Evidence map›Paper›PMID 40267236›Full record

ArticleHuman molecular genetics2025

Linking DNA methylation in brain regions to Alzheimer's disease risk: a Mendelian randomization study.

Hua Zhong, Jingjing Zhu, Shuai Liu, Dan Zhou, Quan Long, Chong Wu, Bingxin Zhao, Chao Cheng, Yaohua Yang, Qing Wu and 8 more

Abstract read
In one paragraph

Article in Human molecular genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
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

18 authors.

Hua ZhongCancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawai'i Cancer Center, University of Hawai'i at Mānoa, 701 Ilalo St, Honolulu, HI 96813, United States.
Jingjing ZhuDepartment of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawai'i at Mānoa, 651 Ilalo Street, Honolulu, HI 96813, United States.
Shuai LiuCancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawai'i Cancer Center, University of Hawai'i at Mānoa, 701 Ilalo St, Honolulu, HI 96813, United States.
Dan ZhouSchool of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, 388 Yuhangtang Road, Hangzhou, Zhejiang 310058, China.
Quan LongDepartment of Biochemistry & Molecular Biology, University of Calgary, 2500 University Drive NW, Calgary Alberta T2N 1N4, Canada.
Chong WuDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, 7007 Bertner Avenue, Houston, TX 77030, United States.
Bingxin ZhaoDepartment of Statistics and Data Science, University of Pennsylvania, 265 South 37th Street, Philadelphia, PA 19104, United States.
Chao ChengInstitute for Clinical and Translational Research, Baylor College of Medicine, One Baylor PlazaBCM 451, Houston, TX 77303, United States.
Yaohua YangDepartment of Genome Sciences, University of Virginia, 200 Jeanette Lancaster Way, Charlottesville, VA 22903, United States.
Qing WuDepartment of Biomedical Informatics, College of Medicine, The Ohio State University, 1800 Cannon Drive 250 Lincoln Tower, Columbus, OH 43210, United States.
Yong WuDivision of Cancer Research and Training, Department of Internal Medicine, Charles R. Drew University of Medicine and Science, 1731 E. 120th St, Los Angeles, CA 90059, United States.
Changwei LiDepartment of Epidemiology, Peter J. O'Donnell School of Public Health, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390, United States.
Zhaoming WangDepartment of Population Sciences, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, United States.
Jianyong WuCollege of Public Health, The Ohio State University, Cunz Hall 250 1841 Neil Ave, Columbus, OH 43210, United States.
Xingyi GuoDepartment of Medicine, Vanderbilt University Medical Center, 1161 21st Ave S # D3300, Nashville, TN 37232, United States.
Degui ZhiSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St Suite 600, Houston, TX 77030, United States.
Youping DengDepartment of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawai'i at Mānoa, 651 Ilalo Street, Honolulu, HI 96813, United States.
Lang WuCancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawai'i Cancer Center, University of Hawai'i at Mānoa, 701 Ilalo St, Honolulu, HI 96813, United States.

Funding

University of Hawaii Cancer Center CCSGP30CA071789 · NCI · UNIVERSITY OF HAWAII AT MANOA · PI Pallav Pokhrel · 1996 to 2026
$56.2M
An innovative integrated computational framework using gene signatures for patient stratificationR01CA269764 · NCI · BAYLOR COLLEGE OF MEDICINE · PI CHAO CHENG · 2023 to 2026
$1.8M
NCI NIH HHS P30 CA071789NCI NIH HHS R01 CA269764University of Hawai'i Cancer Center
6 · The paper itself

Abstract

aimDNA methylation in brain regions represents a potential mechanism linking genetic variation to Alzheimer's disease (ad) risk, yet most studies have focused on blood-derived methylation markers. In this study, we conducted a systematic Mendelian randomization (MR) study to evaluate associations between predicted brain region-specific DNA methylation levels and ad risk, using methylation quantitative trait loci (mQTL) as genetic instruments.

methodsWe analyzed mQTLs from five human brain regions: cerebellum (CRBLM), frontal cortex (FCTX), causal pons (PONS), and temporal cortex (TCTX) from 600 individuals in Gibbs et al's study, as well as mQTLs from dorsolateral prefrontal cortex (DLPFC) of 543 participants in the Religious Orders Study and the Rush Memory and Aging Project (ROSMAP). In our MR analyses, we integrated these mQTLs with single nucleotide polymorphisms (SNP)-ad risk summary statistics derived from 85 934 ad-related cases and 401 577 normal controls.

resultsAmong 62 554 cytosine-guanine dinucleotide (CpG) sites, we identified 597 CpG sites (CpGs) significantly associated with ad risk (false discovery rate (FDR) < 0.05). Of these, 289 were confirmed through colocalization and summary-based MR (SMR) analyses, including one CpG site in CRBLM, 285 in DLPFC, one in FCTX, two in PONS, and one in TCTX. By integrating gene expression data, we identified 19 CpG sites with consistent associations across methylation levels, expression of eight target genes, and ad risk, including novel regulatory mechanisms involving RITA1's modulation of cg11558705 and PCGF3's regulation of cg10009224.

conclusionOur findings highlight brain region-specific DNA methylation as a mediator of genetic risk for ad, offering insights into ad pathogenesis and identifying potential therapeutic targets.

Indexed as

Alzheimer DiseaseBrainDNA MethylationAgedCpG IslandsFemaleGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMaleMendelian Randomization AnalysisPolymorphism, Single NucleotideQuantitative Trait LociRisk FactorsAlzheimer’s disease riskBrain-specific epigeneticsDNA methylation biomarkersGene expression regulationMendelian randomization

Identifiers

PMID40267236
PMCPMC13377460

What Socratic holds

Textmetadata
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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 Socratic graph.