Genome-wide association study of smoking trajectory and meta-analysis of smoking status in 842,000 individuals

Xu, Ke; Li, Boyang; McGinnis, Kathleen A.; Vickers‐Smith, Rachel; Dao, Cecilia; Sun, Ning; Kember, Rachel; Zhou, Hang; Becker, William C.; Gelernter, Joel; Kranzler, Henry R.; Zhao, Hongyu; Justice, Amy C.; Program, VA Million Veteran

doi:10.1038/s41467-020-18489-3

Cited by 74 publications

(103 citation statements)

References 82 publications

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“…These findings align with both the strong phenotypic relationship between smoking and alcohol use and the known genetic overlap between these traits. 5,10,26 Interestingly, in both the AFR and EUR samples, these associations were no longer significant after covarying for the primary TUD diagnosis, suggesting that the association between PRS and these phenotypes may be mediated by the TUD diagnosis.…”

Section: Discussionmentioning

confidence: 96%

Genetic liability for substance use associated with medical comorbidities in electronic health records of African- and European-ancestry individuals

Hartwell

Merikangas

Verma

et al. 2021

Preprint

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Polygenic risk scores (PRS) represent an individual’s summed genetic risk for a trait and can serve as biomarkers for disease. Less is known about the utility of PRS as a means to quantify genetic risk for substance use disorders (SUDs) than for many other traits. Nonetheless, the growth of large, electronic health record-based biobanks makes it possible to evaluate the association of SUD PRS with other traits. We calculated PRS for smoking initiation, alcohol use disorder (AUD), and opioid use disorder (OUD) using summary statistics from the Million Veteran Program sample. We then tested the association of each PRS with its primary phenotype in the Penn Medicine BioBank (PMBB) using all available genotyped participants of African or European ancestry (AFR and EUR, respectively) (N=18,612). Finally, we conducted phenome-wide association analyses (PheWAS) separately by ancestry and sex to test for associations across disease categories. Tobacco use disorder was the most common SUD in the PMBB, followed by AUD and OUD, consistent with the population prevalence of these disorders. All PRS were associated with their primary phenotype in both ancestry groups. PheWAS results yielded cross-trait associations across multiple domains, including psychiatric disorders and medical conditions. SUD PRS were associated with their primary phenotypes, however they are not yet predictive enough to be useful diagnostically. The cross-trait associations of the SUD PRS are indicative of a broader genetic liability. Future work should extend findings to additional population groups and for other substances of abuse.

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Section: Discussionmentioning

confidence: 96%

Genetic liability for substance use associated with medical comorbidities in electronic health records of African- and European-ancestry individuals

Hartwell

Merikangas

Verma

et al. 2021

Preprint

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“…While previous genome-wide association studies (GWAS) have identified numerous genetic variants associated with smoking and smoking-related phenotypes [55][56][57][58][59][60] , functional interpretation of these variants remains challenging. Genetic variants could directly alter poly(A) motifs and RBP binding sites to modulate APA events, and several studies have been undertaken in recent years to systematically map novel apaQTLs and their disease etiologies [61][62][63][64] .…”

Section: Discussionmentioning

confidence: 99%

Cigarette Smoking-Associated Isoform Switching and 3’ UTR Lengthening Via Alternative Polyadenylation

