Evidence map›Paper›PMID 41377971›Full record

ArticleResearch square2025

Distributional genetic effects reveal context-dependent molecular regulation in human brain aging and Alzheimer's disease.

Anjing Liu, Roulan Jiang, Ruixi Li, Xuewei Cao, Zining Qi, Ru Feng, Hao Sun, Masashi Fujita, Natacha Comandante-Lou, Chirag M Lakhani and 9 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in Research square, 2025. 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

19 authors.

Anjing LiuCenter for Statistical Genetics, The Gertrude H. Sergievsky Center, Columbia University, New York, NY, USA.ORCID 0009-0000-0128-1877
Roulan JiangDepartment of Statistics and Data Science, Tsinghua University, Beijing, China.
Ruixi LiCenter for Statistical Genetics, The Gertrude H. Sergievsky Center, Columbia University, New York, NY, USA.
Xuewei CaoCenter for Statistical Genetics, The Gertrude H. Sergievsky Center, Columbia University, New York, NY, USA.
Zining QiCenter for Statistical Genetics, The Gertrude H. Sergievsky Center, Columbia University, New York, NY, USA.
Ru FengCenter for Statistical Genetics, The Gertrude H. Sergievsky Center, Columbia University, New York, NY, USA.
Hao SunCenter for Statistical Genetics, The Gertrude H. Sergievsky Center, Columbia University, New York, NY, USA.
Masashi FujitaCenter for Translational & Computational Neuroimmunology, Columbia University, New York, NY, USA.
Natacha Comandante-LouCenter for Translational & Computational Neuroimmunology, Columbia University, New York, NY, USA.
Chirag M LakhaniNew York Genome Center, New York, NY, USA.
Jenny EmpawiDepartment of Biomedical Genetics, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
David A KnowlesDepartment of Computer Science, Columbia University, New York, NY, USA.
Xiaoling ZhangDepartment of Biomedical Genetics, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
Kushal K DeyComputational and System Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Philip De JagerCenter for Translational & Computational Neuroimmunology, Columbia University, New York, NY, USA.
David BennettRush Alzheimer's Disease Center and Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA.
Alzheimer’s Disease Functional Genomics Consortium
Tianying WangDepartment of Statistics, Colorado State University, Fort Collins, CO, USA.
Gao WangCenter for Statistical Genetics, The Gertrude H. Sergievsky Center, Columbia University, New York, NY, USA.

Funding

FunGen-xQTL: Unraveling the genetic basis of molecular functions in Alzheimer's DiseaseR01AG086467 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Julia TCW, Gao Wang · 2025 to 2026
$7.2M
Multiomics data integration methods to discover putative causal variants, genes and patient heterogeneity for Alzheimers diseaseR01AG076901 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Gao Wang · 2023 to 2026
$2.4M
NIA NIH HHS R01 AG076901NIA NIH HHS R01 AG086467
6 · The paper itself

Abstract

Molecular QTL studies quantify whether genetic variants affect molecular traits, but non-linear effects including distributional patterns, variance, and interactions provide mechanistic insights beyond mean-level associations. Methods for detecting distributional effects have been developed for eQTL analysis, yet applications have focused on method demonstrations rather than large-scale biological discovery. We comprehensively mapped quantile, variance, and interaction QTLs across 34 data-set from 22 molecular contexts in >2,300 human brain donors, revealing that 48.7% of quantile QTLs (qQTLs) exhibit context-dependent regulation invisible to linear models, with enrichment at phenotypic extremes and in cell-type-specific regulatory elements, chromatin accessibility regions, and long-range chromosomal contacts. qQTL variants explained additional trait heritability beyond linear QTLs for brain-related traits. At Alzheimer's disease (AD) risk loci, qQTL analysis revealed complex regulatory architecture including variance effects at

Identifiers

PMID41377971
PMCPMC12687814

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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.