LPM

LPM (Latent Probit Model) is an efficient statistical approach to characterize the relationship among complex traits using summary statistics from multiple GWASs and functional annotations.

Reference: Jingsi Ming, Tao Wang and Can Yang. LPM: a latent probit model to characterize the relationship among complex traits using summary statistics from multiple GWASs and functional annotations. 2018.

  • The R package 'LPM' on github. The R package 'LPM' provides model parameter estimation as well as statistical inference.
  • The source code for simulation studies in the paper [github].
  • Real data sets used in the paper, including functional annotations [link] and summary statistics from GWAS [link].

Figure 1. The estimated correlations for 44 GWAS with 9 genic category annotations and 127 cell-type specific functional annotations integrated.

Figure 2. The −log10 (p -value) of the enrichment test for nine genic category annotations and 127 cell-type specific functional annotations for traits in block 1 of Figure 1.