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Shared genetic variants across substance use disorders implicate common neurobiological pathways, a genome-wide mixed methods study.

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  1. [1] § METHODS › Single locus identification with conditional/conjunctional false discovery rate ↔ plot_Manhattan.m, lines 116–220 · score 0.52 · condFDR, conjFDR, locations, chr

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MATLAB · 307 lines · 12 KB · GPL-3.0 · 1 match

  1. function handles = plot_Manhattan(results,traitname1,traitnames,chrnumvec,options)
  2. %% PLOT_MANHATTAN Make Manhattan plots based on
  3. % results.fdrmat : (SNP x trait) FDR matrix
  4. % results.imat : (SNP x 1) SNPs above -log10(fdrthresh)
  5. % results.imat2 : (SNP x 1) SNP loci after pruning
  6. % options.stattype : condfdr/conjfdr
  7. % options.fdrthresh : FDR threshold
  8. %
  9. % Remember to update that imat, imat2 before plotting
  10. % [imat, imat2, logfdrmat] = ind_loci_idx(fdrmat, flp, LDmat, options);
  11. %
  12. % Developer notes
  13. % 13.06: changed for-loop plot phase
  14. % removed traitsequences option, just use one traitsequence
  15. % 16.06: major cleanup
  16. % removed genenamesubst
  17. % gnlist setorder to 'stable', renamed extmat to textbox
  18. % renamed logpmat_sort into logfdrmat as it should be
  19. % removed p-values (logpvec1, logpmat2), use only results.fdrmat
  20. % 19.06: added handles
  21. % 23.06: added manhattan plot options to options
  22. % 25.06: rescales fontsize.genenames when rescaling y-axis (line 278)
  23. % 30.06: added colorlist as options.manh_colorlist (update pleioOpt.m)
  24. % 02.07: fixed bug for no genes passing threshold (line 274)
  25. % 22.05: don't plot fdrvec0 if snp is NaN on trait2
  26. % fdrvec0( any(isnan(fdrmat),2) ) = NaN; (line 169)
  29. %% Customizable plot options
  31. chrnumlist = { double(unique(chrnumvec)') }; % chromosome subsets
  32. exclude_locations = { }; % start/end points of excluded locations, in cell of arrays
  33. showgenes = false; % switch off for faster plots
  34. fontsize_genenames = 16; %14; %16;
  35. fontsize_legends = 18; %18; %22
  36. fontsize_axes = 13; % 18; %22
  37. fontsize_label = 18; % 18; %22
  38. ymargin = options.manh_ymargin; % 1; %0.25; % margin between points and genenames
  39. yspace = options.manh_yspace; % 0.75; %0.5 % line spacing between genenames
  40. LegendLocation = 'NorthEast';
  41. graylevel = 0.1; % grayness level for chromosome fill
  42. colorlist = options.manh_colorlist;
  44. %% Legacy codes
  46. fdrmat = results.fdrmat;
  47. imat = results.imat;
  48. imat2 = results.imat2;
  49. chrnumvec = results.chrnumvec;
  50. genenamelist = results.genenamelist;
  51. fdrvec0 = results.fdrvec0;
  53. ntraits = size(fdrmat, 2);
  54. %logpmat1 = repmat(logpvec1,[1,ntraits]);
  55. %defmat = isfinite(logpmat1+logpmat2);
  56. logfdrmat = -log10(fdrmat);
  57. %logfdrmat(~defmat) = NaN;
  60. excludevec = false(size(logfdrmat,1),1);
  61. for j = 1:length(exclude_locations)
  62. excludevec(exclude_locations{j}(1):exclude_locations{j}(2)) = true;
  63. end
  66. %% PREPARE LEGEND AND TRAIT PLOTTING SEQUENCE -----------------------
  68. switch(options.stattype)
  69. case 'condfdr' % include unconditioned FDR as additional column
  70. legends = cell(1,ntraits+1);
  71. ylabelstr = 'Conditional -log_{10}(FDR)';
  72. for i = 1:ntraits
  73. legends{i} = sprintf('%s | %s',traitname1,traitnames{i});
  74. end
  75. legends{end} = sprintf('%s',traitname1);
  77. fdrvec0( any(isnan(fdrmat),2) ) = NaN;
  78. logfdrmat = [logfdrmat, -log10(fdrvec0)];
  79. traitsequence = [ntraits+1, 1:ntraits];
  81. case 'conjfdr'
  82. legends = cell(1,ntraits);
  83. ylabelstr = 'Conjunctional -log_{10}(FDR)';
  84. for i = 1:ntraits
  85. legends{i} = sprintf('%s & %s',traitname1,traitnames{i});
  86. end
  87. traitsequence = 1:ntraits;
  88. otherwise
  89. error('')
  90. end
  94. %% PREPARE FIGURE -------------------------------------------------
