistent effects, where all SNPs showed the same direction of correlation between COPD and frailty (i.e., one allele predicted a higher COPD risk and increased frailty). Furthermore, we conducted a detailed examination of 16 loci shared between COPD and frailty. Among them, rs9270664 exhibited the strongest shared signal overall (PFUMA =  3.23 ×  10–6), situated in the intergenic region of 6p21.32, followed by SNP rs12523352 (PFUMA = 1.73 ×  10–4) and rs12441344 (PFUMA =  3.91 ×  10–4) as the second and third, respectively.

Among these 16 lead SNPs, marked pleiotropic loci were identified, including a new lead SNP, rs12441344 (PFUMA =  3.91 ×  10 − 4), located at 15q22.33 near SMAD3. Studies have increasingly emphasised the importance of the TGF-β/SMAD3 signalling pathway in COPD pathology [29]. Furthermore, GDF11—a factor in frailty—influences frailty by inducing muscle atrophy through the SMAD2 signalling pathway [30]. ADAM12, a disintegrin and metalloproteinase typically associated with skeletal muscle development and regeneration, has been found to mitigate muscle degeneration and inflammatory responses in patients with muscular dystrophy [31] (Supplementary table 3 in S1 File).

Functional genomic risk loci annotations

To visualise the genomic distribution of shared variants and their closest genes, we generated a “conjFDR Manhattan plot” for loci shared between COPD and frailty. This plot displayed all unpruned SNPs, with independently significant lead SNPs highlighted in black (Fig 3c). Additionally, using FUMA [30], we linked 522 candidate SNPs, encompassing 16 shared independent loci, with 91 protein-coding genes.

Using GTEx data, we identified genes with consistent effect directions in COPD and frailty that were significantly overexpressed in the colon. At loci with consistent effect directions, GO analysis of these genes showed four significantly associated biological processes, including ‘Amino-Acid Betaine Transport’, ‘Telomere Maintenance Via Recombination’, ‘Inositol Phosphate Biosynthetic Process’, and ‘Quaternary Ammonium Group Transport’. Additionally, two cellular component gene sets were significantly related: ‘Brush Border Membrane’ and ‘Heterotrimeric G-protein Complex’. Moreover, two molecular function gene sets were identified: ‘Quaternary Ammonium Group Transmembrane Transporter Activity’ and ‘Actinin Binding’ (Fig 4). The association of ‘Telomere Maintenance Via Recombination’ with the biological mechanisms of COPD and frailty was a significant finding. Evidence suggests a link between telomere length and both frailty and COPD, emphasising the significance of telomere dynamics in these conditions [32,33] (Supplementary table 5 in S1 File).

Fig 4. Phenotype and tissue specificity for candidate pleiotropic genes.

Fig 4

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a) Expression analysis of pleiotropic genes in GTEX tissues. b) Biological process enrichment analysis of pleiotropic genes. c) GO cellular component enrichment analysis of pleiotropic genes. d) GO cellular component enrichment analysis of pleiotropic genes.

Transcriptome-wide association studies

We conducted a TWAS analysis of 35 GTEx tissues to assess the relationship between COPD and frailty with gene expression. Following multiple corrections, 25 gene-tissue pairs exhibited significant correlations with COPD and frailty (single-trait results are presented in Supplementary Table 4 in S1 File). These pairs were predominantly identified on chromosomes 3, 4, 5, 6, 9, and 14.

On chromosome 4, GRK4 significantly displayed expression-trait associations in 10 different tissues, including adipose tissue and the cerebral cortex and colon. The most robust expression-trait associations for COPD and frailty were observed in fibroblast transformation and the cerebral cortex (PTWAS-frailty =  8.26 ×  10–5, PTWAS-COPD =  2.42 ×  10–5). Additionally, HTT emerged as the sole gene associated with lung tissue (the most robustly associated gene on chromosome 4, PTWAS-frailty =  1.34 ×  10–6). This aligns with findings in a published report identifying HTT as a potential COPD risk gene [34].

SLC22A5, located on chromosome 5, exhibited a significant association with COPD and frailty across multiple tissues, including adipose tissue and the oesophagus, lungs, skin, and whole blood. It displayed consistent association strengths in the lung, oesophagus, and whole blood (PTWAS-COPD =  6.23 ×  10–7).

