RAVAR: a curated repository for rare variant–trait associations

Author:

Cao Chen1ORCID,Shao Mengting1,Zuo Chunman2,Kwok Devin3,Liu Lin1,Ge Yuli1,Zhang Zilong45,Cui Feifei45,Chen Mingshuai5,Fan Rui5,Ding Yijie5ORCID,Jiang Hangjin6,Wang Guishen7ORCID,Zou Quan45

Affiliation:

1. Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University , Nanjing ,  China

2. Institute of Artificial Intelligence, Donghua University , Shanghai , China

3. School of Computer Science, McGill University , Montreal , Canada

4. Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China , Chengdu , China

5. Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China , Quzhou , China

6. Center for Data Science, Zhejiang University , Hangzhou , China

7. College of Computer Science and Engineering, Changchun University of Technology , Changchun , China

Abstract

Abstract Rare variants contribute significantly to the genetic causes of complex traits, as they can have much larger effects than common variants and account for much of the missing heritability in genome-wide association studies. The emergence of UK Biobank scale datasets and accurate gene-level rare variant–trait association testing methods have dramatically increased the number of rare variant associations that have been detected. However, no systematic collection of these associations has been carried out to date, especially at the gene level. To address the issue, we present the Rare Variant Association Repository (RAVAR), a comprehensive collection of rare variant associations. RAVAR includes 95 047 high-quality rare variant associations (76186 gene-level and 18 861 variant-level associations) for 4429 reported traits which are manually curated from 245 publications. RAVAR is the first resource to collect and curate published rare variant associations in an interactive web interface with integrated visualization, search, and download features. Detailed gene and SNP information are provided for each association, and users can conveniently search for related studies by exploring the EFO tree structure and interactive Manhattan plots. RAVAR could vastly improve the accessibility of rare variant studies. RAVAR is freely available for all users without login requirement at http://www.ravar.bio.

Funder

National Natural Science Foundation of China

Publisher

Oxford University Press (OUP)

Subject

Genetics

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