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Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction

Abstract

Behaviors and disorders related to self-regulation, such as substance use, antisocial behavior and attention-deficit/hyperactivity disorder, are collectively referred to as externalizing and have shared genetic liability. We applied a multivariate approach that leverages genetic correlations among externalizing traits for genome-wide association analyses. By pooling data from ~1.5 million people, our approach is statistically more powerful than single-trait analyses and identifies more than 500 genetic loci. The loci were enriched for genes expressed in the brain and related to nervous system development. A polygenic score constructed from our results predicts a range of behavioral and medical outcomes that were not part of genome-wide analyses, including traits that until now lacked well-performing polygenic scores, such as opioid use disorder, suicide, HIV infections, criminal convictions and unemployment. Our findings are consistent with the idea that persistent difficulties in self-regulation can be conceptualized as a neurodevelopmental trait with complex and far-reaching social and health correlates.

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Fig. 1: Genetic correlations and structural equation modeling with genomic SEM.
Fig. 2: Multivariate genome-wide association analysis of EXT with genomic SEM.
Fig. 3: Genome-wide EXT polygenic score associations with behavioral, psychiatric and social outcomes in the independent Add Health and COGA datasets.
Fig. 4: Phenome-wide association study in the BioVU biorepository.

Data availability

All data sources are described in the Supplementary Information and are listed in the Reporting Summary. No new data were collected. Only data from existing studies or study cohorts were analyzed, some of which have restricted access to protect the privacy of the study participants (see Reporting Summary for accession codes or URLs). The minimum dataset necessary to interpret, verify and extend the research, that is, the GWAS summary statistics for the EXT GWAS (our main discovery analysis), can be obtained by following the procedures detailed at https://externalizing.org/request-data/. In brief, summary statistics are derived from analyses based in part on 23andMe data, for which we are restricted to only publicly available report results for up to 10,000 SNPs. The full set of externalizing GWAS summary statistics can be made available to qualified investigators who enter into an agreement with 23andMe that protects participant confidentiality. Once the request has been approved by 23andMe, a representative of the Externalizing Consortium can share the full GWAS summary statistics.