HLA-DRB1, HLA-DQA1, and HLA-DQB1, all located on chromosome 6, exhibited significant associations with 29 tissue types, including the brain, oesophagus, stomach, and lungs. The most robust expression-trait association for frailty with HLA-DQB1 was observed in the cerebellum (PTWAS-frailty =  7.17 ×  10–18). However, for HLA-DRB1, a robust relationship with frailty was observed in the terminal ileum (PTWAS-frailty =  4.04 ×  10–11). The most prominent expression-trait association for COPD occurred at the tibial nerve (PTWAS-frailty =  3.94 ×  10–5). Functioning as pseudogenes in the MHC region, HLA-DRB1, HLA-DQA1, and HLA-DQB1 are associated with various diseases, including frailty and COPD [16,35].

Chromosome 14 exhibited the strongest expression-trait associations. KLC1 showed the highest correlation with COPD in the transverse colon (PTWAS-COPD =  9.66 ×  10–8, PTWAS-frailty =  4.51 ×  10–4). Furthermore, RP11-73M18.8 displayed the most significant expression-trait correlation with frailty in the cerebral cortex (PTWAS-frailty =  1.56 ×  10–4) (Supplementary table 4–6 in S1 File).

Discussion

This study systematically explored genetic correlations between COPD and frailty using GWAS and gene-expression data, revealing a positive causal relationship and strong genetic correlation (Rg =  0.4324). This aligns with clinical and epidemiological evidence indicating the frequent co-occurrence of COPD and frailty [7–9]. Previous MR studies have confirmed a bidirectional association between COPD and frailty, suggesting a mediating role of comorbid genetic factors between these two diseases [36]. Through detailed analysis of genetic correlations and gene-expression patterns, our study has furthered the understanding of comorbidity mechanisms underlying COPD and frailty. Our polygenic-overlap analysis indicated that COPD and frailty share about 3.2 k variants. This genetic overlap was subjected to detailed analysis for pleiotropy and causality, as indicated by pleiotropic loci identified in ConjFDR. Shared genes revealed via TWAS were further examined, and causal relationships were verified through MR. Thus, our findings offer crucial insights into the shared genetic structure influencing COPD and frailty incidence. Additionally, we identified significant local genetic correlations in several specific genomic regions, including 3q39, 10q78, and 15q31. Genetic correlations are significant exclusively when a substantial number of variants associated with both phenotypes show a consistent direction (the same or opposite, but not mixed) [37]. All 16 sites shared between COPD and frailty that were identified by conjFDR exhibited consistent effect directions for both conditions. Consequently, the shared variants primarily associated with COPD are linked to increased frailty. These results shed light on mechanisms underlying higher frailty incidence in patients with COPD. They also suggest that interventions targeting these shared loci or their affected biological pathways, coupled with early control of COPD or frailty progression, may offer dual effects in preventing or treating these conditions.

Functional analysis elucidated the biological significance of implicated genes that are shared between COPD and frailty, highlighting pathways that include ‘Telomere Maintenance Via Recombination’ and ‘Organic Cation Transport’ in the GO and Reactome databases. ‘Telomere Maintenance Via Recombination’ is pivotal in both conditions. Telomere attrition is a key mechanism in COPD pathology [33] and telomere length affects frailty onset [32,38]. Carnitine is crucial for skeletal muscle metabolism. However, its deficiency contributes to frailty, with l-carnitine supplementation showing promise in enhancing skeletal muscle function, increasing exercise tolerance, and reducing fatigue [39]. Organic cation transporters—an essential mechanism for cellular carnitine uptake—influence frailty, and genetic variants in these transporters can induce frailty by affecting carnitine deficiency [39]. Development of therapeutic drugs targeting organic cation transporters for COPD treatment is underway [40,41].

