Binglan Li1, Shefali S. Verma1,2, Yogasudha C. Veturi2, Anurag Verma1,2, Yuki Bradford2, David W. Haas3,4, Marylyn D. Ritchie1,2
1The Huck Institutes of the Life Sciences, The Pennsylvania State University
2Biomedical and Translational Informatics Institute
3Department of Medicine, Pharmacology, Pathology, Microbiology & Immunology, Vanderbilt University School of Medicine
4Department of Internal Medicine, Meharry Medical College
Email: binglan.li@pennmedicine.upenn.edu, shefali.setia@gmail.com, sudhaveturi@gmail.com, anurag21.verma@gmail.com, yuki.bradford@pennmedicine.upenn.edu, david.haas@vanderbilt.edu, marylyn@upenn.edu
Pacific Symposium on Biocomputing 23:448-459(2018)
© 2018 World Scientific
Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License.
Genome-wide association studies (GWAS) have been successful in facilitating the understanding of genetic architecture behind human diseases, but this approach faces many challenges. To identify disease-related loci with modest to weak effect size, GWAS requires very large sample sizes, which can be computational burdensome. In addition, the interpretation of discovered associations remains difficult. PrediXcan was developed to help address these issues. With built in SNP-expression models, PrediXcan is able to predict the expression of genes that are regulated by putative expression quantitative trait loci (eQTLs), and these predicted expression levels can then be used to perform gene-based association studies. This approach reduces the multiple testing burden from millions of variants down to several thousand genes. But most importantly, the identified associations can reveal the genes that are under regulation of eQTLs and consequently involved in disease pathogenesis. In this study, two of the most practical functions of PrediXcan were tested: 1) predicting gene expression, and 2) prioritizing GWAS results. We tested the prediction accuracy of PrediXcan by comparing the predicted and observed gene expression levels, and also looked into some potential influential factors and a filter criterion with the aim of improving PrediXcan performance. As for GWAS prioritization, predicted gene expression levels were used to obtain gene-trait associations, and background regions of significant associations were examined to decrease the likelihood of false positives. Our results showed that 1) PrediXcan predicted gene expression levels accurately for some but not all genes; 2) including more putative eQTLs into prediction did not improve the prediction accuracy; and 3) integrating predicted gene expression levels from the two PrediXcan whole blood models did not eliminate false positives. Still, PrediXcan was able to prioritize GWAS associations that were below the genome-wide significance threshold in GWAS, while retaining GWAS significant results. This study suggests several ways to consider PrediXcan's performance that will be of value to eQTL and complex human disease research.