Myocardial tissue characterization in ischemic heart disease introduces a set of narrative review and original articles by world renowned authors demonstrating such novel advancements and the state-of-the-art techniques in cardiac imaging.The widespread clinical use of cardiovascular imaging inspires constant improvement in imaging technology and post-processing applications. Recent advances in hardware and software have brought about important developments in the assessment of myocardial ischemia such as the rapid evaluation of cardiac volumes and function, ability for detection of subtle myocardial changes, and the combination of anatomic and functional assessment of a coronary artery stenosis via a single modality, which was previously not possible in a noninvasive fashion. These milestones indicate the start of a new era, a paradigm shift that broadens the role of noninvasive imaging. The thematic series  Myocardial tissue characterization in ischemic heart disease. Thematic series are a concept that European Radiology Experimental has adopted to examine and highlight advances in various technical and clinical areas of radiology. While the current pandemic clearly has an ongoing impact on academic productivity causing delays on every level of the publication process, we are grateful to the excellent authors for their tremendous effort to deliver these high-quality articles even under such challenging circumstances. We have the pleasure to kick off our thematic series with a combination of multiple narrative reviews and an original research article.Our editorial introduces a thematic series titled Imaging myocardial ischemia is a vibrant and exciting field of radiology research and clinical care. We spent the last two decades pursuing this field and have witnessed the development and evolution of cardiovascular imaging technology. Over the years, we have seen advances that made modalities more gentle , safer , faster The foundation of this thematic series is built upon three narrative review articles that cover emerging and clinically established imaging technologies used for myocardial ischemia assessment featuring authors from multiple institutions across the globe.Emerging methods for the characterization of ischemic heart disease: ultrafast Doppler angiography, micro-CT, photon-counting CT, novel MRI and PET techniques, and artificial intelligence by Willemink et al. from Stanford University, USA, provides an overview of techniques still in development but with high translational potential [in vivo human cardiac imaging. Second, the potentials of human myocardial imaging at low (≤ 0.35) and ultra-high (7 Tesla) magnetic fields are discussed, featuring the most important benefits and shortcomings of these technologies, new contrast agents with optimal relaxivity profiles at the respective fields, and image examples from Dr. Litt, from the University of Pennsylvania. Finally, this article takes a bit of an unconventional turn by summarizing the current status of artificial intelligence applications in cardiac imaging. While this topic is not strictly related to imaging technology, the plethora of artificial intelligence-based developments in image post-processing have been changing the way we interpret information and what additional information we can derive from images.The first narrative review, titled otential . There aotential , provideComputed tomography for myocardial characterization in ischemic heart disease: a state-of-the-art review, is delivered by van Assen et al. from Emory University, Atlanta, USA [The second review paper in this thematic series, titled nta, USA . This arState of the art CMR for myocardial characterization in ischemic heart disease, is presented by Emrich et al. from Mainz University, Germany [Lastly, the third paper of this review series,  Germany . Cardiac Germany . Such fa Germany . Contrar Germany . T1 and Image quality of late gadolinium enhancement in cardiac magnetic resonance with different doses of contrast material in patients with chronic myocardial infarction, submitted by a group from Milan under this thematic series at European Radiology Experimental [We are also delighted to announce an original research paper, entitled rimental . In thisrimental , the useIn conclusion, the articles in this thematic series demonstrate the growing potential of noninvasive techniques that drive imaging of myocardial ischemia into a direction of fast and quantitative assessment of the myocardium. While it would be too early to predict the future potential of new imaging technology, there is a clear trend towards making cardiac imaging feasible on platforms that were previously unimaginable, such as ultra-high MR fields and spectral CT. These techniques may represent the next milestones in noninvasive imaging where we have the potential to reach substantially improved spatial and temporal resolutions and image contrast, as well as establish currently unknown quantitative biomarkers. Improvements to imaging techniques used in clinical standard of care are expected to gain further efficiency and become more gentle to patients by applying less radiation and contrast. The role artificial intelligence will play in the daily workflow of radiologists is not clear yet, however, developments are targeting both image assessment and image acquisition, by employing artificial intelligence-based algorithms to enhance and optimize patient-centered scan protocols.Finally, we invite the radiology community to help grow this thematic series by submitting original research papers that provide us with the latest and greatest in advanced cardiac imaging and help keep this topical collection on our horizon.


The resources generated by the GTEx consortium offer unprecedented opportunities to advance our understanding of the biology of human diseases. Here, we present an in-depth examination of the phenotypic consequences of transcriptome regulation and a blueprint for the functional interpretation of genome-wide association study-discovered loci. Across a broad set of complex traits and diseases, we demonstrate widespread dose-dependent effects of RNA expression and splicing. We develop a data-driven framework to benchmark methods that prioritize causal genes and find no single approach outperforms the combination of multiple approaches. Using colocalization and association approaches that take into account the observed allelic heterogeneity of gene expression, we propose potential target genes for 47% (2519 out of 5385) of the GWAS loci examined.The online version contains supplementary material available at (10.1186/s13059-020-02252-4). In the last decade, the number of reproducible genetic associations with complex human traits that have emerged from genome-wide association studies (GWAS) has substantially grown. Many of the identified associations lie in non-coding regions of the genome, suggesting that they influence disease pathophysiology and complex traits via gene regulatory changes. Integrative studies of molecular quantitative trait loci (QTL)  have estLarge-scale international efforts such as the Genotype-Tissue Expression (GTEx) Consortium have provided an atlas of the regulatory landscape of gene expression and splicing variation in a broad collection of primary human tissues –9. Nearlenloc [coloc [One of the primary tools for the functional interpretation of GWAS associations has been the integrative analysis of molecular QTLs. Colocalization approaches that seek to establish shared causal variants , enrichc [coloc  and QTLEc [coloc ), or medc [coloc , TWAS [1c [coloc , and Prec [coloc ) have prc [coloc . Their aHere, we present methods and resources that help elucidate how genetic variants associated with gene expression (cis-eQTLs) or splicing (cis-sQTLs) contribute to, or mediate, the functional mechanisms underlying a wide array of complex diseases and quantitative traits. Since splicing QTLs have largely been understudied, we perform a comprehensive integrative study of this class of QTLs, in a broad collection of tissues, and disease associations. We provide predictions of functional mechanisms for 74 distinct complex traits from 87 GWA study results and demonstrate independent validation and evaluation of findings using likely causal gene-disease relationships in the Online Mendelian Inheritance of Man (OMIM) database. Notably, we find widespread dose-dependent effects of cis-QTLs on traits through multiple lines of evidence. We examine the importance of considering, or correcting for, false functional links attributed to GWAS loci due to neighboring but distinct causal variants. We call this confounding LD contamination for the remainder of the paper. To identify predicted causal effects among the complex trait-associated QTLs, we conduct systematic evaluation across different methods. Furthermore, we provide guidelines for employing complementary methods to map the regulatory mechanisms underlying genetic associations with complex traits.The final GTEx data release (v8) included 54 primary human tissues, 49 of which included at least 70 samples with both whole genome sequencing (WGS) and tissue-specific RNA-seq data. A total of 15,253 samples from 838 individuals were used for cis-QTL mapping . After data harmonization, format standardization, missing data imputation, and other quality assurance steps . For this purpose, we applied colocalization, enrichment, and association analyses, and provide a resource to enable investigations into gene prioritization approaches for disease associations.p value thresholds. For example, with a p value threshold of 0.05, over 97% of common variants will be assigned some gene in some tissue associated at that level.Gene expression and alternative splicing dysregulations have been proposed as the underlying mechanism of the association signals in many diseases , 21–24. βgene. That is, a null hypothesis of no dose-dependent effect is equivalent to βgene=0.Nevertheless, enrichment studies can be confounded by many unknown factors. Therefore, we sought to gather stronger evidence for a causal link by testing whether there is a dose-dependent effect of expression and splicing QTLs on complex traits. Figure To reduce unnecessary noise in the analysis, we included only the most likely causal e/sVariant within each credible set as determined by the e/sQTL fine-mapping .