Bingjian Feng, PhD

Research Interests

  • Breast Cancer
  • Head and Neck Cancers
  • Li-Fraumeni Syndrome
  • Arthritis, Psoriatic
  • Personalized Medicine
  • Evaluation of Variants of Uncertain Significance
  • Genetic Variant Analysis
  • Algorithm and Genomics Software Development
  • Medical Diagnosis and Prognosis Using Biomarkers
  • Proteomics
  • bioinformatics
  • Genetic Epidemiology
  • Epidemiology


Lab Website

Academic Information

  • Departments: Dermatology - Research Assistant Professor

Academic Office Information

  • School of Medicine
    30 N 1900 E, Room: 4B465
    Salt Lake City, UT 84132

Academic Bio

Researcher in genomics, proteomics, phenomics, and their clinical applications focusing on cancers and autoimmune diseases.

Research Interests

1. Bioinformatics and biostatistics.

We develop novel bioinformatics and biostatistics tools for genome sequence analysis.

2. Clinical variant classification.

Classification of genetic variants into pathogenic or benign category is an important step in clinical genetic testing. We develop methods and guidelines for variant classification for cancer-related genes including TP53, BRCA1, BRCA2, and mismatch-repair genes.

3. Head and neck cancer.

We conduct multi-omic (genomic, proteomic, interactomic) studies to identify susceptibility genes for head and neck cancer and to discover biomarkers for prognostic prediction.

4. Psoriasis and psoriatic arthritis.

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 tool and diagnostic test for the early detection of PsA among psoriasis patients.


1. PERCH (Polymorphism Evaluation, Ranking, and Classification for Heritable traits).

PERCH is a framework for the interpretation of 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. This framework is useful for both gene discovery research and clinical genetic testing. BayesDel has been selected to be a component of the American College of Medical Genetics and Genomics (ACMG) variant classification guidelines for TP53.

2. VICTOR (Variant Interpretation for Clinical Testing Or Research).

VICTOR is comprehensive software package for variant interpretation starting from a raw Variant Call Format (VCF) file. This package contains PERCH and other software written by Dr. Bing-jian Feng, data files for the genomes hg19, GRCh37, GRCh38, and several slurm script templates for running jobs in a computer cluster. The package has been tested on several large-scale whole-exome or whole-genome sequencing studies on breast cancer, head and neck cancer, colorectal cancer, prostate cancer, interstitial lung disease, and a rare disease cluster of Ehlers-Danlos syndrome, Postural Orthostatic Tachycardia Syndrome, and mast cell activation syndrome.

3. Co-segregation analysis online server.

Classification of germline variants within disease-causing genes into pathogenic or benign category is an important problem in clinical genetic testing. Cosegregation analysis of pedigree data is a powerful tool for assessing germline variant pathogenicity. This server performs cosegregation analysis by Thompson et al. (also called causality analysis) and outputs a Bayes factor that can be integrated into a multifactorial variant classification scheme or the American College of Medical Genetics and Genomics (ACMG) variant classification guidelines. This server provides standard liability classes (penetrance as a function of age, sex, and disease) for BRCA1, BRCA2, and PALB2.

4. Other software.

PedPro: PedPro is a program to handle pedigrees. It can check for errors, detect and break loops, remove uninformative individuals for linkage analysis, fill in missing year-of-birth data, calculate inbreeding coefficient and kinship coefficient, find the most-recent common ancestors, find obligatory carriers, identify clusters of individuals based on connections and/or affection status, identify and remove isolated individuals, merge connected families into one, do parent-of-origin analysis, simulate genotypes by gene dropping, and calculate individual weight to adjust for correlation between family members in an association test.

