Evaluation of PrediXcan for prioritizing GWAS associations and predicting gene expression1*

  title={Evaluation of PrediXcan for prioritizing GWAS associations and predicting gene expression1*},
  author={Binglan Li and Shefali S. Verma and Yogasudha C. Veturi and Anurag Verma and Yuki Bradford and David W. Haas and Marylyn DeRiggi Ritchie},
  booktitle={Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing},
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… CONTINUE READING
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