Abstract
Background
Mutations in ADGRG6 are associated with a variety of cancers and multiple types of diseases. However, the impact of genetic variations in ADGRG6 on chronic obstructive pulmonary disease (COPD) susceptibility has not yet been evaluated.
Methods
Considering the high prevalence of COPD among the elderly population in China, this study specifically targets the elderly Han population in Southern China as the study subject. Following the acquisition of participants’ whole-genome DNA, genotyping was conducted using the Agena MassARRAY platform. The online tool ‘SNPStats’, which utilizes logistic regression, was employed to analyze and assess the correlation. Multi-factor dimensionality reduction was utilized to clarify the impact of “SNP-SNP” interactions on COPD risk. The False-Positive Report Probability (FPRP) was applied to determine whether significant results are noteworthy findings.
Results
The mutant allele “C” of rs11155242 was a protective genetic factor against COPD susceptibility (OR = 0.57, 95% CI = 0.36 to 0.91, p = 0.017). The heterozygous mutant genotype “CA” of rs11155242 was found to be significantly associated with reduced COPD risk (CA Vs AA: OR = 0.53, 95% CI = 0.32 to 0.90, p = 0.018). ADGRG6-rs11155242 was found to be strongly associated with a reduced risk of COPD in males, non-smokers, and subjects with a BMI below 24 kg/m2 (OR < 1, p < 0.05). The FPRP analysis indicated that the positive results identified in this study are noteworthy new findings.
Conclusion
The mutant allele “C” and mutant genotype “CA” of rs11155242 act as protective genetic factors against COPD susceptibility. This study will provide a new research direction for the personalized prevention and treatment of COPD in the elderly Han population in southern China, and lay a potential scientific basis.
Keywords: COPD, ADGRG6, genetic variants, mutant allele
Introduction
Chronic Obstructive Pulmonary Disease (COPD) represents a diverse group of pulmonary disorders marked by ongoing breathing difficulties such as shortness of breath, coughing, and sputum production. These symptoms stem from lasting impairments in the bronchial tubes (bronchitis, bronchiolitis), air sacs (emphysema), and/or blood vessels of the lungs. A definitive diagnosis is made through spirometry, which measures airflow obstruction, and/or by other objective measures indicating lung structural or functional impairment.1,2 As a major public health concern, COPD has emerged as the most common chronic respiratory disease worldwide.3,4 In China, it represents a substantial threat to the health and well-being of residents. From 2012 to 2015, the overall prevalence of COPD in adults over 20 was 8.6%. Notably, the prevalence was only 2.1% among adults aged 20 to 39, but it rose sharply to 13.7% in those aged 40 and above.5,6 These figures highlight a particularly high prevalence among the middle-aged and elderly populations in China, a trend closely linked to aging and prolonged exposure to risk factors. Despite advancements in medical care and increased public health awareness in recent years, the prevention and control of COPD remain critically important.
The development of COPD involves a complex interplay of multiple factors, including genetic contributions and environmental influences. Both environmental and genetic factors synergistically influence the pathogenesis and progression of COPD. Smoking and air pollution are recognized as primary risk factors for COPD,7–9 while occupational and solid biofuel exposures are also acknowledged contributors to its onset and progression.10,11 With industrialization and rapid economic growth, air pollution has become a serious problem in China. There are significant differences in air quality between the northern and southern regions of China, which leads to substantial differences in the prevalence and risk of COPD in the different areas of China.12,13 In addition to the above environmental factors, the familial clustering of COPD highlights the importance of genetic determinants in its development.14 Single nucleotide polymorphisms (SNPs), which are DNA sequence variations caused by changes in a single nucleotide, represent a common form of genetic variation. Beyond the well-established genetic risk factor of alpha-1 antitrypsin deficiency, polymorphisms in several other genes, including HIF1A, CDKN1A, IREB2, ADAM33, ERBB2, ATG16L1, and BAG3, have been implicated in COPD pathogenesis. These genetic variations can influence inflammatory responses, oxidative stress, and the pathological remodeling of lung tissue, thereby playing a crucial role in the emergence and development of COPD.15–17 Although many SNPs related to COPD susceptibility have been identified, the specific pathogenesis of COPD remains unclear. Based on the above, it is necessary to identify genetic markers associated with COPD risk in specific populations.
Adhesion G protein-coupled receptors (aGPCRs) are a complex subgroup within the G protein-coupled receptors (GPCRs) family. aGPCRs are distinguished by their intricate structure, which incorporates a chimeric composition of various functional protein domains. This unique structure enables the integration of extracellular cell adhesion functions with intracellular GPCR signal transduction.18 aGPCRs have a wide distribution and play pivotal roles in immune responses, tumorigenesis, and numerous developmental processes.18,19 Specific aGPCRs, such as ADGRF520 and ADGRB3,21 are linked to lung diseases. Gpr126/ADGRG6, an aGPCR of particular interest, is critical for Schwann cell (SC) myelination among other functions.22,23 Research has revealed that mutations in ADGRG6 are associated with various cancers and diseases.24–26 Notably, a study has identified a functional variant in ADGRG6, which plays a role in gas exchange and hypoxia.27 This variant exhibits differential expression in the lung tissues of COPD patients and individuals with reduced diffusion capacity. While recent studies have underscored the biological significance of ADGRG6 in the development of various diseases, including lung diseases, the specific mechanisms by which this gene influences the onset and progression of COPD in the Chinese population require further exploration.
Focusing on the role of genetic factors in COPD predisposition, our investigation is directed towards examining the influence of genetic variations within the ADGRG6 gene on the susceptibility to COPD among Han Chinese individuals. This research endeavor will pioneer the exploration of the correlation between ADGRG6 genetic predisposition and COPD in the elderly Han population in southern China, offering novel insights for mitigating COPD incidence at the preventative stage. By doing so, the study aspires to contribute to the scientific groundwork necessary for a deeper understanding of ADGRG6’s role in COPD pathogenesis and to inform personalized approaches to COPD prevention and treatment tailored to the elderly Han population in Southern China.
