Genome-wide analysis of screen behaviors among adolescents identifies novel loci and overlap with educational attainment and mental disorders

Genome-wide analysis of screen behaviors among adolescents identifies novel loci and overlap with educational attainment and mental disorders | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Genome-wide analysis of screen behaviors among adolescents identifies novel loci and overlap with educational attainment and mental disorders View ORCID Profile Evgeniia Frei , Tahir Tekin Filiz , Oleksandr Frei , Robert Loughnan , Piotr Jaholkowski , Nora R. Bakken , Viktoria Birkenæs , Alexey A. Shadrin , Helga Ask , Ole A. Andreassen , Olav B. Smeland doi: https://doi.org/10.1101/2025.01.07.25320110 Evgeniia Frei 1 Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo , Oslo, Norway MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Evgeniia Frei For correspondence: evgeniia.frei{at}medisin.uio.no o.b.smeland{at}medisin.uio.no Tahir Tekin Filiz 1 Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo , Oslo, Norway MSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site Oleksandr Frei 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 Centre for Bioinformatics, Department of Informatics, University of Oslo , Oslo, Norway PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Robert Loughnan 4 Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research , Tulsa, Oklahoma, USA 5 Center for Human Development, University of California , La Jolla, California, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Piotr Jaholkowski 1 Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo , Oslo, Norway MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nora R. Bakken 1 Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Viktoria Birkenæs 1 Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo , Oslo, Norway MSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alexey A. Shadrin 1 Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo , Oslo, Norway PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Helga Ask 6 PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health , Oslo, Norway 7 PROMENTA Research Center, University of Oslo , Oslo, Norway PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ole A. Andreassen 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 MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Olav B. Smeland 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 MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: evgeniia.frei{at}medisin.uio.no o.b.smeland{at}medisin.uio.no Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Technological devices play a central role in adolescents’ life. Despite concerns about negative effects of excessive screen time on mental health and development, there is little knowledge of fundamental features of screen behaviours and underlying genetic architecture. Using self-reports from adolescents (14-16 years old) in the Norwegian Mother, Father, and Child Cohort Study (MoBa, n = 18 490), we performed genome-wide association analysis for four screen behaviors: time spent 1) watching movies/series/TV; 2) gaming; 3) sitting/lying down with PC, mobile or tablet; and 4) communicating with friends on social media. The resulting summary statistics were analysed using the conditional false discovery rate (condFDR) approach to increase genetic discovery. We also estimated SNP-based heritabilities of the screen behaviours and the genetic correlations with six major psychiatric disorders (schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, alcohol use disorder), and educational attainment. The screen-based phenotypes displayed significant SNP-based heritabilities (0.048–0.12). We also observed significant genetic correlations between screen behaviours and psychiatric disorders (r g range: 0.21–0.42). Educational attainment demonstrated significant negative genetic correlation with screen behaviours, most strongly with social media use (r g = −0.69). CondFDR analysis identified three novel loci associated with social media use. Thus, we show that screen behaviors are heritable, polygenic traits that partly share genetic signal with mental disorders and educational attainment. Future studies and larger samples are required to clarify causal relationships between these traits and disorders, and to validate the identified genetic loci associated with social media use. Introduction Technological devices have become an integral part of adolescents’ life. The majority of young people spend several hours per day on screen-based activities, and reports indicate that the numbers continue to increase ( 1 , 2 ). Child and adolescent use of technology outpaces our understanding of the fundamental features and health impact of screen behaviours, and additional research is needed to guide children’s access to social media, as well as propose the optimal use of screen devices in primary and secondary education. In the 1990s, the first evidence of genetic influence on television watching emerged, challenging the notion that it was a pure “environmental factor” ( 3 ). Extensive research has since confirmed that screen behaviours are heritable traits ( 4 ), including substantial twin heritability estimates of gaming behaviour (19%-63%) ( 5 ), compulsive internet use (48%) ( 6 ), and problematic internet use (58%-66%) ( 7 ) among adolescents. Recent work investigated SNP-based heritability ( h 2 SNP ) of screen behaviours (TV/video watching, gaming, total screen time) in the Adolescent Brain Cognitive Development Study, and reported estimates that varied from zero to 10-18%, depending on the screen subtype ( 8 ). Despite this, the genetic architecture of screen behaviours among adolescents is poorly understood with a lack of studies detecting specific single nucleotide polymorphisms (SNPs) ( 9 ). A recent genome-wide association study (GWAS) revealed several SNPs significantly associated with internet addiction disorder in adults ( 10 ). Further, GWASs based on middle-aged individuals in the UK Biobank (UKB) cohort identified SNPs associated with television watching and leisure computer use ( 11 ). However, it is unclear whether these results can be generalized to adolescents, who spend more time on screen devices than any other age group ( 12 ). Parallel to the widespread use of digital devices among young people, youth mental health problems are on the rise ( 13 – 15 ). While excessive use of screen devices has been linked to negative mental health outcomes, the proposed explanations vary greatly ( 16 – 19 ). Psychiatric disorders in children and adolescents are affected by genetic factors ( 20 – 22 ), and the potential of shared genetic determinants underlying screen time and mental health problems in children and adolescents is a novel, emerging research topic ( 23 ). Recent studies have indicated that genetic confounding may account for a substantial part of the phenotypic association between screen time and mental health ( 24 , 25 ), and that major psychiatric disorders and screen behaviours may share a common genetic basis ( 23 ). Furthermore, a growing body of evidence suggests that increased screen time could affect academic performance in children and adolescents ( 26 ). For example, television watching, gaming, and social media use are associated with worse academic performance ( 27 – 29 ). Evidence also suggests that prolonged screen time could contribute to a diminished capacity for sustained attention and a heightened susceptibility to distractions ( 30 ). Finally, both twin studies and large genome-wide association studies have demonstrated that genetic factors are important for educational attainment ( 31 , 32 ), although the extent to which academic performance and screen behaviours share genetic underpinnings remains unclear. In this study, we leveraged data from the Norwegian Mother, Father, and Child Cohort Study (MoBa) ( 33 ), a prospective population-based pregnancy cohort, to investigate the genetic architecture of screen behaviours among adolescents and their associations with key mental health traits and disorders. We aimed to identify specific genomic loci associated with screen behaviours in adolescents. To achieve this, we performed GWASs of four single screen behaviours (television watching, gaming, total screen time use, and social media use). We undertook extensive post-GWAS analyses, including