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. 2022 Dec;612(7941):720-724.
doi: 10.1038/s41586-022-05477-4. Epub 2022 Dec 7.

Genetic diversity fuels gene discovery for tobacco and alcohol use

Gretchen R B Saunders #  1 , Xingyan Wang #  2 , Fang Chen #  2 , Seon-Kyeong Jang #  1 , Mengzhen Liu #  1 , Chen Wang #  2 , Shuang Gao  2 , Yu Jiang  3 , Chachrit Khunsriraksakul  2 , Jacqueline M Otto  1 , Clifton Addison  4 , Masato Akiyama  5   6 , Christine M Albert  7   8 , Fazil Aliev  9 , Alvaro Alonso  10 , Donna K Arnett  11 , Allison E Ashley-Koch  12   13 , Aneel A Ashrani  14 , Kathleen C Barnes  15   16 , R Graham Barr  17 , Traci M Bartz  18   19 , Diane M Becker  20 , Lawrence F Bielak  21 , Emelia J Benjamin  22   23 , Joshua C Bis  18 , Gyda Bjornsdottir  24 , John Blangero  25 , Eugene R Bleecker  26 , Jason D Boardman  27 , Eric Boerwinkle  28 , Dorret I Boomsma  29 , Meher Preethi Boorgula  15 , Donald W Bowden  30 , Jennifer A Brody  18 , Brian E Cade  31   32   33 , Daniel I Chasman  8 , Sameer Chavan  15 , Yii-Der Ida Chen  34 , Zhengming Chen  35   36 , Iona Cheng  37   38 , Michael H Cho  39   40 , Hélène Choquet  41 , John W Cole  42   43 , Marilyn C Cornelis  44 , Francesco Cucca  45 , Joanne E Curran  25 , Mariza de Andrade  46 , Danielle M Dick  9 , Anna R Docherty  47   48   49 , Ravindranath Duggirala  25 , Charles B Eaton  50 , Marissa A Ehringer  51   52 , Tõnu Esko  53 , Jessica D Faul  54 , Lilian Fernandes Silva  55 , Edoardo Fiorillo  56 , Myriam Fornage  28   57 , Barry I Freedman  58 , Maiken E Gabrielsen  59 , Melanie E Garrett  12   13 , Sina A Gharib  18   60   61 , Christian Gieger  62 , Nathan Gillespie  48 , David C Glahn  63 , Scott D Gordon  64 , Charles C Gu  65 , Dongfeng Gu  66 , Daniel F Gudbjartsson  24   67 , Xiuqing Guo  68 , Jeffrey Haessler  69 , Michael E Hall  70 , Toomas Haller  53 , Kathleen Mullan Harris  71 , Jiang He  72   73 , Pamela Herd  74 , John K Hewitt  51   75 , Ian Hickie  76 , Bertha Hidalgo  77 , John E Hokanson  78 , Christian Hopfer  79 , JoukeJan Hottenga  29 , Lifang Hou  44 , Hongyan Huang  80   81 , Yi-Jen Hung  82 , David J Hunter  83 , Kristian Hveem  59   84   85 , Shih-Jen Hwang  86 , Chii-Min Hwu  87 , William Iacono  1 , Marguerite R Irvin  77 , Yon Ho Jee  80 , Eric O Johnson  88   89 , Yoonjung Y Joo  44   90 , Eric Jorgenson  91 , Anne E Justice  92   93 , Yoichiro Kamatani  5   94 , Robert C Kaplan  69   95 , Jaakko Kaprio  96 , Sharon L R Kardia  21 , Matthew C Keller  51   75 , Tanika N Kelly  72   73 , Charles Kooperberg  19   69 , Tellervo Korhonen  96 , Peter Kraft  80   81 , Kenneth Krauter  97 , Johanna Kuusisto  98   99 , Markku Laakso  98 , Jessica Lasky-Su  100 , Wen-Jane Lee  101 , James J Lee  1 , Daniel Levy  86 , Liming Li  102 , Kevin Li  103 , Yuqing Li  37 , Kuang Lin  35 , Penelope A Lind  104   105   106 , Chunyu Liu  107 , Donald M Lloyd-Jones  108 , Sharon M Lutz  109   110 , Jiantao Ma  86   111 , Reedik Mägi  52 , Ani Manichaikul  112 , Nicholas G Martin  64 , Ravi Mathur  88 , Nana Matoba  5   113 , Patrick F McArdle  114 , Matt McGue  1 , Matthew B McQueen  115 , Sarah E Medland  104 , Andres Metspalu  53 , Deborah A Meyers  26 , Iona Y Millwood  35   36 , Braxton D Mitchell  114   116 , Karen L Mohlke  117 , Matthew Moll  39   40 , May E Montasser  114 , Alanna C Morrison  28 , Antonella Mulas  56 , Jonas B Nielsen  59   118 , Kari E North  93 , Elizabeth C Oelsner  17 , Yukinori Okada  119   120   121   122 , Valeria Orrù  56 , Nicholette D Palmer  30 , Teemu Palviainen  96 , Anita Pandit  103 , S Lani Park  123 , Ulrike Peters  69   124 , Annette Peters  125   126   127 , Patricia A Peyser  21 , Tinca J C Polderman  128   129 , Nicholas Rafaels  15 , Susan Redline  31   32   130 , Robert M Reed  131 , Alex P Reiner  69   124 , John P Rice  132 , Stephen S Rich  112 , Nicole E Richmond  78 , Carol Roan  133 , Jerome I Rotter  68 , Michael N Rueschman  31 , Valgerdur Runarsdottir  134 , Nancy L Saccone  65   135 , David A Schwartz  136 , Aladdin H Shadyab  137 , Jingchunzi Shi  138 , Suyash S Shringarpure  138 , Kamil Sicinski  133 , Anne Heidi Skogholt  59 , Jennifer A Smith  21   54 , Nicholas L Smith  124   139   140 , Nona Sotoodehnia  18   141 , Michael C Stallings  51   75 , Hreinn Stefansson  24 , Kari Stefansson  24   142 , Jerry A Stitzel  51 , Xiao Sun  72 , Moin Syed  1 , Ruth Tal-Singer  143 , Amy E Taylor  144   145   146 , Kent D Taylor  68 , Marilyn J Telen  12 , Khanh K Thai  41 , Hemant Tiwari  147 , Constance Turman  80   81 , Thorarinn Tyrfingsson  134 , Tamara L Wall  148 , Robin G Walters  35   36 , David R Weir  54 , Scott T Weiss  100 , Wendy B White  149 , John B Whitfield  64 , Kerri L Wiggins  150 , Gonneke Willemsen  29 , Cristen J Willer  151   152   153 , Bendik S Winsvold  59   154   155 , Huichun Xu  114 , Lisa R Yanek  20 , Jie Yin  41 , Kristin L Young  156 , Kendra A Young  78 , Bing Yu  28 , Wei Zhao  21 , Wei Zhou  153   157 , Sebastian Zöllner  158   159 , Luisa Zuccolo  144   146   160 ; 23andMe Research Team; Biobank Japan Project; Chiara Batini  161 , Andrew W Bergen  162   163 , Laura J Bierut  132 , Sean P David  164   165 , Sarah A Gagliano Taliun  166   167   168 , Dana B Hancock  88 , Bibo Jiang  2 , Marcus R Munafò  144   145   169 , Thorgeir E Thorgeirsson  24 , Dajiang J Liu  170 , Scott Vrieze  171
Affiliations

