Increased Genetic Vulnerability to Smoking at CHRNA5 in Early-Onset Smokers

Hartz, Sarah M.; Short, Susan E.; Saccone, Nancy L.; Culverhouse, Robert; Chen, Li Shiun; Schwantes-An, Tae Hwi; Coon, Hilary; Han, Younghun; Stephens, Sarah H.; Sun, Juzhong; Chen, Xiangning; Ducci, Francesca; Dueker, Nicole; Franceschini, Nora; Frank, Josef; Geller, Frank; Guobjartsson, Daniel; Hansel, Nadia N.; Jiang, Changchuan; Keskitalo-Vuokko, Kaisu; Liu, Zhen; Lyytikäinen, Leo-Pekka; Michel, Martha; Rawal, Rajesh; Rosenberger, Albert; Scheet, Paul; Shaffer, John R.; Teumer, Alexander; Thompson, J.; Vink, Jacqueline M.; Vogelzangs, Nicole; Wenzlaff, Angela S.; Wheeler, William; Xiao, Xiangjun; Yang, Bao Zhu; Aggen, Steven H.; Balmforth, Anthony J.; Baumeister, Sebastian E.; Beaty, Terri H.; Bennett, Siiri N.; Bergen, Andrew W.; Boyd, Heather A.; Broms, Ulla; Campbell, Harry; Chatterjee, Nilanjan; Chen, Jingchun; Cheng, Yu Ching; Cichon, Sven; Couper, David J.; Cucca, Francesco; Dick, Danielle M.; Foroud, Tatiana; Furberg, Helena; Giegling, Ina; Gu, Fangyi; Hall, Alistair S.; Hällfors, Jenni; Han, Shizhong; Hartmann, Annette M.; Hayward, Caroline; Heikkilä, Kauko; Hewitt, John K.; Hottenga, Jouke Jan; Jensen, Majken K.; Jousilahti, Pekka; Kaakinen, Marika; Kittner, Steven J.; Konte, Bettina; Korhonen, Tellervo; Landi, Maria Teresa; Laatikainen, Tiina; Leppert, M.; Levy, Steven M.; Mathias, Rasika A.; McNeil, Daniel W.; Medland, Sarah E.; Montgomery, Grant W.; Muley, Thomas; Murray, Tanda; Nauck, Matthias; North, Kari E.; Pergadia, Michele L.; Polašek, Ozren; Ramos, Erin M.; Ripatti, Samuli; Risch, Angela; Ruczinski, Ingo; Rudan, Igor; Salomaa, Veikko; Schlessinger, David; Styrkársdóttir, Unnur; Terracciano, Antonio; Uda, Manuela; Willemsen, Gonneke; Wu, Xifeng; Abecasis, Gonçalo R.; Barnes, Kathleen C.; Bickeböller, Heike; Boerwinkle, Eric; Boomsma, Dorret I.; Caporaso, Neil E.; Duan, Jubao; Edenberg, Howard J.; Francks, Clyde; Gejman, Pablo V.; Gelernter, Joel; Grabe, Hans J.; Hops, Hyman; Järvelin, Marjo‐Riitta; Viikari, Jorma; Kähönen, Mika; Kendler, Kenneth S.; Lehtimäki, Terho; Levinson, Douglas F.; Marazita, Mary L.; Marchini, Jonathan; Melbye, Mads; Mitchell, Braxton D.; Murray, Jeffrey C.; Nöthen, Markus M.; Penninx, Brenda W. J. H.; Raitakari, Olli T.; Rietschel, Marcella; Rujescu, Dan; Samani, Nilesh J.; Sanders, Alan R.; Schwartz, Ann G.; Shete, Sanjay; Shi, Jianxin; Spitz, Margaret R.; Stefánsson, Kári; Swan, Gary E.; Thorgeirsson, Thorgeir E.; Völzke, Henry; Wei, Qingyi; Wichmann, H‐Erich; Amos, Christopher I.; Breslau, Naomi; Cannon, Dale S.; Ehringer, Marissa A.; Grucza, Richard A.; Hatsukami, Dorothy K.; Heath, Andrew C.; Johnson, Eric O.; Kaprio, Jaakko; Madden, Pamela A. F.; Martin, Nicholas G.; Stevens, Victoria L.; Stitzel, Jerry A.; Weiss, Robert B.; Kraft, Peter; Bierut, Laura J.

doi:10.1001/archgenpsychiatry.2012.124

Cited by 70 publications

(86 citation statements)

References 38 publications

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“…11 However, the sample sizes of these studies still fall well short of the nearly 100 000 observations that some have argued are needed to identify true G·E associations. 85 The presence of statistically significant G·E associations within the literature has led some to assert that the bulk of these associations are likely to be false positives and appear in scholarly journals because of publication bias. 8 Concerning the last limitation, most research on G·E interplay in public health and elsewhere is primarily correlative, providing evidence on interactive associations but not necessarily causal ones.…”

Section: Discussionmentioning

confidence: 99%

Defining the Environment in Gene–Environment Research: Lessons From Social Epidemiology

2013

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In this article, we make the case that social epidemiology provides a useful framework to define the environment within gene-environment (G·E) research. We describe the environment in a multilevel, multidomain, longitudinal framework that accounts for upstream processes influencing health outcomes. We then illustrate the utility of this approach by describing how intermediate levels of social organization, such as neighborhoods or schools, are key environmental components of G·E research. We discuss different models of G·E research and encourage public health researchers to consider the value of including genetic information from their study participants. We also encourage researchers interested in G·E interplay to consider the merits of the social epidemiology model when defining the environment. (Am J Public Health.