HGVSreader: HGVS nomenclature is widely used in literature, but not without problems. Many times they don’t tell which transcript was used, sometimes a “p.” can be interpreted by different “c.” changes, and sometimes they may have mistakes. This program reads HGVS and translates them into genomic representations. In doing so, it has the following features: 1) if an HGVS contains a gene symbol but not a transcript ID, use the most biologically relevant transcript; 2) if an HGVS has multiple levels, use the unambiguous one, i.e., use “c.” instead of “p.”; 3) if a protein-level nomenclature have multiple genomic interpretations, it selects the most probable one based on dbSNP, allele frequency, and deleteriousness scores; 4) automatically checks whether the nomenclature matches with the reference sequence; 5) some non-standard or deprecated HGVS are still supported. For example, c.IVS1-5T>C.

TrendTDT (Trend Transmission Disequilibrium Test): TrendTDT is a program to perform a novel family-based trend association test for copy-number variations (CNVs) or variable number tandem repeats (VNTRs).

Education History

Type School Degree
Doctoral Training Erasmus University Rotterdam
Genetic Epidemiology

Global Impact

Education History

Type School Degree Country
Doctoral Training Erasmus University Rotterdam
Genetic Epidemiology
Ph.D. Netherlands


Date Role Description Country
04/12/2019 Invited Participant & Speaker ENIGMA is an international consortium of investigators focused on determining the clinical significance of sequence variants in BRCA1, BRCA2 and other known or suspected breast cancer genes, to provide this expert opinion to global database and classification initiatives, and to explore optimal avenues of communication of such information at the provider and patient level. Australia
02/01/2016 Invited Participant & Speaker The BRCA Exchange supports the exchange of information about BRCA1 and BRCA2 variants. Australia
02/01/2016 Invited Participant & Speaker The Global Alliance for Genomics and Health (GA4GH, is an international, nonprofit alliance formed in 2013 to accelerate the potential of research and medicine to advance human health. GA4GH Driver Projects are real-world genomic data initiatives that help guide our development efforts and pilot our tools. Stakeholders around the globe advocate, mandate, implement, and use our frameworks and standards in their local contexts. As one of the GA4GH Driver Projects, the BRCA Challenge aims to advance our understanding of the genetic basis of breast cancer, ovarian cancer and other diseases by pooling data on BRCA1/2 genetic variants and corresponding clinical data from around the world. The project has developed a publicly available portal, the BRCA Exchange, to make aggregate data accessible to all users and to facilitate expert variant pathogenicity classifications made by the ENIGMA Consortium. Australia
01/07/2015 Expert Panelist TP53 Variant Curation Expert Panel. Variant Curation Expert Panels evaluate evidence to classify a genomic variant on a spectrum from pathogenic to benign with respect to a particular disease and inheritance pattern. They are officially recognized as part of the FDA Human Variant Database Program. Australia

