Evidence map›Paper›PMID 35525699›Full record

ArticleBiological psychiatry2022

A Local Genetic Correlation Analysis Provides Biological Insights Into the Shared Genetic Architecture of Psychiatric and Substance Use Phenotypes.

Zachary F Gerring, Jackson G Thorp, Eric R Gamazon, Eske M Derks

Abstract read
In one paragraph

Article in Biological psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Zachary F GerringTranslational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia. Electronic address: zachary.gerring@qirmberghofer.edu.au.
Jackson G ThorpTranslational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
Eric R GamazonDivision of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee; The Cambridge Centre for Data-Driven Discovery, University of Cambridge, Cambridge, United Kingdom.
Eske M DerksTranslational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.

Funding

Haplotype-aware models of gene and isoform expression with application to genetic studies of disease in diverse populationsR01GM140287 · NIGMS · SEATTLE CHILDREN'S HOSPITAL · PI GAMAZON, ERIC R, MOHAMMADI, PEJMAN · 2021 to 2024
$2.8M
Functional Genomics: A Phenome-wide SurveyR35HG010718 · NHGRI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI GAMAZON, ERIC R · 2019 to 2023
$2.2M
Advancing Multi-Omics and Electronic Health Records Computational MethodologiesR01HG011138 · NHGRI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI GAMAZON, ERIC R · 2020 to 2024
$1.6M
Advancing drug repositioning and development for Alzheimer's Disease using functional genomics and computational phenomicsR56AG068026 · NIA · VANDERBILT UNIVERSITY MEDICAL CENTER · PI GAMAZON, ERIC R · 2021 to 2022
$1.5M
NHGRI NIH HHS R01 HG011138NHGRI NIH HHS R35 HG010718NIA NIH HHS R56 AG068026NIGMS NIH HHS R01 GM140287
6 · The paper itself

Abstract

backgroundGlobal genetic correlation analysis has provided valuable insight into the shared genetic basis between psychiatric and substance use disorders. However, little is known about which regions disproportionately contribute to the global correlation.

methodsWe used Local Analysis of [co]Variant Annotation to calculate bivariate local genetic correlations across 2495 approximately equal-sized, semi-independent genomic regions for 20 psychiatric and substance use phenotypes. We performed a transcriptome-wide association study using expression weights from the prefrontal cortex to identify risk genes for each phenotype, followed by probabilistic fine-mapping to prioritize credible causal genes within each bivariate locus.

resultsWe detected 80 significant (p < 2.08 × 10

conclusionsOur study reveals previously unreported local bivariate genetic correlations between psychiatric and substance use phenotypes, which we fine-mapped to identify shared credible causal genes underlying genetically correlated phenotypes.

Indexed as

SchizophreniaSubstance-Related DisordersGenetic Predisposition to DiseaseGenome-Wide Association StudyGenomicsHumansPhenotypePolymorphism, Single NucleotideGenetic correlationGenome-wide association studyLocal genetic correlationPsychiatric disorderSubstance use disorderTranscriptome-wide association study

Identifiers

PMID35525699
PMCPMC11034779

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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 OpenQuestion graph.