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. 2024 Dec 30;25:1255. doi: 10.1186/s12864-024-11165-6

Genome-wide analysis in northern Chinese twins identifies twelve new susceptibility loci for pulmonary function

Tong Wang 1, Weijing Wang 1, Chunsheng Xu 2, Xiaocao Tian 2,✉, Dongfeng Zhang 1,✉
PMCID: PMC11684132  PMID: 39736507

Abstract

Background

Previous genome-wide association studies (GWAS) have established association between genetic variants and pulmonary function across various ethnics, whereas such associations are scarcely reported in Chinese adults. Therefore, we conducted an GWAS to explore relationships between genetic variants and pulmonary function among middle-aged Chinese dizygotic twins and further validated the top variants using data from the UK Biobank (UKB).

Methods

In the discovery phase, 139 dizygotic twin pairs were drawn from the Qingdao Twin Registry. Pulmonary function was assessed using three parameters: forced expiratory volume the first second (FEV1), forced vital capacity (FVC), and FEV1/FVC ratio. GWAS was performed using GEMMA, Gene-based analysis was conducted by VEGAS2. And pathway enrichment analysis was performed using PASCAL. In the validation phase, Single-nucleotide polymorphisms (SNPs) with suggestive significance were examined through linear regression analysis of the additive effect model among 1573 Chinese ethnic participants from UKB.

Results

The median age of twin pairs in the study was 49 years. 3 SNPs (rs80345886, rs117883876, and 75139439) related to FEV1 achieved genome-wide significance. Moreover, 222, 150, and 73 SNPs surpassed suggestive evidence level (p < 1 × 10− 5) for FEV1, FVC, and FEV1/FVC, respectively. Among them, 16 SNPs located in TBC1D16 for FEV1, 25 SNPs located in GPR126 for FVC, and 2 SNPs located in CCDC110 for FEV1/FVC, the three genes were also revealed by gene-based analysis. Moreover, 12 novel SNPs related to pulmonary function were validated to reach the nominal significance level (p < 0.05) in the UKB, with some located in the TBC1D16, TAFA5, and MTHFD1L genes.

Conclusion

Our GWAS results on Chinese dizygotic twins provide new references for the genetic regulation on pulmonary function. Twelve novel susceptibility loci are considered as possible crucial to pulmonary function.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-024-11165-6.

Keywords: Pulmonary function, GWAS, Twins

Introduction

Pulmonary function, a heritable trait that reflects the physiological state of the lungs and airways [1], declines with age [2]. It can be assessed by spirometry with three key metrics including forced expiratory volume in one second (FEV1), forced vital capacity (FVC), and the FEV1/FVC ratio. Pulmonary function is a predictor of the incidence [3, 4] and mortality [5, 6] of cardiopulmonary diseases and is crucial for diagnosing and evaluating respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD). These lung diseases not only impair lung health but also contribute to the development of other diseases, including diabetes [7] and cardiovascular disease [8]. Therefore, understanding the underlying biological mechanisms and factors affecting pulmonary function is essential for the effective prevention and control of respiratory diseases.

Previous research based on family and twin studies has provided evidence that pulmonary function is influenced by genetic factors, with heritability estimates ranging from 0.3 to 0.9 [9, 10]. Researchers have also sought to identify genetic loci associated with pulmonary function through Genome-Wide Association Study (GWAS), which is an extremely effective method for identifying genetic loci associated with complex phenotypes or diseases [11]. To date, most GWASs related to pulmonary function have focused on European and American populations, identifying numerous genetic variants and genes, such as HHIP, GPR126, FAM13A, AGER, GSTCD, and PRDM11 [12–15]. Additionally, GWAS involving Latin American and East Asian populations, including Koreans, Mongolians, Japanese and Chinese, have also identified genetic loci and genes related to pulmonary function, some of which overlap with those found in European and American populations (e.g., GPR126 [16], FAM13A [17], AGER [18]), while others are novel, such as KCNE2, PPT2, and TTLL2 [16, 17, 19].

Although some previous GWAS have focused on pulmonary function in East Asian populations, sequencing data from the Northeast Asian Reference Database reveal that despite a shared genetic foundation, distinct ancestral components exist among Koreans, Mongolians, Japanese, and mainland East Asians [20]. To our knowledge, only one study conducted with participants from the China has identified 18 novel loci associated with pulmonary function [21]. Therefore, further GWAS on the Chinese population are essential to expand our understanding of the genetic heterogeneity of pulmonary function across different ethnics [22] and to identify genetic variants associated with pulmonary function in the Chinese population. The twin-based GWAS design can excellently control population stratification and gene-environment correlation, identifying direct genetic effects [23]. Thus, in the current study, we conducted the GWAS based the Chinese twin samples to address specific genetic variants related to pulmonary function in Chinese populations.

Materials and methods

Sample

The twin samples were derived from Qingdao Twin Registry [24], and details of the sample recruitment procedure have been described in previous literatures [25]. Participants were excluded if they were pregnant, lactating, or had incomplete measurement of pulmonary function; the incomplete co-twin pairs were also dropped. The final sample consisted of 139 complete dizygotic (DZ) pairs (278 adults, 141 male and 137 female pairs) with a median age of 49 years (ranging from 40 to 70 years). Informed signed consents were achieved for all participants. Ethical approval for the study was granted by the Regional Ethics Committee of the Qingdao Centers for Disease Control and Prevention Institutional Review Boards, following the principles of the Helsinki Declaration.

Pulmonary function, including FEV1 and FVC, both measured in liters, was assessed by the electronic hand-held spirometer (Micro 0102). The FEV1/FVC ratio was calculated by dividing FEV1 by FVC. Details of measurement procedure have been described in previous literatures [26]. The three pulmonary function indices were transformed by Blom’s formula for normality.

