%0 Journal Article %J Nature Genetics %D 2019 %T Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. %A Liu, Mengzhen %A Jiang, Yu %A Wedow, Robbee %A Li, Yue %A Brazel, David M %A Chen, Fang %A Datta, Gargi %A Davila-Velderrain, Jose %A McGuire, Daniel %A Tian, Chao %A Zhan, Xiaowei %A Choquet, Hélène %A Docherty, Anna R %A Jessica Faul %A Foerster, Johanna R %A Fritsche, Lars G %A Gabrielsen, Maiken Elvestad %A Gordon, Scott D %A Jeffrey Haessler %A Jouke-Jan Hottenga %A Huang, Hongyan %A Jang, Seon-Kyeong %A Philip R Jansen %A Ling, Yueh %A Mägi, Reedik %A Matoba, Nana %A McMahon, George %A Mulas, Antonella %A Orrù, Valeria %A Palviainen, Teemu %A Anita Pandit %A Reginsson, Gunnar W %A Skogholt, Anne Heidi %A Smith, Jennifer A %A Taylor, Amy E %A Turman, Constance %A Gonneke Willemsen %A Young, Hannah %A Young, Kendra A %A Zajac, Gregory J M %A Zhao, Wei %A Zhou, Wei %A Bjornsdottir, Gyda %A Boardman, Jason D %A Boehnke, Michael %A Dorret I Boomsma %A Chen, Chu %A Francesco Cucca %A Davies, Gareth E %A Charles B Eaton %A Ehringer, Marissa A %A Tõnu Esko %A Fiorillo, Edoardo %A Gillespie, Nathan A %A Gudbjartsson, Daniel F %A Haller, Toomas %A Kathleen Mullan Harris %A Andrew C Heath %A Hewitt, John K %A Hickie, Ian B %A Hokanson, John E %A Hopfer, Christian J %A Hunter, David J %A Iacono, William G %A Johnson, Eric O %A Kamatani, Yoichiro %A Sharon L R Kardia %A Matthew C Keller %A Kellis, Manolis %A Charles Kooperberg %A Kraft, Peter %A Krauter, Kenneth S %A Laakso, Markku %A Penelope A Lind %A Loukola, Anu %A Lutz, Sharon M %A Pamela A F Madden %A Nicholas G Martin %A McGue, Matt %A Matthew B McQueen %A Sarah E Medland %A Andres Metspalu %A Mohlke, Karen L %A Nielsen, Jonas B %A Okada, Yukinori %A Peters, Ulrike %A Tinca J Polderman %A Posthuma, Danielle %A Reiner, Alexander P %A Rice, John P %A Rimm, Eric %A Rose, Richard J %A Runarsdottir, Valgerdur %A Stallings, Michael C %A Stančáková, Alena %A Stefansson, Hreinn %A Thai, Khanh K %A Hilary A Tindle %A Tyrfingsson, Thorarinn %A Wall, Tamara L %A David R Weir %A Weisner, Constance %A Whitfield, John B %A Winsvold, Bendik Slagsvold %A Yin, Jie %A Zuccolo, Luisa %A Laura Bierut %A Hveem, Kristian %A Lee, James J %A Munafò, Marcus R %A Saccone, Nancy L %A Willer, Cristen J %A Marilyn C Cornelis %A David, Sean P %A Hinds, David A %A Jorgenson, Eric %A Kaprio, Jaakko %A Stitzel, Jerry A %A Stefansson, Kari %A Thorgeirsson, Thorgeir E %A Gonçalo R Abecasis %A Liu, Dajiang J %A Scott Vrieze %K Alcohol Drinking %K Female %K Genetic Variation %K Genome-Wide Association Study %K Humans %K Male %K Middle Aged %K Phenotype %K Risk %K Smoking %K Tobacco %K Tobacco Use Disorder %X

Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders. They are heritable and etiologically related behaviors that have been resistant to gene discovery efforts. In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk. We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures.

%B Nature Genetics %V 51 %P 237-244 %G eng %N 2 %R 10.1038/s41588-018-0307-5 %0 Journal Article %J J Hum Genet %D 2014 %T Testing the key assumption of heritability estimates based on genome-wide genetic relatedness. %A Dalton C Conley %A Mark L Siegal %A Benjamin W Domingue %A Kathleen Mullan Harris %A Matthew B McQueen %A Jason D Boardman %K Body Height %K Body Weight %K Educational Status %K Gene-Environment Interaction %K Genome, Human %K Humans %K Likelihood Functions %K Models, Genetic %K Phenotype %K Quantitative Trait, Heritable %K Urban Population %X

Comparing genetic and phenotypic similarity among unrelated individuals seems a promising way to quantify the genetic component of traits while avoiding the problematic assumptions plaguing twin- and other kin-based estimates of heritability. One approach uses a Genetic Relatedness Estimation through Maximum Likelihood (GREML) model for individuals who are related at less than 0.025 to predict their phenotypic similarity by their genetic similarity. Here we test the key underlying assumption of this approach: that genetic relatedness is orthogonal to environmental similarity. Using data from the Health and Retirement Study (and two other surveys), we show two unrelated individuals may be more likely to have been reared in a similar environment (urban versus nonurban setting) if they are genetically similar. This effect is not eliminated by controls for population structure. However, when we include this environmental confound in GREML models, heritabilities do not change substantially and thus potential bias in estimates of most biological phenotypes is probably minimal.

%B J Hum Genet %I 59 %V 59 %P 342-5 %8 2014 Jun %G eng %N 6 %1 http://www.ncbi.nlm.nih.gov/pubmed/24599117?dopt=Abstract %2 PMC4126504 %4 environmental confound/GREML/heritability %$ 999999 %R 10.1038/jhg.2014.14