Evaluation of PrediXcan for prioritizing GWAS associations and predicting gene expression.

Abstract

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… (More)

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Cite this paper

@article{Li2018EvaluationOP, title={Evaluation of PrediXcan for prioritizing GWAS associations and predicting gene expression.}, author={Binglan Li and Shefali S. Verma and Yogasudha C. Veturi and Anurag Verma and Yuki Bradford and David W. Haas and Marylyn D. Ritchie}, journal={Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing}, year={2018}, volume={23}, pages={448-459} }