estimating genetic correlations across screen behaviours and with six major psychiatric disorders (schizophrenia [SCZ], bipolar disorder [BP], major depressive disorder [MDD], autism spectrum disorder [ASD], attention-deficit hyperactivity disorder [ADHD], and alcohol use disorder [AUD]), as well as educational attainment (EA). In addition, we analysed the resulting summary statistics using the conditional false discovery rate (condFDR) approach to improve statistical power and genetic discovery ( 34 , 35 ). Methods Study sample MoBa is a population-based pregnancy cohort study conducted by the Norwegian Institute of Public Health (NIPH) ( 33 ). Participants were recruited from all over Norway from 1999-2008, and the women consented to participation in 41% of the pregnancies. The cohort includes approximately 114 500 children, 95 200 mothers and 75 200 fathers. For genotyping, blood samples for the children were taken from the umbilical cord after their birth. Biological samples were sent to NIPH where DNA was extracted by standard methods and stored. Genotyping of the MoBa cohort was conducted through multiple research projects spanning several years, involving various selection criteria, and genotyping centres. Full details about the genotyping and quality control procedures are provided elsewhere ( 36 ). The current study is based on version 12 of the quality-assured data files released for research in January 2019, including all adolescents (14-16 years of age) with relevant phenotypic and genetic data available ( n = 18 490). Questionnaire data were collected between 2017-2023. Data of participants who withdrew their consent before August 2024 were not included in the analyses. The establishment of MoBa and initial data collection was based on a license from the Norwegian Data Protection Agency and approval from The Regional Committees for Medical and Health Research Ethics (REK). The MoBa cohort is currently regulated by the Norwegian Health Registry Act, and the current study was approved by REK (2016/1226/REK sør-øst C). Screen behaviors We used single item self-reports about activities during leisure time from the MoBa Q-14year questionnaire. Specifically, adolescents reported how much time they spent on the following screen-based activities: 1) watching movies/series/TV; 2) playing games on PC, TV, tablet, mobile, etc.; 3) sitting/lying down with PC, mobile, or tablet (irrespective of activity); 4) communicating with friends on social media). For each activity, responses were coded from 1 (never/rarely) to 6 (7 hours or more). Detailed information about study variables is presented in the Supplementary Table 1. Genome-Wide Association Analyses GWASs were conducted using an additive multivariate linear regression model with PLINK2 ( 37 ) (v2.00a5.10LM). To ensure that screen use patterns reflect the general adolescent population as accurately as possible, the sample was restricted to participants without a high likelihood of severe disability. In line with another adolescent cohort study ( 38 ), we therefore excluded individuals with current diagnosis of SCZ (F20-F29), mental impairment/intellectual disability (F70-F79), alcohol/substance use disorder (F10-F19), and ASD (F84), based on the data from the Norwegian Patient Registry (NPR), which contains diagnoses registered in specialist healthcare services. Moreover, subjects with ambiguous sex and subjects with chromosomal abnormalities were removed from analyses. This resulted in a sample of 17 945 individuals. For each pair of participants with a kinship coefficient greater than 0.05, one member was randomly excluded from analyses, resulting in a sample of 16 027 unrelated individuals. The first twenty genetic principal components (PCs), age, sex, and genotyping batch (N = 26, as factors) were used as covariates. The analyses were restricted to individuals of European ancestry selected as described previously ( 36 ). All summary statistics underwent quality control and were cleaned using the cleansumstats pipeline ( 39 ). Analyses were conducted using singularity containers ( 40 ). Sensitivity analysis To ensure that the presence of psychiatric diagnoses in the study sample does not confound the results, we performed a sensitivity analysis and re-estimated all genetic correlations using GWAS on screen phenotypes based on subsample of participants without a history of any psychiatric disorder, according to the NPR data. Of the 18 490 participants with relevant phenotypic and genetic data available, 3705 (20.04%) had at least one registered psychiatric diagnosis (see Supplementary Table 2 for more detailed information). Conditional False Discovery Rate (condFDR) analyses To improve statistical power and genetic discovery, we used the condFDR approach. Briefly, it boosts GWAS discovery by leveraging overlapping SNP associations between two GWASs to re-rank the test statistics in a primary phenotype conditional on the associations in a secondary phenotype ( 34 , 35 ). In our study, the primary phenotypes were the four screen time measures, with educational attainment as a secondary phenotype ( 32 ). To facilitate evaluation of identified loci in the UK Biobank, we excluded this cohort from the EA summary statistics. To visualize cross-trait enrichment we used conditional QQ plots, which show p -value distributions for a primary trait for all SNPs, and for SNP strata set by their association with a secondary trait. For QQ plots production, we excluded variants within regions with complex linkage disequilibrium (LD) structure (MHC region: chr6:25119106–33854733, and 8p23 inversion: chr8:7200000–12500000, GRCh37 coordinates). Successive leftward deflection of the variant strata with increasing significance in the conditional phenotype in both directions suggests strong cross-trait enrichment. The FDR significance cut-off for condFDR was set at 0.01, in line with the previous literature ( 34 , 35 ). Evaluation of the identified Loci in an Independent Sample We used GWAS results from the UKB cohort on leisure television watching (TV-UKB) and leisure computer use (PC-UKB) to test whether our results can be supported by data from an independent sample ( 11 ). For this purpose, we checked whether effects of the lead SNPs identified by condFDR analysis are consistent between the MoBa and UKB data sets. Additionally, we obtained the p -values of the lead SNPs from the MoBa cohort in the UKB sample. Functional Analyses Genomic loci were defined by identifying independent significant SNPs with condFDR < 0.01 that were not in close LD with each other (r 2 < 0.60), according to the FUMA protocol( 41 ). Lead SNPs were then defined by the independent significant SNPs with r 2 < 0.1 in approximate LD. Candidate SNPs were defined as all SNPs with condFDR < 0.10 and in LD (r 2 ≥ 0.60) with an independent significant SNP. The loci borders were set by identifying all candidate SNPs in LD (r 2 ≥ 0.6) with one of the independent significant SNPs in the locus. Loci < 250_kb apart were merged, and the lead SNP of the merged locus was selected as the SNP with the most significant condFDR value. Overlapping signals within complex LD regions were represented by one independent lead SNP only. All LD r 2 values were obtained from the 1000 Genomes Project European-ancestry haplotype reference panel ( 42 ). Using FUMA ( 41 ), all candidate SNPs with condFDR < 0.10 were functionally annotated with combined annotation-dependent depletion (CADD) scores to predict deleterious SNP effects on proteins, RegulomeDB scores to predict the likelihood of regulatory function of a SNP, and chromatin state scores to predict transcriptional effects. Candidate SNPs were aligned to genes using three different strategies implemented in FUMA (positional gene mapping, expression quantitative trait locus mapping, and chromatin interaction mapping). The lead SNPs were also mapped to putative causal genes using the Variant to Gene (V2G) tool from the open-source OpenTargets Genetics ( 43 ). The OpenTargets Genetics platform was also used to inspect associations of the mapped genes with other phenotypes. To estimate expression of the mapped genes in human brain, we used the open-source Brain RNA-Seq Database ( 44 ). Estimation of SNP-Based Heritabilities and Genetic Correlations h 2 SNP of screen behaviors were estimated from the GWASs summary statistics using linkage disequilibrium score