Genotyping, quality control, and imputation

Genomic DNA extracted from whole blood of DZ twins were genotyped using Infinium Omni2.5Exome-8v1.2 genotyping BeadChip platform (Illumina Inc, San Diego, USA). Data from autosome and X chromosome were analyzed. Following quality control criteria [27, 28], we included SNPs with locus missing < 0.05, calling rate > 0.98, minor allele frequencies (MAF) > 0.01, and Hardy-Weinberg equilibrium (HWE) p-value > 1 × 10− 4.

Moreover, IMPUTE2 [29] software was used to impute the untyped SNPs based on the linkage disequilibrium (LD) information by using 1,000 Genomes Project Phase 3 East Asian as the reference panel. The filter criteria for imputed SNPs were INFO > 0.8, MAF > 0.05, and HWE > 1 × 10− 4. A total of 7,405,822 SNPs were finally obtained.

GWAS analysis

SNP-based analyses

GEMMA [30] was used to perform the effect of pulmonary function indices on SNP genotypes, adjusting for age, sex, height, smoking status (never, ever, and now), the first five principle components, and the relatedness matrix. The common Bonferroni-corrected threshold (p < 5 × 10− 8) was set as genome-wide statistical significance, and the threshold (p < 1 × 10− 5) was considered for suggestive evidence level. Locus Zoom applied to display regional information of GWAS results. We submitted suggestively significant SNPs located in non-coding region to Enhancer DB to search which enhancer these SNPs were located in, to GTEx project portal (https://www.gtexportal.org/home/) (Accessed data: August 2023; Data Release: Version 8) to identify expression quantitative trait loci (eQTL) related to these SNPs, to SNP2TFBS to find which transcript factor these SNPs affect.

Gene-based analysis

We conducted gene-based analyses using the VEGAS2 [31] tool on GWAS summary data, with linkage disequilibrium accounted for using the Han Chinese in Beijing, China (CHB) sub-population from the 1000 Genomes Project Asian Population. The analysis incorporated 26,056 unique VEGAS2 gene definitions, comprising 24,769 autosomal genes and 1,287 X-chromosomal genes. Significance thresholds were set at p < 1.92(0.05/26,056) × 10− 6 for multiple testing adjustment, and p < 0.05 for suggestive significance.

Pathway enrichment analysis

We conducted pathway enrichment analysis using the PASCAL tool [32], which provides a library of 1077 pathways sourced from the KEGG, REACTOME, and BIOCARTA databases through the Molecular Signatures Database (MsigDB). In our analysis, we aggregated SNP p-values from GWAS, adjusting for linkage disequilibrium (LD), to compute gene scores. These scores were then used to calculate pathway scores based on the gene scores within each of the pathways identified from the library. The significance of the pathway enrichment was assessed using either chi-squared or empirical scoring methods. We established statistical significance with a Bonferroni-corrected emp-p < 4.64 (0.05/1077) × 10− 5, and set a nominal significance level at emp-p < 0.05.

Validation analysis

To confirm the associations, we obtained the phenotype and genotype data from the third release of UK Biobank data (https://www.ukbiobank.ac.uk/) with Applications ID: 95,715. FEV1 and FVC value were calculated from spirometry blow and FEV1/FVC was calculated by these two indicators. The details about data collection, genotyping, and imputation have been described elsewhere [33, 34]. We restricted the validation sample to Chinese ethnic background (n = 1573). As the previous study described [28], the top SNPs were validated by linear regression analysis of the additive effect model, adjusting for age, sex, smoke status, height and the first 10 genetic PCs. A total of 352 SNPs for FEV1, FVC, and FEV1/FVC with p values < 1 × 10− 5 in the discovery set were typed in the UKB data set and selected for validation. The Bonferroni-corrected significance threshold was p < 1.42 × 10− 4 (0.05/352), and the nominal significance level was p < 0.05. Figure 1 showed the outline of overall study design and analysis steps. Moreover, we used the 12 identified susceptibility loci associated with pulmonary function to construct a polygenic risk score (PRS). Each SNP was weighted according to its effect size derived from the UK Biobank validation results. These weights were then applied to the corresponding SNPs in our target population (twin pairs) to calculate the cumulative PRS for each individual. Generalized estimating equation models were used to assess the association between the computed PRS and pulmonary function, adjusting for age, gender, smoking status, and height. Statistical analyses were performed utilizing R version 4.2.0. The code used in this study can be accessed at the following GitHub repository: https://github.com/TongWang0106/Pulmonary-function-GWAS .

Fig. 1.

Fig. 1

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Flowchart of the overall study design and analysis steps

Results

Descriptive statistics of basic characteristics are shown in Supplementary Material 4: Table S1. Our study included 139 dizygotic twin pairs, with a median age of 49 years. The median level (interquartile range) pulmonary function values were 2.06 (0.97) for FEV1, 2.19(0.90) for FVC, and 0.97(0.10) for FEV1/FVC.

SNPs-based genome-wide association study

The association between pulmonary function and SNP genotypes was evaluated using the GEMMA software. The quantile-quantile plot (Fig. 2) illustrates the relationship between the observed and expected GWAS p-values. The deviation of points towards the upper right corner of the QQ plot in our results suggests the presence of significant genetic associations related to pulmonary function. The low level of genomic inflation factor (λ) ranging from 0.997 to 1.007 indicates no evidence of population stratification effects. As the Manhattan plot shows in Fig. 3 SNPs reached the genome-wide significance level for FEV1, and 222 SNPs for FEV1, 150 SNPs for FVC, 73 SNPs for FEV1/FVC reached suggestive association level (p < 1 × 10− 5, Supplementary Material 1: Table S2).

Fig. 2.