Selected Publications

Journal Article

  1. Belman S, Walsh JA, Carroll C, Milliken M, Haaland B, Callis Duffin K, Krueger GG, Feng BJ (2021). Psoriasis characteristics for the early detection of psoriatic arthritis. (Epub ahead of print) J Rheumatol.
  2. Fortuno C, Lee K, Olivier M, Pesaran T, Mai PL, de Andrade KC, Attardi LD, Crowley S, Evans DG, Feng BJ, Foreman AKM, Frone MN, Huether R, James PA, McGoldrick K, Mester J, Seifert BA, Slavin TP, Witkowski L, Zhang L, Plon SE, Spurdle AB, Savage SA, ClinGen TP53 Variant Curation Expert Panel (2020). Specifications of the ACMG/AMP variant interpretation guidelines for germline TP53 variants. Hum Mutat, 42(3), 223-236.
  3. Belman S, Parsons MT, Spurdle AB, Goldgar DE, Feng BJ (2020). Considerations in assessing germline variant pathogenicity using cosegregation analysis. Genet Med, 22(12), 2052-2059.
  4. Boyle JL, Hahn AW, Kapron AL, Kohlmann W, Greenberg SE, Parnell TJ, Teerlink CC, Maughan BL, Feng BJ, Cannon-Albright L, Agarwal N, Cooney KA (2020). Pathogenic Germline DNA Repair Gene and HOXB13 Mutations in Men With Metastatic Prostate Cancer. JCO Precis Oncol, 4.
  5. Yishuo Wu, Hongjie Yu, Siqun Lilly Zheng, Bingjian Feng, Ashley L Kapron, Rong Na, Julie L Boyle, Sameep Shah, Zhuqing Shi, Charles M Ewing, Kathleen E Wiley, Jun Luo, Patrick C Walsh, Herbert Ballentine Carter, Brian T Helfand, Kathleen A Cooney, Jianfeng Xu, William B Isaacs (2018). Germline mutations in PPFIBP2 are associated with lethal prostate cancer. Prostate, 78(16), 1222-1228.
  6. Shimelis H, LaDuca H, Hu C, Hart SN, Na J, Thomas A, Akinhanmi M, Moore RM, Brauch H, Cox A, Eccles DM, Ewart-Toland A, Fasching PA, Fostira F, Garber J, Godwin AK, Konstantopoulou I, Nevanlinna H, Sharma P, Yannoukakos D, Yao S, Feng BJ, Tippin Davis B, Lilyquist J, Pesaran T, Goldgar DE, Polley EC, Dolinsky JS, Couch FJ (2018). Triple-Negative Breast Cancer Risk Genes Identified by Multigene Hereditary Cancer Panel Testing. J Natl Cancer Inst, 110(8), 855-862.
  7. Hart SN, Hoskin T, Shimelis H, Moore RM, Feng B, Thomas A, Lindor NM, Polley EC, Goldgar DE, Iversen E, Monteiro ANA, Suman VJ, Couch FJ (2018). Comprehensive annotation of BRCA1 and BRCA2 missense variants by functionally validated sequence-based computational prediction models. Genet Med.
  8. Fortuno C, James PA, Young EL, Feng B, Olivier M, Pesaran T, Tavtigian SV, Spurdle AB (2018). Improved, ACMG-compliant, in silico prediction of pathogenicity for missense substitutions encoded by TP53 variants. Human Mutation, 39(8), 1061-1069.
  9. Fergus J Couch, Hermela Shimelis, Chunling Hu, Steven N Hart, Eric C Polley, Jie Na, Emily Hallberg, Raymond Moore, Abigail Thomas, Jenna Lilyquist, Bingjian Feng, Rachel McFarland, Tina Pesaran, Robert Huether, Holly LaDuca, Elizabeth C Chao, David E Goldgar, Jill S Dolinsky (2017). Associations Between Cancer Predisposition Testing Panel Genes and Breast Cancer. JAMA Oncol, 3(9), 1190-1196.
  10. Feng BJ (2017). PERCH: A Unified Framework for Disease Gene Prioritization. Hum Mutat, 38(3), 243-251.