regression (LDSC) ( 45 ). To perform more precise estimation of h 2 SNP using individual genotype data, we conducted GCTA-GREML analysis ( 46 , 47 ). We also applied bivariate LDSC ( 45 ) to estimate genetic correlations (r g ) across screen behaviours and with six major psychiatric disorders (SCZ, BP, MDD, ADHD, ASD, AUD) ( 48 – 53 ), as well as EA ( 32 ). Genetic correlations were estimated in the main study sample, as well as in the subsample of participants without a history of a psychiatric disorder. We also estimated genetic correlations between the screen behaviors in MoBa and TV watching and leisure computer use in the UKB cohort. We used the set of LD scores provided by the software’s creators, based on the 1000 Genomes Project’s European sample. Summary statistics from external GWASs of SCZ, BP, MDD, ADHD, ASD, AUD, and EA were harmonized using the cleansumstats pipeline. Additional SNP quality control routines were equivalent to the defaults employed with the LDSC munge_sumstats.py function. Following the recommended practices, we assumed no sample overlap. Correlations are presented as the coefficient ± standard error. Original p -values are reported. Multiple testing correction was performed using the Benjamini-Hochberg procedure with FDR < 0.05. Analyses were done using Python ver. 3.9.5. Results In total, 18 490 participants had relevant phenotypic and genetic data available, and 16 027 unrelated individuals were included in the genetic analysis. Basic demographic characteristics of the initial sample are presented in Table 1 . Descriptive information on study variables is presented in the Supplementary Table 1. View this table: View inline View popup Download powerpoint Table 1. Basic demographic characteristics of the adolescent sample. GWASs of Screen Behaviours The total number of missing values did not exceed 1.5% for any of the screen-based phenotypes. Detailed information about missing values, and sample sizes for each screen-based phenotype are available in the Supplementary Table 1. There was no evidence of stratification artefacts or uncontrolled test statistic inflation in the results for any phenotype (Supplementary Figures 1-2). No SNP reached genome-wide significance in any of the four GWAS. A list of SNPs that reached the level of suggestive genome-wide significance ( p < 1×10 −5 ) for the four screen behaviors is given in the Supplementary Tables 4-7. SNP-Based Heritability Analysis of the GWAS summary statistics indicated that the screen behaviours have significant nonzero h 2 SNP ( Figure 1 , Supplementary Table 3). LDSC results showed that h 2 SNP for television watching, gaming, and social media use was 0.066 (SE = 0.027, p = 0.0073), 0.070 (SE = 0.029, p = 0.0079), and 0.12 (SE = 0.031, p = 5.42×10 −5 ), respectively. LDSC heritability estimate of the total screen time phenotype was not significantly different from zero. Download figure Open in new tab Figure 1. Single nucleotide polymorphism–based heritability (SNP-h 2 ) estimates for screen behaviours, obtained with LDSC (blue bars) and GCTA-GREML (orange bars). Error bars indicate standard errors of the estimated values. TV: watching movies/series/TV; GAMES: playing games on PC, TV, tablet, mobile, etc.; TOTAL: sitting/lying down with PC, mobile or tablet; SoMe: communicating with friends on social media. GCTA-GREML produced concordant heritability estimates. Specifically, h 2 SNP of television watching, gaming, and social media use was 0.060 (SE = 0.019, p = 0.0012), 0.093 (SE = 0.020, p = 4.55×10 −8 ), and 0.10 (SE = 0.020, p = 2.01×10 −7 ), respectively. Total screen time use h 2 SNP estimated with GCTA-GREML was significantly different from zero: 0.048 (SE = 0.019, p = 0.0048). Identification of Loci Associated with Social Media Use Conditional QQ plots demonstrated enrichment of SNP-associations with social media use conditional on increasing levels of significance with EA (Supplementary Figure 3). We leveraged this cross-trait enrichment using condFDR analyses and identified three LD-independent loci associated with social media use at condFDR < 0.01 ( Figure 2 ). The lead SNPs in the identified loci were mapped to putative causal genes using the V2G tool from the Open Targets Genetics ( 43 , 54 , 55 ). The strongest signal was located at an intergenic variant (rs7110805, condFDR = 5.10×10 −5 ), on chromosome 11 ( Figure 3C ). Its nearest gene is MTMR2 , while the region also contains the genes FAM76B and CEP57 (downstream). Three additional independent significant SNPs (rs1727149, rs10765775, rs1893057) were identified in this large region spanning more than 250 000 bp. The second strongest independent condFDR signal was an intergenic variant on chromosome 2 (rs359240, condFDR = 1.28×10 −3 , Figure 3A ). No genes were residing in the direct vicinity of this SNP (the canonical TSS of the nearest gene BCL11A is located at 306 594 bp upstream), and only 34 SNPs were in strong LD (r 2 > 0.6). Nevertheless, rs359240 has a high CADD score of 19.6, indicating deleteriousness( 56 ). Finally, the condFDR analysis identified an ncRNC intronic variant on chromosome 4 (rs6848288, condFDR = 3.65×10 −3 , Figure 3B ), with nearest protein-coding gene SMARCAD1 . rs6848288 tags a broad region of associations, covering around 270 000 bp, and has 139 candidate SNPs in strong LD (r 2 > 0.6). Besides SMARCAD1 , this region also contains the HPGDS gene (upstream). Download figure Open in new tab Figure 2. Common genetic variants significantly associated with social media use (SoMe) among adolescents in the MoBa sample. The variants were identified at conditional false discovery rate (condFDR) < 0.01 after conditioning on educational attainment (EA). The Manhattan plot displays the –log10 transformed condFDR values for each single-nucleotide polymorphism (SNP) on the y-axis with chromosomal positions along the x -axis. The small points represent non-significant SNPs, the bold points represent significant SNPs (condFDR 0.10) have the rs-number written above it. The horizontal grey dotted line shows the significance threshold of condFDR (0.01). Gray dots stand for unconditional FDR values. SoMe: communicating with friends on social media. Download figure Open in new tab Figure 3. The genetic context of the strongest associations identified in the conditional false discovery rate (condFDR) analysis. Values for variants occupying the locus are shown on the left y -axis as –log10(condFDR). In each subplot, a single nucleotide polymorphism (SNP) with the strongest association is shown in the large purple square. The colour of the remaining markers reflects the degree of linkage disequilibrium (LD) with the strongest-associated SNP measured as r 2 coefficient (described in the legend). The dotted line indicates the condFDR threshold of 0.01. A: surrounding of rs359240 (condFDR = 1.28 × 10 −3 ). B: surrounding of rs6848288 (condFDR = 3.65 × 10 −3 ). C: surrounding of rs7110805 (condFDR = 5.10 × 10 −5 ). Functional annotation of the candidate SNPs (see Supplementary Table 8) in the identified loci using FUMA revealed that most candidate SNPs were intronic and intergenic. We found 7 exonic candidate SNPs located on the chromosome 11 locus, and two of these SNPs were non-synonymous. Among the identified loci, 14 candidate SNPs had a CADD-score higher than 12.37 (a threshold suggested to reflect deleteriousness) ( 56 ). Full results of the FUMA analysis are presented in the Supplementary Table 9. Evaluation of the Identified Loci in an Independent Sample We examined the identified loci in the association summary statistics from the independent TV-UKB and PC-UKB GWASs ( 11 ). We also evaluated the respective genetic correlations. PC-UKB was significantly correlated with gaming, but not with the other MoBa phenotypes, whereas TV-UKB showed positive genetic correlations with three screen behaviours in MoBa (r g = 0.38–0.52, see Supplementary Table 13). Positive genetic correlations warrant evaluation of identified loci in TV-UKB and PC-UKB, despite considerable differences between the MoBa and the UKB cohorts (i.e. highly distinct age groups, and different in phenotypes per se ). Locus 3, represented by rs7110805, has the same direction of effect in the MoBa (social media use) and TV-UKB samples, with p < 0.05. Locus 1 and locus 2, represented by rs359240 and rs6848288, respectively, have the same direction of effect in the MoBa (social media use) and