Fig. 2

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Quantile-quantile (Q-Q) plot of genome-wide association study (GWAS) for FEV1 (a), FVC (b), and FEV1/FVC (c). The x-axis shows the -log10 of expected P-values of the association from the chi-square distribution, and the y-axis shows the -log10 of P-values from the observed chi-square distribution. The black dots represent the observed data with the top hit SNP being colored, and the red line is the expectation under the null hypothesis of no association

Fig. 3.

Fig. 3

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Manhattan plot of genome-wide association study (GWAS) of FEV1 (a), FVC (b), and FEV1/FVC (c). The x-axis shows the numbers of autosomes and the X chromosome, and the y-axis shows the -log10 of P-values for statistical significance. The dots represent the SNPs. 222 SNPs for FEV1, 150 SNPs for FVC, 73 SNPs for FEV1/FVC reached suggestive association level

Table 1 displays the top 20 SNPs for pulmonary function. Among the top SNPs for FEV1, three SNPs exceeded genome-wide significance level, including rs80345886 and rs117883876 (p = 8.45 × 10− 10) near MTHFD2P6 gene, and rs75139439 located in the PARM1 gene (p = 2.31 × 10− 9). In addition, three SNPs (rs34982522, rs78238888, and rs78628444, p = 5.88 × 10− 8) were located in the enh8386(chr5: 14,024,025 − 14,042,149) region, which regulates DNAH5 gene. Seven SNPs, including rs4723047 (p = 8.25 × 10− 8) at ADCYAP1R1 gene on chromosome 7p14.3, were illustrated in the locus zoom plots (Supplementary Metarial 4: Figure S1). For FVC, the strongest associated SNP was rs9403380 (p = 1.27 × 10− 7) in the GPR126 gene. Nine other SNPs in the GPR126 gene located on chromosome 6q24.1, are shown in the locus zoom plots (Supplementary Metarial 4: Figure S2). As shown in Figure S2, there was no LD between the nine SNPs and rs9403380, suggesting that these SNPs might independently affect FVC. For FEV1/FVC, including the strongest associated SNP rs61918244 (p = 2.86 × 10− 7), 14 SNPs were located in enh14492 (chr12: 6,369,425-6,399,675) and enh14493 (chr12: 6,399,825-6,409,255), which regulates the PLEKHG6 gene. Supplementary Metarial 4: Figure S3 displays the locus zoom plot of chromosome 12p13.31 regions. In addition, rs4862535 (p = 2.13 × 10− 6) was significantly associated with the expression of CCDC110 gene in lung tissues (p = 4.90 × 10− 5, m-value = 0.934), and rs4950744 (p = 2.81 × 10− 6) was significantly associated with the expression of the TIMM17A gene in lung tissues (p = 1.30 × 10− 6, m-value = 1).

Table 1.

Top 20 SNPs (p < 1 × 10− 5) for association with the pulmonary function in genome-wide association study

SNP Chr band Chr: BP P-value Genes Nearest gene/Functional gene Functional annotation
FEV1
rs80345886 5p13.1 5:41975016 8.45E-10 MTHFD2P6
rs117883876 5p13.3 5:41981801 8.45E-10 MTHFD2P6
rs75139439 4q13.3 4:75872722 2.31E-09 PARM1
rs116821226 3q29 3:195195328 5.14E-08 PDX1,PRRX2 TF
rs191296044 5p13.1 5:41543923 5.23E-08 PLCXD3
