Evidence map›Paper›PMID 39146375›Full record

ArticlePLoS genetics2024

Finemap-MiXeR: A variational Bayesian approach for genetic finemapping.

Bayram Cevdet Akdeniz, Oleksandr Frei, Alexey Shadrin, Dmitry Vetrov, Dmitry Kropotov, Eivind Hovig, Ole A Andreassen, Anders M Dale

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PubMed Full text at PMC Publisher
In one paragraph

Article in PLoS genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Article
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  4. A genomic structural equation modelling analysis of the shared genetic architecture of the aging spine.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026
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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

8 authors.

Bayram Cevdet AkdenizCentre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.ORCID https://orcid.org/0000-0002-9493-3105
Oleksandr FreiCentre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
Alexey ShadrinCentre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
Dmitry VetrovConstructor University Bremen, Bremen, Germany.
Dmitry KropotovConstructor University Bremen, Bremen, Germany.
Eivind HovigCentre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway.
Ole A AndreassenCentre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.ORCID https://orcid.org/0000-0002-4461-3568
Anders M DaleCenter for Multimodal Imaging and Genetics, University of California San Diego, California, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genome-wide association studies (GWAS) implicate broad genomic loci containing clusters of highly correlated genetic variants. Finemapping techniques can select and prioritize variants within each GWAS locus which are more likely to have a functional influence on the trait. Here, we present a novel method, Finemap-MiXeR, for finemapping causal variants from GWAS summary statistics, controlling for correlation among variants due to linkage disequilibrium. Our method is based on a variational Bayesian approach and direct optimization of the Evidence Lower Bound (ELBO) of the likelihood function derived from the MiXeR model. After obtaining the analytical expression for ELBO's gradient, we apply Adaptive Moment Estimation (ADAM) algorithm for optimization, allowing us to obtain the posterior causal probability of each variant. Using these posterior causal probabilities, we validated Finemap-MiXeR across a wide range of scenarios using both synthetic data, and real data on height from the UK Biobank. Comparison of Finemap-MiXeR with two existing methods, FINEMAP and SuSiE RSS, demonstrated similar or improved accuracy. Furthermore, our method is computationally efficient in several aspects. For example, unlike many other methods in the literature, its computational complexity does not increase with the number of true causal variants in a locus and it does not require any matrix inversion operation. The mathematical framework of Finemap-MiXeR is flexible and may also be applied to other problems including cross-trait and cross-ancestry finemapping.

Indexed as

AlgorithmsBayes TheoremGenome-Wide Association StudyLinkage DisequilibriumHumansModels, GeneticPolymorphism, Single NucleotideQuantitative Trait Loci

Identifiers

PMID39146375
PMCPMC11349196

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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.