  97. % CORRECT the options stuff here
  99. %handles = figure();
  100. scrsz = get(0,'ScreenSize');
  101. handles = figure('Position',[1 scrsz(4)/2 scrsz(3) scrsz(4)/2]);
  102. figname = ['Manhattan ', sprintf('%s %s ',traitname1,traitnames{:}) ] ;
  103. set(gca,'FontSize',20)
  104. set(gcf,'Name',figname)
  105. %set(gcf,'units','normalized','position',[0, 0, 1, 1]);
  106. set(gca,'ColorOrder',colorlist)
  107. if showgenes, ylim_extra = 3; else ylim_extra = 1; end
  108. xlim([-100000,size(fdrmat,1)]), ylim([-0.1,max(logfdrmat(:))+ylim_extra ])
  109. switch options.stattype
  110. case 'condfdr'
  111. colorlist(length(legends),:) = [0.5 0.5 0.5]; % black dots for unconditioned FDR
  112. case 'conjfdr'
  113. end
  116. %% DO MANHATTAN PLOT BY CHROMOSOME LIST
  118. for chri = 1:length(chrnumlist)
  120. chr = chrnumlist{chri};
  121. ivec_chr = ismember(chrnumvec,chr);
  122. xtick = nan( length(chr), 1 );
  123. xticklabel = nan( length(chr), 1 );
  124. % Zoom on many or one chromosomes, alternating background color
  125. if length(chr)>1
  126. for k = chr
  127. ind = find(chrnumvec==k);
  128. if isempty(ind), continue; end;
  129. hold on;
  130. h=fill([ind(1) ind(end) ind(end) ind(1)],[0 0 300 300],[1 1 1] - graylevel * (1-mod(k,2)));
  131. set(h,'EdgeColor','none');
  132. if (~is_octave())
  133. set(get(get(h,'Annotation'),'LegendInformation'),'IconDisplayStyle','off');
  134. end
  135. xtick(k) = median(ind);
  136. xticklabel(k) = k;
  137. end
  138. set(gca,'XTick',xtick,'XTickLabel',xticklabel,'FontSize',fontsize_axes)
  139. else
  140. title(sprintf('Chromosome %d',chr),'FontSize',fontsize_axes);
  141. set(gca,'XTickLabel',[],'FontSize',fontsize_axes)
  142. end
  144. % This is a dummy plot for legend purposes
  145. switch(options.stattype)
  146. case 'condfdr'
  147. legendsorder = [ntraits+1 , 1:ntraits];
  148. case 'conjfdr'
  149. legendsorder = 1:ntraits;
  150. end
  151. for i = legendsorder
  152. if ismember(i,traitsequence)
  153. hold on;
  154. plot(-1,-1,'o','MarkerFaceColor',colorlist(i,:),...
  155. 'MarkerEdgeColor',colorlist(i,:),'MarkerSize',8);
  156. end
  157. end
  159. % Draw threshold line
  160. indvec = (1:length(chrnumvec))';
  161. hold on
  162. %plot([indvec(1), indvec(end)],-log10([options.fdrthresh, options.fdrthresh]),':k','LineWidth', 1)
  163. plot([-100000, indvec(end)],-log10([options.fdrthresh, options.fdrthresh]),':','color',[0,0,0],'LineWidth', 1.2)
  165. % Plot in three phases: (1) above threshold, (2) significant, (3) loci
  166. for phase=1:3
  167. for i = 1:size(logfdrmat,2)
  168. if ismember(i,traitsequence)
  169. if i<=ntraits
  170. ivec = imat(:,i);
  171. ivec2 = imat2(:,i);
  172. else % unconditioned FDR
  173. ivec = fdrvec0<=options.fdrthresh; % & isfinite(logfdrmat(:,i));
  174. ivec2 = false(size(ivec));
  175. end
  176. end
  177. mrkface = colorlist(i,:);
  178. switch phase
  179. case 1 % do not pass threshold
  180. selvec = ~ivec&ivec_chr;
  181. mrkedge = colorlist(i,:);
  182. mrksize = 4; lwid=1;
  183. case 2 % pass threshold, but not loci
  184. selvec = ivec&~ivec2&ivec_chr;
  185. mrkedge = colorlist(i,:);
  186. mrksize = 8; lwid=1;
  187. case 3 % pass threshold and loci
  188. selvec = ivec2&ivec_chr;
  189. mrkedge = [0 0 0];
  190. mrksize = 10; lwid=2;
  191. end
  192. hold on;
  193. [x, y] = filter_points_for_plotting(indvec(selvec), logfdrmat(selvec, i), options.manh_image_size);
  194. h=plot(x,y,'o','MarkerFaceColor',...
  195. mrkface,'MarkerEdgeColor',mrkedge,'LineWidth',lwid,'MarkerSize',mrksize);
  196. if (~is_octave())
  197. set(get(get(h,'Annotation'),'LegendInformation'),'IconDisplayStyle','off');
  198. end
  200. if(options.manh_redraw), drawnow, end
  201. end
  202. end
  203. end
  205. if length(chr)>1
  206. h=legend(legends(legendsorder),'Box','off','Location',LegendLocation);
  207. set(h,'FontSize',fontsize_legends);
  208. end % Should automatically modify legends ith "trait1 & trait2"
