Author

Shujing Xu

Claremont Graduate UniversityORCID 0000-0002-7168-613XUnclaimed ORCID profile · follow to get notified of new preprints

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Shujing Xu1 paper · Qingdao Agricultural University

Biology Disciplines

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Bioinformatics· 1 Genetics· 1

Signature Assays, Organisms & Methods

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Assay:GWAS summary statistics· 2 Approach:Bayesian inference· 1 Research Focus:benchmarking· 1 Research Focus:gene regulatory network inference· 1 Research Focus:GWAS / association mapping· 1 Organism:human· 1 Approach:statistical model· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Andrew Joseph SchorkiD· 1 shared Chun Chieh FaniD· 1 shared David V. ContiiD· 1 shared Lai JiangiD· 1 shared Nicholas MancusoiD· 1 shared Paul J. NewcombeiD· 1 shared Richard A. LevineiD· 1 shared Rong ZablockiiD· 1 shared

Activity by Year

2017-2020 · peak 1/yr

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10 top collaborators · 21 mutual co-author links

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1 shared paperBioinformatics

Andrew Joseph SchorkORCID

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2 papers

Recent·Most cited

A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis

Lai Jiang, Shujing Xu, Nicholas Mancuso, Paul J. Newcombe, David V. Conti

This paper introduces hJAM, a hierarchical model that improves causal inference in Mendelian randomization and transcriptome-wide association studies by incorporating prior information and accounting for SNP correlations.

American Journal of Epidemiology · Dec 29, 2020Peer-reviewedGenetics

Semi-Parametric Covariate-Modulated Local False Discovery Rate For Genome-Wide Association Studies

Rong Zablocki, Richard A. Levine, Andrew Joseph Schork, Shujing Xu, Yunpeng Wang, Chun Chieh Fan, Wesley Kurt Thompson

This paper introduces a covariate-modulated local false discovery rate method for GWAS to improve power and pathway analysis for schizophrenia.

The Annals of Applied Statistics · Dec 1, 2017Peer-reviewedBioinformatics