Materials and Methods
Recruitment of Participants
Between August 2020 and January 2023, a cohort of 270 COPD patients was identified from those attending and undergoing treatment at Hainan General Hospital, based on the study’s eligibility requirements. The key diagnostic criterion for COPD was established as a ratio of forced expiratory volume in one second to forced vital capacity (FEV1/FVC) below 70%.28
The inclusion criteria for the COPD case group included: (1) age over 60 years; (2) no prior hormone or immunomodulatory treatment within one month of study entry; (3) no mental health disorders and evidence of compliance; (4) consent to participate, as evidenced by a signed informed consent document.
The exclusion criteria for the COPD case group included: (1) cardiogenic asthma, pulmonary tumors, or allergic alveolitis; serious comorbidities in other organs; (2) a history of thoracic or abdominal surgery within the last three months; (3) non-adherence to the study protocol or withdrawal without authorization.
During the same period, 271 individuals without pulmonary diseases, as confirmed by medical assessment, and free from other organ diseases and recent respiratory infections, were recruited from the hospital’s health check-up center to constitute the control group.
This study received approval from the hospital’s Ethics Committee and was conducted under its supervision. We are committed that our study complies with the Declaration of Helsinki.
Selection of Candidate Genetic Variants
To identify the physical location of ADGRG6, we used it as a keyword in the Ensembl Genome Browser GRCh38.p14 version (http://asia.ensembl.org/Homo_sapiens/Gene/Summary?db=core;g=ENSG00000112414;r=6:142301854-142446266). This led to the download of two essential files, “.info” and “.ped”, associated with ADGRG6, from the “VCF to PED Converter” function module on the Ensembl Tools website (http://asia.ensembl.org/info/docs/tools/index.html). These files were then imported into the Haploview software. Within Haploview, the tagSNPs of ADGRG6 were determined after setting specific parameters in the Tagger module (MAF > 0.05, r2 = 0.80, Min Genotype > 75%, and HWE P-value > 0.01).29 To refine our research focus, we selected six ADGRG6 variants from tagSNPs file (rs9399401,30 rs116955726, rs35699755, rs11155242,31 rs2143390,32 and rs693712133) as candidate genetic variants for this study, based on previous studies examining correlations in populations with varied genetic backgrounds.
DNA Extraction and Genotyping
Upon obtaining informed consent from all participants, we collected their peripheral blood for storage. Whole genome DNA was extracted using the DNA extraction and purification kit from GoldMag Nanobiotech Co., Ltd., Xi’an, China. The Agena MassARRAY platform (Agena Bioscience, San Diego, California, USA) was utilized for genotyping. The design of all primers was accomplished using the “MassARRAY Assay Design” online software (https://support.agenabio.com/s/online-tools), and the sequences of these primers are detailed in Supplemental Table 1.
Statistical Analysis
The candidate variants were assessed for Hardy-Weinberg Equilibrium (HWE) by comparing the observed genotype frequencies against the expected frequencies in the control group. The online tool “SNPStats” (https://www.snpstats.net/start.htm?q=snpstats/start.htm) was utilized to evaluate the association between candidate ADGRG6 variants and COPD susceptibility. All statistical analyses were conducted using SPSS software version 22.0 (SPSS Inc., Chicago, IL, USA). Measurement data, tested for normal distribution, were presented as “mean ± standard deviation” when conforming to normal distribution. The independent sample t-test was employed for comparisons between two groups. Count data were expressed as “n (%)” and analyzed using the chi-square (χ2) test. Furthermore, the False-Positive Report Probability (FPRP) test was applied to evaluate whether all significant results were noteworthy findings.34 Additionally, Multi-factor Dimensionality Reduction (MDR) analysis was conducted to assess the impact of interactions between candidate genetic variants on COPD susceptibility. Results with a “p-value < 0.05” were considered statistically significant.
Results
Participant Characteristics
A total of 541 han participants of Southern China met the recruitment criteria, comprising 270 COPD cases (206 males and 64 females) and 271 healthy individuals (210 males and 61 females). The average age in the case group was 72.26 ± 10.40 years, while it was 69.21 ± 6.61 years in the control group. In the healthy control group, there were 136 (50.2%) smokers and 135 (49.8%) non-smokers; 133 (49.1%) drinkers, and 138 (50.9%) non-drinkers. In the case group, there were 128 (47.4%) smokers and 142 (52.6%) non-smokers; 126 (46.7%) drinkers, and 144 (53.3%) non-drinkers; with an average FEV1/FVC (%) of (58.56 ± 15.05) %. Detailed participant characteristics are presented in Table 1.
Table 1.
Basic Characteristics of Participants (Cases with COPD and Healthy Individuals)
| Characteristics | Cases | Control | p-value | |
|---|---|---|---|---|
| n = 270 | n = 271 | |||
| Age (years) | Mean ± SD | 72.26 ± 10.40 | 69.21 ± 6.61 | < 0.0001a |
| > 71 years old | 162 (60.0%) | 91(33.6%) | ||
| ≤ 71 years old | 108 (40.0%) | 180 (66.4%) | ||
| Gender | Male | 206 (76.3%) | 210 (77.5%) | 0.742b |
| Female | 64 (23.7%) | 61 (22.5%) | ||
| Smoking status | Yes | 128 (47.4%) | 136 (50.2%) | 0.518b |
| No | 142 (52.6%) | 135 (49.8%) | ||
| Drinking status | Yes | 126 (46.7%) | 133 (49.1%) | 0.575b |
| No | 144 (53.3%) | 138 (50.9%) | ||
| BMI | ≥ 24 kg/m2 | 57 (21.1%) | 65 (24.0%) | 0.350b |
| < 24 kg/m2 | 213 (78.9%) | 206 (76.0%) | ||
| FVC (L) | 1.98 ± 0.67 | |||
| FEV1 (L) | 1.18 ± 0.54 | |||
| FEV1/FVC (%) | 58.56 ± 15.05 | |||
Note: a represents the p-value calculated by the t-test; b represents the p-value calculated by the chi-square test.