  210. switch options.stattype
  211. case 'condfdr'
  212. ylabel('-log_{10}(condFDR)','FontSize',fontsize_label);
  213. case 'conjfdr'
  214. ylabel('-log_{10}(conjFDR)','FontSize',fontsize_label);
  215. end
  216. xlabel('Chromosome','FontSize',fontsize_label);
  217. %xtickangle(45)
  218. set(gca,'LineWidth',1.2,'TickDir','out')
  219. set(get(gca,'XAxis'),'TickLength',[0.007 0.007])
  220. set(get(gca,'YAxis'),'TickLength',[0.004 0.004])
  222. %% GENE NAMES -----------------------------------------
  223. % One gene may have multiple loci according to LD
  224. % Here we print one gene once, at one of the loci
  226. if showgenes
  228. % Get snp-index (snpid) of unique genes (gnlist)
  229. idxloci = find( sum(imat2,2)>0 & ivec_chr & ~excludevec ); % snp at loci
  230. gnloci = genenamelist( idxloci ); % gene at loci
  231. if (is_octave())
  232. [gnlist,id,~] = unique(gnloci); % list of unique genes
  233. else
  234. [gnlist,id,~] = unique(gnloci,'stable'); % list of unique genes
  235. end
  236. idxlist = idxloci(id); % SNP-idx of list of unique
  237. chrlist = chrnumvec(idxlist); % chromosome of SNP-idx
  239. % Print text boxes for gene names (textbox: [left,bottom,width,height])
  240. ymaxvec = max(logfdrmat,[],2); % max height of a snp for all traits
  241. ymaxvec(~ivec_chr) = 0;
  242. textbox = NaN(length(gnlist),4);
  243. textboxhandle = NaN(length(gnlist),1);
  244. for i = 1:length(gnlist)
  245. xpos = idxlist(i);
  246. %ypos = ymaxvec(idxlist(i)) + ymargin; % fdr of single snp
  247. %ypos = max(ymaxvec) + ymargin; % max fdr all
  248. %ypos = max( ymaxvec(chrnumvec==chrlist(i)) ) + ymargin; % max fdr within chromosome
  250. % create box then reset y-position
  251. ypos = 0;
  252. h=text( xpos, ypos ,gnlist{i}, 'HorizontalAlignment','center',...
  253. 'FontName','arial','FontSize',fontsize_genenames,'FontWeight','bold',...
  254. 'FontAngle','italic','Color',[0.5 0.5 0.5]); %,'EdgeColor',[0,0,0]);
  255. textbox(i,:) = get(h,'Extent');
  256. x1 = max(floor(xpos-textbox(i,3)/2), 1);
  257. x2 = min(ceil(xpos+textbox(i,3)/2), size(imat2,1));
  258. textbox(i,2) = max( ymaxvec(x1:x2) ) + ymargin;
  259. set(h,'Position',[xpos,textbox(i,2)]);
  261. textboxhandle(i) = h;
  262. end
  263. if (options.manh_redraw), drawnow; end
  264. ysp = yspace*mean(textbox(:,4),1); % white space
  266. % Rearrange text boxes position and color
  267. for i = 1:length(gnlist) % plot from left to right
  269. % check if textbox collides with previous genenames
  270. left1 = textbox(i,1);
  271. right0 = textbox(1:i-1,1)+textbox(1:i-1,3);
  272. bottom1 = textbox(i,2);
  273. bottom0 = textbox(1:i-1,2);
  275. % keep bumping up until it finds a space
  276. collide = left1<right0 & abs(bottom1-bottom0)< ysp;
  277. while any(collide)
  278. %bottom1 = max(bottom0(left1<right0)) + ysp;
  279. %bottom1 = bottom0(end) + ysp;
  280. bottom1 = bottom1 + 0.25*ysp;
  281. textbox(i,2) = bottom1;
  282. collide = left1<right0 & abs(bottom1-bottom0)< ysp;
  283. end
  285. % Color according to trait with maximum conditional FDR
  286. % Special care should be taken to avoid coloring genes with a color of pruned trait.
  287. % For a given idxlist(i) we find which traits do not pass pruning
  288. % procedure, and exclude them from traitsequence. This works
  289. % for both conjfdr and condfdr.
  290. traitsequence_pruned = traitsequence(~ismember(traitsequence, find(~imat2(idxlist(i), :))));
  291. logfdrmat_pruned = logfdrmat(idxlist(i), traitsequence_pruned);
  292. [~, mi] = max(logfdrmat_pruned);
  293. mi = traitsequence_pruned(mi);
  294. xpos = textbox(i,1) + textbox(i,3)/2;
  295. ypos = textbox(i,2);
  296. set( textboxhandle(i), 'Position',[xpos,ypos],...
  297. 'Color',colorlist(mi,:));
  298. end
  300. % Rescale y-axis and gene fontsize
  301. if showgenes && ~isempty(textbox)
  302. yl_old = ylim();
  303. yl = [0, max(textbox(:,2))+ysp];
  304. ylim(yl)
  305. set( textboxhandle(:), 'FontSize', fontsize_genenames*yl_old(2)/yl(2) )
  306. end
  307. end