PC-UKB samples. Moreover, the p -value for rs359240 in the PC-UKB sample was nominally significant ( p < 0.05). These positive evaluation results reassure validity of the identified loci. Genetic Overlap with Key Mental Traits and Disorders We evaluated pairwise genome-wide genetic correlations between the three screen-based phenotypes with significant LDSC estimated SNP-heritability (TV watching, gaming, social media use) and six major psychiatric disorders, as well as EA. In addition, we estimated genetic correlations between the screen-based phenotypes themselves. The results are shown in Figure 4 , and in Supplementary Tables 10-12. We corrected for multiple comparisons using FDR < 0.05. Download figure Open in new tab Figure 4. Genetic correlation estimates A) among screen behaviours and B) between screen behaviours and six major psychiatric disorders and educational attainment. Asterisks indicate significant estimates at FDR < 0.05 (Benjamini-Hochberg procedure). TV: watching movies/series/TV; GAMES: playing games on PC, TV, tablet, mobile, etc.; SoMe: communicating with friends on social media; SCZ, schizophrenia; BP, bipolar disorder; MDD, major depressive disorder; ASD, autism spectrum disorder; ADHD, attention-deficit hyperactivity disorder; AUD, alcohol use disorder; EA, educational attainment. We identified significant genetic correlations between several screen time measures. Specifically, social media use was positively correlated with gaming (r g = 0.83, SE = 0.27, p = 0.0021) and TV watching (r g = 0.69, SE = 0.25, p = 0.0065), while TV and gaming were not significantly correlated. We estimated significant genetic correlations between screen behaviours and psychiatric disorders (r g in range 0.21–0.42). ADHD showed moderate positive genetic correlations with TV watching (r g = 0.33, SE = 0.12, p = 0.006), gaming (r g = 0.39, SE = 0.13, p = 0.0036), and social media use (r g = 0.42, SE = 0.09, p = 3.67×10 −6 ). ASD was positively correlated with gaming, but negatively with social media use. Both MDD and AUD were positively correlated with social media use (r g = 0.21, SE = 0.065, p = 0.0012, and r g = 0.31, SE = 0.12, p = 0.020, respectively), while SCZ was negatively correlated with gaming (r g = −0.30, SE = 0.12, p = 0.0004). Finally, EA showed significant negative genetic correlation with all three screen behaviours, most strongly with social media use (r g = −0.69, SE = 0.097, p = 9.38×10 −13 ). As a sensitivity analysis, we re-estimated all genetic correlations using GWAS on screen-based phenotypes based on subsample of participants without a history of any psychiatric disorder. The genetic correlation estimates in the two samples were highly concordant (Pearson correlation coefficient 0.98; see Supplementary Table 11, Supplementary Figure 4). Discussion The present study investigated the genetic architecture of screen behaviours among adolescents and their associations with mental disorders and educational attainment (EA). Leveraging one of the largest birth cohorts in the world ( 33 ), we demonstrate that screen behaviours are heritable, highly polygenic traits. Furthermore, we identified the first genomic loci associated with adolescent social media use. We also demonstrate that television watching, gaming, and social media use share genetic signals with EA and major psychiatric disorders. According to our results, three screen behaviors – television watching, gaming, and social media use – display significant nonzero h 2 SNP ( Figure 1 , Supplementary Table 3), which are generally in line with estimates for other behavioural traits ( 57 – 59 ). Furthermore, our estimates fall within the same range, but have more narrow standard errors than h 2 SNP reported for time spent on gaming, video watching and total screen time among children ( 8 ). To our knowledge, h 2 SNP estimates of social media use have not been reported before. Therefore, our results provide novel insights into the mechanisms behind screen behaviours. Our study provides new perspectives on the shared genetic basis between screen time use and mental-health related phenotypes. We demonstrate that each of the screen behaviors displayed significant genetic correlations with one or more major psychiatric disorders ( Figure 4 ). The most compelling pattern of associations was observed for ADHD, which has positive generic correlations with all three screen-based phenotypes. Although many studies have linked ADHD and problematic screen usage on a phenotypic level ( 60 , 61 ), genetic studies on the topic are scarce. Overall, our findings are consistent with previous results indicating that higher genetic liability for ADHD can contribute to longer screen time utilization, and that phenotypic association between screen time use and attention problems is partially explained by genetic factors ( 8 , 23 , 62 , 63 ). Notably, social media use displayed positive genetic correlation with both MDD and AUD, in addition to ADHD, but was negatively correlated with ASD. The mechanisms by which genetic factors may increase the susceptibility to both mental illness and screen behaviors remain to be uncovered. Another distinct pattern was observed for EA, which displayed highly significant negative genetic correlations with television watching, gaming, and social media use. The relationship between decline in academic performance and increased screen time is well documented ( 17 , 64 ), but there are no studies investigating the potentially shared genetic background underlying this association. Our results suggest that adolescents with a high load of common genetic variants predisposing to problematic screen use may also be at higher risk for lower EA. While we cannot conclude on the causal relationship underlying these associations, or the specific mechanisms involved, we can hypothesize that the association between screen use and EA may at least partly be mediated by attention difficulties, which is consistent with the significant genetic correlations observed for both EA and screen behaviors with ADHD. Indeed, sustained attention is a key cognitive ability that is critical to successful goal-oriented behavior and is linked to academic achievement ( 65 ). Children genetically predisposed to attention difficulties are not only prone to more problematic screen use but may face an increased risk of lower educational outcomes ( 62 , 66 , 67 ). Further studies are necessary to investigate the possible link with attention problems. Our sensitivity analysis indicate that the identified associations were not driven by participants with a history of mental illness in the screen behaviour GWASs (Supplementary Figure 4). Based on the current findings, we suggest that individuals with a high load of genetic risk factors for a particular psychiatric disorder (but not necessarily with the diagnosis itself) may be at higher risk for displaying more extreme screen behaviours. By combining GWAS summary statistics on social media use and EA ( 32 ) in the condFDR analysis ( 34 , 35 ), we enhanced discovery in the moderately powered social media use GWAS, and identified three genomic loci associated with social media use ( Figure 2 , Supplementary Table 8). The most significant variant (rs7110805, intergenic) is located on chromosome 11 and represents a broad region of associations ( Figure 3C ), which contains three protein-coding genes: MTMR2, FAM67B, and CEP57. MTMR2 encodes myotubularin-related protein 2 and is moderately expressed in the brain( 68 ), mostly in oligodendrocytes and neurons. Its neural functions remain unclear, though synaptically localized MTMR2 might maintain excitatory synapses by inhibiting excessive endosomal production and destructive trafficking to lysosomes ( 69 ). According to the Open Targets Genetics model ( 43 , 54 ), MTMR2 is also predicted to be likely casual (L2G pipeline scores > 0.6) for EA ( 70 , 71 ), consistent with our hypothesis about shared genetic factors between social media use and EA. FAM67B gene encodes its eponymous protein, with low brain expression levels. Nevertheless, it has a moderate chance to be casual at locus discovered in the GWAS of vertex-wise sulcal depth (L2G score = 0.54) ( 72 ). Finally, the CEP57 gene, which encodes centrosomal protein 57, is moderately expressed in neurons and fetal astrocytes ( 68 ). The Cep57 protein controls centriole duplication and centrosome maturation for faithful