rs34982522 5p15.2 5:14026049 5.88E-08 DNAH5 Enhancer
rs78238888 5p15.2 5:14010246 5.88E-08 DNAH5 Enhancer
rs78628444 5p15.2 5:14012334 5.88E-08 DNAH5 Enhancer
rs4723047 7p14.3 7:31139332 8.25E-08 ADCYAP1R1
rs2284225 7p14.3 7:31138503 8.79E-08 ADCYAP1R1
rs1008385 7p14.3 7:31151579 1.05E-07 PRRX2 TF
rs781590047 Xq26.1 23:128565706 1.06E-07 RNA5SP513
rs35031103 3q26.32 3:176086799 1.28E-07 RP11-71G7.1
rs6954589 7p14.3 7:31147965 1.34E-07 ADCYAP1R1
rs4540319 7p14.3 7:31149053 1.47E-07 ADCYAP1R1
rs4503014 7p14.3 7:31149140 1.47E-07 ADCYAP1R1
rs4723046 7p14.3 7:31139200 1.6E-07 ADCYAP1R1
rs10462025 5p13.1 5:41820770 1.62E-07 OXCT1
rs2302476 7p14.3 7:31146455 1.67E-07 ADCYAP1R1
rs117091220 5p14.1 5:27267776 1.8E-07 CTD-3007L5.1
FVC
rs9403380 6q24.1-6q24.2 6:142652416 1.27E-07 GPR126
rs116821226 3q29 3:195195328 1.30E-07 PDX1,PRRX2 TF
rs4540319 7p14.3 7:31149053 1.48E-07 ADCYAP1R1
rs4503014 7p14.3 7:31149140 1.48E-07 ADCYAP1R1
rs972982 6q24.1-6q24.2 6:142666861 1.65E-07 GPR126
rs9496346 6q24.1-6q24.2 6:142669338 1.65E-07 GPR126
rs6686611 1q42.12 1:225610906 2.05E-07 LBR
rs1008385 7p14.3 7:31151579 2.44E-07 PRRX2 TF
rs6954589 7p14.3 7:31147965 2.63E-07 ADCYAP1R1
rs78275852 19q12 19:29708754 3.29E-07 UQCRFS1
rs2302476 7p14.3 7:31146455 3.44E-07 ADCYAP1R1
rs6937121 6q24.1-6q24.2 6:142707133 3.69E-07 GPR126
rs80345886 5p13.1 5:41975016 3.71E-07 MTHFD2P69
rs117883876 5p13.1 5:41981801 3.71E-07 MTHFD2P69
rs9389985 6q24.1-6q24.2 6:142653898 3.99E-07 GPR126
rs2039986 6q24.1-6q24.2 6:142655238 3.99E-07 GPR126
rs1891308 6q24.1-6q24.2 6:142648235 4.29E-07 GPR126
rs2050157 6q24.1-6q24.2 6:142658162 5.27E-07 GPR126
rs6570507 6q24.1-6q24.2 6:142679572 5.27E-07 GPR126
rs7741741 6q24.1-6q24.2 6:142655801 5.28E-07 GPR126
FEV1/FVC
rs61918244 12p13.31 12:6403174 2.86E-07 PLEKHG6 Enhancer
rs61918203 12p13.31 12:6396589 6.47E-07 PLEKHG6 Enhancer
rs144870434 10q23.1 10:85877161 1.04E-06 GHITM Enhancer
rs111457230 12p13.31 12:6399669 1.57E-06 PLEKHG6 Enhancer
rs113872288 12p13.31 12:6399666 1.57E-06 PLEKHG6 Enhancer
rs1366756831 11p15.2 11:13815376 1.77E-06 LINC02548
rs16932500 12p13.31 12:6394730 1.95E-06 PLEKHG6 Enhancer
rs1990385 12p13.31 12:6394635 1.96E-06 PLEKHG6 Enhancer
rs1990386 12p13.31 12:6394443 1.96E-06 PLEKHG6 Enhancer
rs61918201 12p13.31 12:6394883 1.96E-06 PLEKHG6 Enhancer
rs4862535 4q35.1 4:186397351 2.13E-06 CCDC110 eQTL(lung)
rs61918243 12p13.31 12:6402906 2.14E-06 PLEKHG6 Enhancer
rs60560940 12p13.31 12:6402471 2.15E-06 PLEKHG6 Enhancer
rs61918242 12p13.31 12:6402692 2.15E-06 PLEKHG6 Enhancer
rs2363884 12p13.31 12:6394322 2.26E-06 PLEKHG6 Enhancer
rs1178679169 12p13.31 12:6398519 2.43E-06 PLEKHG6 Enhancer
rs61918206 12p13.31 12:6401304 2.59E-06 PLEKHG6 Enhancer
rs4950744 1q32.1 1:201923383 2.75E-06 TIMM17A eQTL(lung)
rs891603236 16q23.2 16:80616315 2.81E-06 LINC01227
rs11615534 12p13.31 12:6409706 2.87E-06 PLEKHG6
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eQTL: expression Quantitative Trait Loci; TF: Transcription Factor