Abbreviations: COPD, chronic obstructive pulmonary disease; FVC, forced vital capacity; FEV1, forced the first second of expiratory volume;
Genotyping of Candidate ADGRG6 Genetic Variants
The ADGRG6 gene is located at Chromosome 6: 142,301,854–142,446,266. There are 36704 variants for this gene. After screening, a total of 6 genetic variations were selected as candidates. Candidate genetic variants (rs9399401, rs116955726, rs35699755, rs11155242, rs2143390, and rs6937121) have been successfully genotyped. All candidate genetic variants conformed to Hardy-Weinberg equilibrium (HWE P-value > 0.01). The minor allele frequencies (MAF) of candidate SNPs in the participants and different genetic backgrounds (Han population in Shanghai/Beijing, China; African; and European) are summarized in Table 2. Notable differences in the MAF of candidate genetic variants among different populations were observed. This finding suggests that it is necessary to identify SNPs associated with COPD susceptibility in different populations.
Table 2.
The Basic Information About the Candidate ADGRG6 Genetic Variants and HWE Analysis
| SNP ID | Function | Chr: Position | Alleles (A/B) |
MAF | HWE (P value) |
Haploreg 4.1 | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Cases | Controls | CHB | CHS | AFR | EUR | ||||||
| rs9399401 | intronic | 6: 142347764 | C/T | 0.318 | 0.306 | 0.286 | 0.367 | 0.424 | 0.309 | 0.061 | Enhancer histone marks; Selected eQTL hits |
| rs116955726 | missense | 6: 142367674 | T/C | 0.061 | 0.065 | 0.058 | 0.052 | 0.021 | 0.001 | 1.000 | SiPhy cons; Promoter histone marks; Enhancer histone marks; DNAse; Proteins bound; Motifs changed |
| rs35699755 | synonymous | 6: 142367690 | T/C | 0.059 | 0.063 | 0.058 | 0.052 | 0.083 | 0.001 | 1.000 | SiPhy cons; Promoter histone marks; Enhancer histone marks; DNAse; Proteins bound; Motifs changed |
| rs11155242 | missense | 6: 142370412 | C/A | 0.057 | 0.096 | 0.083 | 0.090 | 0.230 | 0.228 | 0.285 | Enhancer histone marks; DNAse; Motifs changed; GRASP QTL hits |
| rs2143390 | synonymous | 6: 142382000 | T/C | 0.248 | 0.188 | 0.194 | 0.262 | 0.006 | 0.082 | 0.044 | SiPhy cons; Promoter histone marks; Enhancer histone marks; DNAse |
| rs6937121 | intronic | 6: 142385996 | G/T | 0.374 | 0.367 | 0.350 | 0.419 | 0.939 | 0.320 | 0.694 | Promoter histone marks; Enhancer histone marks; Motifs changed; Selected eQTL hits |
Note: HWE P value > 0.01 indicates that the genotypes were in Hard-Weinberg Equilibrium.
Abbreviations: A: minor allele; B: wild-type allele; HWE: Hardy–Weinberg equilibrium; SNP: Single nucleotide polymorphisms; MAF: minor allele frequency.
Effect of Candidate ADGRG6 Genetic Variants on COPD Susceptibility
The overall analysis results, as shown in Table 3, indicate that the frequency of the mutant allele “C” of rs11155242 in COPD cases (7.74%) is significantly lower compared to the healthy control group (9.59%). This suggests that the mutant allele “C” of rs11155242 acts as a protective genetic factor against COPD susceptibility in the study subjects (OR = 0.57, 95% CI = 0.36 to 0.91, p = 0.017). Additionally, the heterozygous mutant genotype “CA” of rs11155242 was found to be significantly less frequent in COPD cases (10%) than in the healthy control group (16.2%), indicating a significant association with reduced COPD risk (CA Vs AA: OR = 0.53, 95% CI = 0.32 to 0.90, p = 0.018). Furthermore, under the dominant (OR = 0.53, p = 0.011), overdominant (OR = 0.54, p = 0.018), and log-additive (OR = 0.57, p = 0.013) genetic models, rs11155242 demonstrated a significant association with a reduced risk of COPD among the subjects.
Table 3.
The Association Analysis Between Candidate ADGRG6 Genetic Variants and Susceptibility to COPD
| SNP ID | Model | Genotype | control | case | OR (95% CI) | p-value |
|---|---|---|---|---|---|---|
| rs9399401 | Allele | T | 375 (69.44%) | 367 (68.22%) | 1 | |
| C | 165 (30.56%) | 171 (31.78%) | 1.06 (0.82–1.37) | 0.663 | ||
| Codominant | TT | 137 (50.7%) | 126 (46.8%) | 1 | ||
| TC | 101 (37.4%) | 115 (42.8%) | 1.12 (0.77–1.62) | 0.545 | ||
| CC | 32 (11.8%) | 28 (10.4%) | 0.90 (0.51–1.60) | 0.725 | ||
| Dominant | TT | 137 (50.7%) | 126 (46.8%) | 1 | ||
| TC-CC | 133 (49.3%) | 143 (53.2%) | 1.07 (0.76–1.51) | 0.710 | ||
| Recessive | TT-TC | 238 (88.2%) | 241 (89.6%) | 1 | ||
| CC | 32 (11.8%) | 28 (10.4%) | 0.86 (0.50–1.48) | 0.580 | ||
| Overdominant | TT-CC | 169 (62.6%) | 154 (57.2%) | 1 | ||
| TC | 101 (37.4%) | 115 (42.8%) | 1.14 (0.80–1.63) | 0.460 | ||
| Log-additive | - | - | - | 1.00 (0.78–1.29) | 0.990 | |
| rs116955726 | Allele | C | 507 (93.54%) | 507 (93.89%) | 1 | |