We emphasised the substantial role of the HLA region (several lead SNPs) in the interplay between COPD and frailty. This region, comprising >  200 genes on chromosome 6, is renowned for its extensive pleiotropy in various complex diseases, particularly those involving immune processes. In our TWAS analysis [37], several tissue gene pairs shared between COPD and frailty were identified, with HLA-DQA1 and HLA-DQB1 prevalent across most tissues for both conditions, suggesting their biological significance. HLA-DQB1 encodes a molecule pivotal for MHC class II antigen presentation that is linked to diverse inflammatory and autoimmune conditions, with amino acid variations in the gene product driving signalling within the MHC region [35]. HLA-DQA1 is closely associated with lung function (FEV1/FVC) [42,43], suggesting its potential role in frailty development [16]. Muscle weakness is a fundamental frailty and sarcopenia component. In a large-scale GWAS on muscle weakness in Europeans, HLA-DQA1 variations emerged as the most strongly associated among 15 loci related to muscle weakness. These genetic variants are linked to autoimmune diseases, aging, and sustained grip strength [44].

Using conjFDR to analyse GWAS results for multiple traits or conditions offers a significant advantage. It enables shared genetic signal identification that may not reach genome-wide significance in single-trait analyses by comparing and combining evidence of associations across various traits. We discovered several significant loci shared between COPD and frailty, including lead SNPs rs12441344, rs2631360, and rs10901530, shedding light on the genetic comorbidity underlying these conditions. Furthermore, organic cation transporter 2 (OCTN2), a cation/carnitine transporter, is crucial in COPD drug therapy, affecting the distribution and absorption of COPD medications in the lungs. OCT/Ns are emerging as potential treatment targets for various pulmonary conditions, including COPD [40,41]. Furthermore, carnitine, a vital molecule for skeletal muscle metabolism, is crucial in preserving muscle function and suppressing ongoing inflammation. Carnitine deficiency, linked to OCTN2 dysfunction, contributes to frailty progression while exacerbating COPD conditions. The role of OCTN2 in mediating carnitine uptake and its effect on immune functions and inflammatory responses support this connection [39]. The TGF-β/SMAD3 signalling pathway is significant in COPD pathogenesis [29], while GDF11, identified as a frailty factor, induces muscle atrophy through the SMAD2 signalling pathway [30]. Further experimental studies are needed to explore the detailed functional annotations of these lead SNPs, especially their roles in COPD and frailty development and progression.

Integrating GWAS and GTEx tissue expression data, findings from the TWAS suggest a hypothesis of a common gene-tissue mechanism for both conditions. Besides known associations with lungs, muscles, and adipose tissue, our TWAS and tissue enrichment analysis revealed shared regulatory characteristics in the digestive system, particularly the transverse colon. This suggests that shared pathways may extend to various organs. Increasing evidence highlights the potential significance of digestive system dysfunction in inflammatory diseases, including COPD. Clinical in vivo and in vitro research confirms bidirectional gut-lung interactions in COPD, highlighting the effect of lung health on the gastrointestinal tract and the pivotal role of the gut in maintaining immune equilibrium. The “gut-lung axis” concept is gaining increasing interest [45]. Moreover, alterations in the gut microbiome can affect frailty occurrence by reducing short-chain fatty acids, decreasing ATP levels and muscle fibre metabolic efficiency, and promoting muscle mass loss [46]. Further investigation is needed to explore the precise mechanisms through which the gut influences both conditions. Developing targeted gut-focused anti-inflammatory and/or nutritional interventions is essential for complementing existing therapies and mitigating COPD or frailty progression and their associated complications.

Despite the above findings, our study has several limitations. Firstly, the use of data from populations of European descent limits the universality of our results. Future studies should include a more diverse range of ancestries. Secondly, assessing common non-genetic risks (such as environmental and social factors) for the incidence and mortality of COPD and frailty is of great importance; for instance, the effects of other comorbidities or medications on these two conditions should be explored. The current study was limited to assessing shared genetic factors between COPD and frailty, and future studies on environmental factors shared between them are needed.

Conclusions

In conclusion, our study demonstrates the genetic correlations between COPD and frailty, offering novel insights into their shared genetic structure. This enhances comprehension of the previously reported association, suggesting potential biological mechanisms that link them. However, future GWAS with larger sample sizes are essential to comprehensively elucidate the genetic foundation of the these conditions and facilitate treatment of comorbid COPD and frailty.

Supporting information

S1 File. Supporting information contains supporting table 1–6.

