Gene by Environment Investigation of Incident Lung Cancer Risk in African-Americans.
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2016-02
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BACKGROUND: Genome-wide association studies have identified polymorphisms linked to both smoking exposure and risk of lung cancer. The degree to which lung cancer risk is driven by increased smoking, genetics, or gene-environment interactions is not well understood. METHODS: We analyzed associations between 28 single nucleotide polymorphisms (SNPs) previously associated with smoking quantity and lung cancer in 7156 African-American females in the Women's Health Initiative (WHI), then analyzed main effects of top nominally significant SNPs and interactions between SNPs, cigarettes per day (CPD) and pack-years for lung cancer in an independent, multi-center case-control study of African-American females and males (1078 lung cancer cases and 822 controls). FINDINGS: Nine nominally significant SNPs for CPD in WHI were associated with incident lung cancer (corrected p-values from 0.027 to 6.09 × 10(-5)). CPD was found to be a nominally significant effect modifier between SNP and lung cancer for six SNPs, including CHRNA5 rs2036527[A](betaSNP*CPD = - 0.017, p = 0.0061, corrected p = 0.054), which was associated with CPD in a previous genome-wide meta-analysis of African-Americans. INTERPRETATION: These results suggest that chromosome 15q25.1 variants are robustly associated with CPD and lung cancer in African-Americans and that the allelic dose effect of these polymorphisms on lung cancer risk is most pronounced in lighter smokers.
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David, Sean P, Ange Wang, Kristopher Kapphahn, Haley Hedlin, Manisha Desai, Michael Henderson, Lingyao Yang, Kyle M Walsh, et al. (2016). Gene by Environment Investigation of Incident Lung Cancer Risk in African-Americans. EBioMedicine, 4. pp. 153–161. 10.1016/j.ebiom.2016.01.002 Retrieved from https://hdl.handle.net/10161/15151.
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Benjamin Alan Goldstein
I study the meaningful use of Electronic Health Records data. My research interests sit at the intersection of biostatistics, biomedical informatics, machine learning and epidemiology. I collaborate with researchers both locally at Duke as well as nationally. I am interested in speaking with any students, methodologists or collaborators interested in EHR data.
Please find more information at: https://biostat.duke.edu/goldstein-lab
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