γ|,|δ|)) across all fine-mapped variants (for any gene) for each trait-tissue pair. As hypothesized, we found, consistently across all tissue-trait pairs, a positive correlation between the GWAS and QTL effects, which was significantly larger than the permuted null with matched local LD. The average correlations were 0.18  and 0.25  for expression and splicing, respectively with the distribution of the median correlation across tissues for each trait shown in Fig. p values were calculated against permuted null with matched local LD . These results indicate that strong genetic effects on expression or splicing are more likely to have a strong association to complex traits, adding strong support to a dose-dependent relationship between gene regulation and downstream traits.To test and account for mediation effect heterogeneity (different slope/dosage sensitivity for different genes), we modeled the gene-specific mediation effect, σgene2=0 ; Fig. 3cImportantly, by averaging across all genes, the estimates, from both the average and the random-effects approach, of the mediating effect are robust to confounding due to LD, as discussed in the β=δ/γ) inferred from the primary eQTL (βprim) was consistent with the one inferred from the secondary eQTL (βsec). Among the independent eQTLs for a given gene, we called primary the one with the larger effect size. We considered only fine-mapped eQTLs given the low power to detect multiple independent sQTLs. We confirmed this concordance, as reported by the GTEx consortium [Another way to account for mediation effect heterogeneity is to make use of the allelic series of independent eQTLs identified for over half of the eGenes . We examnsortium , demonstNote that genes with discordant effects within the allelic series would be harder to detect and suggest more complex causal relationship or context specificity.coloc and enloc [enloc as our primary approach, due to its use of hierarchical models to estimate colocalization priors [coloc assumption of a single causal variant drastically reduces performance especially in large QTL datasets such as GTEx with widespread allelic heterogeneity. For a more extensive discussion on the benefits of Bayesian colocalization methods and comparison of enloc to other colocalization approaches including SMR-HEIDI, see [enloc, for 12,072,964 tissue-gene-GWAS locus-trait tuples and 67,943,800 tissue-splicing event-GWAS locus-trait tuples. For the tally of colocalized genes, we used rcp > 0.5 as a stringent cutoff as demonstrated below with the low colocalization probabilities of height loci using two different datasets.In addition to genome-wide analyses that shed light on the molecular architecture of complex traits, QTL analysis of GWAS data can identify potential causal genes and molecular changes in individual GWAS loci. Towards this end, we performed association analysis with genetically predicted regulation and colocalization Fig. a. After nd enloc , 17, we n priors  and its IDI, see . We estiIn total, we identified 3477  unique genes colocalizing with GWAS hits (rcp > 0.5) across all traits and tissues analyzed. Similarly, 3157 splicing events  colocalized with GWAS hits, corresponding to 1226 genes with at least one colocalized splicing event .susier [n = 337,119 UKB GWAS vs. n = 253,288 for GIANT), the low colocalization cannot be attributed to lack of power. This result is likely due in part to the sensitivity to small LD differences between different EUR populations that make up large GWAS meta-analysis cohorts such as GIANT. Our analysis illustrates the fact that colocalization probability estimates are highly conservative and may miss many causal genes, and low colocalization probability should not be interpreted as evidence of lack of a causal link between the molecular phenotype and the GWAS trait. Notice that this limitation is not inherent to the colocalization method itself but the limitation of currently available large-scale GWAS meta-analysis results.Colocalization of e/sQTLs with GWAS variants provides important causal support for molecular traits. However, we found their estimates to be overly conservative. To illustrate this point, we tested the colocalization of height with itself, using two large-scale studies of individuals of European-ancestry individuals: GIANT  and UK Bsusier . Notablysusier . Two thimashr) instead of Elastic Net as reported in the main GTEx publication [A complementary approach to colocalization is to estimate the GWAS trait association with genetically predicted gene expression or splicing . The GTElication . Multi-Sp value threshold . Nearly all traits  showed at least one significant gene-level PrediXcan association in at least one tissue  and a trait to estimate the genic effect on the trait, using the summary statistics-based PrediXcan . Given tp<0.05; see To replicate the PrediXcan expression associations in an independent dataset, BioVU, which is a large-scale biobank tied to Electronic Health Records , 31, we 6∼5.7×10−9); we refer to any such region-trait pair by “GWAS locus.” We calculated the proportion of GWAS loci that contain a significantly associated gene via PrediXcan or a colocalized gene via enloc (rcp > 0.5). Briefly, the LD blocks are defined by analyzing empirical patterns of LD observed in 1000 Genomes [Altogether, these results provide abundant links between gene regulation and GWAS loci. To further quantify this, we split the genome into approximately LD-independent blocks  and iden Genomes  and varienloc and PrediXcan support. The distribution of the proportion of associated and colocalized GWAS loci across 87 traits is summarized in Fig. Across the traits, 72% (3899/5385) of GWAS loci had a PrediXcan expression association in the same LD block, of which 55% 2125/3899) had evidence of colocalization with an eQTL; for splicing, 62% 3345/5385) had a PrediXcan association of which 34% (1135/3345) colocalized with an sQTL  GTEx v8 doubles the number of samples with both genotype and RNA-seq relative to v7, (2) we include links based on splicing in addition to expression, (3) a variant may act through both regulation of expression levels and other undetected mechanisms (pleiotropy), and (4) attenuation bias may reduce the estimates given the error in eQTL effect sizes.A recent report estimates that the proportion of trait variance explained by the assayed transcriptome is on average 11% . Even thABCG5 and ABCG8, showed highly significant predicted causal associations using both PrediXcan and enloc for LDL-C levels and self-reported high cholesterol levels. ABCG8 showed more significant associations in both datasets  = 38.43, rcp = 0.985; GLGC LDL-C: −log10(pPrediXcan) = 71.40, rcp = 0.789), compared to ABCG5  = 36.85, rcp = 0.941; −log10(pPrediXcan) = 80.80, rcp = 0.705). Mutations in either of the two ATP-binding cassette (ABC) half-transporters, ABCG5 and ABCG8, lead to reduced secretion of sterols into bile and, ultimately, obstruct cholesterol and other sterols exiting the body [Abcg5 and Abcg8 (G5G8-/-), a 2- to 3-fold increase in the fractional absorption of dietary plan sterols and extremely low biliary cholesterol levels was observed, indicating that disrupting these genes contributes greatly to plasma cholesterol levels [ABCG5 and ABCG8 in transgenic Ldlr-/- mice resulted in 30% reduction in hepatic cholesterol levels and 70% reduced atherosclerotic legion in the aortic root and arch [Of note, two members of the sterolin family, l levels . The oveenloc. Rare protein-truncating variants in APOB have been previously associated with reduced LDL-C and triglyceride levels and reduced coronary heart disease risk [APOB has been predicted as a causal gene in four related traits, coronary artery disease, LDL-C levels, triglyceride levels, and self-reported high cholesterol levels. Among the four traits, PrediXcan showed the highest association to LDL-C levels (−log10(pPrediXcan) = 130.89; rcp = 0.485) while self-reported high cholesterol showed the strongest evidence using enloc at nearly maximum posterior probability (−log10(pPrediXcan) = 93.66; rcp = 0.969). Although APOB has been suggested as a better molecular indicator of predicted cardiac events in place of LDL-C levels [APOB may play in predicting lipid traits.Several other lipid-associated loci were also consistently predicted as causal across OMIM, the rare variant derived set, PrediXcan and ase risk . InteresC levels , 43, itsC levels . Here, wTo compare the ability of different approaches to identify the causal gene that mediates the association between GWAS loci and the traits, we sought to curate sets of “ground truth” genes using information that is independent of GWAS results —the probability that the gene-trait link is causal given that it is called significant or colocalized. Precision recall curves for expression- and splicing-based predictions are shown in Fig. p<0.05/49 × number of gene/trait pairs) were OMIM genes and 14.8% of genes with high colocalization probability (rcp > 0.5) were also OMIM genes for matched traits.For example, 8.7% of genes with PrediXcan significant genes , an OMIM gene was the closest to the top GWAS SNP in 31.6% of the loci, it was the most colocalized in 24.8% of the loci, and it was the most significant in 20.4% of the loci Fig. e, f.p=2.0×10−2, enlocp=6.1×10−3, PrediXcan p=2.5×10−4), indicating that each method provides an additional source of causal evidence even after conditioning on the others. Similar results were obtained using splicing colocalization and association scores and the rare variant-based silver standard, as shown in Additional file To further investigate whether this predictive power could be improved by combining multiple criteria, we performed a joint logistic regression of OMIM gene status on 1) the proximity of the top GWAS variant to the nearest gene (distance to the gene body), (2) posterior probability of colocalization, and (3) PrediXcan association significance between QTL and GWAS variants. To make the scale of the three features more comparable, we used their respective ranking. When genes did not have an enloc or PrediXcan score, they were assigned to the last position in the ranking. All three features were significant predictors of OMIM gene status, with better ranked genes more likely to be OMIM genes , and Interleukins and HLA subunits for asthma, both significant and colocalized for related immunological traits. Significantly associated and colocalized genes that predicted OMIM genes also included FLG (eczema), TPO (hypothyroidism), and NOD2 (inflammatory bowel disease) . To explore the tissue specificity of the PrediXcan z-score vector, we proceeded by assigning the z-score vector to a tissue-pattern category and tested whether certain tissue-pattern categories were over-represented among colocalized PrediXcan genes as compared to non-colocalized genes. We used the FLASH factors identified from matrix factorization applied to the cis-eQTL effect size matrix, as PrediXcan and cis-eQTL shared similar tissue-sharing pattern  and Factor25 (likely to be a tissue-shared factor capturing tissues with large sample size). Additionally, we excluded Factor7 (testis), as it was unlikely to be the mediating tissue but might introduce false positives. We tested the enrichment of each tissue-pattern category by Fisher’s exact test (“positive”/“negative” sets and in/not in tissue-pattern category). Among 87 traits, 82 traits had enloc signal and the enrichment of these was calculated accordingly.The broad sharing of regulatory variation across tissues and the reduced significance of tissue-specific eQTLs make causal tissue identification challenging. To address this problem, we devised a novel approach to identify tissues of relevance for the etiology of complex traits. We investigated the patterns of tissue specificity and tissue sharing of PrediXcan association results across 49 tissues. For each trait-gene pair, the PrediXcan  pattern . To obtaUsing the pattern of tissue classes of non-colocalized genes (rcp = 0) as the expected null, we assessed whether significantly associated and colocalized genes (PrediXcan significant and rcp > 0.01) were over-represented in certain tissue classes Fig. . Consistenloc and PrediXcan were predictive of OMIM genes for matched traits, implying that for a proportion of the genes, the dose-response curve can be extrapolated to the rare and more severe end of the genotype-trait spectrum. The observation that common regulatory variants target genes also implicated by rare coding variants underscores the extent to which these different types of genetic variants converge to mediate a spectrum of similar pathophysiological effects and may provide a powerful approach to drug target discovery.We performed in-depth examination of the phenotypic consequences of the genetic regulation of the transcriptome and provide data-driven analytical approaches to benchmark methods that assign function to GWAS loci and best-practice guidelines for using the GTEx resources to interpret GWAS results. We provide a systematic empirical demonstration of the widespread dose-dependent effect of expression and