Genetic architecture of four smoking behaviors using partitioned h2SNP

Background and Aims Smoking is a leading cause of premature death. Although genome-wide association studies have identified many loci that influence smoking behaviors, much of the genetic variance in these traits remains unexplained. We sought to characterize the genetic architecture of four smoking behaviors through SNP-based heritability ( h 2 SNP ) analyses. Design We applied recently-developed partitioned h 2 SNP approaches to smoking behavior traits assessed in the UK Biobank. Setting UK Biobank. Participants UK Biobank participants of European ancestry. The number of participants varied depending on the trait, from 54,792 to 323,068. Measurements Smoking initiation, age of initiation, cigarettes per day (CPD; count, log-transformed, binned, and dichotomized into heavy versus light), and smoking cessation. Imputed genome-wide SNPs. Findings We estimated h 2 SNP (SE)=0.18(0.01) for smoking initiation and 0.12(0.02) for smoking cessation, which were more than twice the previously reported estimates. Estimated age of initiation h 2 SNP =0.05(0.01) and binned CPD h 2 SNP =0.1(0.01) were similar to previous reports. These estimates remained substantially below published twin-based h 2 of roughly 50%. CPD encoding strongly influenced estimates, with dichotomized CPD h 2 SNP =0.28. We found significant contributions of low-frequency variants and variants in low linkage-disequilibrium (LD) with surrounding genomic regions. Functional annotations related to LD, allele frequency, sequence conservation, and selective constraint also contributed significantly to the partitioned heritability. We found no evidence of dominance genetic variance for any trait. Conclusion h 2 SNP of these four specific smoking behaviors is modest overall. The patterns of partitioned h 2 SNP for these highly polygenic traits is consistent with negative selection. We found a predominant contribution of common variants, and our results suggest a role of low-frequency or rare variants, poorly tagged by surrounding regions. Deep sequencing of large samples and/or improved imputation will be required to fully assess the role of rare variants. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by R01 MH100141-06(PI: Keller); R01 DA 044283, R01 DA 037904, and R01 HG 008983(PI: Vrieze); and the Institute for Behavioral Genetics. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The IRB determined that the proposed activity is not research involving human subjects as defined by DHHS and/or FDA regulations. All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All analyses used data from the UK Biobank.