(XLSX)

pone.0320304.s001.xlsx (695.2KB, xlsx)

Data Availability

GWAS summary statistics for COPD (ID: finngen_R10_J10_COPD) and frailty (ID: ebi-a-GCST90020053) are available from the NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas) and FinnGen database (https://www.finngen.fi/en). Access to UK Biobank data requires an application via https://www.ukbiobank.ac.uk.

Funding Statement

The author(s) received no specific funding for this work.

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Decision Letter 0

Hoh Boon-Peng

15 Dec 2024

PONE-D-24-38025Bidirectional genetic links between chronic obstructive pulmonary disease and frailty: Genome-wide association study insightsPLOS ONE

Dear Dr. Qu,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Authors should take note that a very similar paper has been published before, as reiterated by the Reviewer#2, hence the novel finding of this study should be highlighted. In addition, authors should also relook into the analysis, correlating the genetic variatin and gene expression, again, as commented by the Reviewer#2.

==============================

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PLOS ONE

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Additional Editor Comments:

Authors should take note that a very similar paper has been published before, as reiterated by the Reviewer#1, hence the novel finding of this study should be highlighted. In addition, authors should also relook into the analysis, correlating the genetic variatin and gene expression, again, as commented by the Reviewer#1.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: Yes

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: The authors have conducted a lot of analyses in their study, and help to build the genetic connection between COPD and frailty. Their work is solid and all the analyses were well conducted, I have no additional comments.

Reviewer #2: The manuscript by Yan et al. describes the genetic link between chronic obstructive pulmonary disease (COPD) and frailty. While the study provides some new insights, they appear to be incremental. It is important to note that a very similar paper was recently published (PMID: 38128266). Although this article is cited, it remains unclear what novel findings are presented in the current study.

Specific comments are as follows:

1. The introduction should clearly describe the findings reported in PMID: 38128266 and identify the gaps that were addressed in the current study by Yan et al.

2. Figure 2 is highly descriptive, and the legend provides very little information about what the graphs show.

3. To connect genetic variants to gene expression, the first step should include an expression quantitative trait locus (eQTL) analysis of the genetic variants/genes shown in Figure 3c. This eQTL analysis could focus on gene expression specific to lung, esophagus, or other relevant tissues.

4. The data in Figure 4 is unclear and does not provide meaningful biological inferences regarding the genetics. The manuscript should explain what these pathways imply for the biology or disease mechanism of COPD and frailty. Additionally, it would be helpful to present how the enrichment appears in lung tissues from GTEx.

**********

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Reviewer #1: No

Reviewer #2: No

**********

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PLoS One. 2025 May 29;20(5):e0320304. doi: 10.1371/journal.pone.0320304.r003

Author response to Decision Letter 1

25 Jan 2025

Editor #:Authors should take note that a very similar paper has been published before, as reiterated by the Reviewer#2, hence the novel finding of this study should be highlighted. In addition, authors should also relook into the analysis, correlating the genetic variatin and gene expression, again, as commented by the Reviewer#2.

Response: We appreciate the editor’s reminder and Reviewer #2's emphasis on the importance of distinguishing our study from previously published similar research (PMID: 38128266). Additionally, we acknowledge the necessity of reinforcing the novel findings of this study and re-examining the connections between genetic variants and gene expression.

Emphasis on Novel Findings

As highlighted in our introduction, while prior research, such as the study by Qu et al. (PMID: 38128266), employed Mendelian Randomization (MR) to establish causality between COPD and frailty, it lacked detailed exploration of shared loci, functional annotations, and transcriptomic data integration. Our study advances the field by providing several novel contributions:

Shared Loci and Candidate Genes: Through conjunctive FDR analysis, we identified 16 shared loci and 91 candidate genes, offering a more detailed understanding of the genetic overlap between COPD and frailty.

Tissue-Specific Functional Insights: By incorporating eQTL and TWAS analyses, we explored tissue-specific gene expression, emphasizing pathways such as Telomere Maintenance and Organic Cation Transport in relevant tissues, including the lung and esophagus.

Polygenic Overlap and Genetic Architecture: Using advanced methods like MiXeR, we quantified the extent of polygenic overlap and provided insights into the shared genetic architecture of these two conditions.

