PERCH: a unified framework for disease gene prioritization

By B. Feng. 2017

236 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Yicheng Tu
2. Xiaoming Liu
3. Chang Li
4. Yibo Dong
5. Chengcheng Mou

In Molecular Biology

1. Yicheng Tu
2. Xiaoming Liu
3. Chang Li
4. Yibo Dong
5. Chengcheng Mou

Citations in the corpus

• dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs (2020, background): “Some of the highest agreement ratios are using the same training data, such as MetaLR and MetaSVM (0.96), BayesDel_addAF and BayesDel_noAF (0.94), Polyphen2_HDIV and Polyphen2_HVAR (0.88).”
• The UCSC Genome Browser database: 2025 update (2024, unknown): “Bayes-Del provides a deleteriousness meta-score for coding and non-coding variants, single nucleotide variants and small insertions / deletions ( 6 ).”