Ran Tao

Assistant Professor

Vanderbilt University Medical Center

United States

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About

Dr. Ran Tao is an Assistant Professor at Vanderbilt University Medical Center in the United States. His research focuses on genetic epidemiology and statistical methodologies, with recent work exploring polygenic risk scores, genetic determinants related to diabetes, and methods for analyzing error-prone data in clinical studies. He has contributed significantly to understanding the genetic underpinnings of various diseases, including type 2 diabetes and cancer susceptibility.

Articles (15)

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2025

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2025

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2025

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2025

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2024

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2024

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2024

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2022

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2022

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2022

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2021

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2021

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2021

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2020

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2020

Collaborators (15)

Pamela A Shaw

University of Pennsylvania Perelman School of Medicine

UNITED STATES

Ran Tao

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Articles (15)

RAB19, SERPINB9P1, and Pancreatitis in Patients Taking Azathioprine in Routine Clinical Practice: Genome and Transcriptome‐Wide Association Studies

Polygenic risk score for type 2 diabetes shows context-dependent effects across populations

sleev: An R Package for Semiparametric Likelihood Estimation with Errors in Variables

Ascertainment Conditional Maximum Likelihood for Continuous Outcome Under Two‐Phase Response‐Selective Design

Large-Scale Alternative Polyadenylation-Wide Association Studies to Identify Putative Cancer Susceptibility Genes

Three‐phase generalized raking and multiple imputation estimators to address error‐prone data

Efficient designs and analysis of two-phase studies with longitudinal binary data

Genetic determinants of interleukin-6 levels and risk of end-stage renal disease

Integration of multidimensional splicing data and GWAS summary statistics for risk gene discovery

Multi-ethnic GWAS and fine-mapping of glycaemic traits identify novel loci in the PAGE Study

Efficient odds ratio estimation under two‐phase sampling using error‐prone data from a multi‐national HIV research cohort

Efficient semiparametric inference for two‐phase studies with outcome and covariate measurement errors

Two‐wave two‐phase outcome‐dependent sampling designs, with applications to longitudinal binary data

Genome-Wide Association of Kidney Traits in Hispanics/Latinos Using Dense Imputed Whole-Genome Sequencing Data

Evaluating the Utility of Polygenic Risk Scores in Identifying High-Risk Individuals for Eight Common Cancers

Collaborators (15)

Pamela A Shaw

Laura Raffield

Zhe Wang

Alyson Dickson

Quan Wang

GUOCHONG JIA

Sarah Lotspeich

Gustavo Amorim

Xingyi Guo

Burcu F Darst

Lee Wheless

Ying Liu

Yaohua Yang

Rena Patel

Alexander Bick

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