Gene based analysis

The results of gene-based analysis by VEGAS2 showed that no gene reached the genome-wide significance level (p < 1.92 × 10− 6). However, 698 genes for FEV1,724 genes for FVC, and 355 genes for FEV1/FVC ratio exceeded the suggestive evidence level (p < 0.05), as shown in Supplementary Material 2: Table S3. The top 20 genes for each of the three pulmonary function indicators, ranked by P values, are shown in Table 2. ADCYAP1R1 for FEV1 and FVC, CPR126 for FVC, and CCDC110 for FEV1/FVC had already been shown in the top 20 SNP annotated genes, whereas the other genes were novel, including TBC1D16, FKBP5, AQP4, RPL37, and CD1B.

Table 2.

The top 20 genes from gene-based analysis by using VEGAS2 tool

Chr Gene Number
of SNPs		 Start position Stop position P-value Top-SNP Top-SNP P-value
FEV1
12 TESC 185 117,476,727 117,537,251 6.40E-05 rs7304889 3.75E-05
12 KRT5 28 52,908,358 52,914,243 1.35E-04 rs60314569 4.96E-05
17 ARHGDIA 5 79,825,596 79,829,282 1.57E-04 rs113301975 1.49E-04
23 MBNL3 43 131,503,342 131,623,996 1.71E-04 rs1952453 1.38E-04
6 GPR126 172 142,623,055 142,767,403 1.75E-04 rs2039986 1.60E-05
23 RAP2C 5 131,337,051 131,353,508 1.95E-04 rs5977658 2.00E-04
23 RAP2C-AS1 116 131,352,534 131,566,839 2.05E-04 rs1952453 1.38E-04
17 TBC1D16 279 77,906,141 78,009,657 2.23E-04 rs11653700 2.39E-06
17 FAM195B 18 79,780,236 79,791,170 3.41E-04 rs140438341 1.96E-04
6 FHL5 103 97,010,423 97,064,512 3.50E-04 rs12204342 2.56E-05
7 ADCYAP1R1 133 31,092,075 31,151,093 4.12E-04 rs4723047 8.25E-08
17 PPP1R27 2 79,791,367 79,792,926 4.25E-04 rs34856581 3.43E-04
7 SEPT7P2 79 45,763,385 45,808,617 4.54E-04 rs1004028 3.60E-04
10 FAM196A 106 128,933,689 128,994,422 5.58E-04 rs2486971 9.90E-05
17 P4HB 34 79,801,033 79,818,544 5.91E-04 rs111839893 2.94E-04
23 FRMD7 32 131,211,020 131,262,050 7.96E-04 rs5933079 5.97E-05
12 C12orf80 5 52,599,364 52,604,639 8.36E-04 rs4144987 3.06E-03
23 TAF7L 39 100,523,240 100,548,059 9.90E-04 rs5991927 3.01E-04
12 IRAK4 53 44,152,746 44,183,346 1.02E-03 rs4251463 3.90E-04
11 LOC440040 506 49,580,079 49,831,969 1.02E-03 rs11040429 1.72E-04
FVC
6 GPR126 172 142,623,055 142,767,403 1.50E-05 rs9403380 1.27E-07
6 FKBP5 225 35,541,361 35,696,360 1.80E-05 rs12153967 4.39E-06
13 SLC15A1 173 99,336,054 99,404,929 2.40E-05 rs1768042 2.97E-04
18 AQP4 17 24,432,007 24,445,716 2.60E-05 rs455671 5.10E-05
9 SETX 324 135,136,826 135,230,372 1.78E-04 rs508021 1.22E-04
12 C12orf80 5 52,599,364 52,604,639 2.67E-04 rs1317997 9.70E-04
12 IRAK4 53 44,152,746 44,183,346 3.45E-04 rs4251463 1.55E-04
11 LOC646813 36 50,368,317 50,379,802 3.57E-04 rs692671 2.69E-04
11 LOC440040 506 49,580,079 49,831,969 3.62E-04 rs10742953 9.12E-05
19 CABP5 83 48,532,639 48,547,311 3.82E-04 rs111784716 2.17E-04
23 RAP2C 5 131,337,051 131,353,508 3.85E-04 rs5977658 3.52E-04
7 ADCYAP1R1 133 31,092,075 31,151,093 3.91E-04 rs4503014 1.48E-07
23 RAP2C-AS1 116 131,352,534 131,566,839 4.08E-04 rs142507995 3.36E-04
7 DNAJB6 283 157,129,709 157,210,133 4.26E-04 rs12154512 3.07E-04
16 CALB2 88 71,392,615 71,424,342 4.86E-04 rs11545954 1.40E-05
23 MBNL3 43 131,503,342 131,623,996 6.17E-04 rs5977691 3.36E-04
10 FGF8 2 103,529,886 103,540,126 6.74E-04 rs1348870 1.57E-04
23 FRMD7 32 131,211,020 131,262,050 6.84E-04 rs5933079 1.03E-04
12 MKRN9P 5 88,176,662 88,178,488 7.17E-04 rs1675574 4.63E-04
19 SLC5A5 32 17,982,781 18,005,983 7.43E-04 rs7255433 7.70E-05
FEV1/FVC
5 RPL37 9 40,831,429 40,835,387 2.43E-04 rs2291782 8.52E-04
2 LOC101928113 6 86,116,402 86,119,375 3.86E-04 rs4313976 1.21E-04
2 LOC728730 234 39,664,556 39,828,484 6.32E-04 rs10210842 3.22E-05
1 KLHL17 25 895,966 901,099 7.10E-04 rs41285808 6.56E-05
1 CD1B 4 158,297,739 158,301,321 1.36E-03 rs962879 8.61E-04
2 C2orf81 4 74,641,302 74,644,844 1.42E-03 rs7571984 9.31E-04
3 GSK3B 263 119,540,801 119,813,264 1.52E-03 rs60235167 1.30E-04
3 BTD 77 15,642,858 15,687,328 1.59E-03 rs2455822 2.22E-04
1 C8A 153 57,320,442 57,383,894 1.77E-03 rs2284952 1.16E-03
2 GAL3ST2 62 242,716,239 242,743,702 2.01E-03 rs74704923 5.61E-04
3 BPESC1 39 138,823,026 138,844,005 2.05E-03 rs586043 1.38E-03
2 MDH1 6 63,815,742 63,834,330 2.09E-03 rs17618390 6.18E-04
4 CCDC110 76 186,366,337 186,392,913 2.19E-03 rs57923780 5.62E-06
5 GCNT4 7 74,323,288 74,326,724 2.25E-03 rs3811986 1.78E-03
3 LINC00606 11 10,801,168 10,805,877 2.41E-03 rs35131573 6.57E-04
3 RAD18 281 8,918,879 9,005,159 2.67E-03 rs579898 8.73E-04
3 Section 62 56 169,684,579 169,716,161 2.89E-03 rs3348 3.65E-04
1 HNRNPU 2 245,013,601 245,027,827 2.93E-03 rs3766527 7.68E-03
3 RNF168 48 196,195,656 196,230,639 3.20E-03 rs9869437 7.07E-04
3 DCBLD2 227 98,514,813 98,620,533 3.29E-03 rs2439237 7.02E-04
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The content discussed in detail was bold

Pathway enrichment analysis

Pathway enrichment analysis identified 582, 540, and 633 pathways (emp-p < 0.05) associated with FEV1, FVC, and FEV1/FVC ratio, respectively. The top 20 pathways ranked by empirical p-value for FEV1, FVC, and FEV1/FVC ratio are shown in Table 3. For FEV1, significant pathways included signaling by NGF, olfactory signaling pathway, purine metabolism, P53 signaling pathway, and HIF pathway. For FVC, significant pathways included the PI3K cascade, FGFR signaling relevant pathway including negative regulation of FGFR signaling, FGFR ligand binding and activation. For FEV1/FVC, significant pathways included signaling to ERKS, NTHI pathway, WNT pathway, MET pathway, aquaporin mediated transport, etc. Additionally, 268 pathways were common to FEV1, FVC, and FEV1/FVC, including signaling by NGF, insulin signaling pathway, and aquaporin mediated transport (Supplementary Material 3: Table S4).

Table 3.