cell division. Interestingly, CEP57 has more than a 50% chance to be casual at loci discovered in GWAS for leisure television watching, cortical surface area, and cognitive aspects of EA ( 11 , 73 , 74 ). Again, these findings are consistent with our hypothesis of shared genetic basis between EA and screen behaviors. The second locus associated with social media use is represented by an intergenic variant rs359240 on chromosome 2, with no protein-coding genes in the direct vicinity ( Figure 3A ). Nevertheless, this variant has a CADD score of 19.6, suggesting high deleteriousness( 56 ). Interestingly, rs359240 crossed genome-wide suggestive significance threshold for several brain-related and behavioral traits, like smoking, risk-taking behavior, and age at first sexual intercourse and age at first live birth( 59 , 75 , 76 ). Finally, the third identified locus was located on chromosome 4, and is represented by an ncRNC intronic variant rs6848288, which has multiple LD-linked variants with low condFDR values ( Figure 3B ). The OpenTargets platform links rs6848288 most strongly to the SMARCAD1 gene . SMARCAD1 encodes its eponymous protein and participates in transcriptional regulation, heterochromatin maintenance, DNA repair, and replication, though the molecular basis of its role in these processes is not fully understood ( 77 ). The SMARCAD1 gene is weakly expressed in both human astrocytes and neurons ( 68 ). However, according to the L2G pipeline, this gene has more than a 70% chance to be causal at loci discovered in GWAS for cortical thickness and vertex-wise surface area ( 72 , 74 ). The link between putative causal genes for screen behaviors and brain structures measures is intriguing, especially in light of studies that identified associations between internet gaming disorder and structural and functional brain changes ( 78 ). Brain changes associated to a more moderate level of digital media use remain to be investigated, and further studies in adolescent population are warranted. Functional characterization of the identified loci by FUMA revealed two exonic nonsynymous SNPs (i.e., with a high probability of deleteriousness, as these variants change the produced protein’s amino acid sequence) located on chromosome 11 (Supplementary Table 9). However, like in GWAS on other complex human traits phenotypes, most of the identified candidate SNPs reside in non-coding DNA, suggesting a regulatory and more indirect influence on the phenotype ( 79 ). This may also suggest that the loci do not influence a distinct biological process but represent non-specific genetic effects common to several mental-health related phenotypes. These findings may also reflect insufficient statistical power. Nevertheless, follow-up studies are warranted to determine the specific causal genetic variants underlying the loci detected here. Replication of the identified variants for social media use was not possible due to the absence of comparable independent datasets. Nevertheless, we examined the identified loci in the association summary statistics from the TV-UKB and PC-UKB GWAS conducted in the UKB cohort. The lead SNPs rs359240 and rs7110805 had the same direction of effect as in the MoBa cohort and nominally significant p -values in the PC-UKB and TV-UKB samples, respectively. These findings are notable, given the considerable heterogeneity between the cohorts and different phenotypes. Nevertheless, we acknowledge the possibility that these results may reflect random variation rather than a definitive pattern and recognize this as a limitation. Our study is not without limitations. Generally, selection bias is a major challenge in cohort studies, and MoBa participants were found to not be representative of the entire Norwegian population ( 80 ). Moreover, we were not able to estimate potential discrepancies between self-reported and objectively measured screen time, and we did not have information about the content participants were engaging with during their screen use. The complex associations between phenotypes (attention difficulties and EA, as well as socioeconomic factors that might be linked both to EA and screen use, etc.) prevents us from translating the observed genetic correlations into actual pleiotropy. The initial set of single-trait GWASs performed in our study did not unambiguously identify any loci associated with screen behaviours, though many variants reached the suggestive threshold. We hold the view that this pattern of results is merely due to low power, despite the sample being substantially larger than any prior study of screen behaviours. Insufficient statistical power prevents us from using tools that can be highly valuable in identifying causal relationships or studying genetic overlap between traits (e.g., Mendelian Randomization and MiXeR). Therefore, we urge the research community to continue collecting large-scale data about screen-based activities, as it will greatly improve our understanding of one of the most widespread behavioural traits in the modern society. In summary, we demonstrated that television watching, gaming, and social media use are heritable, highly polygenic traits, which display significant genetic correlations with one or more major psychiatric disorders, and are negatively correlated with EA. Furthermore, we identified three genomic loci associated with adolescent social media use. Overall, our results provide new insights into the genetics of screen behaviours and may generate new hypotheses regarding the relationship between screen time use, mental health, and EA during adolescence. Data Availability The datasets supporting the conclusions of this article are available from the Norwegian Institute of Public Health, but restrictions apply to the availability of these data. The study website provides details on how to access data and information on the available variables (https://www.fhi.no/en/ch/studies/moba/for-forskere-artikler/research-and-data-access/). Conflict of Interest Professor Ole A. Andreassen has received speaker fees from Lundbeck, Janssen, Otsuka, and Sunovion, and is a consultant to Cortechs.ai, and Precision Health AS. Dr. Oleksandr Frei is a consultant to Precision Health AS. No potential conflict of interest was reported by other authors. Acknowledgements The Norwegian Mother, Father and Child Cohort Study is supported by the Norwegian Ministry of Health and Care Services and the Ministry of Education and Research. We are grateful to all the participating families in Norway who take part in this on-going cohort study. We also thank deCODE genetics for genotyping of the main part of the MoBa sample. This work was performed on Services for sensitive data (TSD), University of Oslo, Norway, with resources provided by UNINETT Sigma2 - the National Infrastructure for High Performance Computing and Data Storage in Norway. References 1. ↵ EUROSTAT . Individuals - frequency of internet use . 2023 [cited 2024 Oct 7 ]. 96% of young people in the EU uses the internet daily . Available from: https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20230714-1 2. ↵ Teens, Social Media and Technology 2023 | Pew Research Center [Internet] . [cited 2024 Oct 7 ]. Available from: https://www.pewresearch.org/internet/2023/12/11/teens-social-media-and-technology-2023/ 3. ↵ Plomin R , Corley R , Defries JC , Fulker DW . Individual Differences in Television Viewing in Early Childhood: Nature as Well as Nurture . Psychol Sci . 1990 ; 1 ( 6 ): 371 – 7 . OpenUrl CrossRef Web of Science 4. ↵ Polderman TJC , Benyamin B , De Leeuw CA , Sullivan PF , Van Bochoven A , Visscher PM , et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies . Nat Genet [Internet ]. 2015 May 18 [cited 2022 Aug 4 ]; 47 ( 7 ): 702 – 9 . Available from: https://www.nature.com/articles/ng.3285 OpenUrl 5. ↵ Nilsson A , Kuja-Halkola R , Lichtenstein P , Larsson H , Lundström S , Fatouros-Bergman H , et al. The genetics of gaming: A longitudinal twin study . JCPP Adv [Internet ]. 2023 Dec 1 [cited 2024 Oct 7 ]; 3 ( 4 ). Available from: /pmc/articles/PMC10694538/ 6. ↵ Vink JM , Van Beijsterveldt TCEM , Huppertz C , Bartels M , Boomsma DI . Heritability of compulsive Internet use in adolescents . Addict Biol [Internet ]. 2016 Mar 1 [cited 2024 Oct 8 ]; 21 ( 2 ): 460 – 8 . Available from: https://pubmed.ncbi.nlm.nih.gov/25582809/ OpenUrl 7. ↵ Li M , Chen J , Li N , Li X . A twin study of problematic internet use: Its heritability and genetic association with effortful control . Twin Res Hum Genet [Internet ]. 