Research Associate Professor at the University of Utah.
Permanent URL: BJFengLab.org
We develop novel bioinformatic and biostatistic methods and software for genome sequence analysis.
Classification of genetic variants into either neutral or pathogenic category is an essential step in clinical genetic testing. We develop algorithms and software for this purpose. We participate in the development of national standards and guidelines.
We conduct multi-omic (genomic, proteomic, interactomic) studies to find biomarkers for prognostic prediction in head and neck cancer patients.
Patients with cutaneous psoriasis frequently suffer from unrecognized psoriatic arthritis (PsA). Delays in PsA diagnosis and treatment frequently contribute to functional limitations and irreversible joint damage. We develop screening tools and diagnostic tests for the early detection of PsA among psoriasis patients.
PERCH (Polymorphism Evaluation, Ranking, and Classification for Heritable Traits) (PMID: 27995669) is a framework for interpreting genetic variants identified from next-generation sequencing. This software implements a novel deleteriousness score named BayesDel, an improved guilt-by-association algorithm, rare-variant association tests, and a modified linkage analysis. These components are integrated in a quantitative fashion for gene and variant prioritization. BayesDel (PMID: 27995669) has been selected to be a component of the gene-specific variant classification guidelines for TP53, BRCA1, and BRCA2, compliant with the American College of Medical Genetics and Genomics (ACMG) and the Association of Molecular Pathology (AMP) standards and the quantitative multifactorial variant classification strategy (PMID: 34273903). For ACMG/AMP guidelines for other genes, please refer to Pejaver et al. (PMID: 36413997). If you want to perform secondary and tertiary analyses of large-scale whole-exome / whole-genome sequencing data to search for novel disease genes, please check out the VICTOR package.
References:
Bing-Jian Feng*. Human Mutation 2017 Mar;38(3):243-251. PMID: 27995669.
Fortuno et al. Human Mutation 2021 Mar;42(3):223-236. PMID: 33300245.
Pejaver et al. American Journal of Human Genetics. 2022. PMID: 36413997.
VICTOR (Variant Interpretation for Clinical Testing Or Research) is a software package for the secondary and tertiary analysis of next-generation sequencing data starting from a raw Variant Call Format (VCF) file. It can analyze germline and tumor variants. It has a pipeline for quality control, cryptic relatedness and population structure inference, database querying, functional interpretation, rare-variant association test accounting for ancestry and population substructure, gene-set analysis, cosegregation analysis, variant classification, secondary finding reporting, polygenic risk score calculation, and gene network analysis. This package includes executable programs, data files for the GRCh37 and GRCh38 genomes, and slurm scripts for high-performance computing.
References:
Dumont et al. Cancers. 2022;14(14):3363. PMID:35884425.
Bing-Jian Feng*, Courtney Carroll, Trilokraj Tejasvi, Lam C. Tsoi, Rajan P. Nair, David E. Goldgar, Kristina Callis Duffin, Ana-Maria Orbai, Philip E. Stuart, James T. Elder, Jessica A. Walsh, Gerald G. Krueger. Exome-Guided Proteomic Analysis Identifies Early Biomarkers for the Progression from Psoriasis to Psoriatic Arthritis. http://dx.doi.org/10.2139/ssrn.4586454
Classifying germline variants into pathogenic or neutral categories is essential for interpreting genetic test results. Cosegregation analysis (testing whether a genetic variant and an associated disease segregate within a pedigree) is useful for assessing germline variant pathogenicity. COOL (COsegregation OnLine) (PMID:32773770) is a web server that performs cosegregation analysis by the Full-Likelihood Bayes factor method (Thompson et al.) and outputs a Bayes factor that can be integrated into a multifactorial variant classification scheme. It can also be transformed into a strength category to apply the variant classification guidelines developed by the American College of Medical Genetics and Genomics (ACMG) and the Association of Molecular Pathology (AMP). This server provides penetrance for 16 cancer genes (BRCA1, BRCA2, CDH1, MLH1, MSH2, MSH6, NF1, PALB2, PMS2, PTEN, RAD51C, RAD51D, TP53, ATM, CHEK2, MEN1). It also supports other cancer genes if you provide a relative risk file or non-cancer genes if you provide a penetrance file. The website deletes all pedigree data immediately after computation to protect data privacy. Here are the links for the User Manual and COOL v3.
PedPro: This versatile program is designed to handle pedigrees with ease. It can check for errors, detect and break loops, remove uninformative individuals for linkage analysis, find obligatory carriers, identify clusters of individuals based on connections and affection status, identify and remove isolated individuals, merge connected families, and calculate individual weights for a case-control association test. PedPro also offers the flexibility to convert different pedigree files into a Comprehensive Pedigree Format (CPF), which enhances file sharing among laboratories and improves usability and re-usability. This format contains all the necessary information for cosegregation analysis, risk prediction, and penetrance estimation, the main usages of a pedigree file in clinical genetic testing.
TrendTDT (PMID:17976242) introduces a novel approach to family-based trend association testing. It is designed explicitly for copy-number variations (CNVs) or variable number tandem repeats (VNTRs).
References:
Bing-Jian Feng*, David E Goldgar, Marilys Corbex. Trend-TDT - a transmission/disequilibrium-based association test on functional mini/microsatellites. BMC Genetics. 2007;8:75. PMID:17976242.
Sophie Belman, Michael T Parsons, Amanda B Spurdle, David E Goldgar, Bing-Jian Feng*. Considerations in assessing germline variant pathogenicity using cosegregation analysis. Genetics in Medicine 2020 Dec; 22(12):2052-2059. PMID:32773770.
PAPRIKA (Psoriatic Arthritis Prediction and Identification Question Bank for Various Ancestries) version 1 is an assembly of questions about clinical features for predicting and identifying psoriatic arthritis (PsA). PsA is an inflammatory joint disease that can lead to irreversible joint destruction, functional limitations, and increased mortality. Early treatment of PsA will favorably impact function, quality of life, and workability. This question bank was created to facilitate the detection of PsA early in the disease course. It contains novel clinical predictors we discovered from a prospective cohort study (Belman et al.)
This Bank uses a "Psoriasis Thickness Reference Card." We provide a YouTube video about how to make this Card at home. Patients can complete the questions without assistance from a provider. PAPRIKA contains example images obtained from various databases; we are not allowed to put the images in the public domain due to licensing issues. If you need the images, please get in touch with us. We will point you to the databases.
References:
Sophie Belman, Jessica A Walsh, Courtney Carroll, Michael Milliken, Benjamin Haaland, Kristina Callis Duffin, Gerald G Krueger, Bing-Jian Feng*. Psoriasis Characteristics for the Early Detection of Psoriatic Arthritis. Journal of Rheumatology 2021 Oct;48(10):1559-1565. PMID: 33858978.
Jessica A Walsh, Courtney Carroll, Kristina Callis Duffin, Jing Wang, Gerald G. Krueger, Bing-Jian Feng*. PAPRIKA: A Question Bank for Assessing Psoriatic Arthritis Risk in Individuals of Diverse Ancestries. Arthritis Care & Research 2024 Mar;76(3):421-425. PMID: 37691268.
Carroll C, Adalsteinsson J, Prouty M, Callis Duffin K, Krueger GG, Walsh JA, Feng BJ*. Measuring Psoriasis Severity at Home. Journal of Visualized Experiments 2024 (accepted).
30 N Mario Capecchi Drive
First floor south
Salt Lake City, UT 84112, USA
E: bingjian.feng<at>hsc.utah.edu
P: +1 (801) 581 6465
F: +1 (801) 581 6484
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