plot_Manhattan.m at commit 0da963c, under GPL-3.0 · at the source

Overview

Authors: Børge Holen1,2, Zillur Rahman1,2, Alexey A. Shadrin1,2, Romain Icick1,2,3, Kevin S. O’Connell1,2, Linn Rødevand1,2, Nadine Parker1,2, Markos Tesfaye1,2,4, Piotr Jaholkowski1,2, Oleksandr Frei1,2, Anders M. Dale5,6,7,8,9, Srdjan Djurovic1,2,10, Ole A. Andreassen1,2, Olav B. Smeland1,2
  1. Centre for Precision Psychiatry Institute of Clinical Medicine University of Oslo Oslo Norway
  2. Division of Mental Health and Addiction Oslo University Hospital Oslo Norway
  3. INSERM UMR‐S1144 Université Paris Cité Paris France
  4. Department of Psychiatry Institute for Genomics in Health SUNY Downstate Health Sciences University Brooklyn New York USA
  5. Department of Radiology University of California, San Diego La Jolla California USA
  6. Multimodal Imaging Laboratory University of California, San Diego La Jolla California USA
  7. Department of Cognitive Science University of California, San Diego La Jolla California USA
  8. Department of Psychiatry University of California, San Diego La Jolla California USA
  9. Department of Neurosciences University of California, San Diego La Jolla CA USA
  10. Department of Medical Genetics Oslo University Hospital Oslo Norway
Institutions: Oslo University Hospital (Norway); University of Oslo (Norway); Inserm (France); Université Paris Cité (France); Optimisation Thérapeutique en Neuropsychopharmacologie (France); SUNY Downstate Health Sciences University (United States); University of California San Diego (United States)
Journal: General psychiatry, volume 39, issue 2, article e70017
Dates: received 25 June 2025; accepted 4 March 2026; published online 20 April 2026; in print April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/gps3.70017 · PMID 42016913 · PMCID PMC13095382 · OpenAlex W7154951048
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), other condition (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions
Keywords: alcohol use disorder, cannabis use disorder, common neurobiological pathways, genetic architecture, novel substance use disorder genetic loci, opioid use disorder, shared genetics, substance use disorder
Topic: Genetic Associations and Epidemiology (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Research Council of Norway (324499, 324252, 273291, 223273, 273446); Helse Sør-Øst RHF (2022‐073); NordForsk (164218); H2020 European Institute of Innovation and Technology (964874); Stiftelsen Kristian Gerhard Jebsen (SKGJ‐MED‐021); Universitetet i Oslo; Foundation for the National Institutes of Health (U24DA041123)
Citations: cited by 1 paper (Europe PMC); 81 references in the paper