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Acknowledgements

This research was carried out under the auspices of the Externalizing Consortium. The study was classified as secondary research of de-identified participants, and the study was awarded ethical approval by the internal review board of Virginia Commonwealth University (VCU), with reference number HM20019386. These analyses were made possible by the generous public sharing of summary statistics from published GWAS from the PGC, the Million Veterans Program, the International Cannabis Consortium, the GWAS & Sequencing Consortium of Alcohol and Nicotine use, the Social Science Genetics Association Consortium, the Genetics of Personality Consortium and the Broad Antisocial Behavior Consortium. We thank the many studies that made these consortia possible, the researchers involved and the participants in those studies, without whom this effort would not be possible. We also thank the research participants and employees of 23andMe for making this work possible. This research was conducted in part using the UKB resource under applications 40830 and 11425. We thank all UKB cohort participants for making this study possible. We thank L. K. Davis for providing access to BioVU. Finally, we thank COGA; principal investigators B. Porjesz, V. Hesselbrock, H. Edenberg, L. Bierut; and collaborators at eleven different centers: University of Connecticut (V. Hesselbrock); Indiana University (H. J. Edenberg, J. Nurnberger Jr., T. Foroud and Y. Liu); University of Iowa (S. Kuperman and J. Kramer); SUNY Downstate (B. Porjesz); Washington University in St. Louis (L. Bierut, J. Rice, K. Bucholz and A. Agrawal); University of California, San Diego (M. Schuckit); Rutgers University (J. Tischfield and A. Brooks); Department of Biomedical and Health Informatics, The Children’s Hospital of Philadelphia; Department of Genetics, Perelman School of Medicine, University of Pennsylvania (L. Almasy); Virginia Commonwealth University (D.M.D); Icahn School of Medicine at Mount Sinai (A. Goate); and Howard University (R. Taylor). Other COGA collaborators include: L. Bauer (University of Connecticut); J. McClintick, L. Wetherill, X. Xuei, D. Lai, S. O’Connor, M. Plawecki and S. Lourens (Indiana University); G. Chan (University of Iowa and University of Connecticut); J. Meyers, D. Chorlian, C. Kamarajan, A. Pandey and J. Zhang (SUNY Downstate); J. C. Wang, M. Kapoor and S. Bertelsen (Icahn School of Medicine at Mount Sinai); A. Anokhin, V. McCutcheon and S. Saccone (Washington University); J. Salvatore, F. Aliev and B. Cho (Virginia Commonwealth University); and M. Kos (University of Texas Rio Grande Valley). A. Parsian and H. Chen are the National Institute on Alcohol Abuse and Alcoholism (NIAAA) staff collaborators. All studies included in the externalizing GWAS are listed in the Supplementary Information. Funding: The Externalizing Consortium has been supported by the NIAAA through an administrative supplement (R01AA015416) and by the National Institute of Drug Abuse (R01DA050721). D.M.D. was supported through funding from the NIAAA (K02AA018755, U10AA008401 and P50AA022527). P.D.K. was supported through a European Research Council Consolidator Grant (647648 EdGe). K.P.H. was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD; R01HD092548 and R01HD083613) and the Jacobs Foundation. A.A.P. was supported by the NIAAA (R01AA026281) and the National Institute of Drug Abuse (P50DA037844). S.S.-R. was supported through a NARSAD Young Investigator Award from the Brain and Behavior Foundation (grant no. 27676). Both A.A.P. and S.S.-R. were supported by funds from the California Tobacco-Related Disease Research Program (grant nos. 28IR-0070 and T29KT0526). The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the above funding bodies. This research used data from Add Health, a program project directed by K.M.H. (principal investigator) and designed by J. R. Udry, P. S. Bearman and K.M.H. at the University of North Carolina at Chapel Hill, and funded by grant P01HD031921 from the Eunice Kennedy Shriver NICHD, with cooperative funding from 23 other federal agencies and foundations. Information on how to obtain the Add Health data files is available on the Add Health website (https://addhealth.cpc.unc.edu/). This research used Add Health GWAS data funded by Eunice Kennedy Shriver NICHD grants R01HD073342 to K.M.H. (principal investigator) and R01HD060726 to K.M.H., J. D. Boardman, and M. B. McQueen (multiple principal investigators). COGA is a national collaborative study supported by the National Institutes of Health (NIH) grant U10AA008401 from the NIAAA and the National Institute on Drug Abuse. Data were obtained from Vanderbilt University Medical Center’s BioVU, which is supported by numerous sources, including institutional funding, private agencies and federal grants. These include the NIH-funded shared instrumentation grant S10RR025141, and CTSA grants UL1TR002243, UL1TR000445 and UL1RR024975. Genomic data are also supported by investigator-led projects, including U01HG004798, R01NS032830, RC2GM092618, P50GM115305, U01HG006378, U19HL065962 and R01HD074711; and additional funding sources listed at https://victr.vumc.org/biovu-funding/. Support for data collection for the PNC, acquired through dbGaP (accession no. phs000607, v3.p2), was provided by grant RC2MH089983 awarded to R. Gur and RC2MH089924 was awarded to H. Hakonarson. Participants were recruited and genotyped through the Center for Applied Genomics (CAG) at The Children’s Hospital in Philadelphia (CHOP). Phenotypic data collection occurred at the CAG/CHOP and at the Brain Behavior Laboratory, University of Pennsylvania. A full list of funding for investigator effort is available in the Supplementary Information.

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D.M.D. and P.D.K. conceived the study. The study protocol was developed by D.M.D., K.P.H., R.K.L., P.D.K., T.T.M. and A.A.P. D.M.D., K.P.H., P.D.K. and A.A.P. jointly oversaw the study. D.M.D. and R.K.L. led the writing of the manuscript, with substantive contributions to the writing from K.P.H., P.D.K. and A.A.P. R.K.L. and T.T.M. were the lead analysts, responsible for conducting GWAS, quality control, meta-analysis, genetic correlations and multivariate analyses with genomic SEM, with assistance from A.D.G. R.K.L. led the proxy-phenotype and quasi-replication analyses. P.B.B. led the polygenic score analyses, and R.K.L. and T.T.M. contributed to those analyses. S.S.-R. performed the PheWAS in BioVU. R.d.V. derived analytical s.e. for the within-family analyses. S.S.-R. led the bioinformatics analyses, and R.K.L, S.B.R. and T.I. contributed to those analyses. P.B.B., R.K.L, T.T.M. and S.S.-R. prepared the tables and figures, with assistance from M.N.D., J.W.M. and H.E.P. J.J.T., E.C.J., M.L., H.Z., R.K. and J.A.P. prepared cohort-level GWAS meta-analyses under the supervision of K.J.H.V., D.J.L., S.V., H.R.K. and J.G. K.M.H. assisted with analyses performed in the Add Health study cohort. A.D.G., E.M.T.-D. and I.D.W. provided helpful advice and feedback on various aspects of the study design. All authors contributed to and critically reviewed the manuscript. R.K.L., T.T.M., P.B.B. and S.S.-R. made especially major contributions to the writing and editing.

