PERCH: A Unified Framework for Disease Gene Prioritization

Hum Mutat. 2017 Mar;38(3):243-251. doi: 10.1002/humu.23158. Epub 2017 Jan 28.


To interpret genetic variants discovered from next-generation sequencing, integration of heterogeneous information is vital for success. This article describes a framework named PERCH (Polymorphism Evaluation, Ranking, and Classification for a Heritable trait), available at It can prioritize disease genes by quantitatively unifying a new deleteriousness measure called BayesDel, an improved assessment of the biological relevance of genes to the disease, a modified linkage analysis, a novel rare-variant association test, and a converted variant call quality score. It supports data that contain various combinations of extended pedigrees, trios, and case-controls, and allows for a reduced penetrance, an elevated phenocopy rate, liability classes, and covariates. BayesDel is more accurate than PolyPhen2, SIFT, FATHMM, LRT, Mutation Taster, Mutation Assessor, PhyloP, GERP++, SiPhy, CADD, MetaLR, and MetaSVM. The overall approach is faster and more powerful than the existing quantitative method pVAAST, as shown by the simulations of challenging situations in finding the missing heritability of a complex disease. This framework can also classify variants of unknown significance (variants of uncertain significance) by quantitatively integrating allele frequencies, deleteriousness, association, and co-segregation. PERCH is a versatile tool for gene prioritization in gene discovery research and variant classification in clinical genetic testing.

Keywords: co-segregation analysis; de novo mutation; functional consequence; gene association network; gene prioritization; genetic testing; rare-variant burden test; variant interpretation; variants of unknown significance; whole-exome/whole-genome/gene-panel sequencing.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computational Biology / methods*
  • Genetic Association Studies / methods*
  • Genetic Predisposition to Disease*
  • Humans
  • Polymorphism, Single Nucleotide*
  • Quantitative Trait, Heritable*
  • ROC Curve
  • Reproducibility of Results
  • Software*