Genetic diversity fuels gene discovery for tobacco and alcohol use.
- Authors
- Saunders, Gretchen R B; Wang, Xingyan; Chen, Fang; Jang, Seon-Kyeong; Liu, Mengzhen; Wang, Chen; Gao, Shuang; Jiang, Yu; Khunsriraksakul, Chachrit; Otto, Jacqueline M; Addison, Clifton; Akiyama, Masato; Albert, Christine M; Aliev, Fazil; Alonso, Alvaro; Arnett, Donna K; Ashley-Koch, Allison E; Ashrani, Aneel A; Barnes, Kathleen C; Barr, R Graham; Bartz, Traci M; Becker, Diane M; Bielak, Lawrence F; Benjamin, Emelia J; Bis, Joshua C; Bjornsdottir, Gyda; Blangero, John; Bleecker, Eugene R; Boardman, Jason D; Boerwinkle, Eric; Boomsma, Dorret I; Boorgula, Meher Preethi; Bowden, Donald W; Brody, Jennifer A; Cade, Brian E; Chasman, Daniel I; Chavan, Sameer; Chen, Yii-Der Ida; Chen, Zhengming; Cheng, Iona; Cho, Michael H; Choquet, Hélène; Cole, John W; Cornelis, Marilyn C; Cucca, Francesco; Curran, Joanne E; de Andrade, Mariza; Dick, Danielle M; Docherty, Anna R; Duggirala, Ravindranath; Eaton, Charles B; Ehringer, Marissa A; Esko, Tánu; Faul, Jessica D; Fernandes Silva, Lilian; Fiorillo, Edoardo; Fornage, Myriam; Freedman, Barry I; Gabrielsen, Maiken E; Garrett, Melanie E; Gharib, Sina A; Gieger, Christian; Gillespie, Nathan; Glahn, David C; Gordon, Scott D; Gu, Charles C; Gu, Dongfeng; Gudbjartsson, Daniel F; Guo, Xiuqing; Haessler, Jeffrey; Hall, Michael E; Haller, Toomas; Harris, Kathleen Mullan; He, Jiang; Herd, Pamela; Hewitt, John K; Hickie, Ian; Hidalgo, Bertha; Hokanson, John E; Hopfer, Christian; Hottenga, JoukeJan; Hou, Lifang; Huang, Hongyan; Hung, Yi-Jen; Hunter, David J; Hveem, Kristian; Hwang, Shih-Jen; Hwu, Chii-Min; Iacono, William; Irvin, Marguerite R; Jee, Yon Ho; Johnson, Eric O; Joo, Yoonjung Y; Jorgenson, Eric; Justice, Anne E; Kamatani, Yoichiro; Kaplan, Robert C; Kaprio, Jaakko; Kardia, Sharon L R; Keller, Matthew C; Kelly, Tanika N; Kooperberg, Charles; Korhonen, Tellervo; Kraft, Peter; Krauter, Kenneth; Kuusisto, Johanna; Laakso, Markku; Lasky-Su, Jessica; Lee, Wen-Jane; Lee, James J; Levy, Daniel; Li, Liming; Li, Kevin; Li, Yuqing; Lin, Kuang; Lind, Penelope A; Liu, Chunyu; Lloyd-Jones, Donald M; Lutz, Sharon M; Ma, Jiantao; MÀgi, Reedik; Manichaikul, Ani; Martin, Nicholas G; Mathur, Ravi; Matoba, Nana; McArdle, Patrick F; McGue, Matt; McQueen, Matthew B; Medland, Sarah E; Metspalu, Andres; Meyers, Deborah A; Millwood, Iona Y; Mitchell, Braxton D; Mohlke, Karen L; Moll, Matthew; Montasser, May E; Morrison, Alanna C; Mulas, Antonella; Nielsen, Jonas B; North, Kari E; Oelsner, Elizabeth C; Okada, Yukinori; Orrù, Valeria; Palmer, Nicholette D; Palviainen, Teemu; Pandit, Anita; Park, S Lani; Peters, Ulrike; Peters, Annette; Peyser, Patricia A; Polderman, Tinca J C; Rafaels, Nicholas; Redline, Susan; Reed, Robert M; Reiner, Alex P; Rice, John P; Rich, Stephen S; Richmond, Nicole E; Roan, Carol; Rotter, Jerome I; Rueschman, Michael N; Runarsdottir, Valgerdur; Saccone, Nancy L; Schwartz, David A; Shadyab, Aladdin H; Shi, Jingchunzi; Shringarpure, Suyash S; Sicinski, Kamil; Skogholt, Anne Heidi; Smith, Jennifer A; Smith, Nicholas L; Sotoodehnia, Nona; Stallings, Michael C; Stefansson, Hreinn; Stefansson, Kari; Stitzel, Jerry A; Sun, Xiao; Syed, Moin; Tal-Singer, Ruth; Taylor, Amy E; Taylor, Kent D; Telen, Marilyn J; Thai, Khanh K; Tiwari, Hemant; Turman, Constance; Tyrfingsson, Thorarinn; Wall, Tamara L; Walters, Robin G; Weir, David R; Weiss, Scott T; White, Wendy B; Whitfield, John B; Wiggins, Kerri L; Willemsen, Gonneke; Willer, Cristen J; Winsvold, Bendik S; Xu, Huichun; Yanek, Lisa R; Yin, Jie; Young, Kristin L; Young, Kendra A; Yu, Bing; Zhao, Wei; Zhou, Wei; Zâllner, Sebastian; Zuccolo, Luisa; 23andMe Research Team; Biobank Japan Project; Batini, Chiara; Bergen, Andrew W; Bierut, Laura J; David, Sean P; Gagliano Taliun, Sarah A; Hancock, Dana B; Jiang, Bibo; Munafò, Marcus R; Thorgeirsson, Thorgeir E; Liu, Dajiang J; Vrieze, Scott
- Year
- 2022
- Journal
- Nature
- PMID
- 36477530
- DOI
- 10.1038/s41586-022-05477-4
- PMCID
- PMC9771818
