Skip to main content
Download PDF

Genome-wide association study identifies novel susceptible loci and evaluation of polygenic risk score for chronic obstructive pulmonary disease in a Taiwanese population

BMC Genomics volume 25, Article number: 607 (2024) Cite this article

Abstract

Background

Chronic Obstructive Pulmonary Disease (COPD) describes a group of progressive lung diseases causing breathing difficulties. While COPD development typically involves a complex interplay between genetic and environmental factors, genetics play a role in disease susceptibility. This study used genome-wide association studies (GWAS) and polygenic risk score (PRS) to elucidate the genetic basis for COPD in Taiwanese patients.

Results

GWAS was performed on a Taiwanese COPD case–control cohort with a sample size of 5,442 cases and 17,681 controls. Additionally, the PRS was calculated and assessed in our target groups. GWAS results indicate that although there were no single nucleotide polymorphisms (SNPs) of genome-wide significance, prominent COPD susceptibility loci on or nearby genes such as WWTR1, EXT1, INTU, MAP3K7CL, MAMDC2, BZW1/CLK1, LINC01197, LINC01894, and CFAP95 (C9orf135) were identified, which had not been reported in previous studies. Thirteen susceptibility loci, such as CHRNA4, AFAP1, and DTWD1, previously reported in other populations were replicated and confirmed to be associated with COPD in Taiwanese populations. The PRS was determined in the target groups using the summary statistics from our base group, yielding an effective association with COPD (odds ratio [OR] 1.09, 95% confidence interval [CI] 1.02–1.17, p = 0.011). Furthermore, replication a previous lung function trait PRS model in our target group, showed a significant association of COPD susceptibility with PRS of Forced Expiratory Volume in one second (FEV1)/Forced Vital Capacity (FCV) (OR 0.89, 95% CI 0.83–0.95, p = 0.001).

Conclusions

Novel COPD-related genes were identified in the studied Taiwanese population. The PRS model, based on COPD or lung function traits, enables disease risk estimation and enhances prediction before suffering. These results offer new perspectives on the genetics of COPD and serve as a basis for future research.

Peer Review reports

Background

Chronic Obstructive Pulmonary Disease (COPD) describes some of the inflammatory lung diseases that cause breathing difficulties. The two most common conditions that fall under the umbrella of COPD are chronic bronchitis and emphysema. COPD is characterized by airflow obstruction, owing to various factors such as inflammation and damage to the airways and lung tissue [1]. Some of the key risk factors that potentially cause COPD are as follows: (a) Smoking: Cigarette smoking is by far the most significant risk factor for COPD. Harmful chemicals in tobacco smoke can irritate and damage the airways and lung tissues over time. (b) Environmental factors: Prolonged exposure to indoor and outdoor air pollutants, including fumes from burning fuels for cooking and heating, increases the risk of COPD. (c) Occupational exposure: People working in certain industries such as mining, construction, and manufacturing may be exposed to dust, chemicals, and fumes that can contribute to the development of COPD [1, 2]. (d) Genetic factors: While smoking and environmental factors play dominant roles, genetic factors can also increase the susceptibility of some individuals to COPD. Genetic variations affect how the lungs respond to damage and inflammation [3].

COPD typically involves complex interactions between genetic and environmental factors. The genetics underlying this group of disease is complex, with the specific genetic factors contributing to COPD remaining an active area of research. Alpha-1 antitrypsin deficiency (AATD) is a hereditary condition caused by mutations in the SERPINA1 gene. This deficiency leads to the lack of a protective protein (alpha-1 antitrypsin) in the lungs, making individuals with AATD more susceptible to early onset emphysema and COPD. Individuals with two abnormal alleles on the SERPINA1 gene (homozygous AATD) have a significantly higher risk of developing severe COPD, particularly if they smoke [4]. Variations in certain growth factor genes such as vascular endothelial growth factor, inflammatory and immune response genes such as tumor necrosis factor-alpha and interleukin-6, mucus production genes such as mucin 5B, have been shown to affect the susceptibility to COPD [5,6,7]. These affect the growth and repair of blood vessels as well as responses to lung damage and inflammation, and could cause excessive mucus production, with the latter causing airway obstruction and respiratory symptoms. Furthermore, surfactant protein genes such as those encoding surfactant proteins A, B, and D, are important for maintaining lung function and variations in these genes have also been associated with a predisposition to COPD [8].

Genome-wide association study (GWAS) has revolutionized our understanding of the genetic basis of complex diseases. GWAS identifies genetic variants associated with a disease by comparing the genomes of people with and without a particular disease. This information can be used to develop new treatments and prevention strategies [9, 10]. Numerous GWASs have been conducted to investigate the genetic basis of COPD. The COPD Genetic Epidemiology Study (COPDGene) is one of the most prominent and extensive GWASs. Genetic and clinical data were collected from thousands of patients with COPD and healthy controls. This study has identified several genetic variants associated with COPD susceptibility and severity, including those related to inflammation, lung development, and oxidative stress genes [11, 12]. A large multicenter observational study, the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) conducted a GWAS to identify genetic factors contributing to COPD progression and exacerbations and identified genetic variants associated with lung function decline and the risk of exacerbations in COPD patients [13]. The Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS) is another comprehensive study aimed at uncovering the genetic and environmental factors influencing COPD development and progression. A GWAS within SPIROMICS has identified genetic variations linked to lung function decline, emphysema, and other COPD-related traits [14]. The International COPD Genetics Consortium (ICGC) is a collaborative effort involving researchers from around the world focusing on understanding the genetics of COPD. This consortium conducted a GWAS to identify the genetic risk variants and pathways associated with COPD, including genes involved in lung development, inflammation, and mucin production [15]. The GenKOLS Study (Genetics of Chronic Obstructive Lung Disease Study) was based in Norway and conducted a GWAS to identify genetic factors influencing COPD susceptibility and lung function decline. Specific genetic variants associated with COPD risk have been identified in the Norwegian population [16]. In a recent multi-ancestry GWAS meta-analysis of lung function traits in 580,869 individuals, 1,020 independent association single nucleotide polymorphisms (SNPs) implicating 559 genes were identified. These association study results were used to create a genetic risk score for four lung function traits: Forced Expiratory Volume in 1 s (FEV1), Forced Vital Capacity (FVC), FEV1/FVC ratio, and peak expiratory flow (PEF), which showed a strong association with COPD across ancestry groups [17]. These studies have significantly improved our understanding of the genetic underpinnings of COPD identifying specific disease-associated genetic variations and gene pathways and shedding light on potential targets for future therapeutic interventions.

COPD is a significant health concern in Taiwan, with a prevalence of 6.1% among adults older than 40 years [18]. Determining the specific risk factors and genetic factors associated with COPD in this population is crucial for effective prevention and treatment strategies. Previous studies of COPD in Taiwan were focused on smoking and environmental risk factors [19,20,21]. Target genes association with COPD have already been reported [22,23,24]. A recent global biobank meta-analysis paper performed COPD GWAS in combination with other East Asian population biobank data (including Taiwan Biobank), but without independent GWAS or PRS analysis, nor reports on susceptibility genes within the Taiwanese population [25].

The present study, aimed to use GWAS to understand whether Taiwanese people have special genetic factors in COPD and to construct a genetic risk model. Using a custom-designed TPMv1 SNP array [26] and Taiwanese population data, a GWAS was performed to determine the genes and regulatory pathways involved in COPD. GWAS results were employed to build a polygenic risk score (PRS) model to predict COPD using a genetic approach. In addition, a PRS model established in a previous large study based on four different COPD test traits [17] was applied to our COPD study group to evaluate the risk of COPD in the Taiwanese population. These similar genetic factors could be used to explain the risk of COPD in different populations.