The top 20 pathways (Emp-p < 0.05) by using PASCAL tool

Pathway Chi2
P value		 Emp
P value
FEV1
REACTOME signalling by NGF 3.22E-04 7.10E-05
REACTOME NGF signalling via TRKA from the plasma membrane 8.05E-04 1.44E-04
KEGG p53 signaling pathway 8.15E-04 1.61E-04
BIOCARTA IL7 pathway 7.04E-04 5.10E-04
BIOCARTA IL4 pathway 7.04E-04 7.40E-04
REACTOME olfactory signaling pathway 1.15E-03 7.60E-04
BIOCARTA IL2 pathway 7.04E-04 7.60E-04
BIOCARTA IL2RB pathway 7.04E-04 8.30E-04
REACTOME digestion of dietary carbohydrate 8.72E-04 1.02E-03
REACTOME activated AMPK stimulates fatty acid oxidation in muscle 1.68E-03 1.12E-03
REACTOME passive transport by aquaporins 1.33E-03 1.28E-03
BIOCARTA ARAP pathway 1.26E-03 1.30E-03
REACTOME amine derived hormones 1.41E-03 1.37E-03
KEGG insulin signaling pathway 1.86E-03 1.53E-03
REACTOME APC C CDC20 mediated degradation of mitotic proteins 1.61E-03 1.58E-03
KEGG spliceosome 1.44E-03 1.65E-03
KEGG olfactory transduction 2.56E-03 1.72E-03
REACTOME autodegradation of CDH1 by CDH1 APC C 1.61E-03 1.86E-03
KEGG small cell lung cancer 3.32E-03 1.88E-03
REACTOME collagen formation 5.69E-03 2.11E-03
FVC
REACTOME insulin receptor signalling cascade 2.37E-04 1.53E-04
REACTOME FRS2 mediated cascade 2.37E-04 1.68E-04
REACTOME signaling by insulin receptor 2.37E-04 1.85E-04
REACTOME signalling by NGF 1.35E-03 2.77E-04
REACTOME NGF signalling via TRKA from the plasma membrane 1.48E-03 3.11E-04
REACTOME tandem pore domain potassium channels 8.98E-04 3.24E-04
REACTOME collagen formation 1.14E-03 3.38E-04
REACTOME SHC mediated cascade 1.03E-03 4.47E-04
REACTOME signaling by FGFR mutants 1.03E-03 4.56E-04
REACTOME FGFR ligand binding and activation 1.03E-03 4.57E-04
REACTOME FGFR2C ligand binding and activation 1.03E-03 4.68E-04
REACTOME negative regulation of FGFR signaling 1.03E-03 4.71E-04
REACTOME PI3k cascade 1.03E-03 5.30E-04
REACTOME activated point mutants of FGFR2 1.03E-03 5.60E-04
REACTOME downstream signaling events of b cell receptor BCR 1.42E-03 6.10E-04
REACTOME phospholipase C mediated cascade 1.03E-03 6.30E-04
REACTOME signaling by the B cell receptor BCR 1.40E-03 6.40E-04
BIOCARTA IL7 pathway 7.04E-04 6.50E-04
BIOCARTA IL 4 pathway 7.04E-04 7.20E-04
BIOCARTA IL 2 pathway 7.04E-04 7.40E-04
FEV1/FVC
REACTOME influenza viral RNA transcription and replication 5.97E-04 1.77E-04
REACTOME apoptosis 8.12E-04 1.83E-04
KEGG ubiquitin mediated proteolysis 5.44E-04 1.96E-04
REACTOME p75 NTR receptor mediated signalling 1.42E-03 3.82E-04
KEGG WNT signaling pathway 1.16E-03 5.46E-04
BIOCARTA keratinocyte pathway 3.40E-03 6.90E-04
BIOCARTA TNFR1 pathway 4.70E-03 1.14E-03
BIOCARTA stress pathway 5.94E-03 1.30E-03
REACTOME peptide chain elongation 2.30E-03 1.35E-03
KEGG glycosphingolipid biosynthesis ganglio series 1.39E-03 1.38E-03
REACTOME SRP dependent cotranslational protein targeting to membrane 2.30E-03 1.39E-03
KEGG ribosome 2.30E-03 1.46E-03
REACTOME transferrin endocytosis and recycling 2.47E-03 1.49E-03
BIOCARTA TID pathway 6.30E-03 1.58E-03
BIOCARTA RELA pathway 1.80E-03 1.71E-03
REACTOME insulin receptor recycling 2.47E-03 1.78E-03
BIOCARTA p35 alzheimers pathway 1.59E-03 1.78E-03
BIOCARTA sodd pathway 1.80E-03 1.78E-03
BIOCARTA ceramide pathway 1.80E-03 1.82E-03
BIOCARTA NFKB pathway 1.80E-03 1.88E-03
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Validation analysis

A total of 352 SNPs for the three pulmonary function indicators with p < 1 × 10− 5 in the discovery set were genotyped in the UK Biobank validation set and selected for validation. Although no SNP passed the Bonferroni correction threshold (p < 1.42 × 10− 4), six SNPs for FEV1 and FVC, and six SNPs for FEV1 exceeded the nominal significance level (p < 0.05). Compared to results published in the GWAS catalog, the 12 SNPs related to pulmonary function validated in this study are all novel. Two SNPs (rs10871500 and rs8070465) were located in the TBC1D16 gene, one (rs149612364) in the TAFA5 gene, and one (rs507108) in the MTHFD1L gene (Table 4). After adjusting for confounders, the GEE models indicated that the PRS was positively associated with FEV1 and FVC, with regression coefficients (β) of 1.49 (p < 0.001) and 1.33 (p < 0.001), respectively.

Table 4.