splicing on complex traits, i.e., variants with larger impact at the molecular level have larger impact at the trait level. Furthermore, we found that target genes in GWAS loci identified by enloc, PrediXcan, and proximity ranking outperforms the individual approaches. The significance of the proximity ranking is a sign of the “missing regulability” emphasizing the need to expand the resolution, sample size, and range of contexts of transcriptome studies as well as to examine other molecular mechanisms.We implemented association and colocalization methods that leverage the observed allelic heterogeneity of expression traits. After extensive comparison using two independent sets of silver standard gene-trait pairs, we conclude that combining p values to assign function to a GWAS locus can be misleading. Colocalization approaches can be used to weed out LD contamination, but given the lack of LD references from source studies, they can also be overtly conservative. General purpose reference LD from publicly available sources are not sufficient for fine-mapping and colocalization approaches, which can be highly sensitive to LD misspecification when only summary results are used [We caution that the increased power offered by this release of the GTEx resources also brings higher risk of false links due to LD contamination and that naive use of eQTL or sQTL association are used . The GWAFinally, we generated several resources that can open the door for addressing key questions in complex trait genomics. We present a catalog of gene-level associations, including potential target genes for nearly half of the GWAS loci investigated here that provides a rich basis for studies on the functional mechanisms of complex diseases and traits. We provide a database of optimal gene expression imputation models that were built on the fine-mapping probabilities for feature selection and that leverage the global patterns of tissue sharing of regulation to improve the weights. These imputation models of expression and splicing, which to date has been challenging to study, provide a foundation for transcriptome-wide association studies of the human phenome—the collection of all human diseases and traits—to further accelerate discovery of trait-associated genes. Collectively, these data thus represent a valuable resource, enabling novel biological insights and facilitating follow-up studies of causal mechanisms.∗ alphabetic order Lead Analysts∗Equal contribution Alvaro N Barbeira, Rodrigo Bonazzola, Eric R Gamazon, Yanyu Liang, YoSon ParkAnalysts∗ François Aguet, Lisa Bastarache, Ron Do, Gao Wang, Andrew R Hamel, Farhad Hormozdiari, Zhuoxun Jiang, Daniel Jordan, Sarah Kim-Hellmuth, Boxiang Liu, Milton D Pividori, Abhiram Rao, Marie Verbanck, Dan ZhouGTEx GWAS Working Group∗ François Aguet, Kristin Ardlie, Alvaro N Barbeira, Rodrigo Bonazzola, Christopher D Brown, Lin Chen, Eric R Gamazon, Kevin Gleason, Andrew R Hamel, Farhad Hormozdiari, Hae Kyung Im, Sarah Kim-Hellmuth, Tuuli Lappalainen, Yanyu Liang, Boxiang Liu, Dan L Nicolae, Yoson Park, Milton D Pividori, Abhiram Rao, John M. Rouhana, Ayellet V Segrè, Xiaoquan WenSenior Leadership∗ Kristin Ardlie, Christopher D. Brown, Hae Kyung Im, Tuuli Lappalainen, Mark McCarthy, Stephen Montgomery, Ayellet V Segrè, Matthew Stephens, Xiaoquan WenManuscript Writing Group∗ Eric R Gamazon, Hae Kyung Im, Tuuli Lappalainen, Yanyu Liang, YoSon ParkCorresponding Author∗ Hae Kyung ImLaboratory and Data Analysis Coordinating Center (LDACC): François Aguet1, Shankara Anand1, Kristin G Ardlie1, Stacey Gabriel1, Gad Getz1,2, Aaron Graubert1, Kane Hadley1, Robert E Handsaker3,4,5, Katherine H Huang1, Seva Kashin3,4,5, Xiao Li1, Daniel G MacArthur4,6, Samuel R Meier1, Jared L Nedzel1, Duyen Y Nguyen1, Ayellet V Segrè1,7, Ellen Todres1Analysis Working Group (funded by GTEx project grants): François Aguet1, Shankara Anand1, Kristin G Ardlie1, Brunilda Balliu8, Alvaro N Barbeira9, Alexis Battle10,11, Rodrigo Bonazzola9, Andrew Brown12,13, Christopher D Brown14, Stephane E Castel15,16, Don Conrad17,18, Daniel J Cotter19, Nancy Cox20, Sayantan Das21, Olivia M de Goede19, Emmanouil T Dermitzakis12,22,23, Barbara E Engelhardt24,25, Eleazar Eskin26, Tiffany Y Eulalio27, Nicole M Ferraro27, Elise Flynn15,16, Laure Fresard28, Eric R Gamazon29,30,31,20, Diego Garrido-Martín32, Nicole R Gay19, Gad Getz1,2, Aaron Graubert1, Roderic Guigó32,33, Kane Hadley1, Andrew R Hamel7,1, Robert E Handsaker3,4,5, Yuan He10, Paul J Hoffman15, Farhad Hormozdiari34,1, Lei Hou35,1, Katherine H Huang1, Hae Kyung Im9, Brian Jo24,25, Silva Kasela15,16, Seva Kashin3,4,5, Manolis Kellis35,1, Sarah Kim-Hellmuth15,16,36, Alan Kwong21, Tuuli Lappalainen15,16, Xiao Li1, Xin Li28, Yanyu Liang9, Daniel G MacArthur4,6, Serghei Mangul26,37, Samuel R Meier1, Pejman Mohammadi15,16,38,39, Stephen B Montgomery28,19, Manuel Muñoz-Aguirre32,40, Daniel C Nachun28, Jared L Nedzel1, Duyen Y Nguyen1, Andrew B Nobel41, Meritxell Oliva9,42, YoSon Park14,43, Yongjin Park35,1, Princy Parsana11, Ferran Reverter44, John M Rouhana7,1, Chiara Sabatti45, Ashis Saha11, Ayellet V Segrè1,7, Andrew D Skol9,46, Matthew Stephens47, Barbara E Stranger9,48, Benjamin J Strober10, Nicole A Teran28, Ellen Todres1, Ana Viñuela49,12,22,23, Gao Wang47, Xiaoquan Wen21, Fred Wright50, Valentin Wucher32, Yuxin Zou51Analysis Working Group (not funded by GTEx project grants): Pedro G Ferreira52,53,54, Gen Li55, Marta Melé56, Esti Yeger-Lotem57,58Leidos Biomedical - Project Management: Mary E Barcus59, Debra Bradbury60, Tanya Krubit60, Jeffrey A McLean60, Liqun Qi60, Karna Robinson60, Nancy V Roche60, Anna M Smith60, Leslie Sobin60, David E Tabor60, Anita Undale60Biospecimen collection source sites: Jason Bridge61, Lori E Brigham62, Barbara A Foster63, Bryan M Gillard63, Richard Hasz64, Marcus Hunter65, Christopher Johns66, Mark Johnson67, Ellen Karasik63, Gene Kopen68, William F Leinweber68, Alisa McDonald68, Michael T Moser63, Kevin Myer65, Kimberley D Ramsey63, Brian Roe65, Saboor Shad68, Jeffrey A Thomas68,67, Gary Walters67, Michael Washington67, Joseph Wheeler66Biospecimen core resource: Scott D Jewell69, Daniel C Rohrer69, Dana R Valley69Brain bank repository: David A Davis70, Deborah C Mash70Pathology Mary E Barcus59, Philip A Branton71, Leslie Sobin60ELSI study: Laura K Barker72, Heather M Gardiner72, Maghboeba Mosavel73, Laura A Siminoff72Genome Browser Data Integration & Visualization: Paul Flicek74, Maximilian Haeussler75, Thomas Juettemann74, W James Kentv75, Christopher M Lee75, Conner C Powell75, Kate R Rosenbloom75, Magali Ruffier74, Dan Sheppard74, Kieron Taylor74, Stephen J Trevanion74, Daniel R Zerbino74eGTEx groups: Nathan S Abell19, Joshua Akey76, Lin Chen42, Kathryn Demanelis42, Jennifer A Doherty77, Andrew P Feinberg78, Kasper D Hansen79, Peter F Hickey80, Lei Hou35,1, Farzana Jasmine42, Lihua Jiang19, Rajinder Kaul81,82, Manolis Kellis35,1, Muhammad G Kibriya42, Jin Billy Li19, Qin Li19, Shin Lin83, Sandra E Linder19, Stephen B Montgomery28,19, Meritxell Oliva9,42, Yongjin Park35,1, Brandon L Pierce42, Lindsay F Rizzardi84, Andrew D Skol9,46, Kevin S Smith28, Michael Snyder19, John Stamatoyannopoulos81,85, Barbara E Stranger9,48, Hua Tang19, Meng Wang19NIH program management: Philip A Branton71, Latarsha J Carithers71,86, Ping Guan71, Susan E Koester87, A. Roger Little88, Helen M Moore71, Concepcion R Nierras89, Abhi K Rao71, Jimmie B Vaught71, Simona Volpi90Affiliations1. The Broad Institute of MIT and Harvard, Cambridge, MA, USA 2. Cancer Center and Department of Pathology, Massachusetts General Hospital, Boston, MA, USA 3. Department of Genetics, Harvard Medical School, Boston, MA, USA 4. Program in Medical and Population Genetics, The Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA 5. Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA 6. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA 7. Ocular Genomics Institute, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA 8. Department of Biomathematics, University of California, Los Angeles, Los Angeles, CA, USA 9. Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA v 10. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA 11. Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA 12. Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland 13. Population Health and Genomics, University of Dundee, Dundee, Scotland, UK 14. Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA 15. New York Genome Center, New York, NY, USA 16. Department of Systems Biology, Columbia University, New York, NY, USA 17. Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA 18. Department of Pathology & Immunology, Washington University School of Medicine, St. Louis, MO, USA 19. Department of Genetics, Stanford University, Stanford, CA, USA 20. Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA 21. Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA 22. Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, Switzerland 23. Swiss Institute of Bioinformatics, Geneva, Switzerland 24. Department of Computer Science, Princeton University, Princeton, NJ, USA 25. Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, USA 26. Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA 27. Program in Biomedical Informatics, Stanford University School of Medicine, Stanford, CA, USA 28. Department of Pathology, Stanford University, Stanford, CA, USA 29. Data Science Institute, Vanderbilt University, Nashville, TN, USA 30. Clare Hall, University of Cambridge, Cambridge, UK 31. MRC Epidemiology Unit, University of Cambridge, Cambridge, UK 32. Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain 33. Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain 34. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA 35. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA 36. Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany 37. Department of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, CA, USA 38. Scripps Research Translational Institute, La Jolla, CA, USA 39. Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA 40. Department of Statistics and Operations Research, Universitat Politècnica de Catalunya (UPC), Barcelona, Catalonia, Spain 41. Department of Statistics and Operations Research and Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA 42. Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA 43. Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA 44. Department of Genetics, Microbiology and Statistics, University of Barcelona, Barcelona, Spain 45. Departments of Biomedical Data Science and Statistics, Stanford University, Stanford, CA, USA 46. Department of Pathology and Laboratory Medicine, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL, USA 47. Department of Human Genetics, University of Chicago, Chicago, IL, USA 48. Center for Genetic Medicine, Department of Pharmacology, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA 49. Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK 50. Bioinformatics Research Center and Departments of Statistics and Biological Sciences, North Carolina State University, Raleigh, NC, USA 51. Department of Statistics, University of Chicago, Chicago, IL, USA 52. Department of Computer Sciences, Faculty of Sciences, University of Porto, Porto, Portugal 53. Instituto de Investigação e Inovação em Sauúde, Universidade do Porto, Porto, Portugal 54. Institute of Molecular Pathology and Immunology, University of Porto, Porto, Portugal 55. Columbia University Mailman School of Public Health, New York, NY, USA 56. Life Sciences Department, Barcelona Supercomputing Center, Barcelona, Spain 57. Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev, Beer-Sheva, Israel 58 National Institute for Biotechnology in the Negev, Beer-Sheva, Israel 59. Leidos Biomedical, Frederick, MD, USA 60. Leidos Biomedical, Rockville, MD, USA 61. UNYTS, Buffalo, NY, USA 62. Washington Regional Transplant Community, Annandale, VA, USA 63. Therapeutics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA 64. Gift of Life Donor Program, Philadelphia, PA, USA 65. LifeGift, Houston, TX, USA 66. Center for Organ Recovery and Education, Pittsburgh, PA, USA 67. LifeNet Health, Virginia Beach, VA. USA v 68. National Disease Research Interchange, Philadelphia, PA, USA v 69. Van Andel Research Institute, Grand Rapids, MI, USA 70. Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA 71. Biorepositories and Biospecimen Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, USA 72. Temple University, Philadelphia, PA, USA 73. Virgina Commonwealth University, Richmond, VA, USA 74. European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK v 75. Genomics Institute, UC Santa Cruz, Santa Cruz, CA, USA 76. Carl Icahn Laboratory, Princeton University, Princeton, NJ, USA 77. Department of Population Health Sciences, The University of Utah, Salt Lake City, UT, USA 78. Schools of Medicine, Engineering, and Public Health, Johns Hopkins University, Baltimore, MD, USA 79. Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA 80. Department of Medical Biology, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia 81. Altius Institute for Biomedical Sciences, Seattle, WA, USA v 82. Division of Genetics, University of Washington, Seattle, WA, USA 83. Department of Cardiology, University of Washington, Seattle, WA, USA 84. HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA 85. Genome Sciences, University of Washington, Seattle, WA, USA 86. National Institute of Dental and Craniofacial Research, Bethesda, MD, USA 87. Division of Neuroscience and Basic Behavioral Science, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA 88. National Institute on Drug Abuse, Bethesda, MD, USA 89. Office of Strategic Coordination, Division of Program Coordination, Planning and Strategic Initiatives, Office of the Director, National Institutes of Health, Rockville, MD, USA 90. Division of Genomic Medicine, National Human Genome Research Institute, Bethesda, MD, USAAdditional file 1 Supplementary Materials including detailed methods, tables, and figuresAdditional file 2 The metadata of the full list of 114 GWASsAdditional file 3 Presumed causal genes included in the OMIM databaseAdditional file 4 Genes suggested as causal by rare variant association studiesAdditional file 5 BioVU tableAdditional file 6 OMIM genes included in the analysisAdditional file 7 Rare variant silver standard genes included in the analysisAdditional file 8 PrediXcan and enloc results for predicted causal genes selected based on OMIMAdditional file 9 PrediXcan and enloc results for presumed causal genes in the rare variant based silver standardAdditional file 10 Review history


U-tests and logistic regression models were applied in the data analysis. Participants in the video group reported significantly more frequent headache (P = 0.01), fatigue (P = 0.00), memory loss (P = 0.03), inattention (P = 0.00), and higher level of depression (P = 0.05) than those in the control group. Additionally, the prevalence of memory loss  and inattention  were significantly higher in participants with higher level of depression who watched the video. Media warnings about the possible health hazards of MP use promote people to report physical symptoms and psychological problems. Considering this tendency, more moderate and scientific media information is needed to alleviate public worries about MP use.Along with gradually increases in mobile phone (MP) use, the mass media has played a vital role in informing the public regarding the potential health hazards of MP use. These media warnings have prompted public worries about health. The aim of the present study is to investigate the effects of media warnings about the possible health hazards of MP use on self-reported symptoms. Participants were 703 undergraduate students who volunteered to take part in an experimental study between August 2013 and July 2015. After completing baseline questionnaires containing information on demographics, MP usage and possible confounding variables, the participants were randomly clustered assigned to a video treatment group  or a control group. Then, they completed another set of questionnaires containing 6 self-reported physical symptoms and the Beck Depression Inventory (BDI). Chi-squared tests, Mann-Whitney  At the beginning of the twenty-first century, concerns about the safety of mobile phone (MP) use, environmental pollution, food, etc. have led to a generally heightened awareness of the effects of environmental changes on health . PervasiPeople usually get information from daily media, such as internet, TV, newspapers, magazines. People would seldom search for scientific articles or reviews for health information and look into the results and discussion. These would result in the misleading of people from the media and misinterpretation of media contents. Currently, the mass media plays a vital role in informing the public about the possible health effects of exposure to MP. It is very popular to obtain health information through the Internet, with over 50% of Internet users searching for health information . People In the meantime, scientists are increasingly concerned about the potential health effects of MP use. In some epidemiological studies, headache –14, dizzIn the present study, we investigated the effects of media warnings about the possible health hazards of MP use on self-reported physical symptoms. Additionally, we sought to study the moderation of a psychological factor on these effects.A between-group experimental study was performed between August 2013 and July 2015 at Third Military Medical University. This trial is an analyzer- and participant-blinded trial. Participants were recruited from among the freshmen of undergraduate students and written consents were obtained before experiment. After completing the baseline survey, participants were randomly clustered assigned to either the video or control group. Participants in the video group watched a 5-min video about the health effects of MP use and completed questionnaires containing physical symptoms and the Beck Depression Inventory (BDI). Participants in the control group completed the questionnaires for physical symptoms and the BDI without watching the video.The protocol of the current study was approved by the Third Military Medical University Ethical Committee. All participants who participated in the study completed a written consent form before baseline survey. This study and protocol were not pre-registered.A total of 703 participants were recruited from undergraduate students at Third Military Medical University. During recruitment, participants were informed that they would participate in a study on the relationship between mobile phone use and health symptoms. After obtaining written consent, the baseline survey by the questionnaires including the information on demographics, MP usage and possible confounding variables was conducted. Excluding uncompleted questionnaires, 685 (97.44%) participants were randomly clustered assigned to the video or control group. Of the received questionnaires for physical symptoms and the BDI, 674 (95.87%) valid questionnaires were used in the statistical analysis.Baseline survey questionnaire include three parts: The demographic information, MP usage and possible confounding variables. After participants were assigned to the video group and the control group, they were asked to complete another questionnaire including self-reported physical symptoms and BDI.The demographic information requested included name, gender , age, grade, home address, and telephone number.MP usage included ownership of MP, the years of MP usage, daily duration of MP calls and weekly number of MP calls. The ownership of MP was assessed by the following question: “Do you own a MP?” The years of MP usage was assessed by the following question: “How many years have you used MP?” The daily duration of MP calls was assessed by the following question: “How much time do you spend making MP calls daily?” Finally, the question “How many calls do you make weekly?” was used to assess the weekly number of MP calls. For all of the above questions, MP usage included the use of other people's phones.The self-reported physical symptoms included headache, dizziness, fatigue, memory loss, inattention and irritability, which were taken from the Zerssen complaint list , 34. TheDepression status during the previous week was evaluated using the 21-item version of the BDI, which is a self-report inventory created by Beck et al. . This inDaily physical exercise (yes/no), smoking (yes/no), and drinking (yes/no) as well as gender and age were considered to be confounding factors.In the video group, participants watched a 5-min video, which was made by an information technology professional colleague. The title of the video was “Electromagnetic radiation—a concealed killer around us.” This video included description of electromagnetic radiation in daily life, potential health effects of electromagnetic radiation, the present status of MP use, potential cancers related to MP and some results of the studies that have investigated the possible health effects of MP use. Video was narrated in Chinese, and the English translation for the video narrator was provided as supplementary material .Before watching the video, participants provided written consent and completed the baseline questionnaires for demographics, MP usage and possible confounding factors, such as engaging in daily physical exercise, smoking, and drinking. Then, they were randomly assigned to either the video group or control group. In the video group, participants watched a 5-min video on a block of all video group participants divided into three groups in three different classrooms playing the video in the class time. Participants in control group fill in the rest questionnaire without watching the video at the same time. After watching the video, participants completed another set of the questionnaires containing 6 self-report physical symptoms and the BDI items. In the control group, participants complete these questionnaires without watching the video. The procedure was shown in t-test was used to analyze normally distributed variable, such as age. Mann-Whitney U-tests were applied to analyze non-normally distributed variables, including years of MP usage, daily duration of MP calls, weekly number of MP calls and level of depression between the two groups. Logistic regressions were used to test whether increases in the prevalence of physical symptoms would be predicted by the higher level of depression, by watching the video or by the interaction between these two factors. The inclusion of interaction terms in the logistic regression models references the approach described by Witthöft and Rubin ], and categorical variables were generally summarized using the corresponding percentages. Statistical analysis was undertaken using SPSS version 19.0 .Chi-squared tests were used to compare the distribution of sex, daily physical exercise, smoking, drinking, MP ownership, prevalence of physical symptoms and the BDI score between the two groups. Student nd Rubin . StatistAmong the 703 undergraduate students recruited to participate in this study, 685 (97.44%) participants completed the baseline questionnaires and took part in the experimental portion. After the experiment, 674 (95.87%) valid questionnaires were returned and used in the statistical analysis.The participants included 544 males (80.71%) and 130 females (19.29%). The mean age was 22.69 ± 1.76 