Methods

Data collection and informed consent

The Precision Medicine Project of the China Medical University Hospital (CMUH) was initiated in 2018 to collect biospecimens and recruit study participants from patients visiting the CMUH. The recruitment and sample collection procedures were approved by the Research Ethics Committee of China Medical University Hospital, Taichung, Taiwan, in accordance with the standards of the Declaration of Helsinki. All participants signed an informed consent form. Blood samples were collected from each participant and clinical information was collected from the electronic medical records (EMRs) of CMUH between 2003 and 2021, with approval by the Research Ethics Committee of CMUH, Taichung, Taiwan.

For sample collection: participants who were 20 years of age or older and had a medical record of COPD diagnosis (ICD-10-CM Diagnosis Code: J44.0, J44.1, J44.9) were considered as COPD cases, and those who had no record of lung/trachea/bronchus disease, cancer, neoplasm, or cardiovascular diseases and were 20 years of age or older were selected as COPD controls.

Genotyping, imputation, and genome-wide association study

In the present study, the TPMv1 SNP array (TPMv1, Thermo Fisher Scientific, Inc., Waltham, MA, USA), which was developed by the Academia Sinica and Taiwan Precision Medicine Initiative teams was used for genotyping. This array comprised 714,457 SNPs and was employed according to the manufacturer’s protocol [26,27,28]. SNP data were analyzed using PLINK 2.0 [29]. Participants and SNPs with missing data were excluded if they fulfilled the respective criteria of 10% missing data per individual (–mind 0.1), 10% missing data per marker (–geno 0.1), or heterozygosity > 5 (–het 5 for samples). Next, monomorphic SNPs with a count of < 10 (–mac 10) and multiallelic SNPs were eliminated. Variants with a Hardy–Weinberg equilibrium P-value less than 10−6 (–hwe 10−6) and a minor allele frequency (MAF) less than 10−4 (–maf 0.0001) were also excluded. The following analysis criteria were incorporated into our study methodology: heterozygous outliers exceeding a standard deviation value of 5, principal component analysis (PCA) outliers exceeding an interquartile range (IQR) of 3 (for principal components 1 to 10, PC1-10), and mismatches between genotypic sex and actual sex. We also used the KING-robust kinship estimator18 (PLINK 2.0) to remove duplicate samples from our cohort, ensuring that the genetic data were not affected by inflationary effects. After applying these filters, 508,004 variants successfully passed the quality control. Imputation was performed using Beagle 5.2, and whole-genome sequencing data obtained from Taiwan Biobank was used as reference. The imputed data were further filtered based on the following criteria: an alternate allele dosage < 0.3 and a genotype posterior probability < 0.9 [30, 31]. Following quality control and imputation, 14,064,987 variants were analyzed [27].

Genome-wide association study

The summary statistics were calculated using PLINK 2.0 [29, 32]. The cases and controls were checked using PLINK identity-by-descent (IBD) to remove the first- and second-degree relatives. The selected cases and controls were matched using the MtchIt method [33]. Using PLINK 2.0 in the logistic mode, a GWAS analysis was performed with COPD as the outcome variable. Age and sex were included as covariates in the logistic regression model to account for the potential confounding effects. To address the population structure, PCA was conducted using the EIGENSTRAT method. Adjustments were made for significant PC (PC1–PC10) associated with COPD, as well as demographic variables included age and sex, when estimating odds ratios (ORs) and 95% confidence intervals. The association results were assessed for significance using P-values and effect sizes, and a genome-wide significance threshold (P < 5 × 10–8) was applied to identify significant associations. The R package, ‘qqman’ was used to generate a Manhattan plot and a quantile–quantile (QQ) plot of P-values.

Polygenic risk scores

The objective of our study was to investigate the genetic variations linked to the development of COPD compared to individuals without lung and cardiovascular conditions. We categorized the participants into a base group and a target group for PRS analysis using random allocation (80%: 20%). The base group consisted of 4,354 cases and 14,145 controls, and the target group consisted of 1,088 cases and 3,536 controls. Allocation into COPD cases and controls was based on clinical annotation.

Individual PRS in the target group was estimated using PRSice-2 software (version 2.3.3 for R) by utilizing the ORs obtained from the GWAS data of the base group [34]. SNPs with a P-value < 0.05 were selected from the GWAS results of the base group to ensure a sufficient number of significant variants for constructing the PRS model.

The construction of the PRSs was performed using the “clumping and thresholding” approach in PRSice-2. This algorithm iteratively selected a set of SNPs (P < 0.05) to form clumps around the index SNPs. Each clump comprised SNPs located within 250 kb of the index SNP and in linkage disequilibrium with the index SNP, based on pairwise threshold of r2 = 0.1. A candidate PRS was computed using the resultant index SNPs and the corresponding estimated OR coefficients for its effect allele as weights using the "score" procedure in the GWAS of the base group [35].

To replicate the PRS obtained from a previous multi-ethnic’s study [17], the list of “best SNPs” of a four-traits (FEV1, FVC, FEV1/FVC, and PEF) PRS model and their Beta values were applied to our COPD target group to calculate the PRS score using PRSice-2. A total of 1020 SNPs were reported in the previous PRS model: 223 SNPs for FEV1, 251 for FVC, 406 for FEV1/FVC, and 140 for PEF (Supplementary Table S1). Due to experimental design limitations, only 633 reported SNPs were present in our SNP dataset. For each trait, there were 142 SNPs (64%) for FEV1, 151 (60%) for FVC, 257 (63%) for FEV1/FVC, and 83 (59%) for PEF. These SNPs are referred to as “best SNPs” and were subjected to PRS calculation (Supplementary Table S2). The PRS was z-score-normalized for comparison (PRS_Z). The average PRS and its standard deviation (SD) were calculated for the cases and controls. A two-sample t-test was performed to determine the statistical significance of the difference in PRS between the patients with COPD and controls in target group. We also combined Shrine’s published “best SNPs” [17] and the ORs obtained from our base group to calculate the PRS score in our target group.

Statistical analysis

To test the statistical power of GWAS, the model proposed by Skol et al. [36] as implemented in a web-based calculation tool (https://csg.sph.umich.edu/abecasis/cats/gas_power_calculator/index.html) was used. The association annotation between SNPs and genes was performed using the ENSEMBL web tool (https://www.ensembl.org/info/docs/tools/vep/index.html), and only genes within 100 kbp surrounding the adjacent SNP were included. D prime and R squared of linkage disequilibrium were calculated using LDmatrix Tool (https://ldlink.nih.gov/?tab=ldmatrix) with 1000 Genomes Project dataset (source: GRCh38 High Coverage, all populations) as reference. The characteristics of the study participants were described by expressing categorical data as proportions. The frequencies of categorical variables were compared using the chi-square test. PRS was normalized (z-score normalization, PRS_Z) and treated as a continuous variable in the models. A t-test was used to calculate the significance of PRS in COPD. Receiver-operating characteristic (ROC) curves were generated to quantify the predictive accuracy of PRS models, and the areas under these ROC curves (AUCs) were calculated to assess the discriminatory abilities of the models. Statistical analyses were performed using SPSS (version 21.0; IBM, Armonk, New York, USA) and Excel (2016; Microsoft, Redmond, Washington, USA). All tests were two-sided. Statistical significance was set to a P < 0.05.

Results

The complete research process, including EMRs data mining, GWAS, and PRS calculation, is summarized in Fig. 1. After strict quality control procedures, data from 5,442 patients and 17,681 controls were included in the final analysis. The population characteristics of the patients with COPD are shown in Table 1. The mean ages (standard deviation, SD) of the patients and controls were 67.6 (14.7) and 64.3 (14.0) years, respectively. Approximately 69.2% (N = 3,766) of patients and 63.0% (N = 11,134) of controls were male. A PCA plot of the population structure (PC1 and PC2) is shown in Supplementary Figure S1.

