Integration of Distinct Analysis Strategies Improves Tissue-Trait Association Identification

Zhijian Yang, Wenzheng Xu, Ranran Zhai, Ting Li, Zheng Ning, Yudi Pawitan, Xia Shen

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Integrating genome-wide association studies (GWAS) with transcriptomic data, human complex traits and diseases have been linked to relevant tissues and cell types using different methods. However, different results from these methods generated confusion while no gold standard is currently accepted, making it difficult to evaluate the discoveries. Here, applying three methods on the same data source, we estimated the sensitivity and specificity of these methods in the absence of a gold standard. We established a more specific tissue-trait association atlas by combining the information captured by different methods. Our triangulation strategy improves the performance of existing methods in establishing tissue-trait associations. The results provide better etiological and functional insights for the tissues underlying different human complex traits and diseases.

Original languageEnglish
Pages (from-to)798269
JournalFrontiers in Genetics
Publication statusPublished - 29 Mar 2022


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