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Correspondence to Philipp D. Koellinger or Danielle M. Dick.

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H.R.K. is a member of the American Society of Clinical Psychopharmacology’s Alcohol Clinical Trials Initiative, which was supported in the last 3 years by AbbVie, Alkermes, Ethypharm, Indivior, Lilly, Lundbeck, Otsuka, Pfizer, Arbor and Amygdala Neurosciences. H.R.K. and J.G. are named as inventors on PCT patent application no. 15/878,640 entitled ‘genotype-guided dosing of opioid agonists,’ filed on 24 January 2018. J.G. did paid editorial work for the journal Complex Psychiatry. The authors declare no other competing interests.

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Extended data

Extended Data Fig. 1 Genetic correlations with the genetic externalizing factor (EXT).

Dot plot of genetic correlations (rg) estimated with Genomic SEM between the genetic externalizing factor (EXT) with 91 other complex traits (Supplementary Methods). Error bars are 95% confidence intervals, calculated as 1.96 × SE, centered on the rg estimate (omitted for Agreeableness). The estimates are also reported in Supplementary Table 8, together with the exact number of independent samples used to derive each estimate. This figure displays genetic correlations with personality measures based on GWAS summary statistics from the Genomics of Personality Consortium, while Fig. 1 instead reports genetic correlations with personality measures based on more recent and substantially larger GWAS provided by 23andMe.

Extended Data Fig. 2 Quantile-quantile (Q-Q) plots of the externalizing GWAS and QSNP results.

The panels display Q-Q plots for (a) the externalizing GWAS (Neff = 1,492,085), and (b) SNP-level tests of heterogeneity (QSNP) with respect to the SNP-effects estimated in the externalizing GWAS (for more details see Supplementary Information section 3). The y-axis is the observed association P value on the –log10 scale (based on a two-sided Z-test in a, and based on a one-sided χ2 test scaled to 1 degree of freedom in b). The gray shaded areas represent 95% confidence intervals centered on the expected –log10(P) of the null distribution. The genomic inflation factors displayed here, λGC, is defined as the median χ2 association test statistic divided by the expected median of the χ2 distribution with 1 degree of freedom, and were calculated with 6,132,068 and 6,107,583 SNPs for (a) and (b), respectively. Although there is a noticeable early ‘lift-off’, the estimated LD Score regression intercepts of (a) 1.115 (SE = 0.019) and (b) 0.9556 (SE = 0.013) suggest that most of the inflation of the test statistics is attributable to polygenicity rather than bias from population stratification.

Extended Data Fig. 3 Quantile-quantile (Q-Q) plots of the proxy-phenotypes analyses.

Panels (a–b) show –log10(P values from a two-sided Z-test) for linear regression of the 553 and 579 EXT SNPs (or such SNPs that could be proxied in case of missingness, r2 > 0.8) that were looked up in independent, second-stage GWAS samples on (1) antisocial behavior (N = 32,574) and (2) alcohol use disorder (N = 202,400), respectively (Supplementary Information section 4). Dashed line denotes experiment-wide significance at P < 0.05/553 and 0.05/579 for (1) and (2), respectively. Enrichment P value is the result of a one-sided test of joint enrichment with the non-parametric Mann-Whitney test against an empirical null distribution of 138,250 and 144,750 near-independent (r2 < 0.1) SNPs, matched on MAF, that were randomly selected from the GWAS on (1) and (2), respectively. Sign concordance is the proportion of looked-up SNPs with concordant direction of effect sizes across the externalizing GWAS and the second-stage GWAS, and the sign concordance P value is from a one-sided binomial tests of the sign concordance for the 579 SNPs (against the null hypothesis of 50% concordance that is expected by chance).

Extended Data Fig. 4 MAGMA gene-based association analysis.