Abstract

Background: Substance use disorders (SUDs) are highly heritable, but the extent of shared and distinct genetic architecture across different SUDs is unclear.

Aims: To compare the genetic architectures of alcohol use disorder (AUD), cannabis use disorder (CUD) and opioid use disorder (OUD) and to identify shared and unique genetic loci.

Methods: We analysed large‐scale genome‐wide association study (GWAS) summary statistics from individuals of European ancestry recruited in Europe and the USA. The mixture model MiXeR was used to estimate the unique genetic architecture characteristics of each SUD, including its polygenicity, single nucleotide polymorphism (SNP)‐heritability and discoverability, a measure of the distribution of genetic signal across all causal variants. Pairwise conditional/conjunctional false discovery rate (cond/conjFDR) analyses identified shared loci, followed by biological annotation of implicated genes.

Results: AUD demonstrated the highest polygenicity, followed by CUD and OUD. SNP‐based heritability was 0.10 for AUD and OUD and 0.01 for CUD. Discoverability was highest for OUD, followed by AUD and CUD. Currently, genome‐wide significant SNPs explain 2.0% of AUD, 0.3% of CUD and 0.2% of OUD variance. Cond/conjFDR identified 39 novel loci for AUD, 10 for CUD and 1 for OUD. Of implicated genes, most were expressed in the brain, including several involved in gamma‐aminobutyric acid and dopaminergic neurotransmission, opioid neurophysiology, myelination, DNA recombination, apoptosis and ubiquitin‐dependent protein catabolism.

Conclusions: SUDs have polygenic architectures with many shared loci and are similar with regards to some characteristics. However, the level of polygenicity differs across SUDs, with AUD being considerably more polygenic than OUD, whereas CUD is intermediate in terms of its polygenicity. The novel loci implicate genes primarily expressed in the brain, involving a variety of biological functions. The findings expand our view of the aetiology of these disorders, while supporting the hypothesis of a shared set of pleiotropic SUD genes.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

precimed/mixer

License: GPL-3.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 499bc4955cfb3a6024311064cf5c9c896879cc83, 15 October 2025
Languages: Jupyter (2), Python (1)
Size: 28 files, 3 scripts
Software Heritage: archived
Found in: “DATA AVAILABILITY STATEMENT”
Holds: README, license file, 2 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (3 files), pandas (2 files), Matplotlib (1 file), SciPy (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
5 files
At the source: github.com/precimed/mixer

precimed/GWAS_SUMSTAT

License: none: the authors keep all their rights
State: the link is dead, verified on 29 September 2026
Evidence: found in the paper
Software Heritage: not archived
Found in: “DATA AVAILABILITY STATEMENT”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link is dead
  • 29 September 2026: the link is dead
At the source: github.com/precimed/GWAS_SUMSTAT

precimed/pleiofdr

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 0da963cac22fe8de9030166d7aea974bcbb0a367, 3 June 2025
Languages: MATLAB (33), Python (9), R (3), Shell (2), Jupyter (1)
Size: 55 files, 48 scripts
Software Heritage: archived
Found in: “DATA AVAILABILITY STATEMENT”
Holds: README, license file, 1 notebook
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (8 files), pandas (8 files), Statistics and Machine Learning Toolbox (6 files), data.table (3 files), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
50 files
At the source: github.com/precimed/pleiofdr

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 51 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability statement

Cond/conjFDR analyses were run with MATLAB 2018b and code is available at https://github.com/precimed/pleiofdr (https://uio-my.sharepoint.com/personal/borgeho_uio_no/Documents/Diverse/Søknad til akuttpsyk..docx?web=1). MiXeR software version 2.2.1 was used and code is available at https://github.com/precimed/mixer. Version 1.8.1 of the FUMA web tool was used (https://fuma.ctglab.nl/updates). We used version 8 of the OpenTargets tool (https://genetics.opentargets.org/) and the clean_sumstats pipeline to standardize summary statistics (https://github.com/precimed/GWAS_SUMSTAT).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 14 authors, 8 keywords, 7 funders, 78 references.