| T | 35 (6.46%) | 33 (6.11%) | 0.94 (0.58–1.54) | 0.814 | ||
| Codominant | CC | 237 (87.5%) | 237 (87.8%) | 1 | ||
| CT | 33 (12.2%) | 33 (12.2%) | 0.99 (0.58–1.67) | 0.961 | ||
| TT | 1 (0.4%) | 0 (0%) | / | / | ||
| Dominant | CC | 237 (87.5%) | 237 (87.8%) | 1 | ||
| CT-TT | 34 (12.6%) | 33 (12.2%) | 0.95 (0.57–1.60) | 0.850 | ||
| Recessive | CC-CT | 270 (99.6%) | 270 (100%) | 1 | ||
| TT | 1 (0.4%) | 0 (0%) | / | / | ||
| Overdominant | CC-TT | 238 (87.8%) | 237 (87.8%) | 1 | ||
| CT | 33 (12.2%) | 33 (12.2%) | 0.99 (0.59–1.67) | 0.970 | ||
| Log-additive | - | - | - | 0.92 (0.55–1.53) | 0.740 | |
| rs35699755 | Allele | C | 508 (93.73%) | 508 (94.07%) | 1 | |
| T | 34 (6.27%) | 32 (5.93%) | 0.94 (0.57–1.55) | 0.811 | ||
| Codominant | CC | 238 (87.8%) | 238 (88.2%) | 1 | ||
| TC | 32 (11.8%) | 32 (11.8%) | 0.98 (0.58–1.67) | 0.947 | ||
| TT | 1 (0.4%) | 0 (0%) | / | / | ||
| Dominant | CC | 238 (87.8%) | 238 (88.2%) | 1 | ||
| TC-TT | 33 (12.2%) | 32 (11.8%) | 0.95 (0.56–1.60) | 0.840 | ||
| Recessive | CC-TC | 270 (99.6%) | 270 (100%) | 1 | ||
| TT | 1 (0.4%) | 0 (0%) | / | / | ||
| Overdominant | CC-TT | 239 (88.2%) | 238 (88.2%) | 1 | ||
| TC | 32 (11.8%) | 32 (11.8%) | 0.99 (0.58–1.68) | 0.960 | ||
| Log-additive | - | - | - | 0.91 (0.55–1.53) | 0.730 | |
| rs11155242 | Allele | A | 490 (90.41%) | 509 (94.26%) | 1 | |
| C | 52 (9.59%) | 31 (7.74%) | 0.57 (0.36–0.91) | 0.017 | ||
| Codominant | AA | 223 (82.3%) | 241 (89.3%) | 1 | ||
| CA | 44 (16.2%) | 27 (10%) | 0.53 (0.32–0.9) | 0.018 | ||
| CC | 4 (1.5%) | 2 (0.7%) | 0.47 (0.08–2.6) | 0.385 | ||
| Dominant | AA | 223 (82.3%) | 241 (89.3%) | 1 | ||
| CA-CC | 48 (17.7%) | 29 (10.7%) | 0.53 (0.32–0.87) | 0.011 | ||
| Recessive | AA-CA | 267 (98.5%) | 268 (99.3%) | 1 | ||
| CC | 4 (1.5%) | 2 (0.7%) | 0.51 (0.09–2.82) | 0.420 | ||
| Overdominant | AA-CC | 227 (83.8%) | 243 (90%) | 1 | ||
| CA | 44 (16.2%) | 27 (10%) | 0.54 (0.32–0.90) | 0.018 | ||
| Log-additive | - | - | - | 0.57 (0.36–0.90) | 0.013 | |
| rs6937121 | Allele | T | 342 (63.33%) | 338 (62.59%) | 1 | |
| G | 198 (36.67%) | 202 (37.41%) | 1.03 (0.81–1.32) | 0.801 | ||
| Codominant | TT | 110 (40.7%) | 107 (39.6%) | 1 | ||
| GT | 122 (45.2%) | 124 (45.9%) | 0.95 (0.65–1.38) | 0.781 | ||
| GG | 38 (14.1%) | 39 (14.4%) | 0.98 (0.58–1.67) | 0.941 | ||
| Dominant | TT | 110 (40.7%) | 107 (39.6%) | 1 | ||
| GT-GG | 160 (59.3%) | 163 (60.4%) | 0.96 (0.67–1.36) | 0.800 | ||
| Recessive | TT-GT | 232 (85.9%) | 231 (85.6%) | 1 | ||
| GG | 38 (14.1%) | 39 (14.4%) | 1.01 (0.62–1.65) | 0.970 | ||
| Overdominant | TT-GG | 148 (54.8%) | 146 (54.1%) | 1 | ||
| GT | 122 (45.2%) | 124 (45.9%) | 0.95 (0.67–1.35) | 0.790 | ||
| Log-additive | - | - | - | 0.98 (0.76–1.26) | 0.870 |
Note: “-” indicates Log-additive model, “/” indicates missing data. p-value < 0.05’ and bold text represent statistical significance.
Abbreviations: COPD, chronic obstructive pulmonary disease; SNP: Single nucleotide polymorphism; OR, Odds ratio; CI, Confidence interval.
The other genetic variants, rs9399401, rs116955726, rs35699755, rs2143390, and rs6937121, were not found to be associated with COPD susceptibility in the study subjects.
FPRP results showed that the positive results found in the overall analysis were all noteworthy findings at a probability level of 0.25 and FPRP cut-off value of 0.2 (Supplemental Table 2).
Stratified Analysis of Subgroups
We performed stratified analyses to assess the impact of potential influencing factors, including gender, age, smoking/drinking status, and BMI, on COPD susceptibility. The positive findings from these analyses are depicted in a forest plot, as illustrated in Figure 1.
Figure 1.
Forest map: significant association between ADGRG6 genetic variants and chronic obstructive pulmonary disease (COPD) susceptibility in stratified analysis.
Notably, rs11155242 was found to be strongly associated with a reduced risk of COPD. This association was particularly significant in males (dominant: OR = 0.53, p = 0.032; log-additive: OR = 0.55, p = 0.025), subjects aged 71 years or younger (codominant: OR = 0.35, p = 0.032; overdominant: OR = 0.35, p = 0.020), non-smokers (allele: OR = 0.46, p = 0.014; codominant: OR = 0.44, p = 0.030; dominant: OR = 0.42, p = 0.014; log-additive: OR = 0.46, p = 0.013), non-drinkers (dominant: OR = 0.47, p = 0.042; overdominant: OR = 0.45, p = 0.040), and subjects with a BMI below 24 kg/m2 (allele: OR = 0.56, p = 0.026; codominant: OR = 0.50, p = 0.024; dominant: OR = 0.51, p = 0.019; overdominant: OR = 0.51, p = 0.023; log-additive: OR = 0.56, p = 0.025).
