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The understanding of the molecular genetic contributions to smoking is largely limited to the additive effects of individual single nucleotide polymorphisms (SNPs), but the underlying genetic risk is likely to also include dominance, epistatic, and gene-environment interactions. To begin to address this complexity, we attempted to identify genetic interactions between rs16969968, the most replicated SNP associated with smoking quantity, and all SNPs and genes across the genome. Using the UK Biobank European subsample, we found one SNP, rs1892967, and two genes, PCNA and TMEM230 , that showed a significant genome-wide interaction with rs16969968 for log10 CPD and raw CPD, respectively, in a sample of 116 442 individuals who self-reported currently or previously smoking. We extended these analyses to individuals of South Asian descent and meta-analyzed the combined sample of 117 212 individuals of European and South Asian ancestry. We replicated the gene findings in a meta-analysis of five Finnish samples (N=40 140): FinHealth, FINRISK, Finnish Twin Cohort, GeneRISK, and Health-2000–2011. To our knowledge, this represents the first reliable epistatic association between single nucleotide polymorphisms for smoking behaviors and provides a novel direction for possible future functional studies related to this interaction. Furthermore, this work demonstrates the feasibility of these analyses by pooling multiple datasets across various ancestries, which may be applied to other top SNPs for smoking and/or other phenotypes. • We explored interactions between rs16969968 and all SNPs and genes genome-wide. • One SNP, rs1892967, and two genes, PCNA and TMEM230 , significantly interacted with rs16969968 for smoking quantity. • We replicated the gene findings in a meta-analysis of five Finnish samples comprising over 40, 000 individuals. • This is the first reliable epistatic association between single nucleotide polymorphisms for smoking behaviors. [ABSTRACT FROM AUTHOR]

Supplemental digital content is available in the text. Purpose: Polygenic risk scores (PRS) summarize genome-wide genotype data into a single variable that produces an individual-level risk score for genetic liability. PRS has been used for prediction of chronic diseases and some risk factors. As PRS has been studied less for physical activity (PA), we constructed PRS for PA and studied how much variation in PA can be explained by this PRS in independent population samples. Methods: We calculated PRS for self-reported and objectively measured PA using UK Biobank genome-wide association study summary statistics, and analyzed how much of the variation in self-reported (MET-hours per day) and measured (steps and moderate-to-vigorous PA minutes per day) PA could be accounted for by the PRS in the Finnish Twin Cohorts (FTC; N = 759–11,528) and the Northern Finland Birth Cohort 1966 (NFBC1966; N = 3263–4061). Objective measurement of PA was done with wrist-worn accelerometer in UK Biobank and NFBC1966 studies, and with hip-worn accelerometer in the FTC. Results: The PRS accounted from 0.07% to 1.44% of the variation (R 2) in the self-reported and objectively measured PA volumes (P value range = 0.023 to <0.0001) in the FTC and NFBC1966. For both self-reported and objectively measured PA, individuals in the highest PRS deciles had significantly (11%–28%) higher PA volumes compared with the lowest PRS deciles (P value range = 0.017 to <0.0001). Conclusions: PA is a multifactorial phenotype, and the PRS constructed based on UK Biobank results accounted for statistically significant but overall small proportion of the variation in PA in the Finnish cohorts. Using identical methods to assess PA and including less common and rare variants in the construction of PRS may increase the proportion of PA explained by the PRS. [ABSTRACT FROM AUTHOR]