ArticleHuman mutation2017
PERCH: A Unified Framework for Disease Gene Prioritization.
Article in Human mutation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 188 papers, 1 of them a synthesis that pooled it.
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Who cites it
188 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Domain-Specific Computational, Functional and Structural Methods Enable Interpretation ofCurrent oncology (Toronto, Ont.) · 2026Article
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- Comprehensive evidence for the pathogenicity of theJournal of medical genetics · 2026Article
- Integrating enriched case data from national laboratory testing with population-based case-control analyses: a novel statistical likelihood-ratio methodology for PS4 applied to 325,345 breast cancer cases and 671,006 controls.medRxiv : the preprint server for health sciences · 2026Article
- Gene- and domain-aware calibration increases the clinical utility of variant effect predictors.Research square · 2026Article
- Article
- MetaXVP: an interpretable machine learning framework for deep insight into variant pathogenicity and VUS classification.Scientific reports · 2026Article
- Low-penetrance TP53 variants are mainly hypomorphic: an underestimated issue with high clinical significance.NPJ genomic medicine · 2026Article
128 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
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 http://BJFengLab.org/. 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.
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