2014 [cited 2023 Aug 21 ]; 17 ( 4 ): 279 – 87 . Available from: https://pubmed.ncbi.nlm.nih.gov/24933598/ OpenUrl 8. ↵ Zhang Y , Choi KW , Delaney SW , Ge T , Pingault JB , Tiemeier H . Shared Genetic Risk in the Association of Screen Time With Psychiatric Problems in Children . JAMA Netw Open [Internet ]. 2023 [cited 2024 Oct 4 ]; 6 ( 11 ). Available from: https://pubmed.ncbi.nlm.nih.gov/37930702/ 9. ↵ Shek DTL , Yu L , Sun RCF , Fan Y . Molecular Genetics, Personality, and Internet Addiction Revisited . In: Internet Addiction . Springer International Publishing ; 2022 . p. 141 – 60 . 10. ↵ Haghighatfard A , Ghaderi AH , Mostajabi P , Kashfi SS , Mohabati somehsarayee H , Shahrani M , et al. The first genome-wide association study of internet addiction; Revealed substantial shared risk factors with neurodevelopmental psychiatric disorders . Res Dev Disabil . 2023 Feb 1 ; 133 : 104393 . OpenUrl PubMed 11. ↵ van de Vegte YJ , Said MA , Rienstra M , van der Harst P , Verweij N . Genome-wide association studies and Mendelian randomization analyses for leisure sedentary behaviours . Nat Commun [Internet ]. 2020 Apr 21 [cited 2023 Apr 25 ]; 11 ( 1 ): 1 – 10 . Available from: https://www.nature.com/articles/s41467-020-15553-w OpenUrl 12. ↵ Laricchia F. Statista . 2024 [cited 2024 Oct 8 ]. U.K. : Smartphone usage by age 2012-2023. Available from: https://www.statista.com/statistics/300402/smartphone-usage-in-the-uk-by-age/ 13. ↵ Collishaw S . Annual research review: Secular trends in child and adolescent mental health . J Child Psychol Psychiatry [Internet ]. 2015 Mar 1 [cited 2024 Oct 8 ]; 56 ( 3 ): 370 – 93 . Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/jcpp.12372 OpenUrl 14. World Health Organization . Mental health of adolescents . [cited 2023 Aug 21 ]; Available from: https://www.who.int/news-room/fact-sheets/detail/adolescent-mental-health 15. ↵ Prinstein MJ , Nesi J , Telzer EH . Commentary: An updated agenda for the study of digital media use and adolescent development – future directions following Odgers & Jensen (2020) [Internet]. Vol. 61, Journal of Child Psychology and Psychiatry . J Child Psychol Psychiatry ; 2020 [cited 2024 Oct 8 ]. p. 349 – 52 . Available from: https://pubmed.ncbi.nlm.nih.gov/32064633/ 16. ↵ Stiglic N , Viner RM . Effects of screentime on the health and well-being of children and adolescents: A systematic review of reviews [Internet] . Vol. 9 , BMJ Open . British Medical Journal Publishing Group; 2019 [cited 2023 Aug 22 ]. p. e023191 . Available from: https://bmjopen.bmj.com/content/9/1/e023191 OpenUrl Abstract / FREE Full Text 17. ↵ Paulich KN , Ross JM , Lessem JM , Hewitt JK . Screen time and early adolescent mental health, academic, and social outcomes in 9- And 10-year old children: Utilizing the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study . PLoS One [Internet] . 2021 Sep 1 [cited 2023 Aug 10 ]; 16 ( 9 September ): e0256591 . Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0256591 OpenUrl 18. Santos RMS , Mendes CG , Sen Bressani GY , de Alcantara Ventura S , de Almeida Nogueira YJ , de Miranda DM , et al. The associations between screen time and mental health in adolescents: a systematic review . BMC Psychol [Internet ]. 2023 Dec 1 [cited 2024 Sep 30 ]; 11 ( 1 ). Available from: /pmc/articles/PMC10117262/ 19. ↵ Oswald TK , Rumbold AR , Kedzior SGE , Moore VM . Psychological impacts of “screen time” and “green time” for children and adolescents: A systematic scoping review [Internet] . Vol. 15 , PLoS ONE . PLOS; 2020 [cited 2023 Apr 25 ]. Available from: /pmc/articles/PMC7473739/ 20. ↵ Andreassen OA , Hindley GFL , Frei O , Smeland OB . New insights from the last decade of research in psychiatric genetics: discoveries, challenges and clinical implications . World Psychiatry [Internet ]. 2023 Feb 1 [cited 2023 Aug 10 ]; 22 ( 1 ): 4 – 24 . Available from: https://pubmed.ncbi.nlm.nih.gov/36640404/ OpenUrl 21. Faraone S V. , Larsson H . Genetics of attention deficit hyperactivity disorder [Internet] . Vol. 24 , Molecular Psychiatry . Mol Psychiatry; 2019 [cited 2023 Apr 25 ]. p. 562 – 75 . Available from: https://pubmed.ncbi.nlm.nih.gov/29892054/ OpenUrl CrossRef PubMed 22. ↵ Manoli DS , State MW . Autism spectrum disorder genetics and the search for pathological mechanisms [Internet] . Vol. 178 , American Journal of Psychiatry . Am J Psychiatry; 2021 [cited 2023 Apr 25 ]. p. 30 – 8 . Available from: https://pubmed.ncbi.nlm.nih.gov/33384012/ OpenUrl CrossRef PubMed 23. ↵ Frei E , Jaholkowski PP , Parekh P , Frei O , Shadrin AA , … The relationship between screen-based behaviors and adolescent mental health: a phenotypic and genetic analysis . medRxiv [Internet] . 2023 Sep 14 [cited 2024 Oct 14 ]; 2023.09.14.23295537 . Available from: https://www.medrxiv.org/content/10.1101/2023.09.14.23295537v1 24. ↵ Zhang Y , Choi KW , Delaney SW , Ge T , Pingault JB , Tiemeier H . Shared Genetic Risk in the Association of Screen Time With Psychiatric Problems in Children . JAMA Netw Open [Internet ]. 2023 [cited 2024 Jan 11 ]; 6 ( 11 ). Available from: https://pubmed.ncbi.nlm.nih.gov/37930702/ 25. ↵ Ayorech Z , Baldwin JR , Pingault JB , Rimfeld K , Plomin R . Gene-environment correlations and genetic confounding underlying the association between media use and mental health . Sci Rep [Internet ]. 2023 [cited 2024 Jan 11 ]; 13 ( 1 ): 1030 . Available from : doi: 10.1038/s41598-022-25374-0 OpenUrl CrossRef PubMed 26. ↵ Adelantado-Renau M , Moliner-Urdiales D , Cavero-Redondo I , Beltran-Valls MR , Martínez-Vizcaíno V , Álvarez-Bueno C . Association between Screen Media Use and Academic Performance among Children and Adolescents: A Systematic Review and Meta-analysis . JAMA Pediatr [Internet ]. 2019 Nov 1 [cited 2024 Oct 8 ]; 173 ( 11 ): 1058 – 67 . Available from: /pmc/articles/PMC6764013/ OpenUrl 27. ↵ Gordon MS , Ohannessian CMC . Social Media Use and Early Adolescents’ Academic Achievement: Variations by Parent-Adolescent Communication and Gender . Youth Soc [Internet ]. 2024 Jun 21 [cited 2024 Oct 8 ]; 56 ( 4 ): 651 – 72 . Available from: https://journals.sagepub.com/doi/10.1177/0044118X231180317 OpenUrl 28. Tremblay MS , LeBlanc AG , Kho ME , Saunders TJ , Larouche R , Colley RC , et al. Systematic review of sedentary behaviour and health indicators in school-aged children and youth [Internet] . Vol. 8 , International Journal of Behavioral Nutrition and Physical Activity . BioMed Central; 2011 [cited 2024 Oct 8 ]. p. 1 – 22 . Available from: https://ijbnpa.biomedcentral.com/articles/10.1186/1479-5868-8-98 OpenUrl CrossRef 29. ↵ Ferguson CJ . Do Angry Birds Make for Angry Children? A Meta-Analysis of Video Game Influences on Children’s and Adolescents’ Aggression, Mental Health, Prosocial Behavior, and Academic Performance . Perspect Psychol Sci [Internet ]. 2015 Sep 17 [cited 2024 Oct 8 ]; 10 ( 5 ): 646 – 66 . Available from: https://journals.sagepub.com/doi/10.1177/1745691615592234 OpenUrl 30. ↵ Santos RMS , Mendes CG , Marques Miranda D , Romano-Silva MA . The Association between Screen Time and Attention in Children: A Systematic Review [Internet] . Vol. 47 , Developmental Neuropsychology . Dev Neuropsychol; 2022 [cited 2024 Oct 8 ]. p. 175 – 92 . Available from: https://pubmed.ncbi.nlm.nih.gov/35430923/ OpenUrl CrossRef PubMed 31. ↵ Silventoinen K , Jelenkovic A , Sund R , Latvala A , Honda C , Inui F , et al. Genetic and environmental variation in educational attainment: an individual-based analysis of 28 twin cohorts . Sci Rep [Internet ]. 2020 Jul 29 [cited 2024 Oct 8 ]; 10 ( 1 ): 1 – 11 . Available from: https://www.nature.com/articles/s41598-020-69526-6 OpenUrl 32. ↵ Okbay A , Wu Y , Wang N , Jayashankar H , Bennett M , Nehzati SM , et al. Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals . Nat Genet [Internet ]. 2022 Apr 1 [cited 2024 Oct 8 ]; 54 ( 4 ): 437 – 49 . Available from: /pmc/articles/PMC9005349/ OpenUrl 33. ↵ Magnus P , Birke C , Vejrup K , Haugan A , Alsaker E , Daltveit AK , et al. Cohort Profile Update: The Norwegian Mother and Child Cohort Study (MoBa) . Int J Epidemiol [Internet ]. 2016 Apr 11 [cited 2022 Aug 5 ]; 45 ( 2 ): 382 – 8 . Available from: https://pubmed.ncbi.nlm.nih.gov/27063603/ OpenUrl 34. ↵ Andreassen OA , Thompson WK , Schork AJ , Ripke S , Mattingsdal M , Kelsoe JR , et al. Improved Detection of Common Variants Associated with Schizophrenia and Bipolar Disorder Using Pleiotropy-Informed Conditional False Discovery Rate . PLoS Genet [Internet ]. 