Evidence map›Paper›PMID 41050469›Full record

ArticleComputational and structural biotechnology journal2025

Identifying Alzheimer's disease-related pathways based on whole-genome sequencing data.

Yongheng Wang, Taihang Liu, Yijie He, Yaqin Tang, Pengcheng Tan, Lin Huang, Dongyu Huang, Tong Wen, Lizhen Shao, Jia Wang and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

12 authors.

Yongheng WangDepartment of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China.
Taihang LiuDepartment of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China.
Yijie HeDepartment of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China.
Yaqin TangDepartment of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China.
Pengcheng TanDepartment of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China.
Lin HuangDepartment of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China.
Dongyu HuangDepartment of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China.
Tong WenDepartment of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China.
Lizhen ShaoDepartment of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China.
Jia WangDepartment of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China.
Yingxiong WangDepartment of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China.
Zhijie HanDepartment of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alzheimer's disease (AD) is a highly inheritable neurodegenerative disorder for which pathway-specific genetic profiling provides insights into its key biological mechanisms and potential treatment targets. Traditional disease-pathway analyses for AD have certain limitations, such as environmental interference and arbitrary sample division. We present a comprehensive framework that starts with genome data, avoiding these drawbacks and offering intrinsic pathway-specific genetic profiling for AD. Whole genome sequencing data from 173 individuals were used to quantify transcriptomes in 14 brain regions, estimate individual-level pathway variant scores, and analyze AD risk for each patient. These results were combined to identify AD-related pathways and quantify their interactions. The predicted expression levels were consistent with previous findings, and the estimated AD risk showed a significant correlation with Braak/Thal scores. A total of 3798 pathways were identified as potentially associated with AD, with about 19.7 % previously reported. The pathways identified as AD risk related primarily address six core biological themes, including: Immunity and inflammation, Metabolism, Protein homeostasis, DNA/RNA and Epigenetics, Synapse and structure, Cell cycle. Specifically, key pathways, such as NF-κB signaling and GSK3β activation, were linked to AD pathogenesis. The interactions among pathways highlighted shared gene functions in AD. In summary, we provided an effective framework for disease-pathway analysis, revealing the interdependence or compensatory effects of pathways in AD.

Indexed as

Alzheimer’s diseasePathwayPolygenic risk scoreSingle sample gene set variation analysisStructural equation modeling

Identifiers

PMID41050469
PMCPMC12495422

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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.