years. Nearly every participant (98.96%) owned MP at the time of the survey, and the average length of MP usage was 5.68 ± 2.03 years. Participants spent 18.97 ± 17.15 min per day on making MP calls and made 17.49 ± 2.05 phone calls per week, on average. The detailed distribution of demographics for the two groups is shown in P = 0.01), fatigue (P = 0.00), memory loss (P = 0.03), and inattention (P = 0.00) were significantly higher in the video group , followed by inattention (35.2%) and memory loss (30.9%). In the video group, all physical symptoms were more frequently reported than in the control group. Additionally, the prevalence of headache (eo group . Logistieo group .P = 0.05)  .P = 0.03, P = 0.00, To investigate the interaction effect on physical symptoms between depression and watching video, we estimated a logistic regression. The result showed that the prevalence of physical symptoms was higher among participants with higher level of depression who watched the video . AdditioAs MP is integrated into our lives, more news headlines have reported the health hazards of MP use. Thus, people start to worry about whether MP use is likely to endanger their health. We investigate the effects of media warnings about the health hazards of MP use on the self-reported symptoms of young adults for the first time in China. Our results revealed that watching a video about the health effects of MP use elevated the prevalence of self-reported physical symptoms, and this effect was stronger in participants with higher level of depression.In contrast to participants in the control group, participants who watched the video were significantly more likely to feel headache, fatigue, memory loss, inattention, and depression. Several previous studies have shown that people with long-duration MP use reported more physical symptoms –15 or deUnique findings from our study include several significant interactions between watching the video and the level of depression on self-reported physical symptoms. The BDI score in this study was 4.9 ± 6.7 and 5.2 ± 6.6 in control group and in video group, respectively. This BDI score is comparable with average population, such as young male military recruits in Brazil  , non-patThis experimental study is the first study to indicate that students who watched a video about the possible health hazards of MP use felt physical symptoms more frequently and reported higher level of depression in China. It adds evidence about the impact of the scary media effect from a Chinese student population. Based on similar previous research , the samOur results added evidence to the body of research on the effects of media warnings about the health hazards of MP use on physical and psychological health. However, several limitations should be noted. One of the main limits of this study is that most information about mobile phone use is based on self-reported information collected by questionnaire. To collect mobile phone exposure data by self-reported questionnaire is one of effective and convenient methods to collect information in epidemiological study related with mobile phone use and health which had been used before , 49. HowIn this study, based on similar mobile phone use background in two groups, watching a video about mobile phone health hazards resulted in more frequently reported health symptoms and higher level of BDI score. There is evidence that media warnings about the health hazards of MP use promote people to report physical symptoms and psychological problems in the present study. However, since few participants reported heavy and moderate symptoms, and seldom reported relatively high BDI scores, the effects of video are really minor. Greater collaboration between social media and scientific organizations is needed to provide more credible and pertinent health information about MP use.The datasets generated for this study are available on request to the corresponding author.The studies involving human participants were reviewed and approved by Third Military Medical University Ethical Committee. The patients/participants provided their written informed consent to participate in this study.Z-PY and LZ: conceptualization, funding acquisition, and writing—review and editing. PG, F-ZZ, and PD: formal analysis. PG, F-ZZ, M-DH, ML, and LZ: investigation. PG, F-ZZ, and LZ: methodology. PG, M-DH, and ZZ: validation. PG and F-ZZ: writing—original draft.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In the past 20 years, there has been growing research interest in the association between video games and cognition. Although many studies have found that video game players are better than non-players in multiple cognitive domains, other studies failed to replicate these results. Until now, the vast majority of studies defined video game players based on the number of hours an individual spent playing video games, with relatively few studies focusing on video game expertise using performance criteria. In the current study, we sought to examine whether individuals who play video games at a professional level in the esports industry differ from amateur video game players in their cognitive and learning abilities. We assessed 14 video game players who play in a competitive league  and 16 casual video game players (Amateur) on set of standard neuropsychological tests evaluating processing speed, attention, memory, executive functions, and manual dexterity. We also examined participants’ ability to improve performance on a dynamic visual attention task that required tracking multiple objects in three-dimensions (3D-MOT) over five sessions. Professional players showed the largest performance advantage relative to Amateur players in a test of visual spatial memory , with more modest benefits in a test of selective and sustained attention (d2 Test of Attention), and test of auditory working memory (Digit Span). Professional players also showed better speed thresholds in the 3D-MOT task overall, but the rate of improvement with training did not differ in the two groups. Future longitudinal studies of elite video game experts are required to determine whether the observed performance benefits of professional gamers may be due to their greater engagement in video game play, or due to pre-existing differences that promote achievement of high performance in action video games. Call of Duty, Grand Theft Auto, Halo, Fallout 4, Fortnite, and Overwatch, are among the most popular types of video games in the United States , a high perceptual load, a high degree of distraction, and a requirement for constant switching between focused and distributed states of attention . AVGs arThe vast majority of studies examining the effects of AVGs on perceptual and cognitive function studied video game players who were defined based on a criterion amount of time they spent engaging in video game play over the past 6–12 months, with only a few studies also characterizing players based on their level of performance in a game. Research on expertise has sought to understand whether there are certain characteristics that enable individuals to achieve high levels of performance in various domains . In sporMore broadly, it has been suggested that individuals who achieve high levels of expertise also show enhanced performance across a wide range of core cognitive or perceptual domains in general, outside the specific context of their expertise. To continue with the example of sports, several recent studies have found that elite athletes outperform non-athletes in cognitive tests evaluating attention, multitasking, working memory, and processing speed, with group effects ranging from small to medium effect sizes . ProfessTo our knowledge, no existing studies have examined whether professional action video game players also exhibit enhanced cognitive abilities relative to amateur players. While growing evidence supports a link between playing action video games and cognitive ability , furtherTo address this gap, this study aimed to characterize the cognitive and learning abilities of high-performance action video game players recruited amongst the Houston Outlaws, a professional team in the Overwatch league™. We used a selection of standardized neuropsychological tests to evaluate cognitive abilities, and trained participants on the 3D-MOT task to assess their abilities to learn a novel, dynamic perceptual-cognitive task. We hypothesized that professional players would perform better than amateur players on neuropsychological tasks that evaluate attention, processing speed, executive functions, working memory, and visuo-spatial manipulation, as these aspects of cognition have been found to be more developed in habitual video game players when compared with non-players . We alsoThe experimental protocol was evaluated and approved by the Comité d’Éthique de la Recherche en Santé of Université de Montréal (18-009-CERES-D). Fourteen participants  were recruited amongst competitive players in the Overwatch League™ for the Houston Outlaws . These participants are considered elite video gamers, because they have achieved the necessary performance level to enter a professional league. They reported daily FPS video game usage in the last 6 months and were ranked as Grandmaster or Top 500/Pro in the game. As a comparison group, we recruited habitual video game players (Amateur group) through online advertisements targeted at undergraduate students at the Université de Montréal. Knowing that the video game category and type may be important influencing factors , particiThe d2 Test of Attention  was seleThe WAIS-IV Coding test was selected to evaluate visual processing speed . In thisThe WAIS-IV Visual Puzzles was selected to evaluate visual reasoning and the ability to manipulate visual information . This taThe WAIS-IV Digit Span test  was seleThe Wechsler Memory Scale-III (WMS-III) Spatial Span test  was seleTwo tests from the Delis Kaplan Executive Function System (D-KEFS) were selected to evaluate executive function . The D-KThe D-KEFS Colour-Word Interference test is a version of the Stroop test that evaluates inhibition and cognitive flexibility. In the Inhibition condition, participants are required to name the colour of the ink of a series of words that spell a name of a different colour. In the Flexibility condition, participants are required to read the words that are outlined by a rectangle, but to name the ink colour of the other words that are not outlined. The time required to complete a set of words is recorded .Finally, the Grooved Pegboard test (Lafayette Model 32025) was selected to evaluate hand-eye coordination and dexterity . This teThe 3D-MOT sessions were conducted in a quiet room using a fully immersive environment with a Fove™ virtual reality device. The head-mounted display had a resolution of 2,560 × 1,440 pixels and covered a maximal visual field of 45 degrees. The 3D-MOT task was delivered using a Dell Inspiration 15 700 Gaming Series computer.The 3D-MOT was developed as an optimal training procedure to improve mental abilities critical for processing dynamic scenes, such as those encountered during sports or video gaming . The proDuring the 3D-MOT task , particiParticipants in the Professional group were tested at their headquarters located in Burbank, California, and in Frisco, Texas. Participants in the Amateur group were tested in their homes in Montreal. The same experimenter administered all the tests. In the first visit, participants first completed a demographics and video game experience questionnaire. Then, participants completed the paper and pencil neuropsychological tests in their preferred language (English or French), which lasted approximately one hour. Participants then completed the 3D-MOT