  11. Lanikova L, Reading NS, Hu H, Tashi T, Burjanivova T, Shestakova A, Siwakoti B, Thakur BK, Pun CB, Sapkota A, Abdelaziz S, Feng BJ, Huff CD, Hashibe M, Prchal JT (2017). Evolutionary selected Tibetan variants of HIF pathway and risk of lung cancer. Oncotarget, 8(7), 11739-11747.
  12. Li H, Feng B, Miron A, Chen X, Beesley J, Bimeh E, Barrowdale D, John EM, Daly MB, Andrulis IL, Buys SS, Kraft P, Thorne H, Chenevix-Trench G, Southey MC, Antoniou AC, James PA, Terry MB, Phillips KA, Hopper JL, Mitchell G, Goldgar DE (2017). Breast cancer risk prediction using a polygenic risk score in the familial setting: a prospective study from the Breast Cancer Family Registry and kConFab. Genet Med, 19(1), 30-35.
  13. Young EL, Feng BJ, Stark AW, Damiola F, Durand G, Forey N, Francy TC, Gammon A, Kohlmann WK, Kaphingst KA, McKay-Chopin S, Nguyen-Dumont T, Oliver J, Paquette AM, Pertesi M, Robinot N, Rosenthal JS, Vallee M, Voegele C, Hopper JL, Southey MC, Andrulis IL, John EM, Hashibe M, Gertz J, Breast Cancer Family Registry, Le Calvez-Kelm F, Lesueur F, Goldgar DE, Tavtigian SV (2016). Multigene testing of moderate-risk genes: be mindful of the missense. J Med Genet, 53(6), 366-76.
  14. Meeks HD, Song H, Michailidou K, Bolla MK, Dennis J, Wang Q, Barrowdale D, Frost D, McGuffog L, Ellis S, Feng B, Buys SS, Hopper JL, Southey MC, Tesoriero A, James PA, Bruinsma F, Campbell IG, Broeks A, Schmidt MK, Hogervorst FB, Beckman MW, Fasching PA, Fletcher O, Johnson N, Sawyer EJ, Riboli E, Banerjee S, Menon U, Tomlinson I, Burwinkel B, Hamann U, Marme F, Rudolph A, Janavicius R, Tihomirova L, Tung N, Garber J, Cramer D, Terry KL, Poole EM, Tworoger SS, Dorfling CM, van Rensburg EJ, Godwin AK, Guenel P, Truong T, Stoppa-Lyonnet D, Damiola F, Mazoyer S, Sinilnikova OM, Isaacs C, Maugard C, Bojesen SE, Flyger H, Gerdes AM, Hansen TV, Jensen A, Kjaer SK, Hogdall C, Hogdall E, Pedersen IS, Thomassen M, Benitez J, Gonzalez-Neira A, Osorio A, Hoya Mde L, Segura PP, Diez O, Lazaro C, Brunet J, Anton-Culver H, Eunjung L, John EM, Neuhausen SL, Ding YC, Castillo D, Weitzel JN, Ganz PA, Nussbaum RL, Chan SB, Karlan BY, Lester J, Wu A, Gayther S, Ramus SJ, Sieh W, Whittermore AS, Monteiro AN, Phelan CM, Terry MB, Piedmonte M, Offit K, Robson M, Levine D, Moysich KB, Cannioto R, Olson SH, Daly MB, Nathanson KL, Domchek SM, Lu KH, Liang D, Hildebrant MA, Ness R, Modugno F, Pearce L, Goodman MT, Thompson PJ, Brenner H, Butterbach K, Meindl A, Hahnen E, Wappenschmidt B, Brauch H, Bruning T, Blomqvist C, Khan S, Nevanlinna H, Pelttari LM, Aittomaki K, Butzow R, Bogdanova NV, Dork T, Lindblom A, Margolin S, Rantala J, Kosma VM, Mannermaa A, Lambrechts D, Neven P, Claes KB, Maerken TV, Chang-Claude J, Flesch-Janys D, Heitz F, Varon-Mateeva R, Peterlongo P, Radice P, Viel A, Barile M, Peissel B, Manoukian S, Montagna M, Oliani C, Peixoto A, Teixeira MR, Collavoli A, Hallberg E, Olson JE, Goode EL, Hart SN, Shimelis H, Cunningham JM, Giles GG, Milne RL, Healey S, Tucker K, Haiman CA, Henderson BE, Goldberg MS, Tischkowitz M, Simard J, Soucy P, Eccles DM, Le N, Borresen-Dale AL, Kristensen V, Salvesen HB, Bjorge