Manhattan plot of the –log10(P from a one-sided Z-test) of 18,093 genes that were tested for association in the MAGMA (v.1.08) gene-based association analysis (Supplementary Information section 6). The 10 most significant genes are labeled with gene names. Red dashed line represents Bonferroni-significance, adjusted for the number of tested genes (one-sided P = 2.74 × 10–6). 928 genes were found to be significant, of which 244 have one or more genome-wide significant SNPs from the externalizing GWAS within their gene breakpoints. The results are also report in Supplementary Table 13.

Extended Data Fig. 5 MAGMA gene-property analysis.

Bar plot of the –log10(P from one-sided Z-tests) of the point estimate from a generalized least squares regression. The analysis identified that the externalizing GWAS is significantly enriched in brain and pituitary gland tissues (Supplementary Information section 6). Dashed line denotes Bonferroni-corrected significance, adjusted for testing 54 tissues (one-sided P < 9.26 × 10–4). 14 tissues were significantly associated with the externalizing GWAS, including 13 brain related tissues and the pituitary tissue. The results are also report in Supplementary Table 15.

Extended Data Fig. 6 MAGMA gene-property analysis of enrichment in brain tissues across 11 developmental stages (BrainSpan).

Bar plot of the –log10(P from one-sided Z-tests) of the point estimate from a generalized least squares regression. The analysis identified that the externalizing GWAS is significantly enriched during prenatal developmental stages (Supplementary Information section 6). Dashed line denotes Bonferroni-corrected significance, adjusted for testing 54 tissues (one-sided P < 9.26 × 10–4). The results are also report in Supplementary Table 16.

Extended Data Fig. 7 Gene overlap across multiple gene-association methods.

Venn diagram illustrating the overlap between (1) the nearest genes to the 579 jointly associated lead SNPs (denoted as the COJO EXT SNPs, see Supplementary Table 9), (2) the genes significant in the MAGMA gene-based analysis (Supplementary Table 13), (3) the genes significant in the H-MAGMA adult brain tissue analysis (Supplementary Table 17), and (4) the genes significant in the S-PrediXcan analysis (Supplementary Table 21). Across these four approaches, 34 genes were consistently implicated; these genes include CADM2, PACSIN3, ZIC4, MAPT, and GABRA2. Colored regions of this diagram correspond to the coloring shown in Supplementary Table 22, which lists all identified genes. No new statistical test was performed to generate this figure, and the statistical test used in each gene-based approach is reported in the notes of Supplementary Tables 9, 13, 17, and 21.

Extended Data Fig. 8 Externalizing systems map estimated with the Order Statistics Local Optimization Method (OSLOM) algorithm.

Representation of the externalizing network neighborhood estimated with PCNet as modular gene systems. In the top panel, circles represent distinct systems, with size indicating the number of genes belonging to each system (min 11 for ‘cilium organization’, and max 379 for the ‘externalizing systems map’). System color indicates the fraction of genes in each system that have been mapped to the externalizing phenotype by at least one of the four gene mapping methods (positional, MAGMA, H-MAGMA, and S-PrediXcan). Systems have been annotated with significantly enriched gene ontology terms. Systems without significant enrichment of biological pathways are labeled with a unique system ID (C454, C461, C453, C462), and may represent novel pathways. (i-vi) Visualization of genes within selected systems that have been mapped to the externalizing phenotype by one or more gene mapping methods, and their molecular interactions. In the bottom panel, the gene size is mapped to the number of methods in which the gene was found associated with externalizing (with the largest genes indicating the gene was identified by all 4 methods), and gene color(s) indicates which method(s) have mapped the gene.

Extended Data Fig. 9 Confirmatory factor analysis of phenotypic externalizing factor in Add Health and COGA.

Path diagram of confirmatory factor analysis (CFA) models in (top panel) Add Health (N = 15,107) and (bottom panel) COGA (N = 16,857) (Supplementary Information section 5). The reported model fit statistics and fit indices are degrees of freedom (df), comparative fit index (CFI), root mean square error (RMSEA), standardized root mean squared residual (SRMR). Standardized factor loadings presented as numbers on the paths.

Supplementary information

Supplementary Information

Supplementary Methods and Supplementary Notes.

Reporting Summary

Supplementary Tables 1–34.

Supplementary Data 1

Selected heterogeneity plots.

Supplementary Data 2

Heterogeneity plots for the 579 EXT SNPs.

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Karlsson Linnér, R., Mallard, T.T., Barr, P.B. et al. Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction. Nat Neurosci (2021). https://doi.org/10.1038/s41593-021-00908-3

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