GPA: A statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation

The paper is freely available at PLoS Genetics.

This is a joint work with Dongjun Chung, Cong Li, Joel Gelernter and Hongyu Zhao.

About GPA

GPA (Genetic analysis incorporating Pleiotropy and Annotation)

  1. Pleiotropy: different complex diseases share common risk genetic bases.
  2. Annotation: functionally annotated genetic variants have been consistently demonstrated to be enriched among GWAS hits.

Features of GPA

  1. Easy to use: it only requires the summary statistics (p-values) as its input rather than the genotype and phenotype data.
  2. Computational efficiency: it takes a few minutes to perform analysis with millions of markers.
  3. Rigorous statistical inference: it provides the false discovery rate (FDR), hypothesis testing of pleiotropy and annotation enrichment, and the standard errors of the estimated parameter for understanding the genetic architecture of complex diseases.

The real data sets analyzed in GPA

  1. The summary statistics of five pyschiatry disorders (ADHD, ASD, BPD, MDD, SCZ) from the cross-disorder study section of Psychiatry Genomics Consortium and the CNS annotation [RData download][GPA-results download].
  2. The summary statistics for Bladder Cancer GWAS and 125 DNase I hypersenstivity site annotations from the ENCODE project. [Summary statistics download][125 DNase annotations (flanking region 1kb) download]

The R package of GPA (c++ version) is currently available at github. The MATLAB package is also available upon request.