L, Bandera EV, Risch H, Zheng W, Beeghly-Fadiel A, Cai H, Pylkas K, Tollenaar RA, Ouweland AM, Andrulis IL, Knight JA, Narod S, Devilee P, Winqvist R, Figueroa J, Greene MH, Mai PL, Loud JT, Garcia-Closas M, Schoemaker MJ, Czene K, Darabi H, McNeish I, Siddiquil N, Glasspool R, Kwong A, Park SK, Teo SH, Yoon SY, Matsuo K, Hosono S, Woo YL, Gao YT, Foretova L, Singer CF, Rappaport-Feurhauser C, Friedman E, Laitman Y, Rennert G, Imyanitov EN, Hulick PJ, Olopade OI, Senter L, Olah E, Doherty JA, Schildkraut J, Koppert LB, Kiemeney LA, Massuger LF, Cook LS, Pejovic T, Li J, Borg A, Ofverholm A, Rossing MA, Wentzensen N, Henriksson K, Cox A, Cross SS, Pasini BJ, Shah M, Kabisch M, Torres D, Jakubowska A, Lubinski J, Gronwald J, Agnarsson BA, Kupryjanczyk J, Moes-Sosnowska J, Fostira F, Konstantopoulou I, Slager S, Jones M, Antoniou AC, Berchuck A, Swerdlow A, Chenevix-Trench G, Dunning AM, Pharoah PD, Hall P, Easton DF, Couch FJ, Spurdle AB, Goldgar DE (2016 Feb). BRCA2 Polymorphic Stop Codon K3326X and the Risk of Breast, Prostate, and Ovarian Cancers.LID - 10.1093/jnci/djv315 [doi]LID - djv315 [pii]. J Natl Cancer Inst, 108(2).
  15. Li J, Meeks H, Feng BJ, Healey S, Thorne H, Makunin I, Ellis J, Campbell I, Southey M, Mitchell G, Clouston D, Kirk J, Goldgar D, Chenevix-Trench G (2016). Targeted massively parallel sequencing of a panel of putative breast cancer susceptibility genes in a large cohort of multiple-case breast and ovarian cancer families. J Med Genet, 53(1), 34-42.
  16. Li L, Hamel N, Baker K, McGuffin MJ, Couillard M, Gologan A, Marcus VA, Chodirker B, Chudley A, Stefanovici C, Durandy A, Hegele RA, Feng BJ, Goldgar DE, Zhu J, De Rosa M, Gruber SB, Wimmer K, Young B, Chong G, Tischkowitz MD, Foulkes WD (2015). A homozygous PMS2 founder mutation with an attenuated constitutional mismatch repair deficiency phenotype. J Med Genet, 52(5), 348-52.
  17. Huang YH, Zhang ZF, Tashkin DP, Feng BJ, Straif K, Hashibe M (2015). An Epidemiologic Review of Marijuana and Cancer: An Update. Cancer Epidemiol Biomarkers Prev, 24(1), 15-31.
  18. Laitman Y, Feng BJ, Zamir IM, Weitzel JN, Duncan P, Port D, Thirthagiri E, Teo SH, Evans G, Latif A, Newman WG, Gershoni-Baruch R, Zidan J, Shimon-Paluch S, Goldgar D, Friedman E (2013). Haplotype analysis of the 185delAG BRCA1 mutation in ethnically diverse populations. Eur J Hum Genet, 21(2), 212-6.
  19. Hamel N, Feng BJ, Foretova L, Stoppa-Lyonnet D, Narod SA, Imyanitov E, Sinilnikova O, Tihomirova L, Lubinski J, Gronwald J, Gorski B, Hansen Tv, Nielsen FC, Thomassen M, Yannoukakos D, Konstantopoulou I, Zajac V, Ciernikova S, Couch FJ, Greenwood CM, Goldgar DE, Foulkes WD (2011). On the origin and diffusion of BRCA1 c.5266dupC (5382insC) in European populations. Eur J Hum Genet, 19(3), 300-6.
  20. Feng BJ, Tavtigian SV, Southey MC, Goldgar DE (2011). Design considerations for massively parallel sequencing studies of complex human disease. PLoS ONE, 6(8), e23221.
  21. Bei JX, Li Y, Jia WH, Feng BJ, Zhou G, Chen LZ, Feng QS, Low HQ, Zhang H, He F, Tai ES, Kang T, Liu ET, Liu J, Zeng YX (2010). A genome-wide association study of nasopharyngeal carcinoma identifies three new susceptibility loci. Nat Genet, 42(7), 599-603.