task three times. Subsequent 3D-MOT training sessions were completed in four additional sessions, which were spaced apart by a minimum of 6 h and a maximum of 72 h.Two participants in the Professional group did not have high proficiency in English or French, so they were not tested on the Digit Span and Colour Word Interference tests, since performance in these tests depends on language proficiency. One of these participants also did not complete the Tower test, due to difficulties with understanding instructions. Data for the 3D-MOT training sessions were available for 27 of 30 participants, because three participants in the Amateur group did not complete the five 3D-MOT sessions due to travel restrictions, so their data were excluded from these analyses. Additionally, data from 17 out of 405 blocks across seven participants were missing due to a technical error and were treated as missing at random.t-tests, as the variance in the two groups was unequal for some outcome measures . Hedges’ g was calculated to provide a measure of effect size using the effsize package in R . First, we compared performance on all the neuropsychological measures in the two groups using independent Welch’s age in R . To evalage in R . AdditioP-values for fixed effects were obtained using F-tests with the Kenward-Roger approximation for degrees of freedom  = −1.44, p = 0.16) and in the average age at which participants started playing video games  = 0.18, p = 0.86). Participants in the Professional group devoted approximately five times more time to FPS games than those in the Amateur group in the last six months  = 10.18, p < 0.001). There was no evidence for any differences in depression symptoms between both groups  = 1.01, p = 0.32).p = 0.02, g = 0.85), while maintaining a similar error rate (E%: t(27.3) = 0.70, p = 0. 49, g = 0.25). When accounting for the error rate, Professional players processed a greater number of stimuli correctly (TN-E: t(28) = 2.3; p = 0.03; g = 0.81), but their Concentration Performance score did not differ significantly from the Amateur group (CP: t(26.4) = 1.31; p = 0.20; g = 0.47). The Fluctuation Rate, which measures the consistency of performance throughout the task, was lower in the Professional group (FR: t(28)=−2,14; p = 0.04; g =  − 0.75), indicating better sustained attention.In the d2 Test of Attention, Professional players processed, on average, a greater number of stimuli than those in the Amateur group (TN: t(28) = 2.41, p < 0.001, g = 1.27), with a large effect size. Analyzing each subscale separately revealed a large, reliable effect in the Forward subscale, (t(28) = 3.85 ; p = 0.001, g = 1.36), but no reliable difference in the Backward subscale (t(24.4) = 1.92, p = 0.07, g = 0.7). In the WAIS-IV Digit Span task, the Professional group also showed better performance on the Total score (t(23) = 2.20; p = 0.04, g = 0.82), with a smaller effect size than in the Spatial Span test. Scores for the Digit Span Forward, Backward, and Sequencing subscales were not reliably different in the two groups .In the WAIS-IV Spatial Span task, the Professional group showed better performance than the Amateurs on the Total score (t(25.5) = 3.52, p = 0.14, g = 0.55). There was no evidence for a difference in performance between the two groups on tests of executive function, D-KEFS Towers (ps > 0.08) and D-KEFS Colour-Word Interference (ps > 0.36), nor on the Visual Puzzles test (ps = 0.44), which evaluates perceptual reasoning. There was also no evidence for any difference between groups in the Grooved Pegboard test (ps > 0.17), which evaluates hand-eye coordination and manual dexterity.In the WAIS-IV Coding test, the average score in the Professional group was numerically higher than that of the Amateur group, but this group difference was not reliable (t(26) = 1.54, c, between the composite and Group was 0.67, with a corresponding Rc2 = 0.45, which is the variance accounted for Group in the composite scores. This difference was not statistically significant, Wilk’s Λ = 0.54, F = 1.97, p = 0.11. Examining the standardized discriminant function coefficients revealed that the Spatial Span Total score made the largest contribution to the composite score, with a coefficient of −0.29 and r2 = 0.62. Next, we used a multivariate analysis approach to probe for global differences between the two groups across all neuropsychological assessments. To ward against multicollinearity, we selected one measure per task and conducted a descriptive discriminant analysis DDA;  on this p = 0.03, with the Professional group having higher thresholds, β = 0.28, 95% CI [0.05–0.52]. There was also a main effect of block, F = 76.97, p < 0.001, with thresholds increasing by β = 0.14, 95% CI [0.09–0.20] for every doubling of the block number. The interaction between Group and log2(block) was not statistically significant, F = 1.23, p = 0.28, providing no evidence that the learning rates differed between the two groups.Previous studies have indicated that visual working memory (WM) and selective attention are related both on a behavioral and neuroanatomical level . It has rs = 0.50, p = 0.01), and with the WAIS-IV Coding , but not with Spatial Span Forward . Thus, our measure of selective attention correlated with measures of vWM and processing speed, but not with vSTM. The d2 TN-E score also correlated with the Digit Span Sequencing subscale , but not with the Forward or Backward subscales. The Spatial Span Forward and Backward subscales were moderately correlated with each other . Performance on the 3D-MOT task showed a strong correlation with the Spatial Stan Forward  and Backward  subscales, indicating that these two tasks index similar cognitive capacities. Finally, the Spatial Span Forward was the only variable that correlated with the average weekly hours of video game play . Keeping in mind that the Professional and Amateur groups showed a large difference in their average weekly hours of video game play, this positive association is consistent with the previously-reported difference in Spatial Span performance in the two groups.To gain a better understanding of the cognitive determinants of expertise in action video games, the current study compared the performance of professional action video gamers with that of video gamers who play similar FPS games non-competitively on a set of eight neuropsychological tests, and on their ability to improve their performance on a multiple objects tracking task. The results revealed that Professionals performed better than Amateurs on some measures in the Spatial Span, Digit Span, d2 Test of Attention, and 3D-MOT tasks. These results indicate that high performance in FPS video games is associated with enhanced abilities in visual and auditory short-term memory, selective and sustained attention, and visual spatial attention. There was no evidence for any group differences in performance on tasks that evaluated executive functions, perceptual manipulation, or manual dexterity. Furthermore, both groups showed similar capacity to improve their performance in the 3D-MOT task with training. Given the cross-sectional, observational nature of this group comparison, this study cannot speak to the causality of the differences in cognitive performance and video game expertise. The presence of differences between groups may either indicate that certain cognitive abilities are conducive to achieving high performance in FPS games, or that the greater amount of experience with FPS games in the professional group lead to improved performance in the above-mentioned abilities.The results from the d2 Test of Attention indicate that Professional players may have better selective and sustained attention than Amateur players. This finding is consistent with previous studies that reported attentional benefits in non video-game players following practice with action video games . It is aPrevious studies that used change detection tasks with simple and complex stimuli have reported better vSTM capacities in action video game players . In the While previous studies mainly examined the effects of video games on visual cognition, a few studies have also noted benefits of video games on performance in auditory or multisensory tasks. For example, adult video game players showed better temporal processing of multisensory stimuli than non-players  and traiThe present study suggests a link between the level of video gaming expertise and the ability to perform an abstract dynamic task. The 3D-MOT task strongly engages several attention and mental skills; performing this task well requires selective, dynamic, distributed, and sustained attention skills . ProfessWhile the effect in the multiple object tracking task is consistent with differences in the dorsal pathway, there were no group differences in the Grooved Pegboard task, which relies on action planning and visuomotor coordination that also depends on the dorsal pathway. Previous studies found that players have better hand-eye coordination than non-players, but this benefit was not associated with the amount of time spent engaging in the games . Action The current results also do not provide evidence for any differences in working memory associated with professional gaming, as the average performance on the Backward subscales of the Spatial Span and Digit Span tasks did not differ between groups. As mentioned earlier, previous studies have emphasized that WM, which involves maintaining elements active and quickly accessible, is intricately linked with the concept of selective attention, both on a behavioral and anatomical level . SupportThe present results also do not provide evidence for any differences in executive functions between Professional players and Amateurs, as measured by the inhibition and flexibility subscales of the Stroop task. Mental flexibility has been cited as one of the strongest enhancements in real-time strategy games . The metPrevious studies have found that video game players are faster than non-video game players in reaction time tasks . HoweverBy better understanding Professional videogaming, we can provide clues as to how this expertise and the corresponding performance best develops, and therefore learn how to support it . In ordeThe current study has serval limitations. First, due to our limited sample size, the current study had low power to detect small or medium effects . Thus, we should remain careful in our interpretation of null results, as differences might exist but failed to be detected. Furthermore, the gender of the study participants may have acted as a confounding variable since our Professional players group was composed only of men, whereas our Amateur group had four women, although women’s results did not differ from men’s within the same group. Finally, the experimenter who administered the measures was not blind to the participant group, which may have introduced some bias.In summary, this study was the first to examine the cognitive basis of elite performance in action video game players. Our results revealed that elite players show the greatest performance advantage in tests of visual spatial short-term memory and of visual attention. Furthermore, professional action video gamers showed a better ability to track multiple objects within a complex and dynamic scene than amateur players, but both groups showed similar rates of improvement in the task with training. Further research is needed to clarify whether the observed differences in cognitive abilities emerge as a result of intense practice in action video games, or whether certain cognitive profiles are beneficial for achieving high-level performance in video gaming.10.7717/peerj.10211/supp-1Supplemental Information 1Click here for additional data file.