Tobacco and alcohol use are heritable behaviours associated with 15% and 5.3% of worldwide deaths, respectively, due largely to broad increased risk for disease and injury. These substances are used across the globe, yet genome-wide association studies have focused largely on individuals of European ancestries. Here we leveraged global genetic diversity across 3.4 million individuals from four major clines of global ancestry (approximately 21% non-European) to power the discovery and fine-mapping of genomic loci associated with tobacco and alcohol use, to inform function of these loci via ancestry-aware transcriptome-wide association studies, and to evaluate the genetic architecture and predictive power of polygenic risk within and across populations. We found that increases in sample size and genetic diversity improved locus identification and fine-mapping resolution, and that a large majority of the 3,823 associated variants (from 2,143 loci) showed consistent effect sizes across ancestry dimensions. However, polygenic risk scores developed in one ancestry performed poorly in others, highlighting the continued need to increase sample sizes of diverse ancestries to realize any potential benefit of polygenic prediction.
Ancestry composition and effect size moderation.a, Ancestry compositions of contributing studies (each point is a study). Colours are coded by primary ancestry of individuals in the cohort. Studies with less than 90% of individuals assignable to a single ancestry group are shown in grey. Ancestry component 3 was a northβsouth EUR cline, which was omitted here as we did not conduct meta-analyses stratified by northern versus southern Europe. TOPMed, Trans-Omics for Precision Medicine. b, Extent of effect size moderation as a function of the same ancestry dimensions as shown in a. The full moderation results are in Supplementary Table 2. Each point in b represents an independent variant with the standardized MDS component coefficient from our trans-ancestry models (that is, Ξ³) along the x axes, and the corresponding mean difference in effect sizes (Ξ²) for the ancestry-stratified meta-analysis of the given ancestry versus all other ancestries along the y axes. The grey circles indicate variants showing little to no evidence of effect size heterogeneity across ancestry, whereas the coloured circles represent variants with adequate evidence of effect size heterogeneity. The plots highlight that the majority of variants have similar effect sizes across all ancestry clines, with some potentially interesting exceptions in which the variant effects sizes differ substantially between ancestry clines.
Within-ancestry and across-ancestry performance of polygenic scores in an independent target sample (Add Health35).a, Incremental variance explained for each target ancestry group. The colour of the stacked bars indicates the ancestry from which the polygenic score was derived; the total height of each set of the stacked bars (and 95% confidence intervals) correspond to the total variance explained by all four ancestry-stratified scores combined. For example, in the target EUR subsample, non-EUR polygenic scores add little over and above the EUR score. Note that some comparisons are underpowered to detect differences in predictive accuracy across ancestry (see Supplementary Note). Heritabilities, estimated by LD score regression, of each phenotypeβancestry combination are depicted by the grey dashed bar (with 95% confidence intervals) and corresponding sample sizes; these represent the maximum expected accuracy of the polygenic risk score (PRS). b, The manner in which the phenotype mean in the target sample changes as a function of the EUR PRS deciles. c, Results from an interaction model, in which each phenotype was modelled as a function of an interaction between the EUR-based PRS and target ancestry (coded as a factor with EUR ancestry as the reference and scores scaled within ancestry). The bands around each line denote the 95% confidence intervals. Significant interactions are noted with text. Using SmkInit as an example, the purple line represents the predicted proportion of regular smokers as a function of the EUR PRS in the EUR subsample of Add Health, the blue lines show the predicted proportion of regular smokers by standard deviation of the EUR PRS in the EAS subsample, and so on. In this case, the magnitude of the association between the EUR-based PRS and SmkInit (that is, the slope) was significantly greater in the EUR target ancestry than all other ancestries. Full PRS results are in Supplementary Table 12.
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| Title | Year | PMID |
|---|---|---|
| Integrated single-cell multiomic profiling of caudate nucleus suggests key mechanisms in alcohol use disorder. | 2025 | 41083468 |
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