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Fine-mapping and QTL tissue-sharing information improve causal gene identification and transcriptome prediction performance

By Alvaro N. Barbeira, Yanyu Liang, Rodrigo Bonazzola, Gao Wang, Heather E Wheeler, Owen Melia, Francois Aguet, GTEx Consortiumt, Kristen G Ardlie, Xiaoquan Wen, Hae Kyung Im

Posted 20 Mar 2020
bioRxiv DOI: 10.1101/2020.03.19.997213

The integration of transcriptomic studies and GWAS (genome-wide association studies) via imputed expression has seen extensive application in recent years, enabling the functional characterization and causal gene prioritization of GWAS loci. However, the techniques for imputing transcriptomic traits from DNA variation remain underdeveloped. Furthermore, associations found when linking eQTL studies to complex traits through methods like PrediXcan can lead to false positives due to linkage disequilibrium between distinct causal variants. Therefore, the best prediction performance models may not necessarily lead to more reliable causal gene discovery. With the goal of improving discoveries without increasing false positives, we develop and compare multiple transcriptomic imputation approaches using the most recent GTEx release of expression and splicing data on 17,382 RNA-sequencing samples from 948 post-mortem donors in 54 tissues. We find that informing prediction models with posterior causal probability from fine-mapping (dap-g) and borrowing information across tissues (mashr) lead to better performance in terms of number and proportion of significant associations that are colocalized and the proportion of silver standard genes as indicated by precision-recall and ROC (Receiver Operating Characteristic) curves. All prediction models are made publicly available at predictdb.org.

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