Genetic diversity fuels gene discovery for tobacco and alcohol use

Gretchen R B Saunders et al. Nature. 2022 Dec.

Abstract

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 injury1-4. These substances are used across the globe, yet genome-wide association studies have focused largely on individuals of European ancestries5. 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.

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Conflict of interest statement

The spouse of N.L. Saccone is listed as an inventor on issued U.S. patent 8080371 ‘Markers of addiction’, covering the use of certain single-nucleotide polymorphisms in determining the diagnosis, prognosis and treatment of addiction. M.H.C. has received grant funding from GSK and Bayer, and speaking or consulting fees from AstraZeneca, Illumina and Genentech. R.T.-S. is a former employee and current shareholder of GSK and is currently a non-executive member of the ENA Respiratory board of directors. She reports personal fees from Teva, Immunomet, Vocalis Health and ENA Respiratory (until January 2021). D.A.S. is the founder and chief scientific officer of Eleven P15, a company focused on the early diagnosis of treatment of pulmonary fibrosis. J.B.N. and E.J. are employed by Regeneron Pharmaceuticals, Inc. The spouse of C.J.W. is employed by Regeneron Pharmaceuticals, Inc. L.J.B. is listed as an inventor on Issued U.S. Patent 8080371 ‘Markers for addiction’, covering the use of certain single-nucleotide polymorphisms in determining the diagnosis, prognosis and treatment of addiction. The 23andMe Research Team, including J.S. and S.S.S., are employees of 23andMe, Inc., and hold stock and/or stock options in 23andMe. T.E.T., D.F.G., H.S., G.B. and K. Stefansson are employees of deCODE genetics/AMGEN. M. Moll received grant support from Bayer. A.W.B. is listed as a co-inventor on a U.S. patent application ‘Biosignature discovery for substance use disorder using statistical learning’ assigned to BioRealm, LLC, and serves as a scientific advisor and consultant to BioRealm, LLC. All other authors declare no competing interests.

Figures

Fig. 1
Fig. 1. 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 north–south 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, γ) along the x axes, and the corresponding mean difference in effect sizes (β) 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.
Fig. 2
Fig. 2. Within-ancestry and across-ancestry performance of polygenic scores in an independent target sample (Add Health).
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 phenotype–ancestry 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.
Extended Data Fig. 1
Extended Data Fig. 1. Ancestry space of studies contributing to meta-analysis (panel a), versus individuals from TOPMed and 1000 Genomes (panel b).
The meta-regression within the MEMO model requires specification of ancestry clines. To ensure consistency in the meaning of ancestry clines across all five MEMO analyses (one for each phenotype) we created a single multidimensional scaling solution based on allele frequencies from all phenotypes in all participating cohorts. These solutions are plotted in panel a (circles correspond to TOPMed cohorts, squares are all other cohorts which used imputed microarray genotypes, and triangles are 1000 Genomes ancestry groups). Colors of points correspond to the primary assigned ancestry of each cohort (studies with < 90% of individuals coming from a single ancestry group are shown in grey). Panel b shows projection of principal components (after OADP transformation) of TOPMed individuals onto PCs of 1000 Genomes individuals, in colored triangles. Each 1000 Genomes individual is colored by their known ancestry. This PC information was used in assigning ancestry to TOPMed individuals for the purpose of reference panel creation (individuals of South Asian ancestry were not included in analyses). The PCs in panel b were reordered or reversed in some cases to align with panel a. These transformations are noted in the axis labels.
Extended Data Fig. 2
Extended Data Fig. 2. Multi-ancestry meta-analysis Manhattan plots.
Black horizontal line corresponds to P = 5 × 10−9, the GWAS significance threshold used for all analyses. Note that some y-axis scales are discontinuous to better illustrate variants with very small P-values (e.g., the Drinks per Week y-axis is cut at 30 with a maximum value of 307.7, denoting a P-value of 1.9 × 10−308). All P-values are from two-sided statistical tests.
Extended Data Fig. 3
Extended Data Fig. 3. Tissue expression and brain cell type enrichment in high priority genes.
Panel a shows tissue expression enrichment in ‘high priority’ genes. We define high priority genes here as those located nearest to the variants in fine-mapped credible intervals containing less than five variants. These genes were compared to ‘control’ genes identified in the same way, but from variants in credible intervals with PIP < 0.01 from the trans-ancestry fine-mapping. The x-axis denotes GTEx tissue types. The y-axis represents relative risk estimates comparing high priority to control genes. Panel b shows similar relative risk comparisons with 39 brain cell types. Data are presented as relative risk values with error bars denoting bootstrapped 95% confidence intervals. Further details on estimating relative risk are included in the Supplementary Note section ‘Functional enrichment’.

References

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    1. World Health Organization. Alcohol. WHOhttps://www.who.int/news-room/fact-sheets/detail/alcohol (2022).
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