Na Qin,Cheng Wang,Zhibin Hu
Genetic insights into lung function inform better management of respiratory diseases
Shrine et al. conducted the largest multi-ancestry genome-wide meta-analysis of lung function and identified 1,020 signals associated with lung function. These provide novel insights into the genetic underpins of lung function and may inform better clinical management of respiratory disorders.
Genetic insights into lung function inform better management of respiratory diseases

Shrine et al. conducted the largest multi-ancestry genome-wide meta-analysis of lung function and identified 1,020 signals associated with lung function. These provide novel insights into the genetic underpins of lung function and may inform better clinical management of respiratory disorders.

Lung function serves as an important indicator of respiratory health, as it allows for the prediction of susceptibility to a variety of respiratory disorders, including but not limited to chronic obstructive pulmonary disease (COPD), lung cancer, and COVID-19. A comprehensive understanding of the genetic variants that influence lung function has the potential to improve risk prediction and management of these respiratory diseases. Despite significant progress in genome-wide association studies (GWASs) in the last decade, which have implicated approximately 300 genetic loci associated with lung function, the current variants account for less than 10% of the heritability, leaving the problem of “missing heritability” unresolved. In a recent issue of Nature Genetics, Shrine et al. conducted the largest genome-wide meta-analysis of lung function to date. They assembled genotype data from 49 cohorts, comprising of 580,869 participants from five different ancestries. Combing genetic variants associated with each of the four spirometry indices (i.e., forced expiratory volume in 1s [FEV1], forced vital capacity [FVC], FEV1/FVC, and peak expiratory flow rate) as well as those co-localized across these traits, a total of 1,020 signals were identified, including 731 newly defined ones, which enabled a tripling of the number of confirmed susceptibility loci for lung function. Notably, the signals defined in this study substantially increased the estimated heritability of lung function from around 10% to 33%. These findings greatly expand our knowledge on genetic loci that contribute to lung function and suggest that biobank-level meta-analysis of GWASs from diverse populations is an efficient strategy for identifying hundreds of common and low-frequency genetic variations associated with a target phenotype. However, it should be noted that rare variants, which can hardly be captured or imputed by array-based GWASs like this study, also contribute to the heritability of lung function or related respiratory diseases., Although still in infancy, recent application of whole-genome or exome sequencing in GWAS has been growing, which presents an excellent opportunity to better assess the role of rare variants in lung function. Another key challenge in a GWAS study is to dissect the causal variants in trait-associated loci and interpret their biological mechanisms. In this study, Shrine et al. fine-mapped all newly and previously identified signals by integrating tissue-specific functional genomic data, including chromatin accessibility, transcriptional-factor bindings sites, and gene and protein expression quantitative trait loci. This allowed them to implicate 438 putative causal signals and 559 causal genes, highlighting the importance of PI3K-Akt signaling, integrin pathways, endochondral ossification, and several other molecular pathways in the etiology of impaired lung function. Importantly, many of the causal genes are also druggable targets, providing promising opportunities for drug repurposing in respiratory disorders. In addition to pinpointing the target genes for genetic signals, identifying the relevant cell type is also critical for subsequent functional studies. SNP enrichment analysis revealed that lung function signals were overrepresented in the functionally active regions in alveolar type 1 cells, fibroblasts, myofibroblasts, and bronchial epithelial cells, all of which have previously been postulated to be involved in the development of COPD. The above findings substantially facilitate our understanding of the genetic architecture of lung function, which paves the way for translating the genetic signals into actionable causal variants and genes for following mechanism experiments. Nevertheless, the causal variants determined by the annotation process require further functional evaluation. In recent years, several scalable and high-throughput technologies have been developed to firmly establish the causality of GWAS-nominated variants and demonstrate the molecular functionality of related elements and genes, such as the massively parallel reporter assay (MPRA), self-transcribing active regulatory region sequencing (STARR-seq), and CRISPR-Cas9, which enables the assessment of many genetic variants and genes in vitro or in vivo simultaneously. In recent years, developing a genetic risk score (GRS) to assess individuals’ susceptibility to respiratory disorders is one of the direct clinical applications of lung function GWAS findings.,, However, previous GWASs for lung function often suffered from a lack of ancestry diversity in signal identification as the majority of the study participants are of European descent, leading to poor performance of GRS in non-European ancestries. Interestingly, in this current study, the integration of newly identified genetic signals with previous GWAS hits into a GRS showed a substantially improved predictive performance across a wide range of ancestries, suggesting that an increased number of genetic signals and improved effect estimates could motivate the development of a better and more transferable GRS for complex traits or diseases. Another strategy to accelerate the clinical translation of GWAS findings is through phenome-wide associations study (PheWAS), which assesses the association between GRS and a wide range of phenotypes simultaneously. In this study, PheWAS revealed a broad genetic overlap between lung function GRS and other respiratory phenotypes, such as asthma, COPD, chronic bronchitis/emphysema, etc., which was concordant with previous epidemiological and clinical evidence. Interestingly, the study also provided novel evidence for the extensive pleiotropic properties of lung function with autoimmune traits or diseases (e.g., C-reactive protein and type 2 diabetes). Although the precise mechanisms underlying the comorbidities remains unclear, these findings raise the possibility of shared clinical trajectories and etiology mechanisms between these traits or diseases, which may help to accelerate the drug development and repurposing as well as subsequent genomic mechanism research for respiratory diseases. Additionally, to further maximize the predictive power of lung function GRS and detect more specific associations to biologically informative traits, the authors provided the first trial of applying pathway-partitioned GRS analyses in lung function, providing novel insights into understanding the mechanisms underlying the transition from impaired lung function to other diseases. In summary, this largest multi-ancestry GWASs provides valuable insights into the genetic architecture of lung function, highlighting new genetic signals, causal genes, and related pathways that contribute to respiratory diseases, which can potentially improve risk prediction, facilitate drug discovery, and advance our understanding on the etiology of respiratory diseases. As impaired lung function is taking center stage in the diagnosis of multiple respiratory diseases, we eagerly await these results leveraged for subsequent clinical translation in the near future.