Article in Biological psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
4 authors.
Lu WangDepartment of Psychiatry, Yale University School of Medicine, New Haven, Connecticut; Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut.
Henry R KranzlerDepartment of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania; Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania.
Joel GelernterDepartment of Psychiatry, Yale University School of Medicine, New Haven, Connecticut; Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut; Department of Genetics, Yale School of Medicine, New Haven, Connecticut; Department of Neuroscience, Yale School of Medicine, New Haven, Connecticut. Electronic address: joel.gelernter@yale.edu.
Hang ZhouDepartment of Psychiatry, Yale University School of Medicine, New Haven, Connecticut; Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut; Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut; Center for Brain and Mind Health, Yale School of Medicine, New Haven, Connecticut. Electronic address: hang.zhou@yale.edu.
Funding
Translational Technologies CoreP50AA012870 · NIAAA · YALE UNIVERSITY · PI John H. Krystal · 2001 to 2026
$43.7M
Sex-specific analysis of opioid dependence GWAS and beyondR01DA012690 · NIDA · YALE UNIVERSITY · PI GELERNTER, JOEL · 2000 to 2017
$14.0M
Yale-SCORE Resource Support CoreU54AA027989 · NIAAA · YALE UNIVERSITY · PI Sherry Ann McKee · 2020 to 2026
$13.0M
Genetic & Social Determinants of Health: Center for Admixture Science and TechnologyRM1HG011558 · NHGRI · YALE UNIVERSITY · PI FRAZER, KELLY A, GYMREK, MELISSA · 2021 to 2025
$11.2M
Methamphetamine and Other Substance Use Disorder Genetics in ThailandR01DA037974 · NIDA · YALE UNIVERSITY · PI JOEL GELERNTER, Marc N Potenza · 2015 to 2026
$6.7M
Linkage Disequilibrium Studies of Alcohol DependenceR01AA011330 · NIAAA · YALE UNIVERSITY · PI GELERNTER, JOEL · 1997 to 2013
$6.4M
Genetic Basis of the Risk and Consequences of Cannabis Exposure in HumansR01DA058862 · NIDA · YALE UNIVERSITY · PI DEEPAK Cyril D'SOUZA, JOEL GELERNTER · 2023 to 2026
$2.6M
Leveraging GWAS Findings to Map Variants and Identify Novel Effector Genes for Alcohol-Related TraitsR01AA030056 · NIAAA · UNIVERSITY OF PENNSYLVANIA · PI Struan F A Grant, MATTHEW S KAYSER · 2023 to 2026
$2.5M
Genetics of Alcohol Dependence in African Americans: RecruitmentR01AA026364 · NIAAA · YALE UNIVERSITY · PI GELERNTER, JOEL · 2018 to 2022
$2.0M
Genetic Causality of Alcohol Intake and Alcohol Use Disorder on Cancer RiskR21CA252916 · NCI · YALE UNIVERSITY · PI ZHOU, HANG · 2021 to 2022
backgroundAlcohol use disorder (AUD) is a leading cause of death and disability worldwide. There has been substantial progress in identifying genetic variants that underlie AUD. However, whole-exome sequencing studies of AUD have been hampered by the lack of available samples.
methodsWe analyzed whole-exome sequencing data of 4530 samples from the Yale-Penn cohort and 469,835 samples from the UK Biobank, which represent an unprecedented resource for exploring the contribution of coding variants in AUD. After quality control, 1750 African-ancestry (1142 cases) and 2039 European-ancestry (1420 cases) samples from the Yale-Penn and 6142 African-ancestry (130 cases), 415,617 European-ancestry (12,861 cases), and 4607 South Asian (130 cases) samples from the UK Biobank cohorts were included in the analyses.
resultsWe confirmed the well-known functional variant rs1229984 in ADH1B (p = 4.88 × 10
conclusionsThis study extends our understanding of the genetic architecture of AUD by providing insights into the contribution of rare coding variants, separately and convergently with common variants in AUD.
Indexed as
Alcohol DehydrogenaseAlcoholismAdultAsian PeopleBlack PeopleCohort StudiesExome SequencingFemaleGenetic Predisposition to DiseaseHumansMaleMiddle AgedPolymorphism, Single NucleotideUnited KingdomWhite PeopleADH1B protein, humanADH1C protein, humanAlcohol DehydrogenaseAlcohol use disorderCoding variantsMultiancestryRare variantsSubstance use disorderWhole-exome sequencing
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.
Investigating the Contribution of Coding Variants in Alcohol Use Disorder Using Whole-Exome Sequencing Across Ancestries. · full record | Socratic