Evidence map›Paper›PMID 35883662›Full record

ArticleCells2022

A Method for Bridging Population-Specific Genotypes to Detect Gene Modules Associated with Alzheimer's Disease.

Yulin Dai, Peilin Jia, Zhongming Zhao, Assaf Gottlieb

Open access · goldAbstract read
In one paragraph

Article in Cells, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
0.4field-weighted citation impact, top 42% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. GRPa-PRS: A risk stratification method to identify genetically-regulated pathways in polygenic diseases.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    Article
  4. Article
  5. Article
4 · The record

Corrections and comments

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 at 1 institution in 1 country.

Yulin DaiCenter for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.ORCID 0000-0003-1874-7893
Peilin JiaCenter for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Zhongming ZhaoCenter for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.ORCID 0000-0002-3477-0914
Assaf GottliebCenter for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.ORCID 0000-0003-4904-631X
The University of Texas Health Science Center at Houston · US

Funding

Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer's Disease (Parent grant)U01AG070112 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI FORNAGE, MYRIAM, JI, SHUIWANG · 2021 to 2025
$7.2M
Finding Combinatorial Drug Repositioning Therapy For Alzheimer'S Disease And Related DementiasR01AG066749 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI JIANG, XIAOQIAN, ZHENG, WENJIN JIM · 2020 to 2024
$4.4M
Transforming dbGaP genetic and genomic data to FAIR-ready by artificial intelligence and machine learning algorithmsR01LM012806 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Zhongming Zhao · 2017 to 2026
$3.7M
Role of cellular metabolism in palate morphogenesisR01DE029818 · NIDCR · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI IWATA, JUNICHI, ZHAO, ZHONGMING · 2020 to 2024
$1.8M
Deep learning for decoding genetic regulation and cellular maps in craniofacial developmentR01DE030122 · NIDCR · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI IWATA, JUNICHI, ZHAO, ZHONGMING · 2021 to 2023
$1.7M
Constructing A Transcriptomic Atlas of Retrotransposon in Alzheimer's DiseaseR03AG077191 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI ZHAO, ZHONGMING · 2022 to 2022
$312k
NIA NIH HHS R01 AG066749NIA NIH HHS R03 AG077191NIA NIH HHS U01 AG070112NIDCR NIH HHS R01 DE029818NIDCR NIH HHS R01 DE030122NLM NIH HHS R01 LM012806
6 · The paper itself

Abstract

backgroundGenome-wide association studies have successfully identified variants associated with multiple conditions. However, generalizing discoveries across diverse populations remains challenging due to large variations in genetic composition. Methods that perform gene expression imputation have attempted to address the transferability of gene discoveries across populations, but with limited success.

methodsHere, we introduce a pipeline that combines gene expression imputation with gene module discovery, including a dense gene module search and a gene set variation analysis, to address the transferability issue. Our method feeds association probabilities of imputed gene expression with a selected phenotype into tissue-specific gene-module discovery over protein interaction networks to create higher-level gene modules.

resultsWe demonstrate our method's utility in three case-control studies of Alzheimer's disease (AD) for three different race/ethnic populations (Whites, African descent and Hispanics). We discovered 182 AD-associated genes from gene modules shared between these populations, highlighting new gene modules associated with AD.

conclusionsOur innovative framework has the potential to identify robust discoveries across populations based on gene modules, as demonstrated in AD.

Indexed as

Alzheimer DiseaseGene Regulatory NetworksGenome-Wide Association StudyGenotypeHumansPhenotypeAlzheimer’s diseasedense gene modulesgene expression imputationgenetically regulated expression (GReX)

Identifiers

PMID35883662
PMCPMC9319087
OpenAlexW4285732628

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.