Evidence map›Paper›PMID 41232530›Full record

ArticleAmerican journal of human genetics2025

A deep dive into statistical modeling of RNA splicing QTLs reveals variants that explain neurodegenerative disease.

David Wang, Matthew R Gazzara, San Jewell, Benjamin D Wales-McGrath, Kevin Yang, Christopher D Brown, Peter S Choi, Yoseph Barash

Abstract read
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In one paragraph

Article in American journal of human genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

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No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

David WangDepartment of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: wangdavid758@gmail.com.
Matthew R GazzaraDepartment of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
San JewellDepartment of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Benjamin D Wales-McGrathDepartment of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Kevin YangDepartment of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Christopher D BrownDepartment of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Peter S ChoiDepartment of Pathology & Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Division of Cancer Pathobiology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Yoseph BarashDepartment of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Computer and Information Sciences, School of Engineering, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: yosephb@upenn.edu.

Funding

Methods for RNA splicing variations detection, quantification, visualization, and association from large heterogeneous datasetsR01GM128096 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI BARASH, YOSEPH · 2018 to 2021
$1.8M
Methods for improving clinical diagnostic by detection, prediction, interpretation and prioritization of aberrant transcriptome variationsR01LM013437 · NLM · UNIVERSITY OF PENNSYLVANIA · PI BARASH, YOSEPH · 2020 to 2023
$1.4M
Developing Machine Learning Models for the Analysis of Splicing Data in Large Heterogeneous CohortsF31CA265218 · NCI · UNIVERSITY OF PENNSYLVANIA · PI WANG, DAVID · 2021 to 2023
$121k
NCI NIH HHS F31 CA265218NIGMS NIH HHS R01 GM128096NLM NIH HHS R01 LM013437
6 · The paper itself

Abstract

Genome-wide association studies (GWASs) have identified thousands of putative disease-causing variants with unknown regulatory effects. Efforts to connect these variants with splicing quantitative trait loci (sQTLs) have provided functional insights, yet sQTLs reported by existing methods cannot explain many GWAS signals. We show that current sQTL modeling approaches can be improved by considering alternative splicing representation, model calibration, and covariate integration. We then introduce MAJIQTL, a pipeline for sQTL discovery. MAJIQTL includes two statistical methods: a weighted multiple-testing approach for sGene discovery and a model for sQTL effect-size inference to improve variant prioritization. By applying MAJIQTL to GTEx, we find significantly more sGenes harboring sQTLs with functional significance. Notably, our analysis implicates the variant rs528823 in Alzheimer disease. Using antisense oligonucleotides, we test this variant's effect by blocking the implicated YBX3 binding site, leading to exon skipping in MS4A3.

Indexed as

Models, StatisticalNeurodegenerative DiseasesQuantitative Trait LociRNA SplicingAlternative SplicingAlzheimer DiseaseExonsGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansModels, GeneticPolymorphism, Single NucleotideAlzheimereffect sizeMAJIQMS4A3ParkinsonRNA-seqRNA splicingsQTLstatistical geneticsweighted hypothesis testing

Identifiers

PMID41232530
PMCPMC12626415

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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.