The study aimed to investigate the risk factors of malnutrition in children with congenital heart defect (CHD) in China.This cohort study was performed at the biggest pediatric heart center in China; 3252 patients with CHD who underwent cardiac surgeries in 2013 were included. Anthropometric measurements included weight for age Z score (WAZ), weight for height Z score (WHZ), and height for age Z score (HAZ). The patients were classified as normal nutritional status and malnutrition, based on a cut-off Z score of <− 2. Factors associated with malnutrition were determined using logistic regression analysis.P < 0.001), HAZ , and WHZ  at 2 years after surgery. The prevalence of underweight, stunted, and wasting declined to 3.2, 2.7, and 1.9% 3 years after surgery. Malnutrition after surgery was associated with cardiac residual cardiac abnormalities , high Ross classification of heart function , and long-term taking oral diuretics .The prevalence of preoperative WAZ < -2 (underweight), HAZ < -2 (stunting), and WHZ < -2 (wasting) was 23.3, 23.3, and 14.3%, respectively. The multivariable analysis of preoperative malnutrition showed that hospitalization, age at surgery, risk adjustment for congenital heart surgery-1 > 3, mechanical ventilation, pulmonary hypertension, and acyanotic heart disease were associated with underweight. Parents’ height, single ventricle, and cyanotic heart disease were associated with stunting. Hospitalization and pulmonary hypertension were associated with wasting. After surgery, the patients presented a significant improvement in growth within the first year in all three parameters and grew to the normal range of WAZ (− 0.3 ± 0.9, Malnutrition is still a problem in children with CHD in China, especially before the surgery. There is need to strengthen the nutrition support for children with CHD before surgery. Hemodynamic factors were found to be the risk factors associated with malnutrition after operation. Globally, congenital heart defect (CHD) represents one-third of all major congenital anomalies and are often associated with malnutrition and failure to thrive in children . Growth In developed countries, advancements in pediatric cardiac care, early prenatal and postnatal diagnosis, and timely corrective interventions for cardiac lesions have improved the outcomes of those children, but malnutrition still remains a challenge. In a recent paper from the UK, Marino et al.  reportedIn China, as in many developing countries, the epidemiological data on CHD-related malnutrition are not clear. There might be many more risk factors associated with malnutrition than in developed countries. Therefore, this study aimed to investigate the effect and risk factors of malnutrition in children with CHD in China.Children who underwent radical or palliative surgery for CHD at the Shanghai Children’s Medical Center  between January 2013 and December 2013 were retrospectively selected from the medical charts and prospectively followed up. The study was approved by the ethics committee of the Shanghai Children’s Medical Center.The patients< 18 years for cardiac surgery were enrolled in. The exclusions criteria were: 1) gestational age < 36 weeks; or 2) a recognizable chromosomal or phenotypic syndrome associated with growth failure. 3252 children were enrolled in this study with median age of 275 days , median weight of 8 kg , and median height 70 cm.2), and risk adjustment for congenital heart surgery-1 (RACHS-1) scores was collected. Pulmonary hypertension was diagnosed based on tricuspid valve regurgitation velocity of ≥3.5 m/s by echocardiography. The anthropometric measurements, including weight and height, were performed according to standard World Health Organization (WHO) procedures. Z-scores for weight for age (WAZ), weight for height (WHZ), and height for age (HAZ) were calculated using the anthropometric calculator module of WHO Anthro software  based on the 2006 WHO child growth standards.Demographic and preoperative characteristics of the children, including sex, age, weight, height/length, diagnosis, preoperative percutaneous oxygen saturation , low HAZ (stunting), and low WHZ (wasting) as malnutrition . After t2, residual cardiac abnormalities, Ross classification of heart function for children . There were no significant differences between years 2 and 3.There were 107 in-hospital deaths among the 3252 . Among 3165 cases who were discharged, there were 790 malnutrition cases with WAZ < -2 or HAZ < -2, or WHZ < -2 followed after surgery. The 3-year follow-up data of 699 (88.5%) malnutrition patients were analyzed. There were 13 deaths . The growth trends of the survivors are presented in Fig. P < 0.001), Ross classification of 3 and 4 , and long-term oral diuretics treatment  , stunting , and wasting  children at the end of follow-up. The frequencies of palliative suegery, re-operation, residual anatomy, long-term oral diuretics, and Ross classification of 2–4 were significantly higher in the malnutrition group than in the normal nutrition group. More children lived in the suburban area in the malnutrition group, and their parent’s education was poor , 23.3% were stunted (HAZ < -2), and 14.3% were wasted (WHZ < -2) before the surgery, which was comparatively lower than that of other developing countries like Egypt, Uganda, and India . PreviouAs shown in the present study, hospitalization, mechanical ventilation, pulmonary hypertension, acyanotic heart disease, RACHS-1 > 3 and age at surgery were associated with underweight. RACHS-1 > 3 had the strongest association, demonstrating that the severity of heart defects  affects the degree of nutrition . InteresSingle ventricle, cyanotic heart disease, and parents’ height were associated with being stunted, and parents’ height had the strongest impact. Hospitalization and pulmonary hypertension were associated with wasting, and pulmonary hypertension had the strongest association. Malnutrition is commonly seen in children with cyanotic disease, single ventricle, and pulmonary hypertension. In the present study, children with acyanotic CHD were more likely to be underweight, while those with cyanotic CHD were more likely to be stunted. Similarly, Radman et al.  also repAfter surgery, malnutrition patients showed dramatic decreases in all parameters. The patients need more energy in the early period after surgery, but their gut function is not good enough to get the energy . In addiOnly three factors were associated with malnutrition by multivariable logistic regression analysis: residual abnormalities, Ross classification of 3 and 4, and long-term oral diuretics treatment. Those results suggest that correction of the anatomy is the key point for the somatic development of the children.This study has some limitations. It was carried out at a highly developed and specialized center with substantial expertise and, therefore, might not reflect the entire situation in China. Some studies in other provinces showed the incidence of malnutrition was in the range of 25.3–47.1% , 24, whiMalnutrition is a very common complication in children with symptomatic CHD. About 25% of the children with CHD suffered from malnutrition. Hospitalization, mechanical ventilation, pulmonary hypertension, acyanotic heart disease, RACHS-1 > 3, and age at surgery were associated with preoperative malnutrition. Most of the survivors would have normal development after anatomical correction, but some patients were still malnourished 3 years after surgery. Incomplete anatomical correction could be associated with malnutrition after surgery.


