Author

Matthew Stephens

University of Chicago

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

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Biology Disciplines

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Bioinformatics· 4 Genetics· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 6 Organism:human· 4 Research Focus:benchmarking· 2 Research Focus:differential expression· 2 Research Focus:fine-mapping· 2 Assay:GWAS summary statistics· 2 Research Focus:variant calling· 2 Assay:bulk RNA-seq· 1

Frequent Co-Authors

Peter S. CarbonettoiD· 2 shared William R. P. DenaultiD· 2 shared Alexandra J. ScottiD· 1 shared Alexis J. BattleiD· 1 shared Alvaro BarbeiraiD· 1 shared Andrew B. NobeliD· 1 shared Andrew BrowniD· 1 shared Andrey A. ShabaliniD· 1 shared

Activity by Year

2015-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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2 shared papersGenetics

Peter S. CarbonettoORCID

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Author profile →

6 papers

Recent·Most cited

fSuSiE enables fine-mapping of QTLs from genome-scale molecular profiles

William R. P. Denault, Hao H. Sun, Peter S. Carbonetto, Anjing Liu, Philip L. De Jager, David Bennett, Gao T. Wang, Matthew Stephens

fSuSiE improves fine-mapping of QTLs by modeling spatial genetic effects on molecular traits, achieving higher resolution in identifying causal variants and their molecular targets.

bioRxiv · Aug 17, 2025 · PreprintGenetics

Accounting for uncertainty in residual variances improves calibration of the Sum of Single Effects model for small sample sizes

William R. P. Denault, Peter S. Carbonetto, Ruixi Li, Gao Wang, Matthew Stephens

The paper modifies the SuSiE model to improve calibration in small-sample studies, reducing false positives in genetic fine-mapping.

bioRxiv · May 16, 2025 · PreprintBioinformatics

Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics

Alvaro Barbeira, Scott P. Dickinson, Rodrigo Bonazzola, Jiamao Zheng, Heather E. Wheeler, Jason M. Torres, Eric S. Torstenson, Kaanan P. Shah, Tzintzuni Garcia, Todd L. Edwards, Eli Stahl, Laura M. Huckins, François Aguet, Kristin Ardlie, Beryl B. Cummings, , Benjamin J. Strober

The paper develops S-PrediXcan to compute gene expression associations from GWAS summary data and applies it to 44 GTEx tissues and 100+ phenotypes, finding tissue-specific effects and colocalized associations.

Nature Communications · May 2, 2018Peer-reviewedGenomics

False Discovery Rates: A New Deal.

Matthew Stephens

The paper introduces a new Empirical Bayes method for large-scale hypothesis testing that estimates False Discovery Rates and effect sizes with improved accuracy and robustness.

bioRxiv · Jun 8, 2016Bioinformatics

Variance Adaptive Shrinkage (vash): Flexible Empirical Bayes estimation of variances

Mengyin Lu, Matthew Stephens

The paper introduces a flexible Empirical Bayes method for variance estimation in genomics, showing it performs competitively or better than existing methods in various scenarios.

bioRxiv · Apr 13, 2016Bioinformatics

A simple model-based approach to inferring and visualizing cancer mutation signatures

Yuichi Shiraishi, Georg Tremmel, Satoru Miyano, Matthew Stephens

This paper introduces a simplified model-based approach for inferring and visualizing cancer mutation signatures, showing improved robustness and interpretability.

PLOS Genetics · Dec 2, 2015Peer-reviewedBioinformatics