Fig. 1
figure 1

Diagram illustrating the steps involved in electronic medical record (EMR) data mining, genome-wide association study, and polygenic risk score calculation

Full size image
Table 1 Selected information on the study population and composition of the base and target groups
Full size table

The QQ plot of SNPs, which compares observed versus expected χ2 test results, did not reveal significant deviation from chance expectations (inflation factor λ = 1.029; Fig. 2A). Although 85 variants exhibited associations with COPD that reached P < 1 × 10−5 (Fig. 2B, Supplementary Table S3), none reached genome-wide significance (P < 5 × 10−8). We selected the SNPs with P < 1 × 10−5 to include SNPs and neighboring genes that showed promising associations with COPD susceptibility. This adjustment allowed us to explore potential relationships with the disease while ensuring a reasonable level of statistical significance. According to the calculation of statistical power using Skol’s model [36], adjustments of MAF and OR were necessary (MAF > 0.05, OR > 1.1) for higher statistical power (0.5 ~ 0.6). The 16 SNPs showing maximum associations when filtered by these conditions are listed in Table 2, marked within genes or adjacent genes (within 100 kbp) following the annotation at the ENSEMBL web tool. The variant with the highest association on chromosome 15p26.2, rs1994147, was found in the LINC01197 (LETR1) region. The other 15 SNPs with maximum association were located in or near the genic region included WWTR1 on chromosome 3q25.1 (rs6802474/ rs11925206/ rs6783721), CFAP95 (C9orf135) on chromosome 9q21.12 (rs10780705/ rs11140930), EXT1 on chromosome 8q24.11 (rs12682151), INTU on chromosome 4q28.1 (chr4:127564977_G_GT), MAP3K7CL on chromosome 21q21.3 (rs57220716), MAMDC2 on chromosome 9q21.12 (rs10511980), BZW1/CLK1 on chromosome 2q33.1 (rs2881881/ rs6735908), and a locus in LINC01894 on chromosome 18q11.2 (rs1786166). Rs58352046, rs76053630, and rs60298813 are located on chromosome 2q14.2. There are no known genes within a distance of 100 kbp. (Supplementary Figure S2).

Fig. 2
figure 2

A Quantile–quantile plot showing the distribution of observed P-values for the identified associations. The plot demonstrates minimal population inflation with a genomic inflation factor (λ) of 1.029. B Manhattan plot displaying genome-wide P-values for the identified associations. The red line represents the threshold of P < 5 × 10–8

Full size image
Table 2 The 16 highly associated SNPs identified in the genome-wide association study of COPD
Full size table

Previous GWASs conducted in several different populations identified 1150 susceptibility loci associated with COPD or lung functions (Supplementary Table S4). Hence, these loci were queried in the study population, and the consistent ones with P < 0.005, are listed in Table 3 [37,38,39,40,41,42,43]. We focused on SNPs with P < 0.005 to emphasize high correlations between the datasets without overwhelming complexity. These included several important variants or genes associated with COPD or lung function, such as rs2273500 in CHRNA4, rs4488938/rs9654093 in AFAP1, rs72731149 in DTWD1, rs8070954 in SMG6, rs11049488 in CCDC91, rs12894780/rs35584079/rs2180369 in ITPK1, rs503464 in CHRNA5, rs7170068 in CHRNA3, rs116921376 in CYP2F2P/CYP2A6, and rs72927213 in TUT1. The findings of other replication analyses with P-values larger than 0.005 in our population are presented in Supplementary Table S5.

Table 3 Replication analysis of SNPs associated with COPD reported in previous GWAS in the Taiwanese population
Full size table

In this study, 16 SNPs significantly associated with COPD susceptibility were identified. However, the linkage disequilibrium (LD) between these SNPs and previously identified SNPs associated with COPD or lung function traits was found to be low. This indicates that the genetic variants identified in this study may represent novel loci specific to the studied population. The detailed LD relationships, along with the corresponding effect sizes, P-values, and MAFs, are summarized in Supplementary Table S6 and Figure S3.

The PRS was computed using the summary statistics of the base group and the raw genotypes of the target group using PRSice-2. An optimal SNP combination was derived through iterative calculations. A total of 13,348 SNPs were ultimately selected, with a maximum P-value threshold of 0.195 (according to the GWAS of base group). The PRS based on the selected SNPs was calculated for each participant (Supplementary Table S7). A t-test was used to test the explanatory capabilities of COPD and PRS_Z (z-score normalization). In the target group, the comparison between cases and controls yielded a P-value of 0.011 (P < 0.05) (Table 4, Fig. 3), indicating that applying the COPD-PRS model resulted in statistically significant differences.

Table 4 t-test of PRS_Z for COPD cases and controls in the target group
Full size table
Fig. 3
figure 3

Polygenic risk scoring analysis using the 80% dataset as base and the 20% dataset as target. The t-test result of polygenic risk score (Z-score normalization) of COPD cases and controls in target group, P-value = 0.011 was statistical significance. PRS_Z, PRS Z-score normalization

Full size image

A previously described four-trait PRS model [17] was also applied to the COPD target group. Based on the “best SNPs” and their Beta values, and according to the trait of lung function (FEV1/FVC), the average PRS_Z for patients with COPD in the cases of our target group was -0.0918 (SD = 0.9828), while that for controls was 0.0282 (SD = 1.0037). The t-test analysis indicated a significant association (P = 0.001) between the PRS for FEV1/FVC and COPD susceptibility. This suggests that individuals with a higher genetic risk for low FEV1/FVC PRS may have an increased genetic predisposition to COPD. The PRS for the other three traits (FEV1, FVC, and PEF) did not show any statistical significance in our target group (P-values of 0.086, 0.090 and 0.426, respectively) (Table 5).

Table 5 t-test comparing COPD target group PRS_Zs using published lung function traits PRS modelsa (best SNPs and Beta)
Full size table

Next, the PRS in the target group was calculated for the combined “best SNPs” and the OR values obtained from our analysis of the base group. The averages and SD of PRS_Z for lung function traits are shown in Table 6. With this condition, none of the PRS model of lung function traits reached statistical significance.

Table 6 t-tests comparing COPD target group PRS_Zs using published SNP list (best SNPs)a and incorporating odds ratios obtained in this study
Full size table

The trend of a PRS is inherently linked to the trait it aims to assess. In multi ethnics studies, using lung function as the indicator for PRS establishment, lung function values represent health status numerically and higher values denote better lung function. As shown in Table 5, we found that the PRS for controls was higher (indicating better lung function), while that for cases was lower. Conversely, when we based the PRS on the presence or absence of COPD aiming to predict COPD risk, the scenario changed to one where the PRS for cases tended to be higher, signifying a greater risk of COPD, while it was relatively low for control (Table 6). Consequently, evaluation of the two tables must be based on the chosen perspective.

We also investigated the ability of the PRSs to distinguish between individuals with and without COPD. The significance (P-value), odds ratio, and the amount of variance explained (R2) derived from this analysis are shown in Supplementary Table S8. In the target group, an increase in the PRS was associated with increased COPD risk in the logistic regression model (OR 1.094, 95% CI 1.020–1.172, R2 = 0.0021). Of the four examined lung function traits PRS model in the target group, only the FEV1/FVC trait calculated as “best SNPs + Beta” showed an improved distinguishing capability (OR 0.886, 95% CI 0.828–0.949). In the target group, the AUC was 0.528 (95% CI 0.508–0.548) and 0.534 (95% CI 0.514–0.553), respectively, for the PRS of the regression models using our study (COPD PRS) and FEV1/FVC trait PRS. Other results are shown in Supplementary Figure S4.

Discussion

Based on the relationship between SNPs and genes, the 16 identified SNPs showing maximum association could be divided into three groups: 1) Intron variant; most SNPs belonged to this group, including rs11925206, rs6783721, rs6802474, rs10511980, rs1994147, rs1786166, and rs57220716. 2) Downstream gene variant; the SNP is located within 20 kbp downstream of adjacent genes, including rs6735908, rs2881881, and rs10780705. 3) Intergenic variant; all other SNPs belonged to this group, including rs76053630, rs60298813, rs58352046, chr4:127564977_G_GT, rs12682151, and rs11140930. The aforementioned 16 SNPs still require further research to confirm their effects on gene expression or regulation. The known genes most strongly associated with these SNPs, within genes or adjacent genes (within 100 kbp), were WWTR1, EXT1, MAP3K7CL, MAMDC2, BZW1/CLK1, INTU, CFAP95, LINC01197 (LETR1), and LINC01894. These genes were not identified in previous GWAS.