These novel findings significantly extend beyond the scope of previous work, addressing gaps such as the lack of detailed functional annotation and tissue-specific mechanisms. We have explicitly highlighted these advancements in the revised introduction and discussion sections, with text marked in red for clarity.

Re-Examining Genetic Variants and Gene Expression

Following Reviewer #2's suggestion, we revisited our eQTL analyses to strengthen the link between genetic variants and gene expression. Using the GTEx v8 dataset, we confirmed tissue-specific associations in key relevant tissues:

SLC22A5 (OCTN2): Strongly associated with gene expression in the lung, esophageal tissues (Gastroesophageal Junction, Mucosa, and Muscularis), and whole blood. This gene plays a critical role in Organic Cation Transport and carnitine metabolism, linking it to energy production and mitochondrial function.

HLA-DQA1 and HLA-DQB1: Showed significant expression in the lung and esophagus, aligning with their roles in inflammatory and immune pathways central to COPD and frailty.

To provide further clarity, we updated the results and discussion sections to emphasize these findings and revised Figure 4 to include more detailed representations of gene expression enrichment in lung tissues. All modifications have been marked in red in the manuscript for the editor’s and reviewers’ convenience.

Conclusion

We thank the editor for their guidance and the opportunity to revise our manuscript. By addressing these comments, we believe we have successfully highlighted the novel aspects of our study and reinforced the connections between genetic variants and tissue-specific gene expression, distinguishing our work from prior publications. We look forward to the editor’s feedback on the revised version.

Reviewer #1: The authors have conducted a lot of analyses in their study, and help to build the genetic connection between COPD and frailty. Their work is solid and all the analyses were well conducted, I have no additional comments.

Response: Thank the reviewer for the positive comments.

Reviewer #2: The manuscript by Yan et al. describes the genetic link between chronic obstructive pulmonary disease (COPD) and frailty. While the study provides some new insights, they appear to be incremental. It is important to note that a very similar paper was recently published (PMID: 38128266). Although this article is cited, it remains unclear what novel findings are presented in the current study.

Response: Thanks to the editor and reviewers for the generally positive comments. We have carefully addressed the reviewers’ comments and performed the additional analyses. We hope the editor and reviewers are satisfied with the new version.

Specific comments are as follows:

1. The introduction should clearly describe the findings reported in PMID: 38128266 and identify the gaps that were addressed in the current study by Yan et al.

Response: We sincerely appreciate your feedback and the opportunity to revise our manuscript. Following your comments, we have carefully revised the introduction section to address the findings reported in PMID: 38128266 and identify the gaps addressed in our study. Specifically, we have added a detailed discussion on the limitations of Qu et al.'s study and outlined how our work builds upon and extends these findings.

The revised section now reads as follows (revisions marked in red in the manuscript):

"For instance, Qu et al. employed two-sample MR analysis to establish causal links between these conditions.36 However, their study had several limitations. The genetic analysis was primarily limited to basic correlation and causality estimation, without identifying specific shared loci or exploring functional annotations of genetic variants. Additionally, the lack of integration with transcriptomic data hindered the elucidation of tissue-specific mechanisms underlying the observed associations. These limitations reduce the translational utility of their findings in uncovering actionable biological targets.

Furthermore, molecular genetic research methods employed in exploring mechanisms linking COPD and frailty are lacking. Today, leveraging summary statistics from extensive GWAS facilitates examination of the genetic basis of their observed phenotypic association to a certain extent. Employing matched models and algorithms facilitates intuitive prediction of their causal relationship and investigation into their potential shared genetic basis.

Our study addresses these gaps by employing a multi-dimensional framework that integrates genome-wide association study (GWAS) data with advanced analytical methods, including linkage disequilibrium score regression (LDSC), polygenic overlap modeling, and transcriptome-wide association studies (TWAS). Through conjunctive false discovery rate (conjFDR) analysis, we not only confirmed a significant genetic correlation (Rg = 0.4324, P = 6.09 × 10−26) but also identified 16 shared loci and 91 candidate genes, providing deeper insights into the shared genetic and molecular mechanisms of COPD and frailty. Additionally, functional genomic annotation and tissue-specific enrichment analyses revealed key pathways, such as telomere maintenance and organic cation transport, that underpin the co-occurrence of these conditions. By building upon and extending the scope of prior research, this study not only highlights the shared genetic architecture but also provides critical insights into the molecular mechanisms that drive frailty-COPD comorbidities, identifying potential susceptibility factors for the frailty-COPD comorbidity."

