title: A two-stage approach to genetic risk assessment in primary care.
abstract: Genetic risk prediction models such as BRCAPRO are used routinely in genetic counseling for identification of potential BRCA1 and BRCA2 mutation carriers. They require extensive information on the counselee and her family history, and thus are not practical for primary care. To address this gap, we develop and test a two-stage approach to genetic risk assessment by balancing the tradeoff between the amount of information used and accuracy achieved. The first stage is intended for primary care wherein limited information is collected and analyzed using a simplified version of BRCAPRO. If the assessed risk is sufficiently high, more extensive information is collected and the full BRCAPRO is used (stage two: intended for genetic counseling). We consider three first-stage tools: BRCAPROLYTE, BRCAPROLYTE-Plus, and BRCAPROLYTE-Simple. We evaluate the two-stage approach on independent clinical data on probands with family history of breast and ovarian cancers, and BRCA genetic test results. These include population-based data on 1344 probands from Newton-Wellesley Hospital and mostly high-risk family data on 2713 probands from Cancer Genetics Network and MD Anderson Cancer Center. We use discrimination and calibration measures, appropriately modified to evaluate the overall performance of a two-stage approach. We find that the proposed two-stage approach has very limited loss of discrimination and comparable calibration as BRCAPRO. It identifies a similar number of carriers without requiring a full family history evaluation on all probands. We conclude that the two-stage approach allows for practical large-scale genetic risk assessment in primary care.
authors.affiliation: Department of Mathematical Sciences, University of Texas at Dallas, FO 35, 800 West Campbell Road, Richardson, TX, 75080-3021, USA. swati.biswas@utdallas.edu.
authors.forename: Swati
authors.initials: S
authors.lastname: Biswas
authors.affiliation: Department of Biostatistics, School of Public Health, University of North Texas Health Science Center, Fort Worth, USA.
authors.forename: Philamer
authors.initials: P
authors.lastname: Atienza
authors.affiliation: Department of Biostatistics, Vanderbilt School of Medicine, Nashville, USA.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Chipman
authors.affiliation: Division of Biostatistics and Bioinformatics, School of Medicine, Johns Hopkins University, Baltimore, USA.
authors.forename: Amanda L
authors.initials: AL
authors.lastname: Blackford
authors.affiliation: Department of Breast Medical Oncology and Clinical Cancer Genetics, University of Texas M.D. Anderson Cancer Center, Houston, USA.
authors.forename: Banu
authors.initials: B
authors.lastname: Arun
authors.affiliation: Massachusetts General Hospital and Harvard School of Medicine, Boston, USA.
authors.forename: Kevin
authors.initials: K
authors.lastname: Hughes
authors.affiliation: Department of Biostatistics and Computational Biology, Dana Farber Cancer Institute, Boston, USA.|Department of Biostatistics, Harvard School of Public Health, Boston, USA.
authors.forename: Giovanni
authors.initials: G
authors.lastname: Parmigiani
mesh_terms: D019313:BRCA1 Protein / Q000235:genetics; D024682:BRCA2 Protein / Q000235:genetics; D001943:Breast Neoplasms / Q000235:genetics*; D005260:Female; D005817:Genetic Counseling / Q000379:methods; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D006801:Humans; D008297:Male; D008875:Middle Aged; D009154:Mutation / Q000235:genetics; D010051:Ovarian Neoplasms / Q000235:genetics*; D010375:Pedigree; D011320:Primary Health Care / Q000379:methods; D012189:Retrospective Studies; D018570:Risk Assessment; D012307:Risk Factors
chemical_list: D019313:BRCA1 Protein; D024682:BRCA2 Protein
keywords: BRCA1; BRCA2; BRCAPRO; BayesMendel; CancerGene