LINC01197 (LETR1) and LINC01894 are long noncoding RNAs (lncRNAs). Several studies have identified dysregulated expression of lncRNAs in COPD patients compared to healthy individuals. These lncRNAs have been implicated in various cellular processes involved in COPD pathogenesis, such as inflammation, oxidative stress, and airway remodeling. Some lncRNAs have also been proposed as potential biomarkers for COPD diagnosis, prognosis, and treatment response [44,45,46,47,48]. In addition, LINC01197 (LETR1) is a lymphatic endothelium-specific long noncoding RNA governing cell proliferation and migration [49]. However, its significance to respiratory disease, specifically COPD, requires further investigation.

WWTR1 is involved in various cellular processes including cell proliferation and tissue repair. Variations in WWTR1 may influence lung tissue repair mechanisms and airway remodeling [50]. In a recent study, downregulation of WWTR1 was observed in COPD samples compared to healthy samples [51]. This suggests that WWTR1 gene expression is crucial for normal cellular function. Our results indicate that the SNPs located in WWTR1 have ORs less than 1 (OR = 0.87), implying a protective effect against COPD. This finding aligns with the higher expression of WWTR1 in normal cells observed in cell expression analyses. Currently, there are no reports on whether these three intronic SNPs influence the gene expression of WWTR1. Further experiments are needed in the future to establish this association. Additionally, WWTR1 is known to be associated with ferroptosis, a form of programmed cell death induced by lipid peroxidation through an iron-dependent pathway [52,53,54]. Ferroptosis has been implicated in various lung diseases, including COPD [53,54,55], highlighting the potential importance of WWTR1 in COPD pathogenesis. These observations underscore the need for further investigation into the role of WWTR1 and ferroptosis-related pathways in COPD development and progression.

The EXT1 gene encodes a glycosyltransferase enzyme called exostosin-1. This enzyme is involved in the biosynthesis of heparan sulfate (HS), a type of polysaccharide that is a component of proteoglycans. Proteoglycans are important for the structure and function of connective tissues, including cartilage and bone. Mutations in the EXT1 gene can lead to a condition called hereditary multiple exostoses, which is characterized by the formation of benign bone tumors called osteochondromas [56]. In chronic lung diseases like asthma and COPD, macrophages exhibit a phenotype similar to that of alternatively activated (M2) macrophages, characterized by an upregulation of HS biosynthesis genes. However, EXT1 expression is not significantly regulated in M2-like macrophages from patients with chronic lung diseases, suggesting a different role for EXT1 under these conditions compared to other diseases like rheumatoid arthritis and atherosclerosis, where EXT1 expression is increased [57]. In addition, an SNP, rs74701635, located approximately 49 kbp downstream of the EXT1 gene, has been associated with smoking behavior [58]. This SNP is about 776 bp away from another SNP, rs12682151, which was identified in this study. While the exact functional significance of these SNPs in relation to EXT1 and COPD remains unclear, their proximity to the EXT1 gene suggests a potential link between genetic variation in this region and smoking behavior, which is a known risk factor for COPD.

The MAP3K7CL gene, also known as MAP3K7 C-terminal like, may be involved in signaling pathways that regulate various cellular processes such as cell growth, differentiation, and apoptosis. In a gene expression study on tumor-educated leukocytes mRNA isolated from non-small cell lung cancer patients, MAP3K7CL was found to be downregulated [59]. Research on its specific role in COPD is currently lacking. The MAMDC2 gene, also known as MAM domain containing 2, is involved in various biological processes, including cell adhesion, migration, and signaling. A study reported that MAMDC2 exhibited tumor-suppressive activity and may constitute a biomarker for breast cancer treatment [60]. The BZW1 gene, also known as Basic Leucine Zipper and W2 Domains 1, encodes a protein involved in transcriptional regulation. Abnormal expression of this gene is associated with a variety of cancers [61, 62]. In addition, BZW1, as a translation initiation regulation factor, plays an important role in preimplantation embryo protein synthesis [63]. However, its association with COPD remains to be studied. The CLK1 gene, also known as CDC2-Like Kinase 1, encodes a protein belonging to the CLK family of serine/threonine kinases. These kinases play crucial roles in regulating pre-mRNA splicing, which is essential for the production of mature mRNA transcripts [64]. While CLK1's direct role in lung biology is unclear, its involvement in mRNA splicing suggests an indirect influence on lung function and disease, given the importance of proper splicing for lung health. INTU (Inturned Planar Cell Polarity Protein) is associated with embryonic digit and mouth development, functioning in the ciliary basal body and motile cilium. It is linked to conditions like asphyxiating thoracic dystrophy and orofaciodigital syndrome XVII. INTU plays a crucial role in ciliogenesis, regulating cilia formation and cell polarity, indirectly impacting Hedgehog signaling. Mutations in INTU and related ciliary genes contribute to orofacial-digital syndromes and ciliopathies, highlighting its significance in cilia formation and cellular processes [65, 66]. While its direct association with lung function has not been well established, planar cell polarity pathways may indirectly affect lung development [67]. CFAP95 (C9orf135) encodes a membrane-associated protein that may serve as a surface marker for undifferentiated human embryonic stem cells [68]. The function of the CFAP95 (C9orf135) gene has not been extensively studied, and its specific role in lung biology remains unclear. Further research is needed to determine any potential relevance to the lungs.

In addition to the highly associated genes discovered, our results identified those previously reported as COPD-or lung function-related genes including CHRNA3, CHRNA4, CHRNA5, AFAP1, SMG6, ITPK1, CYP2A6, TUT1, DTWD1, and CCDC91 in our study cohort. CHRNA3, CHRNA4, and CHRNA5 encode the subunits of nicotinic acetylcholine receptors (nAChRs) involved in the neurotransmission of acetylcholine. Variations in these genes render individuals more susceptible to nicotine dependence. Because smoking is a major risk factor for COPD, individuals with these genetic variants are at a higher risk of developing COPD. Furthermore, these genes have been linked to changes in lung function even in patients without COPD. Variations in CHRNA3 and CHRNA5 levels are associated with reduced lung function, FEV1 and FVC, which may contribute to the development of COPD [69]. AFAP1 is involved in actin cytoskeleton organization and cell motility. Variations in the gene related to cytoskeletal dynamics can potentially affect airway remodeling and lung function in COPD [70]. SMG6 is involved in the nonsense-mediated mRNA decay pathway, which is involved in mRNA surveillance and degradation. Variations in the gene involved in mRNA stability and processing may affect the regulation of inflammation and tissue repair in COPD [71]. ITPK1 is involved in the regulation of inositol phosphate metabolism, which affects cell signaling pathways. Variations in genes involved in intracellular signaling pathways may have downstream effects on inflammatory responses in the lungs [72]. CYP2A6 is an enzyme responsible for metabolizing nicotine and other tobacco-related compounds. Genetic variants of CYP2A6 influence an individual's ability to metabolize nicotine, which may in turn affect smoking behavior and susceptibility to COPD [73]. TUT1 is involved in RNA modification and degradation. Variations in RNA processing genes may influence the stability and regulation of genes associated with lung function and inflammation [74]. DTWD1 possesses tRNA-uridine aminocarboxypropyltransferase activity and is involved in tRNA modification. Its role in lung function and COPD is not well established, and further research is required to understand its significance in respiratory health. However, the specific role of CCDC91 in COPD has not been well documented. Genetic variants of this gene may influence processes related to lung function and airway inflammation [75].

Based on the GWAS results, many genes have been previously linked to either COPD or lung function traits, indicating their potential relevance to respiratory health. However, it is important to note that the genetic basis of COPD is multifactorial, and that these genes likely interact with other genetic and environmental factors to contribute to disease susceptibility and severity. Further research is needed to elucidate the specific mechanisms by which these genes influence COPD and lung function.