Cite

This paper

Holen, B., Rahman, Z., Shadrin, A. A., Icick, R., O’Connell, K. S., Rødevand, L., Parker, N., Tesfaye, M., Jaholkowski, P., Frei, O., Dale, A. M., Djurovic, S., Andreassen, O. A., & Smeland, O. B. (2026). Shared genetic variants across substance use disorders implicate common neurobiological pathways, a genome-wide mixed methods study. General psychiatry, 39(2), e70017. https://doi.org/10.1002/gps3.70017

BibTeX

@article{holen2026shared,
author = {Holen, Børge and Rahman, Zillur and Shadrin, Alexey A. and Icick, Romain and O’Connell, Kevin S. and Rødevand, Linn and Parker, Nadine and Tesfaye, Markos and Jaholkowski, Piotr and Frei, Oleksandr and Dale, Anders M. and Djurovic, Srdjan and Andreassen, Ole A. and Smeland, Olav B.},
title = {{Shared genetic variants across substance use disorders implicate common neurobiological pathways, a genome-wide mixed methods study}},
journal = {General psychiatry},
year = {2026},
month = apr,
volume = {39},
number = {2},
pages = {e70017},
publisher = {Shanghai Mental Health Center},
issn = {2517-729X},
doi = {10.1002/gps3.70017},
url = {https://doi.org/10.1002/gps3.70017},
pmid = {42016913},
pmcid = {PMC13095382}
}

RIS

TY - JOUR
AU - Holen, Børge
AU - Rahman, Zillur
AU - Shadrin, Alexey A.
AU - Icick, Romain
AU - O’Connell, Kevin S.
AU - Rødevand, Linn
AU - Parker, Nadine
AU - Tesfaye, Markos
AU - Jaholkowski, Piotr
AU - Frei, Oleksandr
AU - Dale, Anders M.
AU - Djurovic, Srdjan
AU - Andreassen, Ole A.
AU - Smeland, Olav B.
TI - Shared genetic variants across substance use disorders implicate common neurobiological pathways, a genome-wide mixed methods study
T2 - General psychiatry
J2 - Gen Psychiatr
PY - 2026
DA - 2026/04/20
VL - 39
IS - 2
SP - e70017
SN - 2517-729X
PB - Shanghai Mental Health Center
DO - 10.1002/gps3.70017
UR - https://doi.org/10.1002/gps3.70017
LA - en
ER -

CSL-JSON

{
"id": "10.1002/gps3.70017",
"type": "article-journal",
"title": "Shared genetic variants across substance use disorders implicate common neurobiological pathways, a genome-wide mixed methods study",
"container-title": "General psychiatry",
"author": [
{
"family": "Holen",
"given": "Børge"
},
{
"family": "Rahman",
"given": "Zillur"
},
{
"family": "Shadrin",
"given": "Alexey A."
},
{
"family": "Icick",
"given": "Romain"
},
{
"family": "O’Connell",
"given": "Kevin S."
},
{
"family": "Rødevand",
"given": "Linn"
},
{
"family": "Parker",
"given": "Nadine"
},
{
"family": "Tesfaye",
"given": "Markos"
},
{
"family": "Jaholkowski",
"given": "Piotr"
},
{
"family": "Frei",
"given": "Oleksandr"
},
{
"family": "Dale",
"given": "Anders M."
},
{
"family": "Djurovic",
"given": "Srdjan"
},
{
"family": "Andreassen",
"given": "Ole A."
},
{
"family": "Smeland",
"given": "Olav B."
}
],
"container-title-short": "Gen Psychiatr",
"volume": "39",
"issue": "2",
"page": "e70017",
"DOI": "10.1002/gps3.70017",
"PMID": "42016913",
"PMCID": "PMC13095382",
"ISSN": "2517-729X",
"publisher": "Shanghai Mental Health Center",
"URL": "https://doi.org/10.1002/gps3.70017",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
20
]
]
}
}

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