Platig

Lee

Boueiz

et al. 2021

Preprint

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Cigarette smoking accounts for approximately one in five deaths in the United States. Previous genomic studies have primarily focused on gene level differential expression to identify related molecular signatures and pathways, but the genome-wide effects of smoking on alternative isoform regulation and posttranscriptional modulation have not yet been described. We conducted RNA sequencing (RNA-seq) in whole-blood samples of 454 current and 767 former smokers in COPDGene Study. We assessed the association of current smoking with differential expression of genes and isoforms and differential usage of isoforms and exons. At 10% FDR, we detected 3,167 differentially expressed genes, 2,014 differentially expressed isoforms, 945 differentially used isoforms and 160 differentially used exons. Genes containing differentially used isoforms were enriched in biological pathways involving GTPase activity and innate immunity. The majority of these genes were not differentially expressed, thus not identifiable from conventional differential gene expression analysis. Isoform switch analysis revealed for the first time widespread 3-prime UTR lengthening associated with cigarette smoking, where current smokers were found to have higher expression and usage of isoforms with markedly longer 3-prime UTRs. The lengthening of 3-prime UTRs appears to be mediated through alternative usage of distal polyadenylation sites, and these extended 3-prime UTR regions are significantly enriched with functional sequence elements including adenylate-uridylate (AU)-rich elements, microRNA and RNA-protein binding sites. Expression quantitative trait locus analyses on differentially used 3-prime UTRs identified 79 known GWAS variants associated with multiple smoking-related human diseases and traits. Smoking elicits widespread transcriptional and posttranscriptional alterations with disease implications. It induces alternative polyadenylation (APA) events resulting in a switch towards the usage of isoforms with strikingly longer 3-prime UTRs in genes related to multiple biological pathways including GTPase activity and innate immunity. The extended 3-prime UTR regions are enriched with functional sequence elements facilitating post-transcriptional regulation of protein expression and mRNA stability. These findings warrant further studies on APA events as potential biomarkers and novel therapeutic targets for smoking-related diseases.

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“…To address this gap in knowledge, we leverage the Million Veteran Program (MVP) cohort, a longitudinal mega biobank with on-going recruitment of Veterans who receive care from the Veterans Health Administration (VA) and has contributed to numerous biomedical and genomics studies [29][30][31]. The VA is the largest single-payer healthcare system in the US and has over 20 years of electronic health record (EHR) data for 6 million annual active users nationwide which will allow to examine the history of clinical risk factors and comorbidities that may be associated with COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Phenome-wide association of 1809 phenotypes and COVID-19 disease progression in the Veterans Health Administration Million Veteran Program

Song

Schubert

et al. 2021

PLoS ONE

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Background The risk factors associated with the stages of Coronavirus Disease-2019 (COVID-19) disease progression are not well known. We aim to identify risk factors specific to each state of COVID-19 progression from SARS-CoV-2 infection through death. Methods and results We included 648,202 participants from the Veteran Affairs Million Veteran Program (2011-). We identified characteristics and 1,809 ICD code-based phenotypes from the electronic health record. We used logistic regression to examine the association of age, sex, body mass index (BMI), race, and prevalent phenotypes to the stages of COVID-19 disease progression: infection, hospitalization, intensive care unit (ICU) admission, and 30-day mortality (separate models for each). Models were adjusted for age, sex, race, ethnicity, number of visit months and ICD codes, state infection rate and controlled for multiple testing using false discovery rate (≤0.1). As of August 10, 2020, 5,929 individuals were SARS-CoV-2 positive and among those, 1,463 (25%) were hospitalized, 579 (10%) were in ICU, and 398 (7%) died. We observed a lower risk in women vs. men for ICU and mortality (Odds Ratio (95% CI): 0.48 (0.30–0.76) and 0.59 (0.31–1.15), respectively) and a higher risk in Black vs. Other race patients for hospitalization and ICU (OR (95%CI): 1.53 (1.32–1.77) and 1.63 (1.32–2.02), respectively). We observed an increased risk of all COVID-19 disease states with older age and BMI ≥35 vs. 20–24 kg/m2. Renal failure, respiratory failure, morbid obesity, acid-base balance disorder, white blood cell diseases, hydronephrosis and bacterial infections were associated with an increased risk of ICU admissions; sepsis, chronic skin ulcers, acid-base balance disorder and acidosis were associated with mortality. Conclusions Older age, higher BMI, males and patients with a history of respiratory, kidney, bacterial or metabolic comorbidities experienced greater COVID-19 severity. Future studies to investigate the underlying mechanisms associated with these phenotype clusters and COVID-19 are warranted.

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Genome-wide association study of smoking trajectory and meta-analysis of smoking status in 842,000 individuals

Cited by 74 publications

References 82 publications

Genetic liability for substance use associated with medical comorbidities in electronic health records of African- and European-ancestry individuals

Genetic liability for substance use associated with medical comorbidities in electronic health records of African- and European-ancestry individuals

Cigarette Smoking-Associated Isoform Switching and 3’ UTR Lengthening Via Alternative Polyadenylation

Phenome-wide association of 1809 phenotypes and COVID-19 disease progression in the Veterans Health Administration Million Veteran Program

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