The Shared Genetic Architectures Between Lung Cancer and Multiple Polygenic Phenotypes in Genome-Wide Association Studies

Abstract

Background: Prior genome-wide association studies have identified numerous lung cancer risk loci and reveal substantial etiologic heterogeneity across histologic subtypes. Analyzing the shared genetic architecture underlying variation in complex traits can elucidate common genetic etiologies across phenotypes. Exploring pairwise genetic correlations between lung cancer and other polygenic traits can reveal the common genetic etiology of correlated phenotypes. Methods: Using cross-trait linkage disequilibrium score regression, we estimated the pairwise genetic correlation and heritability between lung cancer and multiple traits using publicly available summary statistics. Identified genetic relationships were also examined after excluding genomic regions known to be associated with smoking behaviors, a major risk factor for lung cancer. Results: We observed several traits showing moderate single nucleotide polymorphism–based heritability and significant genetic correlations with lung cancer. We observed highly significant correlations between the genetic architectures of lung cancer and emphysema/chronic bronchitis across all histologic subtypes, as well as among lung cancer occurring among smokers. Our analyses revealed highly significant positive correlations between lung cancer and paternal history of lung cancer. We also observed a strong negative correlation with parental longevity. We observed consistent directions in genetic patterns after excluding genomic regions associated with smoking behaviors. Conclusions: This study identifies numerous phenotypic traits that share genomic architecture with lung carcinogenesis and are not fully accounted for by known smoking-associated genomic loci. Impact: These findings provide new insights into the etiology of lung cancer by identifying traits that are genetically correlated with increased risk of lung cancer.

Shared genomic architecture between COVID-19 severity and numerous clinical and physiologic parameters revealed by LD score regression analysis

“…Genome-wide association studies (GWAS) have revealed the genetic underpinnings of complex human traits, helping to elucidate the heritability of susceptibility to both chronic diseases and infectious diseases—including COVID-19 2 . Such complex diseases typically have multifactorial etiologies, with contributions from germline genetic variation and environmental exposures 3 , and they frequently present with related, comorbid medical conditions 4 .…”

“…In addition, we determined the directionality of these associations, and whether the genetic architecture of two traits are correlated or anti-correlated 8 . Examining the shared genetic architecture underlying co-occurrence of disease can help elucidate their common genetic etiology 4 , but there has been no comprehensive evaluation of the shared genetic architecture between COVID-19 disease severity and additional diseases or traits. Previous analyses of lung cancer risk reveal shared genetic architecture with emphysema, a common co-morbid condition, as well as with cigarette consumption, the leading cause of lung cancer 4 .…”

“…Examining the shared genetic architecture underlying co-occurrence of disease can help elucidate their common genetic etiology 4 , but there has been no comprehensive evaluation of the shared genetic architecture between COVID-19 disease severity and additional diseases or traits. Previous analyses of lung cancer risk reveal shared genetic architecture with emphysema, a common co-morbid condition, as well as with cigarette consumption, the leading cause of lung cancer 4 . In the setting of COVID-19 disease severity, such an evaluation has similar potential to identify co-morbid conditions and to uncover traits that are causally involved in worsening clinical course.…”

An integrated genome and phenome-wide association study approach to understanding Alzheimer’s disease predisposition

“…However, both GWAS and PheWAS typically test SNP-trait associations through the same multivariable regression-based frameworks, including adjustment for subject ancestry to control for population stratification. (Bush, Oetjens, and Crawford 2016) By expanding the list of phenotypes associated with a variant of interest, performing PheWAS on a known disease-associated SNP can identify potential co-morbid conditions, (Byun et al 2021) as well as traits that may mediate the SNP-disease association. (Pettit et al 2021) For example, a PheWAS of the lung cancer risk-variant rs16969968 reveals numerous strong associations with various smoking-related behaviors (e.g., cigarettes per day) that mediate the variant's association with lung cancer risk.…”

“…By expanding the list of phenotypes associated with a variant of interest, performing PheWAS on a known disease-associated SNP can identify potential co-morbid conditions,(Byun et al 2021) as well as traits that may mediate the SNP-disease association. (Pettit et al 2021) For example, a PheWAS of the lung cancer risk-variant rs16969968 reveals numerous strong associations with various smoking-related behaviors ( e .…”