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Section: Discussionmentioning

confidence: 99%

Defining the Environment in Gene–Environment Research: Lessons From Social Epidemiology

2013

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“…A range of outcomes have been used, including dichotomous outcomes (e.g. having an oral cleft [24], colorectal adenoma and colorectal cancer [25], bladder cancer [26], and heavy smoking [27]), categorical outcomes (e.g. amount smoked coded as an ordinal variable [27]) or continuous outcome (e.g.…”

Section: Examples Of Gxe Meta-analysesmentioning

confidence: 99%

Meta-Analyses to Investigate Gene-Environment Interactions in Neuroepidemiology

Mei

Otáhal²,

Simpson³

et al. 2013

Neuroepidemiology

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Background: Most chronic neurological diseases are caused by a combination of multiple genetic and environmental factors. Increasingly, gene-environment interactions (GxE) are being examined, providing opportunities to combine studies systematically using meta-analysis. Methods: Systematic review of the literature on how to examine GxE using observational study designs, and how to conduct a meta-analysis of studies on GxE. Results: Most methods and challenges related to a standard meta-analysis apply to a GxE meta-analysis. There are, however, some substantive differences. With GxE, there is the capability of using a case-only design. Research on GxE interactions may be more prone to publication bias, since interactions are usually not the primary hypothesis and only ‘exciting' significant GxE findings are reported out of a range of secondary analyses. In disease aetiology research, there has been debate whether to measure interaction on a multiplicative or additive scale. There are some significant challenges associated with measuring interaction on an additive scale, and thus the uptake of the measures of additive interaction has been limited. As a result, the methods of analysing interaction have been less consistent and reporting has been highly variable. We suggest using the STROBE/STREGA reporting guidelines to allow evaluation of interaction on both scales. Conclusions: We identified a number of differences of a GxE meta-analysis over a standard meta-analysis. Awareness of these issues is important. Using established reporting guidelines for GxE studies is recommended. The development of consortia for neurological disorders that include both genetic and environmental data might offer benefits for GxE meta-analyses in the future.

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“…The remaining 686 pedigrees had an average of 7.2 genotyped members, including 13 pedigrees with .30 members. As an illustration of our proposed method, we focused on 24 SNPs within a genomic region between 50,000 base pairs upstream and downstream of a candidate gene, CHRNA5, which had been repeatedly reported for its association with nicotine dependence and lung cancer (Saccone et al 2010;Chen et al 2012;Hartz et al 2012). Both family-U and FBAT were applied to evaluate the association between these 24 SNPs and the quantitative phenotype, the number of cigarettes smoked per day.…”

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confidence: 99%

A Powerful Nonparametric Statistical Framework for Family-Based Association Analyses

Schaid

et al. 2015

Genetics

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Family-based study design is commonly used in genetic research. It has many ideal features, including being robust to population stratification (PS). With the advance of high-throughput technologies and ever-decreasing genotyping cost, it has become common for family studies to examine a large number of variants for their associations with disease phenotypes. The yield from the analysis of these family-based genetic data can be enhanced by adopting computationally efficient and powerful statistical methods. We propose a general framework of a family-based U-statistic, referred to as family-U, for family-based association studies. Unlike existing parametricbased methods, the proposed method makes no assumption of the underlying disease models and can be applied to various phenotypes (e.g., binary and quantitative phenotypes) and pedigree structures (e.g., nuclear families and extended pedigrees). By using only withinfamily information, it can offer robust protection against PS. In the absence of PS, it can also utilize additional information (i.e., betweenfamily information) for power improvement. Through simulations, we demonstrated that family-U attained higher power over a commonly used method, family-based association tests, under various disease scenarios. We further illustrated the new method with an application to large-scale family data from the Framingham Heart Study. By utilizing additional information (i.e., between-family information), family-U confirmed a previous association of CHRNA5 with nicotine dependence.KEYWORDS population stratification; pedigree structure; within-family information; between-family information; nicotine dependence B OTH population-based and family-based designs have been commonly used in genetic association studies. Case-control study is a typical population-based design, by which unrelated cases are recruited and compared with healthy controls. When cases and controls come from different ethnicity backgrounds, they could have distinct ancestry distribution, leading to false-positive association results due to population stratification (PS) (Knowler et al. 1988;Freedman et al. 2004). Many statistical methods have been proposed to address the issue of PS (Devlin and Roeder 1999;Pritchard and Rosenberg 1999; Pritchard et al. 2000a,b;Satten et al. 2001;Chen et al. 2003;Price et al. 2006;Bauchet et al. 2007). However, these methods can infer only the average effect of PS based on a large number of genetic variants, but not the locus-specific PS. Population stratification related to a particular locus may vary and deviate from the average effect. In the presence of locus-specific PS, these approaches may overadjust or underadjust the PS effect, leading to either low power or inflated type I error (Marchini et al. 2004;Qin and Zhu 2012). Unlike population-based studies, family-based studies offer robust protection against PS (Weinberg et al. 1998;Cardon and Palmer 2003;Weinberg 2003). In a typical family-based association study, the alleles transmitted to affected individu...

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Increased Genetic Vulnerability to Smoking at CHRNA5 in Early-Onset Smokers

Cited by 70 publications

References 38 publications

Defining the Environment in Gene–Environment Research: Lessons From Social Epidemiology

Defining the Environment in Gene–Environment Research: Lessons From Social Epidemiology

Meta-Analyses to Investigate Gene-Environment Interactions in Neuroepidemiology

A Powerful Nonparametric Statistical Framework for Family-Based Association Analyses

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