The results of the GWAS in the Taiwanese COPD study group suggested a significant genetic component of COPD. The PRS analysis using PRSice-2 also supported this finding, showing statistical significance in the target groups. The t-test yielded a P-value of 0.011 and logistic regression yielded OR 1.09 (95% CI 1.02–1.17) and AUC 0.528 (95% CI 0.508–0.548), suggesting that the identified genetic variants were significantly correlated with COPD.

Furthermore, a previously established PRS model for lung function traits [17] was applied to our target group, which included a set of SNPs associated with lung function traits such as FEV1, FVC, FEV1/FVC, and PEF. The PRS model of FEV1/FVC revealed statistical significance between our COPD cases and controls. The FEV1/FVC ratio is used to assess pulmonary mechanical limitations, such as airflow restriction commonly seen in COPD patients. A lower ratio may indicate more impaired lung function. Using the lung function trait, FEV1/FVC, to establish a genetic PRS model, higher scores may indicate better lung function and lower chances of developing COPD, leading to a decrease in the odds ratio for risk. Using associations found through GWAS and PRS, there is potential to elucidate the molecular mechanisms underlying changes in lung function, thereby understanding the pathogenesis of COPD at a molecular level. This might include more information about lung function measurements and further explanation of the relationship between the FEV1/FVC ratio and COPD. However, the PRS for FEV1, FVC, and PEF did not show statistically significant associations with COPD in our target group. Furthermore, when using the “best SNPs” and our ORs to calculate the PRS, no PRS model of the four lung function traits reached statistical significance.

Shrine et al. [17] generated a PRS for four lung function traits based on 49 study cohosts. In these ethnic groups, 80.6% were of European ancestry and 14.7% were of East Asian ancestry, which is closer to our ethnicity. Interestingly, replicating their PRS model to our COPD target group, on the “best SNPs”, could distinguish between cases and controls in a comparable manner to our PRS. This indicates common genetic factors for COPD or lung function traits across ethnic groups. However, based on our GWAS and PRS results, we found that some novel risk variants or loci are associated with COPD.

PRS are often developed based on GWAS conducted on specific populations or ethnic groups. This means that the genetic variants and their effect sizes used to calculate the PRS may be more applicable and accurate within the population from which they were derived. Consequently, the PRS developed in one ethnic group may not perform as well in individuals from different ethnic backgrounds. Historically, many GWAS have been conducted in populations of European ancestry, leading to biases in the available genetic data. Consequently, PRSs developed using these data may not be informative for individuals from non-European ethnic backgrounds. To address this limitation, researchers have attempted to include diverse populations in their genetic studies. Genetic variants associated with certain traits or diseases occur at different frequencies across ethnic groups. Variants common to one population may be rare in another. This can influence the performance of a PRS when applied to individuals from different ethnic backgrounds. The PRS may need to be recalibrated or adapted for specific populations [76, 77].

PRS is a valuable tool for assessing an individual's genetic inclination towards specific diseases, allowing for personalized prevention and screening approaches. Moreover, PRS assists in disease identification, prognosis, and treatment selection, aiding in the identification of suitable candidates for clinical trials based on their genetic risk profiles. It is important to note that PRS analysis relies on statistical associations rather than causation, necessitating further research to validate the connection between genetic variants and understand the underlying biological mechanisms [78].

Overall, current GWAS investigations on COPD have provided valuable insights into the genetic foundations of this intricate condition. Although the identified genetic variants may exert only a modest influence and elucidate only a fraction of the genetic complexity of COPD, they offer valuable insights into the underlying biological processes associated with the disease. The replication findings presented here provide important information regarding lung function traits in the Taiwanese population with meaningful implications for both clinical practice and public health. The susceptibility genes identified in this study may serve as promising targets for future prevention and treatment strategies involving drug development and personalized therapeutic approaches [79, 80]. In this study, PRS demonstrated statistical significance based on genetic information, but future investigations with larger sample sizes have the potential to enhance the identification of highly representative genetic susceptibility loci, enabling the simplification of personalized PRS. This approach can further incorporate both genetic and environmental factors to identify individuals at heightened risk of developing COPD. The capacity for prediction or early diagnosis can guide timely management and intervention.

Our study is subject to some limitations. Firstly, while we acknowledge the influence of factors such as smoking, environmental exposures, socioeconomic status, and disease severity or specific phenotypes on COPD susceptibility, the incomplete records in the EMRs prevented us from including these variables in our analysis. This may have introduced bias into our results, given the established associations between these factors and COPD risk. Additionally, the limited number of cases available for analysis within the timeframe of our study resulted in insufficient statistical power (> 0.8; the necessary sample size would exceed 8000 cases), which may have affected the robustness of our findings. As a result, we were unable to explore potential associations between these factors and COPD susceptibility. To address these limitations, we will continue to collect more comprehensive patient data and collaborate with other medical centers to obtain replication cohorts for further validation in future studies.

Conclusions

This study performed GWAS and PRS construction using data from a Taiwanese cohort of 5,442 COPD cases and 17,681 non-COPD individuals as controls. Common and novel COPD susceptibility loci were identified and compared with previous GWAS results from different populations. Although no SNP reached the genome-wide significance, we identified WWTR1, EXT1, INTU, MAP3K7CL, MAMDC2, BZW1/CLK1, LINC01197, LINC01894, and CFAP95 (C9orf135) as prominent COPD susceptibility loci found in Taiwan. Furthermore, replication and confirmation of susceptibility loci between Taiwanese and other populations were achieved. The PRS results obtained in our study group or other population groups could be an effective tool for the quantification of polygenic contributions to COPD at the individual level. Our findings demonstrated a significant association between the PRS and COPD susceptibility in the study population. The established PRS model may serve as a valuable genetic tool for identifying individuals at a higher risk of developing COPD.

Availability of data and materials

Data supporting the findings of this study are available from the corresponding author upon request. GWAS summary statistics data link: https://my.locuszoom.org/gwas/255056/?token=42c73f97b16c476eb75b23a928aec182

References

  1. Labaki WW, Rosenberg SR. Chronic obstructive pulmonary disease. Ann Intern Med. 2020;173:ITC17–ITC32.

    Article  PubMed  Google Scholar 

  2. Yang IA, Jenkins CR, Salvi SS. Chronic obstructive pulmonary disease in never-smokers: risk factors, pathogenesis, and implications for prevention and treatment. Lancet Respir Med. 2022;10:497–511.

    Article  CAS  PubMed  Google Scholar 

  3. Silverman EK. Genetics of COPD. Annu Rev Physiol. 2020;82:413–31.

    Article  CAS  PubMed  Google Scholar 

  4. Cazzola M, Stolz D, Rogliani P, Matera MG. α1-Antitrypsin deficiency and chronic respiratory disorders. Eur Respir Rev. 2020;29: 190073.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Laddha AP, Kulkarni YA. VEGF and FGF-2: Promising targets for the treatment of respiratory disorders. Respir Med. 2019;156:33–46.

    Article  PubMed  Google Scholar 

  6. Seifart C, Dempfle A, Plagens A, Seifart U, Clostermann U, Müller B, Vogelmeier C, von Wichert P. TNF-alpha-, TNF-beta-, IL-6-, and IL-10-promoter polymorphisms in patients with chronic obstructive pulmonary disease. Tissue Antigens. 2005;65:93–100.

    Article  CAS  PubMed  Google Scholar 

  7. Saco TV, Breitzig MT, Lockey RF, Kolliputi N. Epigenetics of mucus hypersecretion in chronic respiratory diseases. Am J Respir Cell Mol Biol. 2018;58:299–309.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Ma T, Liu X, Liu Z. Functional polymorphisms in surfactant protein genes and chronic obstructive pulmonary disease risk: a meta-analysis. Genet Test Mol Biomarkers. 2013;17:910–7.

