AnnoPred specifications


Unique identifier OMICS_19027
Name AnnoPred
Software type Framework/Library
Interface Command line interface
Restrictions to use None
Input data Some GWAS Summary statistics.
Input format TXT
Output data A set of files including two types of AnnoPred polygenic risk scores, phenotypes of testing data, prediction accuracy, posterior expectation estimation of the effect size of each snp.
Operating system Unix/Linux
Programming languages Python
Computer skills Advanced
Stability Stable
h5py, plinkio, scipy, numpy
Maintained Yes



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  • person_outline Hongyu Zhao <>

Additional information


Publication for AnnoPred

AnnoPred in publication

PMCID: 5482506
PMID: 28598966
DOI: 10.1371/journal.pgen.1006836

[…] of eight methods, corresponding to infinitesimal and non-infinitesimal versions of single-trait and two-trait approaches with and without functional annotations. as shown in [, ], ldpred and annopred outperform other state-of-the-art prs methods, we therefore use these two approaches as the representative single-trait prediction methods., annopred-inf/annopred: single-trait prediction […]

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AnnoPred institution(s)
Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA; Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Yale College, New Haven, CT, USA; Department of Mathematics, Hong Kong Baptist University, Kowloon, Hong Kong; Department of Genetics, Yale University School of Medicine, New Haven, CT, USA; Clinical Epidemiology Research Center (CERC), Veterans Affairs (VA) Cooperative Studies Program, VA Connecticut Healthcare System, West Haven, CT, USA
AnnoPred funding source(s)
Supported in part by the National Institutes of Health grants R01 GM59507, the VA Cooperative Studies Program of the Department of Veterans Affairs, Office of Research and Development, and the Yale World Scholars Program sponsored by the China Scholarship Council.

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