Author

Brian E. Henderson

University of Southern California

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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Genetic & Genomic Medicine· 1 Genetics· 1

Signature Assays, Organisms & Methods

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Organism:human· 2 Approach:statistical model· 2 Research Focus:GWAS / association mapping· 1 Assay:GWAS summary statistics· 1 Research Focus:variant effect prediction· 1 Assay:whole-exome sequencing· 1

Frequent Co-Authors

Abigail SvedeniD· 1 shared Adam Stuart KibeliD· 1 shared Adolfo CorreaiD· 1 shared Alanna C. MorrisoniD· 1 shared Alex P. ReineriD· 1 shared Alexander GuseviD· 1 shared Ali Amin Al OlamaiD· 1 shared Alicja WolkiD· 1 shared

Activity by Year

2018-2021 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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1 shared paperGenetic & Genomic Medicine

Abigail SvedenORCID

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2 papers

Recent·Most cited

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

Large-scale transcriptome-wide association study identifies new prostate cancer risk regions

Nicholas Mancuso, Simon A. Gayther, Alexander Gusev, Wei Zheng, Kathryn L. Penney, Zsofia Kote‐Jarai, Rosalind A. Eeles, Matthew L. Freedman, Christopher A. Haiman, Bogdan Paşaniuc, Brian E. Henderson, Sara Benlloch, Fredrick R. Schumacher, Ali Amin Al Olama, Kenneth Ross Muir, , Peter Kraft

This study integrates a large prostate cancer GWAS with gene expression data to identify 235 genes associated with risk, prioritizing causal genes through Bayesian credible sets.

Nature Communications · Sep 28, 2018Peer-reviewedGenetics