    Article  CAS  PubMed  Google Scholar 

  9. McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JP, Hirschhorn JN. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet. 2008;9:356–69

    Article  CAS  PubMed  Google Scholar 

  10. Chiou JS, Cheng CF, Liang WM, Chou CH, Wang CH, Lin WD, Chiu ML, Cheng WC, Lin CW, Lin TH, Liao CC, Huang SM, Tsai CH, Lin YJ, Tsai FJ. Your height affects your health: genetic determinants and health-related outcomes in Taiwan. BMC Med. 2022;20:250.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Castaldi PJ, Cho MH, Litonjua AA, Bakke P, Gulsvik A, Lomas DA, Anderson W, Beaty TH, Hokanson JE, Crapo JD, Laird N, Silverman EK, COPDGene and Eclipse Investigators. The association of genome-wide significant spirometric loci with chronic obstructive pulmonary disease susceptibility. Am J Respir Cell Mol Biol. 2011;45:1147–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Kim DK, Cho MH, Hersh CP, Lomas DA, Miller BE, Kong X, Bakke P, Gulsvik A, Agustí A, Wouters E, et al. Genome-wide association analysis of blood biomarkers in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2012;186:1238–47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Vestbo J, Anderson W, Coxson HO, Crim C, Dawber F, Edwards L, Hagan G, Knobil K, Lomas DA, MacNee W, Silverman EK, Tal-Singer R, ECLIPSE investigators. Evaluation of COPD longitudinally to identify predictive surrogate end-points (ECLIPSE). Eur Respir J. 2008;31:869–73.

    Article  CAS  PubMed  Google Scholar 

  14. Couper D, LaVange LM, Han M, Barr RG, Bleecker E, Hoffman EA, Kanner R, Kleerup E, Martinez FJ, Woodruff PG, Rennard S, SPIROMICS Research Group. Design of the Subpopulations and Intermediate Outcomes in COPD Study (SPIROMICS). Thorax. 2014;69:491–4.

    Article  PubMed  Google Scholar 

  15. Hobbs BD, de Jong K, Lamontagne M, Bossé Y, Shrine N, Artigas MS, Wain LV, Hall IP, Jackson VE, Wyss AB, et al. Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis. Nat Genet. 2017;49:426–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Sørheim IC, Gulsvik A. Genetics of chronic obstructive pulmonary disease: a case-control study in Bergen. Norway Clin Respir J. 2008;2(Suppl 1):129–31.

    Article  PubMed  Google Scholar 

  17. Shrine N, Izquierdo AG, Chen J, Packer R, Hall RJ, Guyatt AL, Batini C, Thompson RJ, Pavuluri C, Malik V, Hobbs BD, Moll M, Kim W, Tal-Singer R, Bakke P, et al. Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk. Nat Genet. 2023;55:410–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Cheng SL, Chan MC, Wang CC, Lin CH, Wang HC, Hsu JY, Hang LW, Chang CJ, Perng DW, Yu CJ. COPD in Taiwan: a national epidemiology survey. Int J Chron Obstruct Pulmon Dis. 2015;10:2459–67.

    PubMed  PubMed Central  Google Scholar 

  19. Wu CF, Feng NH, Chong IW, Wu KY, Lee CH, Hwang JJ, Huang CT, Lee CY, Chou ST, Christiani DC, Wu MT. Second-hand smoke and chronic bronchitis in Taiwanese women: a health-care based study. BMC Public Health. 2010;10:44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Huang HC, Lin FC, Wu MF, Nfor ON, Hsu SY, Lung CC, Ho CC, Chen CY, Liaw YP. Association between chronic obstructive pulmonary disease and PM2.5 in Taiwanese nonsmokers. Int J Hyg Environ Health. 2019;222:884–8.

    Article  CAS  PubMed  Google Scholar 

  21. Guo SE, Chi MC, Lin CM, Yang TM. Contributions of burning incense on indoor air pollution levels and on the health status of patients with chronic obstructive pulmonary disease. PeerJ. 2020;8: e9768.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Chen YC, Liu SF, Chin CH, Wu CC, Chen CJ, Chang HW, Wang YH, Chung YH, Chao TY, Lin MC. Association of tumor necrosis factor-alpha-863C/A gene polymorphism with chronic obstructive pulmonary disease. Lung. 2010;188:339–47.

    Article  CAS  PubMed  Google Scholar 

  23. Chen CZ, Ou CY, Wang RH, Lee CH, Lin CC, Chang HY, Hsiue TR. Association of Egr-1 and autophagy-related gene polymorphism in men with chronic obstructive pulmonary disease. J Formos Med Assoc. 2015;114:750–5.

    Article  CAS  PubMed  Google Scholar 

  24. Hou HH, Wang HC, Cheng SL, Chen YF, Lu KZ, Yu CJ. MMP-12 activates protease-activated receptor-1, upregulates placenta growth factor, and leads to pulmonary emphysema. Am J Physiol Lung Cell Mol Physiol. 2018;315:L432–42.

    Article  CAS  PubMed  Google Scholar 

  25. Zhou W, Kanai M, Wu KH, Rasheed H, Tsuo K, Hirbo JB, Wang Y, Bhattacharya A, Zhao H, Namba S, et al. Global Biobank meta-analysis initiative: Powering genetic discovery across human disease. Cell Genom. 2022;2: 100192.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Wei CY, Yang JH, Yeh EC, Tsai MF, Kao HJ, Lo CZ, et al. Genetic profiles of 103,106 individuals in the Taiwan biobank provide insights into the health and history of Han Chinese. npj Genom Med. 2021;6:10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Liu TY, Lin CF, Wu HT, Wu YL, Chen YC, Liao CC, et al. Comparison of multiple imputation algorithms and verification using whole-genome sequencing in the CMUH genetic biobank. Biomedicine (Taipei). 2021;11:57–65.

    Article  PubMed  Google Scholar 

  28. Kelly TN, Takeuchi F, Tabara Y, Edwards TL, Kim YJ, Chen P, et al. Genome-wide association study meta-analysis reveals transethnic replication of mean arterial and pulse pressure loci. Hypertension. 2013;62:853–9.

    Article  CAS  PubMed  Google Scholar 

  29. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience. 2015;4:7.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Browning BL, Zhou Y, Browning SR. A one-penny imputed genome from next-generation reference panels. Am J Hum Genet. 2018;103:338–48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Liao WL, Liu TY, Cheng CF, Chou YP, Wang TY, Chang YW, et al. Analysis of HLA variants and Graves’ disease and its comorbidities using a high resolution imputation system to examine electronic medical health records. Front Endocrinol (Lausanne). 2022;13: 842673.

    Article  PubMed  Google Scholar 

  32. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Ho DE, Imai K, King G, Stuart EA. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Polit Anal. 2007;15:199–236.

    Article  Google Scholar 

  34. Choi SW, O’Reilly PF. PRSice-2: Polygenic Risk Score software for biobank-scale data. Giga Science. 2019;8:giz082.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Liao WL, Huang YN, Chang YW, Liu TY, Lu HF, Tiao ZY, Su PH, Wang CH, Tsai FJ. Combining polygenic risk scores and human leukocyte antigen variants for personalized risk assessment of type 1 diabetes in the Taiwanese population. Diabetes Obes Metab. 2023;25:2928–36.

    Article  PubMed  Google Scholar 

  36. Skol AD, Scott LJ, Abecasis GR, Boehnke M. Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat Genet. 2006;38:209–13.

    Article  CAS  PubMed  Google Scholar 

  37. Sakornsakolpat P, Prokopenko D, Lamontagne M, Reeve NF, Guyatt AL, Jackson VE, Shrine N, Qiao D, Bartz TM, Kim DK, Lee MK, Latourelle JC, Li X, Morrow JD, Obeidat M, Wyss AB, et al. Genetic landscape of chronic obstructive pulmonary disease identifies heterogeneous cell-type and phenotype associations. Nat Genet. 2019;51:494–505.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ishigaki K, Akiyama M, Kanai M, Takahashi A, Kawakami E, Sugishita H, Sakaue S, Matoba N, Low SK, Okada Y, Terao C, Amariuta T, Gazal S, Kochi Y, Horikoshi M, Suzuki K, et al. Large-scale genome-wide association study in a Japanese population identifies novel susceptibility loci across different diseases. Nat Genet. 2020;52:669–79.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Kim W, Prokopenko D, Sakornsakolpat P, Hobbs BD, Lutz SM, Hokanson JE, Wain LV, Melbourne CA, Shrine N, Tobin MD, Silverman EK, Cho MH, Beaty TH. Genome-wide gene-by-smoking interaction study of chronic obstructive pulmonary disease. Am J Epidemiol. 2021;190:875–85.