The SNPs with nominal significance (P < 0.05) in UK Biobank validation analysis

Discovery Validation
SNP Chr BP P value P value Enhancers Gene*
FEV1
rs35031103 3 176,086,799 1.28E-07 3.09E-02 -
rs34981006 3 176,087,566 2.58E-07 2.14E-02
rs7632772 3 176,103,902 4.25E-07 2.30E-02
rs34817694 3 176,118,748 4.38E-07 2.35E-02 enh113420
rs66475255 3 176,107,428 5.02E-07 2.10E-02
rs13084577 3 176,113,219 8.33E-07 4.76E-02
rs186195940 4 138,380,119 1.29E-06 1.29E-02
rs192813943 4 138,326,821 2.34E-06 2.86E-02
rs149612364 22 48,967,243 3.71E-06 1.45E-02 TAFA5
rs10871500 17 77,980,564 4.08E-06 2.14E-02 TBC1D16 enh17427

TBC1D16,

CCDC40, GAA
rs8070465 17 77,978,990 9.36E-06 2.47E-02 TBC1D16 enh17427

TBC1D16,

CCDC40, GAA
rs507108 6 151,327,692 5.75E-06 4.65E-02 MTHFD1L enh9447
FVC
rs35031103 3 176,086,799 2.42E-06 3.23E-02
rs34981006 3 176,087,566 4.73E-06 2.51E-02
rs34817694 3 176,118,748 4.87E-06 2.08E-02
rs7632772 3 176,103,902 7.40E-06 2.80E-02
rs66475255 3 176,107,428 8.70E-06 2.77E-02
rs186195940 4 138,380,119 9.26E-06 9.35E-03
Open in a new tab

*: Genes possibly regulated by Enhancers

Discussion

In this study, we conducted a GWAS of pulmonary function in northern Chinese twins. We identified 222 SNPs associated with FEV1, 150 SNPs associated with FVC, and 73 SNPs associated with the FEV1/FVC ratio, all of which reached a suggestive level of association. Among these, we corroborated the involvement of 52 SNPs located in 14 genes, including DNAH5, C5orf67 [35], CXCR4 [36, 37], ETV1 [38], MAP3K1 [35], MTHFD1L [39], TENM2 [38, 40], ZBTB43 [40], GPR126 [13, 16, 41, 42], AKAP6 [39, 40], FKBP5 [43], GPC5 [21, 39, 40], LRBA [39, 40], and TMOD1 [40], as supported by previous studies. Additionally, we validated 12 novel SNPs associated with pulmonary function in an independent UK Biobank cohort, with some located in the TBC1D16, TAFA5, and MTHFD1L genes. Gene-based and pathway enrichment analyses further identified hundreds of suggestively significant associations involving genes and pathways across FEV1, FVC, and the FEV1/FVC ratio, underscoring the complex genetic architecture underlying pulmonary function traits.

In this study, we identified 222, 150, and 73 SNPs associated with FEV1, FVC, and the FEV1/FVC ratio, respectively, that reached suggestive significance levels. For FEV1, three SNPs located in enhancers were found to regulate the transcription of the DNAH5 gene. This gene, previously associated with ciliary defects [38, 40, 44], was linked to FEV1 in our study. Additionally, two SNPs near the lesser-studied MTHFD2P6 pseudogene and one at the PARM1 gene, which may influence airway epithelium maintenance and repair [45], reached genome-wide significance. For FVC, 11 highly correlated SNPs within the GPR126 gene showed significant associations. GPR126, previously identified in GWAS studies as related to pulmonary function [16, 46, 47], is crucial for lung development [48] and may influence cell proliferation and airway remodeling [49]. Several SNPs located the ADCYAP1R1 gene showed associations with both FEV1 and FVC. This gene is widely distributed in airway nerve fibers and has significant anti-inflammatory and protective functions in the lungs [50, 51]. For the FEV1/FVC ratio, 14 SNPs in enhancers regulating the PLEKHG6 gene reached suggestive significance. Animal studies suggest that PLEKHG6 may contribute to lung protection against hypercapnia [52], and methylation of PLEKHG6 CpG sites, associated with NO2 exposure, may mediate the effects of air pollution on pulmonary function [53].

During the validation phase in the UK Biobank Chinese cohort, 12 novel SNPs were found to be nominally significant for lung function, including two in the TBC1D16 gene, one in the TAFA5 gene, and one in the MTHFD1L gene. TBC1D16 is a GTPase-activating protein that regulates Rab GTPases [54], influencing cell migration and growth, which may be related to alveolar development [55, 56]. The TAFA5 gene, previously associated with wheezing [57], which is the symptomatic manifestation of diseases that cause airway obstruction, also inhibits post-injury neointima formation [58] and may influence lung function. The MTHFD1L-encoded protein is involved in the synthesis of tetrahydrofolate in mitochondria, which could impact lung tissue repair and regeneration, and has been associated with lung function [39, 59]. Additionally, six SNPs located in non-coding regions suggest that these sites may represent functional coding variants or regulatory elements, warranting further exploration.

The gene-based analysis found 698, 724, and 355 genes exceeding the suggestive significance level for FEV1, FVC, and FEV1/FVC ratio, respectively. Some of these genes, such as GPR126, TBC1D16, and ADCYAP1R1, have been discussed above. Several genes of these genes might play a role in pulmonary function through the following mechanisms. FKBP51 encodes a major component of the glucocorticoid receptor and regulates its response to corticosteroids [60]. Glucocorticoids are involved in late fetal lung development and intrauterine lung maturation in humans [61, 62]. AQP4 encodes an aquaporins expressed in large- and small-airway epithelia, and AQP4 deletion affects lung water permeability [63]. RPL37, as a component of the 60 S ribosomes subunit, is a target gene of miR-4516 and forms the miR-4516/RPL37 pathway, which is involved in autophagy. This pathway plays a critical role in the cellular response to PM2.5 and metal components, potentially affecting pulmonary function [64]. CD1B is a lipid-presenting protein that may be influenced by the sphingosine pathway, which is relevant to COPD and other chronic inflammatory lung diseases [65].

