Computational Exploration of Lung Function Genetics Across Populations via Public GWAS Data Integration

Abstract

Abstract Background: Chronic obstructive pulmonary disorder (COPD) is a highly prevalent disease, making it a leading cause of death worldwide. Several GWAS have been performed across multiple populations to measure lung function and identify loci associated with COPD. Population-specific GWAS shows that every population has a different ancestral genetic composition for the same disease in different populations. To analyze trans-ethnic genetics, GWAS meta-analysis is the commonly used method; however, meta-analysis has some limitations in terms of genetic heterogeneity when used for cross-population GWAS analysis, even though transethnic analyses are becoming increasingly important for personalized medicine in each population. In this study, we proposed a transethnic linkage disequilibrium LD analysis to identify common and unique functional variants in different population cohorts. Methods: Lung function measurement is used as an indicator for the risk prediction of COPD; therefore, we used lung function GWAS data from two populations. The results from the Japanese and European population GWAS for lung function were re-evaluated using a trans-ethnic LD approach. Results: This study identified nine novel independent significant single nucleotide variants SNVs and four lead SNVs in three genomic risk loci in the Japanese GWAS, whereas five novel lead SNVs and 17 novel independent significant SNPs were identified in 21 genomic risk loci in the European population. Comparative analysis revealed 28 genes that were similar in the prioritized gene lists of both populations. We also performed a meta-analysis-based post-GWAS analysis that identified 18 common genes in both populations less frequently than in our approach. Our approach identified significant novel associations and genes that have not been previously reported or were missed in the meta-analysis. Conclusions: This study proposes a trans-ethnic LD approach for cross-population GWAS analysis that will help understand genetic diversity among different populations and identify generalized and population-specific treatment and diagnostic options.