    Article  PubMed  Google Scholar 

  40. Moll M, Jackson VE, Yu B, Grove ML, London SJ, Gharib SA, Bartz TM, Sitlani CM, Dupuis J, O’Connor GT, Xu H, Cassano PA, Patchen BK, Kim WJ, Park J, Kim KH, et al. A systematic analysis of protein-altering exonic variants in chronic obstructive pulmonary disease. Am J Physiol Lung Cell Mol Physiol. 2021;321(1):L130–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Sakaue S, Kanai M, Tanigawa Y, Karjalainen J, Kurki M, Koshiba S, Narita A, Konuma T, Yamamoto K, Akiyama M, Ishigaki K, Suzuki A, Suzuki K, Obara W, Yamaji K, Takahashi K, et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet. 2021;53:1415–24.

    Article  CAS  PubMed  Google Scholar 

  42. John C, Guyatt AL, Shrine N, Packer R, Olafsdottir TA, Liu J, Hayden LP, Chu SH, Koskela JT, Luan J, Li X, Terzikhan N, Xu H, Bartz TM, Petersen H, Leng S, et al. Genetic associations and architecture of asthma-COPD overlap. Chest. 2022;161:1155–66.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Cosentino J, Behsaz B, Alipanahi B, McCaw ZR, Hill D, Schwantes-An TH, Lai D, Carroll A, Hobbs BD, Cho MH, McLean CY, Hormozdiari F. Inference of chronic obstructive pulmonary disease with deep learning on raw spirograms identifies new genetic loci and improves risk models. Nat Genet. 2023;55:787–95.

    Article  CAS  PubMed  Google Scholar 

  44. Chen Y, Thomas PS, Kumar RK, Herbert C. The role of noncoding RNAs in regulating epithelial responses in COPD. Am J Physiol Lung Cell Mol Physiol. 2018;315:L184–92.

    Article  CAS  PubMed  Google Scholar 

  45. Zhang J, Zhu Y, Wang R. Long noncoding RNAs in respiratory diseases. Histol Histopathol. 2018;33:747–56.

    PubMed  Google Scholar 

  46. Devadoss D, Long C, Langley RJ, Manevski M, Nair M, Campos MA, Borchert G, Rahman I, Chand HS. Long noncoding transcriptome in chronic obstructive pulmonary disease. Am J Respir Cell Mol Biol. 2019;61:678–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Wang Y, Chen J, Chen W, Liu L, Dong M, Ji J, Hu D, Zhang N. LINC00987 Ameliorates COPD by regulating LPS-induced cell apoptosis, oxidative stress, inflammation and autophagy through Let-7b-5p/SIRT1 axis. Int J Chron Obstruct Pulmon Dis. 2020;15:3213–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Xie J, Wu Y, Tao Q, Liu H, Wang J, Zhang C, Zhou Y, Wei C, Chang Y, Jin Y, Ding Z. The role of lncRNA in the pathogenesis of chronic obstructive pulmonary disease. Heliyon. 2023;9: e22460.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Ducoli L, Agrawal S, Sibler E, Kouno T, Tacconi C, Hon CC, Berger SD, Müllhaupt D, He Y, Kim J, D’Addio M, Dieterich LC, Carninci P, de Hoon MJL, Shin JW, Detmar M. LETR1 is a lymphatic endothelial-specific lncRNA governing cell proliferation and migration through KLF4 and SEMA3C. Nat Commun. 2021;12:925.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. LaCanna R, Liccardo D, Zhang P, Tragesser L, Wang Y, Cao T, Chapman HA, Morrisey EE, Shen H, Koch WJ, Kosmider B, Wolfson MR, Tian Y. Yap/Taz regulate alveolar regeneration and resolution of lung inflammation. J Clin Invest. 2019;129:2107–22.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Cao Y, Pan H, Yang Y, Zhou J, Zhang G. Screening of potential key ferroptosis-related genes in Chronic Obstructive Pulmonary Disease. Int J Chron Obstruct Pulmon Dis. 2023;18:2849–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Dixon SJ, Lemberg KM, Lamprecht MR, Skouta R, Zaitsev EM, Gleason CE, Patel DN, Bauer AJ, Cantley AM, Yang WS, Morrison B 3rd, Stockwell BR. Ferroptosis: an iron-dependent form of nonapoptotic cell death. Cell. 2012;149:1060–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Han C, Liu Y, Dai R, Ismail N, Su W, Li B. Ferroptosis and its potential role in human diseases. Front Pharmacol. 2020;11:239.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Ho T, Nichols M, Nair G, et al. Iron in airway macrophages and infective exacerbations of chronic obstructive pulmonary disease. Respir Res. 2022;23:8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Meng D, Zhu C, Jia R, Li Z, Wang W, Song S. The molecular mechanism of ferroptosis and its role in COPD. Front Med Lausanne. 2022;9:1052540.

    Article  PubMed  Google Scholar 

  56. Lin WD, Hwu WL, Wang CH, Tsai FJ. Mutant EXT1 in Taiwanese patients with multiple hereditary exostoses. Biomedicine (Taipei). 2014;4:11.

    Article  PubMed  Google Scholar 

  57. Swart M, Troeberg L. Effect of polarization and chronic Inflammation on macrophage expression of heparan sulfate proteoglycans and biosynthesis enzymes. J Histochem Cytochem. 2019;67:9–27.

    Article  CAS  PubMed  Google Scholar 

  58. Sung YJ, Winkler TW, de Las FL, Bentley AR, Brown MR, Kraja AT, Schwander K, Ntalla I, Guo X, Franceschini N, Lu Y, et al. A large-scale multi-ancestry genome-wide study accounting for smoking behavior identifies multiple significant loci for blood pressure. Am J Hum Genet. 2018;102:375–400.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Niu L, Guo W, Song X, Song X, Xie L. Tumor-educated leukocytes mRNA as a diagnostic biomarker for non-small cell lung cancer. Thorac Cancer. 2021;12:737–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Lee H, Park BC, Soon Kang J, Cheon Y, Lee S, Jae MP. MAM domain containing 2 is a potential breast cancer biomarker that exhibits tumour-suppressive activity. Cell Prolif. 2020;53: e12883.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Ge J, Mu S, Xiao E, Tian G, Tao L, Li D. Expression, oncological and immunological characterizations of BZW1/2 in pancreatic adenocarcinoma. Front Genet. 2022;13:1002673.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Zhao L, Song C, Li Y, Yuan F, Zhao Q, Dong H, Liu B. BZW1 as an oncogene is associated with patient prognosis and the immune microenvironment in glioma. Genomics. 2023;115: 110602.

    Article  CAS  PubMed  Google Scholar 

  63. Zhang J, Pi SB, Zhang N, Guo J, Zheng W, Leng L, Lin G, Fan HY. Translation regulatory factor BZW1 regulates preimplantation embryo development and compaction by restricting global non-AUG Initiation. Nat Commun. 2022;13:6621.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Lindberg MF, Meijer L. Dual-specificity, tyrosine phosphorylation-regulated kinases (DYRKs) and cdc2-like kinases (CLKs) in human disease, an overview. Int J Mol Sci. 2021;22:6047.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Bruel AL, Franco B, Duffourd Y, Thevenon J, Jego L, Lopez E, Deleuze JF, Doummar D, Giles RH, Johnson CA, et al. Fifteen years of research on oral-facial-digital syndromes: from 1 to 16 causal genes. J Med Genet. 2017;54:371–80.

