Article in Genes, 2021. 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
0.4field-weighted citation impact, top 40% of its field
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
4 citing papers in PubMed, 4 citations in OpenAlex.
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
5 authors at 3 institutions in 2 countries.
Vânia TavaresInstituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal.
Joana MonteiroInstituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal.
Evangelos VassosSocial, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK.
Jonathan ColemanSocial, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK.ORCID 0000-0002-6759-0944
Diana PrataInstituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal.
King's College London · GBUniversity of Lisbon · PTIscte – Instituto Universitário de Lisboa · PT
Funding
VESICULAR LOCALIZATION AND FUNCTION OF PRESENILIN 1 FRAGMENTP50AG005138 · NIA · MOUNT SINAI SCHOOL OF MEDICINE OF NYU · PI GROSSMAN, HILLEL · 1985 to 2019
$37.9M
QUANTITATIVE ANALYSIS OF MICROVASCULAR CHANGES IN THE AGING BRAINP01AG002219 · NIA · MOUNT SINAI SCHOOL OF MEDICINE OF NYU · PI HAROUTUNIAN, VAHRAM · 1985 to 2009
$19.2M
White Matter Abnormalities in SchizophreniaP50MH066392 · NIMH · MOUNT SINAI SCHOOL OF MEDICINE OF NYU · PI O'DONOVAN, MICHAEL J · 2002 to 2012
$18.6M
Project 5: In vivo measurement of GABA transmission in healthy controls & subjectP50MH084053 · NIMH · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI LEWIS, DAVID A · 2008 to 2012
$11.5M
International Cohort Collection for Bipolar DisorderR01MH085542 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI SMOLLER, JORDAN W · 2008 to 2012
$10.7M
Cis-Regulatory Epigenome Mappings in SchizophreniaU01MH103392 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI AKBARIAN, SCHAHRAM, SKLAR, PAMELA · 2014 to 2017
$7.6M
Molecular Profiling of SchizophreniaR01MH110921 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI CHESS, ANDREW J, ROUSSOS, PANAGIOTIS · 2016 to 2020
$6.9M
Genetics Association in Schizophrenia and Other DisordersR37MH057881 · NIMH · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI DEVLIN, BERNIE · 2013 to 2022
$5.4M
Risk genetic variants and cis regulation of gene expression in Bipolar DisorderR01MH109677 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI ROUSSOS, PANAGIOTIS · 2016 to 2020
$4.6M
Src mediates molecular alterations leading to NMDAR hypofunction in schizophreniaR01MH075916 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI BORGMANN-WINTER, KARIN, HAHN, CHANG-GYU · 2006 to 2019
$3.3M
Integrated Multiscale Networks in SchizophreniaR01MH109897 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI BRENNAND, KRISTEN JENNIFER, ROUSSOS, PANAGIOTIS · 2016 to 2020
$3.0M
Identifying therapeutic targets for autism using Shank3-deficient miceR01MH093725 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI BUXBAUM, JOSEPH D. · 2011 to 2015
Predicting gene expression from genotyped data is valuable for studying inaccessible tissues such as the brain. Herein we present eGenScore, a polygenic/poly-variation method, and compare it with PrediXcan, a method based on regularized linear regression using elastic nets. While both methods have the same purpose of predicting gene expression based on genotype, they carry important methodological differences. We compared the performance of expression quantitative trait loci (eQTL) models to predict gene expression in the frontal cortex, comparing across these frameworks (eGenScore vs. PrediXcan) and training datasets (BrainEAC, which is brain-specific, vs. GTEx, which has data across multiple tissues). In addition to internal five-fold cross-validation, we externally validated the gene expression models using the CommonMind Consortium database. Our results showed that (1) PrediXcan outperforms eGenScore regardless of the training database used; and (2) when using PrediXcan, the performance of the eQTL models in frontal cortex is higher when trained with GTEx than with BrainEAC.
Indexed as
GenotypeModels, GeneticTranscriptomeBrainDatabases, GeneticGenome-Wide Association StudyHumansMultifactorial InheritanceQuantitative Trait LociSoftwareexpression quantitative trait locigene expressiongenome wide association studypolygenic scoretranscriptome
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.
Evaluation of Genotype-Based Gene Expression Model Performance: A Cross-Framework and Cross-Dataset Study. · full record | OpenQuestion