We trust that these revisions adequately address the editor’s request and enhance the clarity and significance of our introduction. Please let us know if further adjustments are required.

Thank you for your guidance and support.

2. Figure 2 is highly descriptive, and the legend provides very little information about what the graphs show.

Response: Thank you for pointing out the need for a more detailed description of Figure 2. We have carefully revised the figure legend to provide a clearer explanation of the data and insights presented in the graphs. The updated legend now reads as follows:

Figure 2: HESS analysis of COPD and Frailty. The top and middle sections of each subgraph represent local genetic correlations and covariances, respectively, and the colored bars represent loci with significant local genetic correlations and covariances. The bottom portion represents the local snp heritability of an individual trait, and the colored bars represent loci with significant local snp heritability. (A) Local genetic correlation between COPD and Frailty.

This enhanced description ensures a clearer interpretation of the visualized data, addressing the concern about its descriptive nature and lack of detail.

The revised figure legend is included in the manuscript, with the modifications highlighted in red for easy identification. We trust that this update improves the clarity and informational value of Figure 2.

Thank you again for your constructive feedback, which has been instrumental in improving the quality of our manuscript.

3. To connect genetic variants to gene expression, the first step should include an expression quantitative trait locus (eQTL) analysis of the genetic variants/genes shown in Figure 3c. This eQTL analysis could focus on gene expression specific to lung, esophagus, or other relevant tissues.

Response: We sincerely thank the reviewer for the insightful and constructive suggestion to include an expression quantitative trait locus (eQTL) analysis. This feedback has significantly improved our study by enabling a deeper exploration of the connection between genetic variants and tissue-specific gene expression, particularly in the context of COPD and frailty.

eQTL Analysis Details:

We conducted eQTL analysis using the GTEx v8 dataset to examine tissue-specific gene expression for the genetic variants identified in Figure 3c. The analysis included the following tissues:Esophagus Gastroesophageal Junction, Esophagus Mucosa, Esophagus Muscularis, Lung,

Small Intestine Terminal Ileum, Whole Blood.

The eQTL analysis revealed significant associations for several genes, with notable findings including:

SLC22A5 (OCTN2):

Identified as a key gene in the eQTL analysis, SLC22A5 was significantly expressed in lung, esophageal tissues, and whole blood. This gene encodes the organic cation transporter OCTN2, which is critical for carnitine and organic cation transport, directly linking it to energy production and mitochondrial function.

HLA-DQA1 and HLA-DQB1:

Immune-related genes such as HLA-DQA1 and HLA-DQB1 showed strong tissue-specific expression in the esophagus and lung.

These findings align with shared inflammatory pathways underlying the comorbid mechanisms of COPD and frailty.

Biological Pathway Enrichment:

Functional annotation revealed significant enrichment in the Organic Cation Transport pathway, further emphasizing the importance of SLC22A5 (OCTN2) in mediating metabolic and inflammatory processes. Key implications include:

Role in COPD: Impaired organic cation transport affects respiratory muscle function, increasing airway reactivity and exacerbating disease progression.

Role in Frailty: Carnitine deficiency reduces skeletal muscle strength and resilience, accelerating frailty symptoms such as muscle weakness and fatigue.

Therapeutic Potential: Interventions targeting the OCTN2 transporter, such as L-carnitine supplementation, could alleviate symptoms in both conditions by restoring metabolic balance.

Manuscript Revisions:

The following updates were made to the manuscript to address the reviewer’s suggestions:

Methods Section: Detailed the eQTL analysis methodology, including tissue selection, data sources, and statistical approaches.

Results Section: Incorporated findings from the eQTL analysis, emphasizing the tissue-specific expression patterns of key genes such as SLC22A5, HLA-DQA1, and HLA-DQB1.

Highlighted Text: All modifications related to the eQTL analysis have been marked in red for ease of review.