    Article  CAS  PubMed  Google Scholar 

  66. Martín-Salazar JE, Valverde D. CPLANE Complex and Ciliopathies Biomolecules. 2022;12:847.

    PubMed  Google Scholar 

  67. Chan HYE, Chen ZS. Multifaceted investigation underlies diverse mechanisms contributing to the downregulation of Hedgehog pathway-associated genes INTU and IFT88 in lung adenocarcinoma and uterine corpus endometrial carcinoma. Aging (Albany NY). 2022;14:7794–823.

    Article  PubMed  Google Scholar 

  68. Zhou S, Liu Y, Ma Y, Zhang X, Li Y, Wen J. C9ORF135 encodes a membrane protein whose expression is related to pluripotency in human embryonic stem cells. Sci Rep. 2017;7:45311.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Yang L, Yang Z, Zuo C, Lv X, Liu T, Jia C, Chen H. Epidemiological evidence for associations between variants in CHRNA genes and risk of lung cancer and chronic obstructive pulmonary disease. Front Oncol. 2022;12:1001864.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Röhl A, Baek SH, Kachroo P, Morrow JD, Tantisira K, Silverman EK, Weiss ST, Sharma A, Glass K, DeMeo DL. Protein interaction networks provide insight into fetal origins of chronic obstructive pulmonary disease. Respir Res. 2022;23:69.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Morrow JD, Cho MH, Platig J, Zhou X, DeMeo DL, Qiu W, Celli B, Marchetti N, Criner GJ, Bueno R, Washko GR, Glass K, Quackenbush J, Silverman EK, Hersh CP. Ensemble genomic analysis in human lung tissue identifies novel genes for chronic obstructive pulmonary disease. Hum Genomics. 2018;12:1.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Vucic EA, Chari R, Thu KL, Wilson IM, Cotton AM, Kennett JY, Zhang M, Lonergan KM, Steiling K, Brown CJ, McWilliams A, Ohtani K, Lenburg ME, Sin DD, Spira A, Macaulay CE, Lam S, Lam WL. DNA methylation is globally disrupted and associated with expression changes in chronic obstructive pulmonary disease small airways. Am J Respir Cell Mol Biol. 2014;50:912–22.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Yuan JM, Nelson HH, Carmella SG, Wang R, Kuriger-Laber J, Jin A, Adams-Haduch J, Hecht SS, Koh WP, Murphy SE. CYP2A6 genetic polymorphisms and biomarkers of tobacco smoke constituents in relation to risk of lung cancer in the Singapore Chinese Health Study. Carcinogenesis. 2017;38:411–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Yamashita S, Tomita K. Mechanism of U6 snRNA oligouridylation by human TUT1. Nat Commun. 2023;14:4686.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Soler Artigas M, Wain LV, Miller S, Kheirallah AK, Huffman JE, Ntalla I, Shrine N, Obeidat M, Trochet H, McArdle WL, et al. Sixteen new lung function signals identified through 1000 Genomes Project reference panel imputation. Nat Commun. 2015;6:8658.

    Article  PubMed  Google Scholar 

  76. Ruan Y, Lin YF, Feng YA, Chen CY, Lam M, Stanley Global Asia Initiatives, Guo Z, He L, Sawa A, Martin AR, Qin S, Huang H, Ge T. Improving polygenic prediction in ancestrally diverse populations. Nat Genet. 2022;54:573–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Wang Y, Tsuo K, Kanai M, Neale BM, Martin AR. Challenges and opportunities for developing more generalizable polygenic risk scores. Annu Rev Biomed Data Sci. 2022;5:293–320.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Lambertx SA, Abraham G, Inouye M. Towards clinical utility of polygenic risk scores. Hum Mol Genet. 2019;28:R133–42.

    Article  Google Scholar 

  79. Liao WL, Tsai FJ. Personalized medicine: a paradigm shift in healthcare. Biomedicine. 2013;3:66–72.

    Article  Google Scholar 

  80. Tsai FJ, Ho TJ, Cheng CF, Liu X, Tsang H, Lin TH, Liao CC, Huang SM, Li JP, Lin CW, Lin JG, Lin JC, Lin CC, Liang WM, Lin YJ. Effect of Chinese herbal medicine on stroke patients with type 2 diabetes. J Ethnopharmacol. 2017;200:31–44.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by a grant from the China Medical University Hospital, Taichung, Taiwan (# DMR 112-143).

Funding

This work was supported by a grant from the China Medical University Hospital, Taichung, Taiwan (# DMR 112–143).

Author information

Authors and Affiliations

  1. Department of Medical Research, China Medical University Hospital, Taichung, 404327, Taiwan

    Wei-De Lin & Fuu-Jen Tsai

  2. School of Post Baccalaureate Chinese Medicine, China Medical University, Taichung, 404333, Taiwan

    Wei-De Lin

  3. Graduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical University, Taichung, 404333, Taiwan

    Wen-Ling Liao

  4. Center for Personalized Medicine, China Medical University Hospital, Taichung, 404327, Taiwan

    Wen-Ling Liao

  5. Department of Internal Medicine, Pulmonary and Critical Care Medicine, China Medical University Hospital, Taichung, 404333, Taiwan

    Wei-Cheng Chen

  6. Graduate Institute of Biomedical Sciences, China Medical University, Taichung, 404327, Taiwan

    Wei-Cheng Chen

  7. Department of Medical Research, Million-Person Precision Medicine Initiative, China Medical University Hospital, Taichung, 404327, Taiwan

    Ting-Yuan Liu & Yu-Chia Chen

  8. School of Chinese Medicine, China Medical University, Taichung, 404333, Taiwan

    Fuu-Jen Tsai

  9. Division of Genetics and Metabolism, China Medical University Children’s Hospital, Taichung, 404327, Taiwan

    Fuu-Jen Tsai

  10. Department of Medical Genetics, China Medical University Hospital, Taichung, 404327, Taiwan

    Fuu-Jen Tsai

  11. Department of Medical Laboratory Science and Biotechnology, Asia University, Taichung, 413305, Taiwan

    Fuu-Jen Tsai

  12. Department of Medical Research, China Medical University Hospital, No. 2, Yude Road, North District, Taichung, 404327, Taiwan

    Fuu-Jen Tsai

Authors
  1. Wei-De Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wen-Ling Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei-Cheng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ting-Yuan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yu-Chia Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Fuu-Jen Tsai
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

WDL performed data curation, formal analysis, writing, review, and editing the manuscript; WLL carried out investigation, formal analysis, writing, and review the manuscript; WCC provided conceptualization and clinical information of study and manuscript revision; TYL performed data acquisition, analysis, drafting, and manuscript revision; YCC carried out data acquisition, analysis and writing; FJT provided conceptualization and design of study, supervision, and manuscript revision; all authors read and approved the final manuscript.

Corresponding author

Correspondence to Fuu-Jen Tsai.

Ethics declarations

Ethics approval and consent to participate

The China Medical University Hospital's Precision Medicine Project, initiated in 2018, gathered biospecimens and recruited participants from hospital visitors with the approval by the Research Ethics Committee of China Medical University Hospital, Taichung, Taiwan (CMUH-107-REC3-058, CMUH-110-REC3-005, and CMUH-110-REC1-095). Informed consent was obtained from all participants. Blood samples were collected from each participant and clinical information was collected from the electronic medical records (EMRs) of CMUH between 2003 and 2021, with approval by the Research Ethics Committee of China Medical University Hospital, Taichung, Taiwan (CMUH-110-REC1-095). All the experimental procedures were performed by the standards of the Declaration of Helsinki 1964.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, WD., Liao, WL., Chen, WC. et al. Genome-wide association study identifies novel susceptible loci and evaluation of polygenic risk score for chronic obstructive pulmonary disease in a Taiwanese population. BMC Genomics 25, 607 (2024). https://0-doi-org.brum.beds.ac.uk/10.1186/s12864-024-10526-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://0-doi-org.brum.beds.ac.uk/10.1186/s12864-024-10526-5

Keywords

BMC Genomics

ISSN: 1471-2164

Contact us