Pathway enrichment analysis identified several biological pathways significantly associated with pulmonary function: 582 pathways for FEV1, 540 pathways for FVC, and 633 pathways for FEV1/FVC. Some of these pathways are biologically plausible. Signaling by nerve growth factor (NGF) was associated with FEV1 and FVC. As a member of the neurotrophin family, NGF is highly expressed in the lungs and can change protein expression levels and mediate cellular function, impacting the development of lung diseases such as pulmonary fibrosis and asthma [66]. Olfactory signaling pathway and olfactory transduction were also related to FEV1 and FVC [67]. Notably, olfactory transduction was the most significant KEGG pathway in a previous GWAS of pulmonary function in Koreans [68]. WNT pathway was related to FEV1/FVC and is essential for proper lung development [69, 70]. Additionally, WNT signaling pathway has been revealed in asthma pathogenesis in previous GWASs [68, 71]. Besides these plausible pathways, other pathways might also have biological associations with pulmonary function. More studies need to be conducted to verify these associations.

This study has several advantages. First, our study, based on Qingdao twin samples, provides valuable genetic evidence for the East Asian population. This contrasts with previous pulmonary function GWAS, which were conducted primarily on European populations. Second, we performed the pulmonary function GWAS in twin pairs. The twin-based GWAS approach effectively controls population stratification and passive gene-environment correlations due to the shared genetic and environmental factors of twins. This methodology enhances the accuracy of genetic association studies by enabling the detection of direct genetic effects [23], while simultaneously reducing the risk of false positives and clarifying indirect genetic influences.

However, our study still has some limitations. First, the relatively small sample size of our study may limit the ability to detect significant associations between genetic markers and pulmonary function, largely due to the difficulty in recruiting and identifying qualified twin samples. More studies are needed to confirm our results. Second, although 12 suggestive SNPs were identified and validated in our study, none reached the statistically significant level. Further research is necessary to validate these potential candidate biomarkers of pulmonary function. Third, since our sample is based on a limited number of Han Chinese twins from the Qingdao region, the generalizability of our findings may be constrained. Future studies with more diverse populations and larger sample sizes are needed to validate the broader applicability of our results.

In conclusion, we identified and validated 12 novel SNPs associated with pulmonary function, including those located in the TBC1D16, TAFA5, and MTHFD1L genes. These findings enhance our understanding of the genetic factors influencing pulmonary function and highlight potential biomarkers for future investigation. While these SNPs show promise, further studies are needed to confirm their roles and explore their potential as candidate biomarkers for pulmonary function.

Electronic supplementary material

Below is the link to the electronic supplementary material.

12864_2024_11165_MOESM1_ESM.xlsx (36.5KB, xlsx)

Supplementary Material 1: Table 2S All suggestively significant SNPs for association with the pulmonary function in genome-wide association study: FEV1, FVC and FEV1/FVC

12864_2024_11165_MOESM2_ESM.xlsx (137.5KB, xlsx)

Supplementary Material 2: Table S3:Nominal significant genes of pulmonary function from gene-based analysis by using VEGAS2 tool: FEV1, FVC and FEV1/FVC

12864_2024_11165_MOESM3_ESM.xlsx (46.1KB, xlsx)

Supplementary Material 3: Table S4: Common pathways for FEV1, FVC and FEV1/FVC by PASCAL tool

12864_2024_11165_MOESM4_ESM.pdf (414.3KB, pdf)

Supplementary Material 4: Table S1. Descriptive statistics of basic characteristics of the sample., Figure S1 Regional association plot showing signals around chromosomal loci (6q24.1) for genome-wide association study of FVC, Figure S2 Regional association plot showing signals around chromosomal loci (6q24.1) for genome-wide association study of FVC, Figure S3 Regional association plot showing signals around chromosomal loci (12p13.31) for genome-wide association study of FEV1/FVC.

Acknowledgements

The authors appreciate Dr. Gu Zhu for his technical guidance of data analysis, and all participants and contributors of Qingdao Twins Registry and the UK Biobank.

Author contributions

T.W conducted the analysis, wrote original draft, and prepare the figures and tables. W.W, C.X and X.T collected and provided data and other resources. D.Z helped make interpretation of data and substantively revised it. All authors reviewed the draft for intellectual content, and approved submission of the manuscript.

Funding

None.

Data availability

The dataset analysed during the current study are available in the European Variation Archive (EVA) repository (Accession No. PRJEB23749).

Declarations

Ethics approval and consent to participate

Informed written consents were obtained from all participants. Regional Ethics Committee of the Qingdao Centers for Disease Control and Prevention Institutional Review Boards has approved this study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

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

Contributor Information

Xiaocao Tian, Email: txc_2006911@126.com.

Dongfeng Zhang, Email: zhangdf1961@126.com.

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Associated Data

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

Supplementary Materials

12864_2024_11165_MOESM1_ESM.xlsx (36.5KB, xlsx)

Supplementary Material 1: Table 2S All suggestively significant SNPs for association with the pulmonary function in genome-wide association study: FEV1, FVC and FEV1/FVC

12864_2024_11165_MOESM2_ESM.xlsx (137.5KB, xlsx)

Supplementary Material 2: Table S3:Nominal significant genes of pulmonary function from gene-based analysis by using VEGAS2 tool: FEV1, FVC and FEV1/FVC

12864_2024_11165_MOESM3_ESM.xlsx (46.1KB, xlsx)

Supplementary Material 3: Table S4: Common pathways for FEV1, FVC and FEV1/FVC by PASCAL tool

12864_2024_11165_MOESM4_ESM.pdf (414.3KB, pdf)

Supplementary Material 4: Table S1. Descriptive statistics of basic characteristics of the sample., Figure S1 Regional association plot showing signals around chromosomal loci (6q24.1) for genome-wide association study of FVC, Figure S2 Regional association plot showing signals around chromosomal loci (6q24.1) for genome-wide association study of FVC, Figure S3 Regional association plot showing signals around chromosomal loci (12p13.31) for genome-wide association study of FEV1/FVC.

Data Availability Statement

The dataset analysed during the current study are available in the European Variation Archive (EVA) repository (Accession No. PRJEB23749).

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