Evidence map›Paper›PMID 41690978›Full record

ReviewNPJ systems biology and applications2026

Genetic risk in Alzheimer's disease.

Yining Pan, Hwayoung Cho, Qian Lou, Wei Zhang, Breton Asken, Qianqian Song

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in NPJ systems biology and applications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Yining PanDepartment of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA.
Hwayoung ChoDepartment of Family, Community and Health System Science, College of Nursing, University of Florida, Gainesville, FL, USA.
Qian LouDepartment of Computer Science, University of Central Florida, Orlando, FL, USA.
Wei ZhangDepartment of Computer Science, University of Central Florida, Orlando, FL, USA.
Breton AskenDepartment of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA.
Qianqian SongDepartment of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA. qsong1@ufl.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alzheimer's disease (AD) has a strong genetic predisposition. Genome-wide association studies have identified multiple risk loci, yet many non-coding variants remain uncharacterized. Machine learning-based polygenic risk scores (PRS) enhance prediction by modeling genetic epistasis and sex-specific risks. This review summarizes AD genetic risk factors, PRS methodologies, and ML-based AD risk prediction. It also highlights challenges such as population bias, functional validation, and integrating multi-omics for precision medicine.

Indexed as

Alzheimer DiseaseGenetic Predisposition to DiseaseEpistasis, GeneticGenetic Risk ScoreGenome-Wide Association StudyHumansMachine LearningMultifactorial InheritancePolymorphism, Single NucleotidePrecision MedicineRisk Factors

Identifiers

PMID41690978
PMCPMC13022224

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.