Author

Amanda L. Tapia

University of North Carolina at Chapel HillORCID 0000-0001-7348-7372Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Genetics· 2 Bioinformatics· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 4 Organism:human· 3 Research Focus:benchmarking· 2 Assay:bulk RNA-seq· 2 Research Focus:database / resource· 1 Research Focus:differential expression· 1 Research Focus:fine-mapping· 1 Research Focus:GWAS / association mapping· 1

Frequent Co-Authors

Yun LiiD· 4 shared Alex P. ReineriD· 3 shared Jonathan D. RoseniD· 3 shared Laura M. RaffieldiD· 3 shared Mariaelisa GraffiD· 3 shared Alanna C. MorrisoniD· 2 shared Bryce RowlandiD· 2 shared Charles L KooperbergiD· 2 shared

Activity by Year

2019-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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4 shared papersGenetics

Yun LiORCID

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4 papers

Recent·Most cited

Transcriptome-wide association study in UK Biobank Europeans identifies associations with blood cell traits.

Bryce Rowland, Sanan Venkatesh, Manuel Tardáguila, Jia Wen, Jonathan D. Rosen, Amanda L. Tapia, Quan Sun, Mariaelisa Graff, Dragana Vuckovic, Guillaume Lettre, Vijay G. Sankaran, Georgios Voloudakis, Panos Roussos, Jennifer E. Huffman, Alex P. Reiner, , Yun Li

This study identifies 557 new gene-trait associations for hematological traits using transcriptome-wide association analysis in UK Biobank data.

Human Molecular Genetics · Feb 9, 2022Peer-reviewedGenetics

MRLocus: identifying causal genes mediating a trait through Bayesian estimation of allelic heterogeneity

Anqi Zhu, Nana Matoba, Emmaleigh Wilson, Amanda L. Tapia, Yun Li, Joseph G. Ibrahim, Jason L. Stein, Michael I. Love

MRLocus estimates gene-to-trait mediation effects using Bayesian methods on eQTL and GWAS data, showing high accuracy and better uncertainty estimation in simulations and real applications.

PLoS Genetics · Apr 19, 2021Peer-reviewedBioinformatics

A large-scale transcriptome-wide association study (TWAS) of ten blood cell phenotypes reveals complexities of TWAS fine-mapping

Amanda L. Tapia, Bryce Rowland, Jonathan D. Rosen, Michael H Preuss, Kris Young, Mariaelisa Graff, Hélène Choquet, David J. Couper, Steven G. Buyske, Stephanie A. Bien, Eric Jorgenson, Charles L Kooperberg, Ruth J. F. Loos, Alanna C. Morrison, Kari E. North, , Laura M. Raffield

This paper performs a large-scale TWAS of blood cell phenotypes, identifies 239 gene-trait associations, and develops an R Shiny app to visualize TWAS results.

bioRxiv · Feb 23, 2021 · PreprintGenetics

Use of >100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations

Madeline H. Kowalski, Huijun Qian, Ziyi Hou, Jonathan D. Rosen, Amanda L. Tapia, Yue Shan, Deepti Jain, Maria Argos, Donna K. Arnett, Christy L. Avery, Kathleen C. Barnes, Lewis C. Becker, Stephanie A. Bien, Joshua C. Bis, John Blangero, , Yun Li

This study uses TOPMed whole genome sequences to improve imputation quality and detect rare variant associations in admixed African and Hispanic/Latino populations, identifying two novel associations with hematological traits.

PLoS Genetics · Dec 23, 2019Peer-reviewedGenomics