FPRP analysis, as shown in Supplemental Table 2, indicates that the positive results observed in males, non-smokers, and subjects with a BMI of less than 24 kg/m² are all considered notable findings. However, the observed prior probabilities of positive results in subjects aged 71 years or younger and non-drinkers exceed 0.2 (with a prior probability level set at 0.25 and an FPRP threshold of 0.2). This suggests that these positive results might not be particularly noteworthy.
MDR Analysis
The MDR analysis, as detailed in Table 4, reveals that of all the genetic models, the three-site model comprising rs116955726, rs11155242, and rs6937121 demonstrates the highest testing balanced accuracy (0.5195) and perfect cross-validation consistency (10/10). This indicates that this specific three-site model is the most effective for predicting COPD susceptibility in the study subjects, with a significant p-value of 0.0072. Figure 2 showed a dendrogram and a Fruchterman-Reingold, both illustrating the interactions among pairwise genetic variants across the five candidate variants. Figure 2A illustrates that rs11155242 and rs6937121 have a strong synergistic effect in predicting COPD susceptibility. In addition, Figure 2B showed that the information gain (IG) between rs11155242和rs6937121 was the highest.
Table 4.
The Influence of Interaction Between ADGRG6 SNP-SNP on the Susceptibility to COPD Analyzed by MDR
| Model | Training Bal. Acc | Testing Bal. Acc | OR (95% CI) | p-value | CVC |
|---|---|---|---|---|---|
| rs9399401 | 0.5258 | 0.4935 | 1.23 (0.87–1.74) | 0.2379 | 8/10 |
| rs116955726, rs11155242 | 0.5425 | 0.4881 | 1.56 (1.04–2.36) | 0.0319 | 6/10 |
| rs9399401, rs11155242, rs6937121 | 0.5570 | 0.5195 | 1.64 (1.14–2.36) | 0.0072 | 10/10 |
| rs9399401, rs116955726, rs11155242, rs6937121 | 0.5578 | 0.5158 | 1.64 (1.14–2.36) | 0.0072 | 10/10 |
| rs9399401, rs116955726, rs35699755, rs11155242, rs6937121 | 0.5578 | 0.5158 | 1.64 (1.14–2.36) | 0.0072 | 10/10 |
Note: p values were calculated using χ2 tests; “p-value < 0.05” and “bold text” represent statistical significance.
Abbreviations: COPD, chronic obstructive pulmonary disease; MDR, multifactor dimensionality reduction; Bal. Acc., balanced accuracy; CVC, cross-validation consistency; OR, odds ratio; 95% CI, 95% confidence interval.
Figure 2.
MDR analysis of the interaction between ADGRG6 SNP-SNP. (A) SNP-SNP Interaction Dendrogram: the color represents the degree of redundancy or synergy between SNP-SNP; the closer the color is to red, the more synergy, and the closer to blue, the more redundancy. (B) Fruchterman-Reingold: values in nodes represent the IGs of individual attributes (main effects). Values between nodes are IGs of each pair of attributes (interaction effects).
Discussion
An analysis of online databases revealed six candidate ADGRG6 variants exhibiting significant variations in minor allele frequencies across populations with diverse genetic backgrounds. This underscores the importance of investigating ADGRG6’s role in COPD susceptibility among individuals with varying genetic backgrounds. We examined the relationship between these candidate ADGRG6 variants and COPD susceptibility in the elderly Han Chinese population of Southern China. Our findings indicate that the rs11155242 variant is significantly associated with a decreased risk of developing COPD: the mutant allele “C” and the heterozygous mutant genotype “CA” of rs11155242 acted as protective genetic factors against COPD susceptibility in the study subjects. Rs11155242 was found to be strongly associated with a reduced risk of COPD in males, non-smokers, and subjects with a BMI below 24 kg/m2. More importantly, the FPRP analysis indicated that the above positive results identified in this study are noteworthy new findings.
In a meta-analysis encompassing genome-wide association studies with individuals of European descent, the rs11155242 variant was significantly correlated with the pulmonary function metric Forced Expiratory Volume in one second (FEV1), a pivotal physiological biomarker for COPD.28,35 This association suggests a potential role for rs11155242 in the pathogenesis of COPD. Our investigation has substantiated this correlation, revealing a robust association between rs11155242 and decreased COPD susceptibility among the elderly Han Chinese population in southern China. It is postulated that the “C” allele and the heterozygous genotype “CA” of rs11155242 may mitigate COPD risk by modulating the FEV1 index; however, this hypothesis requires validation through further functional studies to ascertain the variant’s effect on FEV1 and, consequently, COPD susceptibility. Despite this, our findings present a novel perspective for COPD diagnostics within a distinct demographic. Discrepancies in the literature have been noted. Young, R. P.et al, reported no association between rs11155242 and COPD in their GWAS study concentrating on lung cancer susceptibility.36 Additionally, a predictive model analysis within a Shanghai cohort detected no significant allele frequency disparities in rs11155242 between COPD patients and healthy controls.37 These inconsistencies may arise from differences in study population selection criteria. Genetic background and age are significant modifiers of COPD susceptibility. To the best of our knowledge, this study is the inaugural investigation to identify a genetic variant (rs11155242) associated with COPD susceptibility in the elderly Han population of southern China, thus paving the way for novel diagnostics research in targeted demographics.
To mitigate the impact of confounding factors on genetic associations, we conducted a stratified analysis focusing on potential risk factors for COPD. This analysis reinforced the genetic association of the ADGRG6-rs11155242 variant with increased susceptibility to COPD. Notably, a pronounced association was observed between rs11155242 and COPD susceptibility in men, non-smokers, and individuals with a Body Mass Index (BMI) below 24 kg/m2. Emerging evidence suggests a slower increase in COPD prevalence among men compared to women.38 Given that COPD is primarily caused by the inhalation of toxic particles, particularly tobacco smoke and environmental pollutants, the incidence of COPD tends to be lower in non-smokers compared to smokers.39 Building on the findings of this study, we propose that the ADGRG6-rs11155242 variant is linked to a reduced risk of Chronic Obstructive Pulmonary Disease (COPD). Additionally, this genetic association appears to be more pronounced in men and non-smokers. This supports the notion that COPD results from a combination of environmental and genetic factors. Moreover, existing research indicates that low BMI and weight loss can increase COPD mortality in men,40 which seems to contradict our findings. Consequently, we hypothesize that the ADGRG6-rs11155242 variant influences COPD susceptibility independently of the participants’ BMI. In the elderly Han population of southern China, the genetic variation rs11155242 seems to have a more substantial impact on COPD susceptibility compared to BMI.