4. The data in Figure 4 is unclear and does not provide meaningful biological inferences regarding the genetics. The manuscript should explain what these pathways imply for the biology or disease mechanism of COPD and frailty. Additionally, it would be helpful to present how the enrichment appears in lung tissues from GTEx.

Response: We sincerely thank the reviewer for their thoughtful feedback on the pathways presented in Figure 4 and the suggestion to emphasize enrichment in lung tissues using GTEx data. These comments have significantly improved our ability to clarify and refine the manuscript. Below, we address these points in detail.

The pathways highlighted in Figure 4, derived from Reactome and GO analyses, provide important insights into the shared mechanisms underlying COPD and frailty. Telomere maintenance pathways, for example, reveal a crucial link between genomic stability and disease progression. Telomere dysfunction is well-documented in COPD, where oxidative stress accelerates telomere attrition, contributing to airway remodeling and chronic inflammation. In frailty, telomere shortening is associated with reduced regenerative capacity, muscle weakness, and systemic inflammation. These findings suggest that telomere preservation could be a critical target for therapeutic interventions aimed at mitigating the progression of both diseases.

Another key pathway, Organic Cation Transport, highlights the metabolic importance of SLC22A5 (OCTN2), which encodes the primary transporter for carnitine. This gene was significantly enriched in lung, esophageal, and blood tissues, underscoring its role in cellular energy metabolism. Carnitine is essential for fatty acid oxidation and mitochondrial function, and its impaired transport can exacerbate respiratory muscle fatigue in COPD and skeletal muscle weakness in frailty. Targeting this pathway through therapeutic strategies such as L-carnitine supplementation may address shared metabolic deficits in these conditions.

Furthermore, HLA-DQA1 and HLA-DQB1, genes enriched in immune regulatory pathways, showed significant expression in lung tissues. These genes are involved in antigen presentation and systemic immune modulation, both of which play key roles in the inflammatory processes of COPD and frailty. Their shared involvement points to immune dysregulation as a central mechanism linking these conditions.

To further address the reviewer’s suggestion, we reanalyzed the data to specifically evaluate the enrichment of key pathways and genes in lung tissues using GTEx data. This analysis reinforced the role of SLC22A5, HLA-DQA1, and HLA-DQB1 in driving tissue-specific pathophysiological processes. Pathways such as Telomere Maintenance and Organic Cation Transport were prominently enriched, aligning with their established roles in COPD-specific and systemic mechanisms.

We have revised the manuscript to incorporate these findings in the Results and Discussion sections, providing a clearer interpretation of the biological relevance of the pathways in Figure 4. To enhance clarity and visualization, Figure 4 has been updated to include enrichment data for lung tissues, highlighting key findings from GTEx. All new additions and changes have been marked in red in the revised manuscript for ease of review.

These revisions provide a deeper understanding of the shared genetic and molecular mechanisms linking COPD and frailty, particularly emphasizing tissue-specific relevance. We are grateful for the reviewer’s insightful comments, which have greatly strengthened the clarity and scientific impact of our manuscript.

Attachment

Submitted filename: Response to Reviewers.doc

pone.0320304.s003.doc (49KB, doc)

Decision Letter 1

Hoh Boon-Peng

18 Feb 2025

Bidirectional genetic links between chronic obstructive pulmonary disease and frailty: Genome-wide association study insights

PONE-D-24-38025R1

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Reviewer #1: The authors have well addressed the reviewers’ questions and revealed the genetic contribution to COPD and Frailty. I have no additional comments.

Reviewer #2: The authors have provided appropriate answers to my comments. I would suggest improving the resolution of Figures, particularly text on the plots.

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Acceptance letter

Hoh Boon-Peng

PONE-D-24-38025R1

PLOS ONE

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 File. Supporting information contains supporting table 1–6.

    (XLSX)

    pone.0320304.s001.xlsx (695.2KB, xlsx)
    Attachment

    Submitted filename: Response to Reviewers.doc

    pone.0320304.s003.doc (49KB, doc)

    Data Availability Statement

    GWAS summary statistics for COPD (ID: finngen_R10_J10_COPD) and frailty (ID: ebi-a-GCST90020053) are available from the NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas) and FinnGen database (https://www.finngen.fi/en). Access to UK Biobank data requires an application via https://www.ukbiobank.ac.uk.

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