2013 Apr [cited 2022 Aug 18 ]; 9 ( 4 ): e1003455 . Available from: https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1003455 OpenUrl 35. ↵ Smeland OB , Frei O , Shadrin A , O’Connell K , Fan CC , Bahrami S , et al. Discovery of shared genomic loci using the conditional false discovery rate approach [Internet] . Vol. 139 , Human Genetics . Hum Genet; 2020 [cited 2024 Sep 30 ]. p. 85 – 94 . Available from: https://pubmed.ncbi.nlm.nih.gov/31520123/ OpenUrl CrossRef PubMed 36. ↵ Corfield EC , Shadrin AA , Frei O , Rahman Z , Lin A , Athanasiu L , et al. The Norwegian Mother, Father, and Child cohort study (MoBa) genotyping data resource: MoBaPsychGen pipeline v.1 . bioRxiv [Internet] . 2024 May 7 [cited 2024 Oct 1 ]; Available from: https://www.biorxiv.org/content/10.1101/2022.06.23.496289v4 37. ↵ Purcell S , Neale B , Todd-Brown K , Thomas L , Ferreira MAR , Bender D , et al. PLINK: A tool set for whole-genome association and population-based linkage analyses . Am J Hum Genet [Internet ]. 2007 [cited 2024 Oct 10 ]; 81 ( 3 ): 559 – 75 . Available from: /pmc/articles/PMC1950838/ OpenUrl 38. ↵ ABCD Study [Internet] . [cited 2024 Dec 5 ]. Available from: https://abcdstudy.org/ 39. ↵ Gadin JR , Zetterberg R , Meijsen J , Schork AJ . Zenodo . 2023 . Cleansumstats: Converting GWAS sumstats to a common format to facilitate downstream applications . Available from: https://github.com/BioPsyk/cleansumstats 40. ↵ Akdeniz BC , Frei O , Hagen E , Filiz TT , Karthikeyan S , Pasman J , et al. COGEDAP: A COmprehensive GEnomic Data Analysis Platform . arXiv: 221214103 [Internet] . 2022 Dec 28 [cited 2024 Oct 10 ]; Available from: https://arxiv.org/abs/2212.14103v1 41. ↵ Watanabe K , Taskesen E , Van Bochoven A , Posthuma D . Functional mapping and annotation of genetic associations with FUMA . Nat Commun [Internet ]. 2017 Nov 28 [cited 2024 Oct 14 ]; 8 ( 1 ): 1 – 11 . Available from: https://www.nature.com/articles/s41467-017-01261-5 OpenUrl 42. ↵ Auton A , Abecasis GR , Altshuler DM , Durbin RM , Bentley DR , Chakravarti A , et al. A global reference for human genetic variation . Nature [Internet ]. 2015 Sep 30 [cited 2024 Oct 14 ]; 526 ( 7571 ): 68 – 74 . Available from: https://pubmed.ncbi.nlm.nih.gov/26432245/ OpenUrl 43. ↵ Ochoa D , Hercules A , Carmona M , Suveges D , Baker J , Malangone C , et al. The next-generation Open Targets Platform: reimagined, redesigned, rebuilt . Nucleic Acids Res [Internet ]. 2023 Jan 6 [cited 2024 Oct 14 ]; 51 ( D1 ): D1353 – 9 . Available from: https://pubmed.ncbi.nlm.nih.gov/36399499/ OpenUrl 44. ↵ Brain RNA-Seq [Internet] . [cited 2024 Dec 4 ]. Available from: https://brainrnaseq.org/ 45. ↵ Bulik-Sullivan B , Finucane HK , Anttila V , Gusev A , Day FR , Loh PR , et al. An atlas of genetic correlations across human diseases and traits . Nat Genet [Internet ]. 2015 Sep 28 [cited 2024 Sep 30 ]; 47 ( 11 ): 1236 – 41 . Available from: https://www.nature.com/articles/ng.3406 OpenUrl 46. ↵ Yang J , Lee SH , Goddard ME , Visscher PM . GCTA: A tool for genome-wide complex trait analysis . Am J Hum Genet [Internet ]. 2011 Jan 1 [cited 2023 Mar 6 ]; 88 ( 1 ): 76 – 82 . Available from: /pmc/articles/PMC3014363/ OpenUrl 47. ↵ Yang J , Benyamin B , McEvoy BP , Gordon S , Henders AK , Nyholt DR , et al. Common SNPs explain a large proportion of the heritability for human height . Nat Genet [Internet ]. 2010 Jul [cited 2022 Aug 5 ]; 42 ( 7 ): 565 – 9 . Available from: https://pubmed.ncbi.nlm.nih.gov/20562875/ OpenUrl 48. ↵ Mullins N , Forstner AJ , O’Connell KS , Coombes B , Coleman JRI , Qiao Z , et al. Genome-wide association study of over 40,000 bipolar disorder cases provides new insights into the underlying biology . Nat Genet [Internet ]. 2021 Jun 1 [cited 2023 Aug 10 ]; 53 ( 6 ): 817 – 29 . Available from: /pmc/articles/PMC8192451/ OpenUrl 49. Grove J , Ripke S , Als TD , Mattheisen M , Walters RK , Won H , et al. Identification of common genetic risk variants for autism spectrum disorder . Nat Genet [Internet ]. 2019 Mar 1 [cited 2023 Aug 10 ]; 51 ( 3 ): 431 – 44 . Available from: https://pubmed.ncbi.nlm.nih.gov/30804558/ OpenUrl 50. Demontis D , Walters GB , Athanasiadis G , Walters R , Therrien K , Nielsen TT , et al. Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains . Nat Genet [Internet ]. 2023 Jan 26 [cited 2024 Nov 11 ]; 55 ( 2 ): 198 – 208 . Available from: https://www.nature.com/articles/s41588-022-01285-8 OpenUrl 51. Levey DF , Stein MB , Wendt FR , Pathak GA , Zhou H , Aslan M , et al. Bi-ancestral depression GWAS in the Million Veteran Program and meta-analysis in >1.2 million individuals highlight new therapeutic directions . Nat Neurosci [Internet ]. 2021 May 27 [cited 2024 Nov 11 ]; 24 ( 7 ): 954 – 63 . Available from: https://www.nature.com/articles/s41593-021-00860-2 OpenUrl 52. Trubetskoy V , Pardiñas AF , Qi T , Panagiotaropoulou G , Awasthi S , Bigdeli TB , et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia . Nature [Internet ]. 2022 Apr 8 [cited 2023 Aug 10 ]; 604 ( 7906 ): 502 – 8 . Available from: https://www.nature.com/articles/s41586-022-04434-5 OpenUrl 53. ↵ Kranzler HR , Zhou H , Kember RL , Vickers Smith R , Justice AC , Damrauer S , et al. Genome-wide association study of alcohol consumption and use disorder in 274,424 individuals from multiple populations . Nat Commun [Internet ]. 2019 Apr 2 [cited 2024 Nov 11 ]; 10 ( 1 ): 1 – 11 . Available from: https://www.nature.com/articles/s41467-019-09480-8 OpenUrl CrossRef 54. ↵ Mountjoy E , Schmidt EM , Carmona M , Schwartzentruber J , Peat G , Miranda A , et al. An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci . Nat Genet [Internet ]. 2021 Oct 28 [cited 2024 Nov 4 ]; 53 ( 11 ): 1527 – 33 . Available from: https://www.nature.com/articles/s41588-021-00945-5 OpenUrl 55. ↵ Ghoussaini M , Mountjoy E , Carmona M , Peat G , Schmidt EM , Hercules A , et al. Open Targets Genetics: Systematic identification of trait-associated genes using large-scale genetics and functional genomics . Nucleic Acids Res [Internet ]. 2021 Jan 8 [cited 2024 Nov 4 ]; 49 ( D1 ): D1311 – 20 . Available from : doi: 10.1093/nar/gkaa840 OpenUrl CrossRef 56. ↵ Kircher M , Witten DM , Jain P , O’roak BJ , Cooper GM , Shendure J . A general framework for estimating the relative pathogenicity of human genetic variants . Nat Genet [Internet ]. 2014 [cited 2024 Oct 16 ]; 46 ( 3 ): 310 – 5 . Available from: /pmc/articles/PMC3992975/ OpenUrl 57. ↵ Liu M , Jiang Y , Wedow R , Li Y , Brazel DM , Chen F , et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use [Internet] . Vol. 51 , Nature Genetics . Nat Genet; 2019 [cited 2024 Oct 29 ]. p. 237 – 44 . Available from: https://pubmed.ncbi.nlm.nih.gov/30643251/ OpenUrl CrossRef PubMed 58. Day FR , Ong KK , Perry JRB . Elucidating the genetic basis of social interaction and isolation . Nat Commun [Internet ]. 2018 Jul 3 [cited 2024 Oct 29 ]; 9 ( 1 ): 1 – 6 . Available from: https://www.nature.com/articles/s41467-018-04930-1 OpenUrl CrossRef 59. ↵ Clifton EAD , Perry JRB , Imamura F , Lotta LA , Brage S , Forouhi NG , et al. Genome– wide association study for risk taking propensity indicates shared pathways with body mass index . Commun Biol [Internet ]. 2018 May 3 [cited 2024 Oct 29 ]; 1 ( 1 ): 1 – 10 . Available from: https://www.nature.com/articles/s42003-018-0042-6 OpenUrl 60. ↵ Dekkers TJ , van Hoorn J . Understanding Problematic Social Media Use in Adolescents with Attention-Deficit/Hyperactivity Disorder (ADHD): A Narrative Review and Clinical Recommendations [Internet] . Vol. 12 , Brain Sciences . MDPI; 2022 [cited 2024 Oct 29 ]. p. 1625 . Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC9776226/ OpenUrl PubMed 61. ↵ Thorell LB , Burén J , Ström Wiman J , Sandberg D , Nutley SB . Longitudinal associations between digital media use and ADHD symptoms in children and adolescents: a systematic literature review [Internet] . Vol. 1 , European Child and Adolescent Psychiatry . Springer Science and Business Media Deutschland GmbH; 2022 [cited 2023 Apr 25 ]. p. 1 – 24 . Available from: https://link.springer.com/article/10.1007/s00787-022-02130-3 OpenUrl CrossRef 62. ↵ Yang A , Rolls ET , Dong G , Du J , Li Y , Feng J , et al. Longer screen time utilization is associated with the polygenic risk for Attention-deficit/hyperactivity disorder with mediation by brain white matter microstructure . eBioMedicine . 