In this study, we investigated six candidate ADGRG6 genetic variants; however, only ADGRG6-RS11155242 demonstrated an association with COPD susceptibility. Previous research has identified a link between rs9399401 and COPD risk in Northern Swedish populations,41 while rs6937121 has shown a significant genome-wide association with FEV1/FVC levels in European ancestry populations.42 Additionally, a notable correlation was found between rs2143390 and COPD susceptibility in a Northern Swedish cohort.32 In contrast, these genetic variants did not exhibit a similar association with COPD susceptibility in the elderly Han population of Southern China. These findings suggest that the impact of ADGRG6 genetic variations on COPD susceptibility varies across different genetic backgrounds, highlighting the importance of exploring the relationship between ADGRG6 variants and COPD risk in specific populations.
ADGRG6, a well-studied member of the adhesion G protein-coupled receptor (aGPCR) family, plays a crucial role in peripheral nerve development, angiogenesis, and inner ear formation.22,43–47 Although there has been some progress in understanding the function of ADGRG6 genetic variants in various diseases,48–51 their mechanisms related to lung function remain less explored. It is reported that ADGRG6 expression varies throughout human lung development, indicating its significance in lung function.52 Studies have also demonstrated that individuals carrying protective alleles exhibit reduced GPR126 expression, leading to decreased IL33 production.53 In COPD patients, serum IL33 levels have been found to be elevated compared to control groups.54 Integrating findings from previous research and this study, we hypothesize that individuals in our study population carrying the mutant allele “C” of rs11155242 may have a reduced risk of COPD. This could be attributed to the genetic variant’s potential to lower ADGRG6 expression, subsequently affecting the expression of certain inflammatory factors. However, this hypothesis is speculative, and further functional validation experiments are essential to confirm these findings.
In summary, our study demonstrates that ADGRG6 genetic variants are associated with the reduced risk of COPD in elderly Han Chinese populations in Southern China. This research contributes a novel perspective to the personalized prevention and treatment of COPD among the elderly Han Chinese. However, the limitations of our study should not be overlooked. The genetic background of our sample is relatively simple, underscoring the importance of expanding the sample size in subsequent validation studies. Including other minority groups in Hainan in future studies to expand the sample size will help to enrich our research. In addition, considering the important association between ADGRG6 and the pathogenesis of COPD, it is necessary to further explore the correlation between the ADGRG6-rs11155242 locus and clinical indicators in COPD patients. At the same time, it is imperative to design functional verification experiments for rs11155242. These experiments will lay a solid theoretical foundation for elucidating the role of ADGRG6 in the pathogenesis and progression of COPD.
Conclusion
The “C” allele and “CA” genotype of the rs11155242 variant in ADGRG6 are identified as in ADGRG6 are identified as genetic factors that confer protection against the development protection against the development of COPD. Our research marks a pioneering effort to establish a robust correlation between a robust correlation between the ADGRG6-rs11155242 polymorphism and COPD risk. In the clinic, this study is expected to provide significant insights for the development of strategies for COPD risk screening, diagnosis, classification, assessment, and stratification in the elderly Han Chinese population in southern China who possess these protective genetic markers.
Acknowledgments
We thank all authors for their contributions and support.
Funding Statement
This paper was supported by the Hainan Province Key Research and Development Program (ZDYF2024SHFZ094), the Youth Foundation of Hainan Provincial Natural Science Foundation of China (No. 822QN447), the National Natural Science Foundation of China (No.82460010), and the Innovation Platform for Academicians of Hainan Province.
Data Sharing Statement
All data generated or analyzed during this study are included in this manuscript.
Ethics Approval Statement
This study was conducted under the standard approved by the Biomedical Ethics Committee of Hainan General Hospital.
Patient Consent Statement
All participants signed informed consent forms before participating in this study.
Author Contributions
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis, and interpretation, or in all these areas; took part in drafting, revising, or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
Disclosure
The authors declare that they have no conflict of interest.