Alcohol problems in parents have been revealed to affect adolescent alcohol misuse. However, few studies examine the effects of parental drinking on adolescent risky drinking (including binge drinking) in the general population. In particular, previous study findings are inconsistent regarding the influence of parental drinking according to parental composition. In this study, we aimed to examine the relationship between parental drinking, according to parental composition, and binge drinking among high school students in Japan.We performed a secondary analysis of the Nationwide High School Survey on Drug Use and Lifestyle 2018, Japan. A total of 46,848 valid surveys from high school students of 78 schools were included for analysis. Logistic regression analysis with a generalized linear mixed model was conducted with binge drinking as the dependent variable and “parental drinking according to parental composition”  as the independent variable, after adjusting with covariates. Binge drinking was defined as five or more alcoholic drinks for male adolescents or four or more alcoholic drinks for females on the same occasion within two hours.In the fully adjusted models, adolescents whose mothers drink (adjusted odds ratio (AOR): 1.50, 95% confidence interval (CI): 1.06–2.12) were significantly associated with adolescent binge drinking. This risk was significantly higher among students with neither parent living at home .Parental drinking and absence do affect adolescent binge drinking; our findings show that adolescents are more likely to engage in binge drinking if their mothers drink or if they are not living with either parent. Therefore, it is important to engage parents and non-parental family members in future programs and interventions to prevent adolescent binge drinking. Alcohol misuse is common among adolescents. A recent survey found that approximately 2.4% of high school students in Japan  engage iEmerging health research suggests that alcohol use is determined by multidimensional  factors . GeneticPsychological traits related to cognitive and emotional susceptibility to substance abuse are also crucial factors for the initiation and development of adolescent substance abuse . Among tRegarding sociocultural factors, etiological mechanisms representing multiple systems  interact across development to influence binge drinking tendencies . Here, fRecent studies have focused on the influence of alcohol use on others, including the potential impact of parental drinking on children, especially adolescent alcohol misuse. Whereas most studies focused on alcoholics and parents who had problems with alcohol , some reParental alcohol use increases the intensity of alcohol consumption in late adolescent life , 45. WheSimilarly, the effects of parental drinking according to gender differences remain unclear. The study that revealed that paternal drinking frequency and intensity predicted excessive adolescent drinking for younger adolescents, also conceded that higher overall alcohol consumption rates may have influenced the results reflecting the father’s dominant influence . In contWomen are more susceptible to alcohol-related problems than men, their blood alcohol concentration tends to increase rapidly, and apart from potentially causing fetal alcohol syndrome (from drinking during pregnancy) , their pAnother concern related to parental drinking, is how both parents’ drinking influences the risk of binge drinking among adolescents. Previous studies have found that both the mother’s and father’s drinking increased the risk of adolescent drinking , and botAnother challenge is whether parental composition  will affect child binge drinking. Most studies that examine the relationship between parental factors and adolescent drinking fail to separate the father’s and mother’s influence , report Regarding the effects of gender differences (in single-parent households) on children’s drinking behaviors, alcohol use and delinquent behaviors among adolescents were higher in single-father homes than in single-mother homes, owing to lower levels of parental supervision observed in single-father homes . ResearcThus, adolescents from non-intact families are ostensibly associated with binge drinking. Also, since men and women have important but different roles affecting a child’s development, both living without a mother or a father, may hold binge drinking risks for adolescents. Next, we predict that for adolescents not living with either parent influences the prevalence of binge drinking. Related to this, past studies found that children living without both their biological parents engage more frequently in heavy drinking , 33, 69.Considering the above, we aimed to investigate the relationship between current parental drinking, according to parental composition, and binge drinking among high school students in Japan. Our hypothesis is that parental drinking (mother drinking and both parents drinking) and parental composition  would affect adolescent binge drinking. Particularly, we test whether the effects of father drinking and mother drinking separately, as well as the effects of father’s absence and mother’s absence separately, are associated with adolescent binge drinking behavior. Next, we test whether the combined effects of the (a) father and mother drinking and/or (b) father’s and mother’s absence, further influence adolescent binge drinking.The aim of the Nationwide High School Student Survey on Drug Use and Lifestyle 2018 was to examine the current situation of drug use, including alcohol and tobacco, among Japanese high school students, whose findings informed a drug abuse prevention strategy in Japan. The survey was a cross-sectional study conducted between October 2018 and March 2019.This study used secondary data from the Nationwide High School Student Survey on Drug Use and Lifestyle 2018 which was conducted by the National Institute of Mental Health in Japan.Participants were high school students randomly selected using a stratified single-stage cluster method, with six regions as the strata and schools as the cluster. Schools were selected using random sampling with probability proportionate to the number of high school students in each region. Self-administered questionnaires were distributed to students in each classroom; these were selected by the person in charge of substance abuse prevention at each school. The teacher in each classroom collected the completed questionnaires and returned them to our research center. A total of 116,313 students at 140 high schools nationwide were surveyed.A self-developed questionnaire (43 items) was used in the original survey. The analysis included demographic and lifestyle variables, information on students’ binge drinking experiences, and their parents’ drinking status and absence, as below.We defined binge drinking as five or more alcoholic drinks for male participants or four or more alcoholic drinks for female participants on the same occasion within two hours . StudentStudents were asked the status of their parents’ alcohol use. First, they were asked: “Does your father consume alcohol regularly?” with response options No (1), Sometimes (2), Every day (3), and No father at home  (4). Second, the following question was asked: “Does your mother consume alcohol regularly?” with response options No (1), Sometimes (2), Every day (3), and No mother at home  (4). For both questions, single answers were allowed. If respondents answered, “No father at home” or “No mother at home,” they were not allowed to answer the question related to their parents’ drinking status.Then, the responses were recoded as a) “Father’s alcohol use status and absence” with response options: FN = Father does not drink (1), FD = Father drinks (2), or NF = No father at home (3), and b) “Mother’s alcohol use status and absence” with options: MN = Mother does not drink (1), MD = Mother drinks (2), or NM = No mother at home (3). We finally combined and re-encoded the two items as “Parental drinking according to parental composition” with response options: FN/MN = Neither father nor mother drinks (1), FN/MD = Father does not drink but mother drinks (2), FN/NM = Father does not drink and no mother at home (3), FD/MN = Father drinks but mother does not drink (4), FD/MD = Both father and mother drink (5), FD/NM = Father drinks and no mother at home (6), NF/MN = No father at home and mother does not drink (7), NF/MD = No father at home and mother drinks (8), and NF/NM = Neither father nor mother at home (9).The family options were: “both father and mother (two-parent) family” , father-only family (FN/NM [3] or FD/NM [6]), mother-only family (NF/MN [7] or NF/MD [8]), and neither-parent family (NF/NM [9]).Models 2 and 3 included demographic variables that had been associated with adolescent binge drinking in prior research. Covariates included sex, grade , lifestyWe performed descriptive analysis  to characterize the sample Table  and asseSubsequently, the association between parental drinking  and adolescent binge drinking was evaluated in generalized linear mixed models, addressing the clustering of adolescents in sample areas and schools . Multivap value < .05 was considered statistically significant.All data were coded and analyzed using IBM SPSS version 24 . A two-tailed N = 47,280; response rate: 56%) were collected from students in 78 schools and 46,848 valid surveys were included for analysis. The demographic distribution of participants , given in Table The completed surveys  and binge drinking in a randomly selected nationally representative sample of high school students in Japan. In adjusted models (including 14 variables) the findings partly supported our hypothesis that adolescents whose mothers drink are more likely to engage in binge drinking. Also, adolescents who were not living with either parent may have been at greater risk of binge drinking.This study revealed that mothers’ drinking habits are significantly associated with binge drinking in adolescents, supporting our hypothesis that maternal drinking is associated with adolescent risky (including binge) drinking. Many studies reported that maternal drinking affect adolescent drinking behavior , 49, 50,Moreover, we mentioned that women are more likely to become problematic drinkers than men . The numIn our study, fathers’ drinking did not predict adolescent binge drinking. However, previous studies showed frequent and intense paternal drinking predicted excessive adolescent drinking , implyinAnother thought is that drinking habits are very common among fathers; the prevalence of drinking habits among men is overwhelmingly higher than among women . HoweverOur study did not show that living in households where both parents drink may affect binge drinking in adolescents, thus failing to support the related hypothesis. Thus, the findings did not add to existing evidence that both parents (a) using alcohol, significantly predicted adolescent alcohol misuse (increasing the risk of drinking) –58, and We applied the social learning theory to the effects of both parents drinking alcohol on their children’s drinking behavior , 59, 60.Previous research suggested that there is a very strong association between adolescent drinking and excessive and frequent drinking by both parents , and higApart from parental drinking, parenting itself is important since parental control (including monitoring), support, and warmth are crucial against risky drinking behavior . ModeratThe current study showed that paternal absence and maternal absence were not significantly associated with adolescent binge drinking. This result did not support our hypothesis that paternal absence and maternal absence are associated with adolescent binge drinking. Our findings do not support the evidence that non-intact families are more likely to drink , 40, discustodial grandchildren typically receive care from grandparents because of predicaments among their parents, such as substance abuse, child abuse and neglect, teenage pregnancy, death, illness, divorce, incarceration, and HIV/AIDS.” [Our results revealed that adolescents without either parent are associated with binge drinking, supporting previous findings that children who do not live with both their biological parents consume alcohol more frequently and heavily , 33, 69.V/AIDS.”  Still, wV/AIDS.” . Binge dV/AIDS.” . Thus, oThere are several limitations to our research. First, owing to its cross-sectional design, we were unable to examine the temporal relationship between parental and child binge drinking; longitudinal design studies are recommended. Owing to secondary data analysis, we were unable to consider several potentially important factors including genetic predispositions, adverse childhood experiences, social influences, and parental mental health. In addition, data regarding parental drinking were collected from the children and depended on their perceptions; therefore, the representativeness thereof may not be accurate. No survey questions regarding the amount and frequency of drinking by parents were included, so we were unable to distinguish whether parents were problematic drinkers or not. In future research, data should be collected from both the parents and children, including items querying the amount and frequency of drinking. Furthermore, our questionnaire did not include items regarding the reasons for single- or neither parent household, therefore the definitions of “single” and “neither” parent were ambiguous. The cause of family deficiency varies depending on country and culture, and hence, which parent group most likely influences Japanese children’s risk behavior needs to be determined. Finally, we did not examine whether parental drinking influenced adolescents according to sex and further longitudinal studies assessing the deferential effects are needed, as past results have been inconsistent . FurtherUsing a nationally representative sample of Japanese high school students, the findings of the present research suggest that adolescents whose mothers drink or do not live with them at home may be more at risk of binge drinking. Our findings add substantial evidence to that of previous studies regarding parental drinking and adolescent alcohol misuse. Focusing on parental engagement  in future programs and interventions is important in preventing adolescent binge drinking. We hope our findings will prove useful to future binge drinking research. Future analyses should explore whether parental drinking , considering the amount and frequency of drinking, affects adolescent binge drinking by gender differences.