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  "Title": "graph-GPA: A graphical model for prioritizing GWAS results and\ninvestigating pleiotropic architecture",
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  "Author": "Dongjun Chung, Hang J. Kim, Carter Allen",
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  "Description": "Genome-wide association studies (GWAS) is a widely used\ntool for identification of genetic variants associated with\nphenotypes and diseases, though complex diseases featuring many\ngenetic variants with small effects present difficulties for\ntraditional these studies. By leveraging pleiotropy, the\nstatistical power of a single GWAS can be increased. This\npackage provides functions for fitting graph-GPA, a statistical\nframework to prioritize GWAS results by integrating pleiotropy.\n'GGPA' package provides user-friendly interface to fit\ngraph-GPA models, implement association mapping, and generate a\nphenotype graph.",
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