References
- 1.Celli B, Fabbri L, Criner G, et al. Definition and Nomenclature of Chronic Obstructive Pulmonary Disease: time for Its Revision. American Journal of Respiratory and Critical Care Medicine. 2022;206(11):1317–1325. doi: 10.1164/rccm.202204-0671PP [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Confalonieri M, Braga L, Salton F, Ruaro B, Confalonieri P. Chronic Obstructive Pulmonary Disease Definition: is It Time to Incorporate the Concept of Failure of Lung Regeneration? Amer J Resp Criti Care Med. 2023;207(3):366–367. doi: 10.1164/rccm.202208-1508LE [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.GCRD C, Kendrick PJ, Paulson KR. Prevalence and attributable health burden of chronic respiratory diseases, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Respir Med. 2020;8(6):585–596. doi: 10.1016/S2213-2600(20)30105-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Zhou M, Wang H, Zeng X, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2019;394(10204):1145–1158. doi: 10.1016/S0140-6736(19)30427-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Wang C, Xu J, Yang L, et al. Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health [CPH] study): a national cross-sectional study. Lancet. 2018;391(10131):1706–1717. doi: 10.1016/S0140-6736(18)30841-9 [DOI] [PubMed] [Google Scholar]
- 6.Fang L, Gao P, Bao H, et al. Chronic obstructive pulmonary disease in China: a nationwide prevalence study. Lancet Respir Med. 2018;6(6):421–430. doi: 10.1016/S2213-2600(18)30103-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Wain LV, Shrine N, Miller S, et al. Novel insights into the genetics of smoking behaviour, lung function, and chronic obstructive pulmonary disease (UK BiLEVE): a genetic association study in UK Biobank. Lancet Respir Med. 2015;3(10):769–781. doi: 10.1016/S2213-2600(15)00283-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Vij N, Chandramani-Shivalingappa P, Van Westphal C, Hole R, Bodas M. Cigarette smoke-induced autophagy impairment accelerates lung aging, COPD-emphysema exacerbations and pathogenesis. American J Physi Cell Physi. 2018;314(1):C73–c87. doi: 10.1152/ajpcell.00110.2016 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Doiron D, de Hoogh K, Probst-Hensch N, et al. Air pollution, lung function and COPD: results from the population-based UK Biobank study. European Respir J. 2019;54(1):1802140. doi: 10.1183/13993003.02140-2018 [DOI] [PubMed] [Google Scholar]
- 10.Grahn K, Gustavsson P, Andersson T, et al. Occupational exposure to particles and increased risk of developing chronic obstructive pulmonary disease (COPD): a population-based cohort study in Stockholm, Sweden. Environ Res. 2021;200:111739. doi: 10.1016/j.envres.2021.111739 [DOI] [PubMed] [Google Scholar]
- 11.Sana A, Somda SMA, Meda N, Bouland C. Chronic obstructive pulmonary disease associated with biomass fuel use in women: a systematic review and meta-analysis. BMJ Open Resp Rese. 2018;5(1):e000246. doi: 10.1136/bmjresp-2017-000246 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Yan P, Liu P, Lin R, et al. Effect of ambient air quality on exacerbation of COPD in patients and its potential mechanism. Int J Chronic Obstr. 2019;14:1517–1526. doi: 10.2147/COPD.S190600 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Yan P, Lin R, Jia Y, et al. Influence of environmental factors on the migratory birds population with chronic obstructive pulmonary disease. Zhonghua yi xue za zhi. 2015;95(36):2924–2927. [PubMed] [Google Scholar]
- 14.Silverman EK. Genetics of COPD. Annual review of physiology. Annual Rev Physi. 2020;82:413–431. doi: 10.1146/annurev-physiol-021317-121224 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Sun S, Shen Y, Wang J, Li J, Cao J, Zhang J. Identification and Validation of Autophagy-Related Genes in Chronic Obstructive Pulmonary Disease. Int J Chronic Obstr. 2021;16:67–78. doi: 10.2147/COPD.S288428 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Xiao J, Han J, Wang X, et al. Association of ADAM33 gene with susceptibility to COPD in Tibetan population of China. Molecular Biology Reports. 2011;38(8):4941–4945. doi: 10.1007/s11033-010-0637-6 [DOI] [PubMed] [Google Scholar]
- 17.DeMeo DL, Mariani T, Bhattacharya S, et al. Integration of genomic and genetic approaches implicates IREB2 as a COPD susceptibility gene. American J Human Genet. 2009;85(4):493–502. doi: 10.1016/j.ajhg.2009.09.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Hamann J, Aust G, Araç D, et al. International Union of Basic and Clinical Pharmacology. XCIV. Adhesion G protein-coupled receptors. Pharm Revi. 2015;67(2):338–367. doi: 10.1124/pr.114.009647 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Scholz N. Cancer Cell Mechanics: adhesion G Protein-coupled Receptors in Action? Front oncology. 2018;8:59. doi: 10.3389/fonc.2018.00059 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Kubo F, Ariestanti DM, Oki S, et al. Loss of the adhesion G-protein coupled receptor ADGRF5 in mice induces airway inflammation and the expression of CCL2 in lung endothelial cells. Respir Res. 2019;20(1):11. doi: 10.1186/s12931-019-0973-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Shiu FH, Wong JC, Bhattacharya D, et al. Generation and initial characterization of mice lacking full-length BAI3 (ADGRB3) expression. Basic Clin Pharm Toxic. 2023;133(4):353–363. doi: 10.1111/bcpt.13917 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Geng FS, Abbas L, Baxendale S, et al. Semicircular canal morphogenesis in the zebrafish inner ear requires the function of gpr126 (lauscher), an adhesion class G protein-coupled receptor gene. Development. 2013;140(21):4362–4374. doi: 10.1242/dev.098061 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Waller-Evans H, Prömel S, Langenhan T, et al. The orphan adhesion-GPCR GPR126 is required for embryonic development in the mouse. PLoS One. 2010;5(11):e14047. doi: 10.1371/journal.pone.0014047 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Garinet S, Pignot G, Vacher S, et al. High Prevalence of a Hotspot of Noncoding Somatic Mutations in Intron 6 of GPR126 in Bladder Cancer. Molecu Cancer Rese. 2019;17(2):469–475. doi: 10.1158/1541-7786.MCR-18-0363 [DOI] [PubMed] [Google Scholar]
- 25.Nik-Zainal S, Davies H, Staaf J, et al. Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature. 2016;534(7605):47–54. doi: 10.1038/nature17676 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Piraino SW, Furney SJ. Identification of coding and non-coding mutational hotspots in cancer genomes. BMC Genomics. 2017;18(1):17. doi: 10.1186/s12864-016-3420-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Terzikhan N, Sun F, Verhamme FM, et al. Heritability and genome-wide association study of diffusing capacity of the lung. European Respi J. 2018;52(3). doi: 10.1183/13993003.00647-2018. [DOI] [PubMed] [Google Scholar]
