Evidence map›Paper›PMID 41676477›Full record

ArticlebioRxiv : the preprint server for biology2026

AlphaGenome-enabled analysis of non-coding regulatory variants underlying

Miao Liu, Ziyang Shen, You Kyeong Jeong, Na Yu, Shang-Chuen Wu, Andre Wittig, Danielle Tenen, Yanjing Liu, Yanjing Liu, Li Chai

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 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

10 authors.

Miao LiuDepartment of Pathology, Joint Program in Transfusion Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.ORCID 0000-0003-3365-0213
Ziyang ShenDepartment of Pathology, Joint Program in Transfusion Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
You Kyeong JeongDepartment of Data Science, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA.
Na YuDepartment of Pathology, Joint Program in Transfusion Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Shang-Chuen WuDepartment of Pathology, Joint Program in Transfusion Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Andre WittigDepartment of Pathology, Joint Program in Transfusion Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Danielle TenenDepartment of Pathology, Joint Program in Transfusion Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Yanjing LiuGenetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA.
Yanjing LiuDepartment of Pathology, Joint Program in Transfusion Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Li ChaiDepartment of Pathology, Joint Program in Transfusion Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.ORCID 0000-0003-1937-4750

Funding

Novel Strategies to Improve Blood Transfusion PracticeP01HL158688 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI Hongbo R Luo · 2022 to 2026
$12.9M
NHLBI NIH HHS P01 HL158688
6 · The paper itself

Abstract

Systematic identification of functional non-coding regulatory variants remains a major challenge in human genetics. Conventional approaches such as large-scale CRISPR screening and genome-wide association studies (GWAS) are powerful but often prohibitively expensive, time-consuming, and experimentally intensive, limiting their scalability for locus-specific mechanistic studies. Recent advances in artificial intelligence offer the potential to partially replace or substantially augment these approaches by prioritizing regulatory variants with high functional likelihood. The

Identifiers

PMID41676477
PMCPMC12889522

What OpenQuestion holds

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Registered trials

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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.