2022 Jun 1 ; 80 : 104039 . OpenUrl PubMed 63. ↵ Lee SA , Hur YM . Common Genetic Influence on the Relationship Between Gaming Addiction and Attention Deficit Hyperactivity Disorder in Young Adults: A Twin Study . Twin Res Hum Genet [Internet ]. 2024 Oct 28 [cited 2024 Oct 29 ]; 1 – 6 . Available from: https://pubmed.ncbi.nlm.nih.gov/39463167/ 64. ↵ Peiró-Velert C , Valencia-Peris A , González LM , García-Massó X , Serra-Añó P , Devís-Devís J . Screen media usage, sleep time and academic performance in adolescents: Clustering a self-organizing maps analysis . PLoS One [Internet ]. 2014 Jun 18 [cited 2024 Oct 29 ]; 9 ( 6 ): e99478 . Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0099478 OpenUrl 65. ↵ Gallen CL , Schaerlaeken S , Younger JW , Younger JW , O’Laughlin KD , Anguera JA , et al. Contribution of sustained attention abilities to real-world academic skills in children . Sci Rep [Internet ]. 2023 Feb 15 [cited 2024 Oct 30 ]; 13 ( 1 ): 1 – 11 . Available from: https://www.nature.com/articles/s41598-023-29427-w OpenUrl 66. ↵ Pingault JB , Tremblay RE , Vitaro F , Carbonneau R , Genolini C , Falissard B , et al. Childhood trajectories of inattention and hyperactivity and prediction of educational attainment in early adulthood: A 16-year longitudinal population-based study . Am J Psychiatry [Internet ]. 2011 [cited 2024 Oct 30 ]; 168 ( 11 ): 1164 – 70 . Available from: https://pubmed.ncbi.nlm.nih.gov/21799065/ OpenUrl 67. ↵ Polderman TJC , Boomsma DI , Bartels M , Verhulst FC , Huizink AC . A systematic review of prospective studies on attention problems and academic achievement: Review [Internet] . Vol. 122 , Acta Psychiatrica Scandinavica . Acta Psychiatr Scand; 2010 [cited 2024 Oct 30 ]. p. 271 – 84 . Available from: https://pubmed.ncbi.nlm.nih.gov/20491715/ OpenUrl CrossRef PubMed Web of Science 68. ↵ Zhang Y , Sloan SA , Clarke LE , Caneda C , Plaza CA , Blumenthal PD , et al. Purification and Characterization of Progenitor and Mature Human Astrocytes Reveals Transcriptional and Functional Differences with Mouse . Neuron [Internet ]. 2016 Jan 6 [cited 2024 Oct 29 ]; 89 ( 1 ): 37 – 53 . Available from: http://www.cell.com/article/S0896627315010193/fulltext OpenUrl 69. ↵ Lee HW , Kim Y , Han K , Kim H , Kim E . The phosphoinositide 3-phosphatase MTMR2 interacts with PSD-95 and maintains excitatory synapses by modulating endosomal traffic . J Neurosci [Internet ]. 2010 Apr 21 [cited 2024 Oct 29 ]; 30 ( 16 ): 5508 – 18 . Available from: https://pubmed.ncbi.nlm.nih.gov/20410104/ OpenUrl 70. ↵ Kichaev G , Bhatia G , Loh PR , Gazal S , Burch K , Freund MK , et al. Leveraging Polygenic Functional Enrichment to Improve GWAS Power . Am J Hum Genet [Internet ]. 2019 Dec 27 [cited 2024 Oct 30 ]; 104 ( 1 ): 65 – 75 . Available from: https://europepmc.org/articles/PMC6323418 OpenUrl 71. ↵ Lee JJ , Wedow R , Okbay A , Kong E , Maghzian O , Zacher M , et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals . Nat Genet [Internet ]. 2018 Jul 23 [cited 2024 Oct 30 ]; 50 ( 8 ): 1112 – 21 . Available from: https://europepmc.org/articles/PMC6393768 OpenUrl 72. ↵ Van Der Meer D , Kaufmann T , Shadrin AA , Makowski C , Frei O , Roelfs D , et al. The genetic architecture of human cortical folding . Sci Adv [Internet ]. 2021 Dec 15 [cited 2024 Oct 30 ]; 7 ( 51 ): eabj9446 – eabj9446 . Available from: https://europepmc.org/articles/PMC8673767 OpenUrl 73. ↵ Demange PA , Malanchini M , Mallard TT , Biroli P , Cox SR , Grotzinger AD , et al. Investigating the genetic architecture of noncognitive skills using GWAS-by-subtraction . Nat Genet [Internet ]. 2021 Jan 7 [cited 2024 Oct 30 ]; 53 ( 1 ): 35 – 44 . Available from: https://europepmc.org/articles/PMC7116735 OpenUrl 74. ↵ Shadrin AA , Kaufmann T , van der Meer D , Palmer CE , Makowski C , Loughnan R , et al. Vertex-wise multivariate genome-wide association study identifies 780 unique genetic loci associated with cortical morphology . Neuroimage [Internet ]. 2021 Sep 21 [cited 2024 Oct 30 ]; 244 : 118603 – 118603 . Available from: https://europepmc.org/articles/PMC8785963 OpenUrl 75. ↵ Mills MC , Tropf FC , Brazel DM , van Zuydam N , Vaez A , Agbessi M , et al. Identification of 371 genetic variants for age at first sex and birth linked to externalising behaviour . Nat Hum Behav [Internet ]. 2021 Jul 1 [cited 2024 Oct 30 ]; 5 ( 12 ): 1717 – 30 . Available from: https://europepmc.org/articles/PMC7612120 OpenUrl 76. ↵ Pan UKBB Team . Pan UKBB [Internet] . [cited 2024 Nov 11 ]. Available from: https://pan.ukbb.broadinstitute.org/ 77. ↵ Sachs P , Bergmaier P , Treutwein K , Mermoud JE . The Conserved Chromatin Remodeler SMARCAD1 Interacts with TFIIIC and Architectural Proteins in Human and Mouse . Genes (Basel) [Internet ]. 2023 Sep 1 [cited 2024 Oct 30 ]; 14 ( 9 ): 1793 . Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC10530723/ OpenUrl 78. ↵ Jeong BS , Han DH , Kim SM , Lee SW , Renshaw PF . White matter connectivity and Internet gaming disorder . Addict Biol [Internet] . 2016 May 1 [cited 2024 Oct 30 ]; 21 ( 3 ): 732 – 42 . Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC5609481/ OpenUrl 79. ↵ Sullivan PF , Geschwind DH . Defining the Genetic, Genomic, Cellular, and Diagnostic Architectures of Psychiatric Disorders [Internet] . Vol. 177 , Cell . Cell; 2019 [cited 2022 Aug 10 ]. p. 162 – 83 . Available from: https://pubmed.ncbi.nlm.nih.gov/30901538/ OpenUrl CrossRef PubMed 80. ↵ Nilsen RM , Vollset SE , Gjessing HK , Skjærven R , Melve KK , Schreuder P , et al. Self-selection and bias in a large prospective pregnancy cohort in Norway . Paediatr Perinat Epidemiol [Internet] . 2009 Nov 1 [cited 2023 Aug 24 ]; 23 ( 6 ): 597 – 608 . Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1365-3016.2009.01062.x OpenUrl View the discussion thread. Back to top Previous Next Posted January 07, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Genome-wide analysis of screen behaviors among adolescents identifies novel loci and overlap with educational attainment and mental disorders Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Genome-wide analysis of screen behaviors among adolescents identifies novel loci and overlap with educational attainment and mental disorders Evgeniia Frei , Tahir Tekin Filiz , Oleksandr Frei , Robert Loughnan , Piotr Jaholkowski , Nora R. Bakken , Viktoria Birkenæs , Alexey A. Shadrin , Helga Ask , Ole A. Andreassen , Olav B. Smeland medRxiv 2025.01.07.25320110; doi: https://doi.org/10.1101/2025.01.07.25320110 Share This Article: Copy Citation Tools Genome-wide analysis of screen behaviors among adolescents identifies novel loci and overlap with educational attainment and mental disorders Evgeniia Frei , Tahir Tekin Filiz , Oleksandr Frei , Robert Loughnan , Piotr Jaholkowski , Nora R. Bakken , Viktoria Birkenæs , Alexey A. Shadrin , Helga Ask , Ole A. Andreassen , Olav B. Smeland medRxiv 2025.01.07.25320110; doi: https://doi.org/10.1101/2025.01.07.25320110 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Psychiatry and Clinical Psychology Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (297) Cardiovascular Medicine (4421) Dentistry and Oral Medicine (443) Dermatology (382) Emergency Medicine (606) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1507) Epidemiology (15212) Forensic Medicine (30) Gastroenterology (1121) Genetic and Genomic Medicine (6581) Geriatric Medicine (667) Health Economics (996) Health Informatics (4520) Health Policy (1366) Health Systems and Quality Improvement (1611) Hematology (539) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15906) Intensive Care and Critical Care Medicine (1103) Medical Education (620) Medical Ethics (144) Nephrology (667) Neurology (6580) Nursing (345) Nutrition (998) Obstetrics and Gynecology (1141) Occupational and Environmental Health (956) Oncology (3324) Ophthalmology (970) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (663) Pediatrics (1689) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5433) Public and Global Health (9212) Radiology and Imaging (2193) Rehabilitation Medicine and Physical Therapy (1368) Respiratory Medicine (1194) Rheumatology (593) Sexual and Reproductive Health (709) Sports Medicine (529) Surgery (709) Toxicology (99) Transplantation (288) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ff5777d183d4807',t:'MTc3OTM4NjI4MA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();