- 28.Kahnert K, Jörres RA, Behr J, Welte T. The Diagnosis and Treatment of COPD and Its Comorbidities. Deutsches Arzteblatt int. 2023;120(25):434–444. doi: 10.3238/arztebl.m2023.027 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Barnes MR. Navigating the HapMap. Briefings Bioinformatics. 2006;7(3):211–224. doi: 10.1093/bib/bbl021 [DOI] [PubMed] [Google Scholar]
- 30.Sakornsakolpat P, Prokopenko D, Lamontagne M, et al. Genetic landscape of chronic obstructive pulmonary disease identifies heterogeneous cell-type and phenotype associations. Nature Genet. 2019;51(3):494–505. doi: 10.1038/s41588-018-0342-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Moll M, Jackson VE, Yu B, 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–l43. doi: 10.1152/ajplung.00009.2021 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Matsson H, Söderhäll C, Einarsdottir E, et al. Targeted high-throughput sequencing of candidate genes for chronic obstructive pulmonary disease. BMC Pulmonary Medicine. 2016;16(1):146. doi: 10.1186/s12890-016-0309-y [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Castaldi PJ, Cho MH, Litonjua AA, et al. The association of genome-wide significant spirometric loci with chronic obstructive pulmonary disease susceptibility. American J Respir Cell Mole Biol. 2011;45(6):1147–1153. doi: 10.1165/rcmb.2011-0055OC [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Jiao L, Song J, Chen H, et al. Genetic architecture of tuberculosis susceptibility: a comprehensive research synopsis, meta-analyses, and epidemiological evidence. Infect gene evol. 2022;104:105352. doi: 10.1016/j.meegid.2022.105352 [DOI] [PubMed] [Google Scholar]
- 35.Hancock DB, Eijgelsheim M, Wilk JB, et al. Meta-analyses of genome-wide association studies identify multiple loci associated with pulmonary function. Nature Genet. 2010;42(1):45–52. doi: 10.1038/ng.500 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Young RP, Hopkins RJ, Whittington CF, Hay BA, Epton MJ, Gamble GD. Individual and cumulative effects of GWAS susceptibility loci in lung cancer: associations after sub-phenotyping for COPD. PLoS One. 2011;6(2):e16476. doi: 10.1371/journal.pone.0016476 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Guo YI, Qian Y, Gong YI, Pan C, Shi G, Wan H. A predictive model for the development of chronic obstructive pulmonary disease. Biomedical Rep. 2015;3(6):853–863. doi: 10.3892/br.2015.503 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Matera MG, Ora J, Calzetta L, Rogliani P, Cazzola M. Sex differences in COPD management. Expert Review Clinical Pharm. 2021;14(3):323–332. doi: 10.1080/17512433.2021.1888713 [DOI] [PubMed] [Google Scholar]
- 39.Christenson SA, Smith BM, Bafadhel M, Putcha N. Chronic obstructive pulmonary disease. Lancet. 2022;399(10342):2227–2242. doi: 10.1016/S0140-6736(22)00470-6 [DOI] [PubMed] [Google Scholar]
- 40.Wada H, Ikeda A, Maruyama K, et al. Low BMI and weight loss aggravate COPD mortality in men, findings from a large prospective cohort: the JACC study. Scientific Reports. 2021;11(1):1531. doi: 10.1038/s41598-020-79860-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Eriksson Ström J, Kebede Merid S, Pourazar J, et al. Chronic Obstructive Pulmonary Disease Is Associated with Epigenome-Wide Differential Methylation in BAL Lung Cells. American J Respir Cell Molec Biology. 2022;66(6):638–647. doi: 10.1165/rcmb.2021-0403OC [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Obeidat M, Hao K, Bossé Y, et al. Molecular mechanisms underlying variations in lung function: a systems genetics analysis. Lancet Respir Med. 2015;3(10):782–795. doi: 10.1016/S2213-2600(15)00380-X [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Mogha A, Benesh AE, Patra C, et al. Gpr126 functions in Schwann cells to control differentiation and myelination via G-protein activation. J Neuroscie. 2013;33(46):17976–17985. doi: 10.1523/JNEUROSCI.1809-13.2013 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Cui H, Wang Y, Huang H, et al. GPR126 protein regulates developmental and pathological angiogenesis through modulation of VEGFR2 receptor signaling. J Biological Chem. 2014;289(50):34871–34885. doi: 10.1074/jbc.M114.571000 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Monk KR, Oshima K, Jörs S, Heller S, Talbot WS. Gpr126 is Essential for Peripheral Nerve Development and Myelination in Mammals. Development. 138. 2673–2680. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Petersen SC, Luo R, Liebscher I, et al. The adhesion GPCR GPR126 has distinct, domain-dependent functions in Schwann cell development mediated by interaction with laminin-211. Neuron. 2015;85(4):755–769. doi: 10.1016/j.neuron.2014.12.057 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Glenn TD, Talbot WS. Analysis of Gpr126 function defines distinct mechanisms controlling the initiation and maturation of myelin. Development. 2013;140(15):3167–3175. doi: 10.1242/dev.093401 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Kou I, Takahashi Y, Johnson TA, et al. Genetic variants in GPR126 are associated with adolescent idiopathic scoliosis. Nature Genet. 2013;45(6):676–679. doi: 10.1038/ng.2639 [DOI] [PubMed] [Google Scholar]
- 49.Xu JF, Yang GH, Pan XH, et al. Association of GPR126 gene polymorphism with adolescent idiopathic scoliosis in Chinese populations. Genomics. 2015;105(2):101–107. doi: 10.1016/j.ygeno.2014.11.009 [DOI] [PubMed] [Google Scholar]
- 50.Qin X, Xu L, Xia C, et al. Genetic Variant of GPR126 Gene is Functionally Associated With Adolescent Idiopathic Scoliosis in Chinese Population. Spine. 2017;42(19):E1098–e103. doi: 10.1097/BRS.0000000000002123 [DOI] [PubMed] [Google Scholar]
- 51.Kou I, Watanabe K, Takahashi Y, et al. A multi-ethnic meta-analysis confirms the association of rs6570507 with adolescent idiopathic scoliosis. Scientific Rep. 2018;8(1):11575. doi: 10.1038/s41598-018-29011-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Miller S, Melén E, Merid SK, Hall IP, Sayers I. Genes associated with polymorphic variants predicting lung function are differentially expressed during human lung development. Resp Res. 2016;17(1):95. doi: 10.1186/s12931-016-0410-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Hall RJ, O’Loughlin J, Billington CK, Thakker D, Hall IP, Sayers I. Functional genomics of GPR126 in airway smooth muscle and bronchial epithelial cells. FASEB J. 2021;35(7):e21300. doi: 10.1096/fj.202002073R [DOI] [PubMed] [Google Scholar]
- 54.Xia J, Zhao J, Shang J, et al. Increased IL-33 expression in chronic obstructive pulmonary disease. Am J Physiol Lung Cell Mol Physiol. 2015;308(7):L619–27. doi: 10.1152/ajplung.00305.2014 [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
All data generated or analyzed during this study are included in this manuscript.