Author

Andy Dahl

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.

Biology Disciplines

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Genetics· 2 Genetic & Genomic Medicine· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Organism:human· 4 Approach:statistical model· 4 Assay:GWAS summary statistics· 2 Research Focus:polygenic risk· 2 Research Focus:variant effect prediction· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:eQTL / molecular QTL mapping· 1

Frequent Co-Authors

Manuela CostantinoiD· 2 shared Abigail SvedeniD· 1 shared Adolfo CorreaiD· 1 shared Alanna C. MorrisoniD· 1 shared Alex P. ReineriD· 1 shared Allan R LinnebergiD· 1 shared Amanda RantaiD· 1 shared Andrew D. MorrisiD· 1 shared

Activity by Year

2021-2026 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 22 mutual co-author links

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2 shared papersGenetics

Manuela CostantinoORCID

Author profile →

4 papers

Recent·Most cited

Locus-specific gene-context interactions improve polygenic prediction

Renée Fonseca, Christa Caggiano, Manuela Costantino, Ophelia Dominguez, Eimear E. Kenny, Andy Dahl

This paper introduces PGSC, a polygenic score framework that incorporates locus-specific gene-context interactions, showing improved prediction across multiple traits and ancestries.

bioRxiv · Aug 28, 2026 · PreprintGenetics

FastGxC: Fast and Powerful Context-Specific eQTL Mapping in Bulk and Single-Cell Data

Lena Krockenberger, Andrew Y. Lu, Mike Thompson, Lise A. Tucker, Cuining Liu, Mary Grace Gordon, Amanda Ranta, Ivan Carcamo‐Orive, Joshua W. Knowles, Andy Dahl, Chun Ye, Noah Zaitlen, Brunilda Balliu

FastGxC efficiently maps context-specific eQTLs in bulk and single-cell RNA-seq data, improving power and speed while enhancing biological relevance and trait association precision.

Cell Genomics · May 18, 2026Peer-reviewedGenomics

Optimizing phenotype scale improves genetic analyses in large-scale biobanks

Zhenhong Huang, Manuela Costantino, Andy Dahl

SIQReg optimizes phenotype scales in genetic analyses, improving both additive and non-additive signal detection in large biobanks.

bioRxiv · May 7, 2026 · PreprintGenetics

Determinants of penetrance and variable expressivity in monogenic metabolic conditions across 77,184 exomes

Julia K. Goodrich, Moriel Singer‐Berk, Rachel Son, Abigail Sveden, Jordan C. Wood, Eleina M. England, Joanne B. Cole, Ben Weisburd, Nick Watts, Lizz Caulkins, Peter Dornbos, Ryan Koesterer, Zachary Zappala, Haichen Zhang, Kristin A. Maloney, , Kerrin S. Small

The paper evaluates penetrance and variable expressivity of monogenic metabolic conditions using exome data from 77,184 individuals, finding that monogenic variants have larger effect sizes than polygenic scores but low penetrance.

Nature Communications · Jun 9, 2021Peer-reviewedGenetic & Genomic Medicine