  22. Feng BJ, Khyatti M, Ben-Ayoub W, Dahmoul S, Ayad M, Maachi F, Bedadra W, Abdoun M, Mesli S, Bakkali H, Jalbout M, Hamdi-Cherif M, Boualga K, Bouaouina N, Chouchane L, Benider A, Ben-Ayed F, Goldgar DE, Corbex M (2009). Cannabis, tobacco and domestic fumes intake are associated with nasopharyngeal carcinoma in North Africa. Br J Cancer, 101(7), 1207-12.
  23. Feng BJ, Sun LD, Soltani-Arabshahi R, Bowcock AM, Nair RP, Stuart P, Elder JT, Schrodi SJ, Begovich AB, Abecasis GR, Zhang XJ, Callis-Duffin KP, Krueger GG, Goldgar DE (2009). Multiple Loci within the major histocompatibility complex confer risk of psoriasis. PLoS Genet, 5(8), e1000606.
  24. Nair RP, Duffin KC, Helms C, Ding J, Stuart PE, Goldgar D, Gudjonsson JE, Li Y, Tejasvi T, Feng BJ, Ruether A, Schreiber S, Weichenthal M, Gladman D, Rahman P, Schrodi SJ, Prahalad S, Guthery SL, Fischer J, Liao W, Kwok PY, Menter A, Lathrop GM, Wise CA, Begovich AB, Voorhees JJ, Elder JT, Krueger GG, Bowcock AM, Abecasis GR (2009). Genome-wide scan reveals association of psoriasis with IL-23 and NF-kappaB pathways. Nat Genet, 41(2), 199-204.
  25. Feng BJ, Goldgar DE, Corbex M (2007). Trend-TDT - a transmission/disequilibrium based association test on functional mini/microsatellites. BMC Genet, 8, 75.
  26. Feng BJ, Jalbout M, Ayoub WB, Khyatti M, Dahmoul S, Ayad M, Maachi F, Bedadra W, Abdoun M, Mesli S, Hamdi-Cherif M, Boualga K, Bouaouina N, Chouchane L, Benider A, Ben Ayed F, Goldgar D, Corbex M (2007). Dietary risk factors for nasopharyngeal carcinoma in Maghrebian countries. Int J Cancer, 121(7), 1550-5.
  27. Jia WH, Feng BJ, Xu ZL, Zhang XS, Huang P, Huang LX, Yu XJ, Feng QS, Yao MH, Shugart YY, Zeng YX (2004). Familial risk and clustering of nasopharyngeal carcinoma in Guangdong, China. Cancer, 101(2), 363-9.
  28. Chen HK, Feng BJ, Liang H, Zhang RH, Zeng YX (2003). The susceptibility for familial nasopharyngeal carcinoma mapping on chromosome 4p11-p14 by haplotype analyses. Chin Sci Bull, 48(16), 1776-9.
  29. Shugart YY, Feng BJ, Collins A (2002). The power and statistical behaviour of allele-sharing statistics when applied to models with two disease loci. J Genet, 81(3), 99-103.
  30. Feng BJ, Huang W, Shugart YY, Lee MK, Zhang F, Xia JC, Wang HY, Huang TB, Jian SW, Huang P, Feng QS, Huang LX, Yu XJ, Li D, Chen LZ, Jia WH, Fang Y, Huang HM, Zhu JL, Liu XM, Zhao Y, Liu WQ, Deng MQ, Hu WH, Wu SX, Mo HY, Hong MF, King MC, Chen Z, Zeng YX (2002). Genome-wide scan for familial nasopharyngeal carcinoma reveals evidence of linkage to chromosome 4. Nat Genet, 31(4), 395-9.


  1. Thompson BA, Snow AK, Koptiuch C, Kohlmann WK, Mooney R, Johnson S, Huff CD, Yu Y, Teerlink CC, Feng BJ, Neklason DW, Cannon-Albright LA, Tavtigian SV (2019). A novel ribosomal protein S20 variant in a family with unexplained colorectal cancer and polyposis. [Letter to the editor]. Clin Genet, 97(6), 943-944.


  1. Bingjian Feng (2016). PERCH: a unified framework for genetic variant interpretation.