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Clark SL, McClay JL, Adkins DE, Aberg KA, Kumar G, Nerella S, Xie L, Collins AL, Crowley JJ, Quakenbush CR, Hillard CE, Gao G, Shabalin AA, Peterson RE, Copeland WE, Silberg JL, Maes H, Sullivan PF, Costello EJ, van den Oord EJ. Deep Sequencing of Three Loci Implicated in Large-Scale Genome-Wide Association Study Smoking Meta-Analyses. Nicotine Tob Res 2015; 18:626-31. [PMID: 26283763 DOI: 10.1093/ntr/ntv166] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2015] [Accepted: 07/17/2015] [Indexed: 12/11/2022]
Abstract
INTRODUCTION Genome-wide association study meta-analyses have robustly implicated three loci that affect susceptibility for smoking: CHRNA5\CHRNA3\CHRNB4, CHRNB3\CHRNA6 and EGLN2\CYP2A6. Functional follow-up studies of these loci are needed to provide insight into biological mechanisms. However, these efforts have been hampered by a lack of knowledge about the specific causal variant(s) involved. In this study, we prioritized variants in terms of the likelihood they account for the reported associations. METHODS We employed targeted capture of the CHRNA5\CHRNA3\CHRNB4, CHRNB3\CHRNA6, and EGLN2\CYP2A6 loci and flanking regions followed by next-generation deep sequencing (mean coverage 78×) to capture genomic variation in 363 individuals. We performed single locus tests to determine if any single variant accounts for the association, and examined if sets of (rare) variants that overlapped with biologically meaningful annotations account for the associations. RESULTS In total, we investigated 963 variants, of which 71.1% were rare (minor allele frequency < 0.01), 6.02% were insertion/deletions, and 51.7% were catalogued in dbSNP141. The single variant results showed that no variant fully accounts for the association in any region. In the variant set results, CHRNB4 accounts for most of the signal with significant sets consisting of directly damaging variants. CHRNA6 explains most of the signal in the CHRNB3\CHRNA6 locus with significant sets indicating a regulatory role for CHRNA6. Significant sets in CYP2A6 involved directly damaging variants while the significant variant sets suggested a regulatory role for EGLN2. CONCLUSIONS We found that multiple variants implicating multiple processes explain the signal. Some variants can be prioritized for functional follow-up.
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Affiliation(s)
- Shaunna L Clark
- Center for Biomarker Research and Precision Medicine, School of Pharmacy, Virginia Commonwealth University, Richmond, VA;
| | - Joseph L McClay
- Center for Biomarker Research and Precision Medicine, School of Pharmacy, Virginia Commonwealth University, Richmond, VA
| | - Daniel E Adkins
- Center for Biomarker Research and Precision Medicine, School of Pharmacy, Virginia Commonwealth University, Richmond, VA
| | - Karolina A Aberg
- Center for Biomarker Research and Precision Medicine, School of Pharmacy, Virginia Commonwealth University, Richmond, VA
| | - Gaurav Kumar
- Center for Biomarker Research and Precision Medicine, School of Pharmacy, Virginia Commonwealth University, Richmond, VA
| | - Sri Nerella
- Center for Biomarker Research and Precision Medicine, School of Pharmacy, Virginia Commonwealth University, Richmond, VA
| | - Linying Xie
- Center for Biomarker Research and Precision Medicine, School of Pharmacy, Virginia Commonwealth University, Richmond, VA
| | - Ann L Collins
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - James J Crowley
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Corey R Quakenbush
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Christopher E Hillard
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Guimin Gao
- Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, VA
| | - Andrey A Shabalin
- Center for Biomarker Research and Precision Medicine, School of Pharmacy, Virginia Commonwealth University, Richmond, VA
| | - Roseann E Peterson
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA
| | - William E Copeland
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC
| | - Judy L Silberg
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA
| | - Hermine Maes
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA
| | - Patrick F Sullivan
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Elizabeth J Costello
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC
| | - Edwin J van den Oord
- Center for Biomarker Research and Precision Medicine, School of Pharmacy, Virginia Commonwealth University, Richmond, VA
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