Introduction: Genetic risk modifier testing (GRMT), an emerging form of genetic testing based on common single nucleotide polymorphisms and polygenic risk scores, has the potential to refine estimates of BRCA1/2 mutation carriers’ breast cancer risks. However, for women to benefit from GRMT, effective approaches for communicating this novel risk information are needed. Objective: To evaluate patient preferences regarding risk communication materials for GRMT. Methods: We developed four separate presentations (panel of genes, icon array, verbal risk estimate, graphical risk estimate) of hypothetical GRMT results, each using varying risk communication strategies to convey different information elements including number of risk modifier variants present, variant prevalence among BRCA1/2 carriers, and implications and uncertainties of test results for cancer risk. Thirty BRCA1/2 carriers evaluated these materials (randomized to low, moderate, or high breast cancer risk versions). Qualitative and quantitative data were obtained through in-person interviews. Results: Across risk versions, participants preferred the presentation of the graphical risk estimate, often in combination with the verbal risk estimate. Interest in GRMT was high; 76.7% of participants wanted their own GRMT. Participants valued the potential for GRMT to clarify their cancer susceptibility and provide actionable information. Many (65.5%) anticipated that GRMT would make risk management decisions easier. Conclusions: Women with BRCA1/2 mutations could be highly receptive to GRMT, and the minimal amount of necessary information to be included in result risk communication materials includes graphical and verbal estimates of future cancer risk. Findings will inform clinical translation of GRMT in a manner consistent with patients’ preferences.

Introduction: Genetic risk modifier testing (GRMT), an emerging form of genetic testing based on common single nucleotide polymorphisms and polygenic risk scores, has the potential to refine estimates of BRCA1/2 mutation carriers’ breast cancer risks. However, for women to benefit from GRMT, effective approaches for communicating this novel risk information are needed. Objective: To evaluate patient preferences regarding risk communication materials for GRMT. Methods: We developed four separate presentations (panel of genes, icon array, verbal risk estimate, graphical risk estimate) of hypothetical GRMT results, each using varying risk communication strategies to convey different information elements including number of risk modifier variants present, variant prevalence among BRCA1/2 carriers, and implications and uncertainties of test results for cancer risk. Thirty BRCA1/2 carriers evaluated these materials (randomized to low, moderate, or high breast cancer risk versions). Qualitative and quantitative data were obtained through in-person interviews. Results: Across risk versions, participants preferred the presentation of the graphical risk estimate, often in combination with the verbal risk estimate. Interest in GRMT was high; 76.7% of participants wanted their own GRMT. Participants valued the potential for GRMT to clarify their cancer susceptibility and provide actionable information. Many (65.5%) anticipated that GRMT would make risk management decisions easier. Conclusions: Women with BRCA1/2 mutations could be highly receptive to GRMT, and the minimal amount of necessary information to be included in result risk communication materials includes graphical and verbal estimates of future cancer risk. Findings will inform clinical translation of GRMT in a manner consistent with patients’ preferences.

Though genome-wide association studies (GWAS) have identified numerous susceptibility loci for common diseases, their use is limited due to the expense of genotyping large cohorts of individuals. One potential solution is to use ‘additional controls’, or genotype data from control individuals deposited in public repositories. While this approach has been used by several groups, the genetically heterogeneous nature of the population of the United States makes this approach potentially problematic. We empirically investigated the utility of this approach in a US-based GWAS. In a small GWAS of pancreatic cancer in New York, we observed clear population structure differences relative to controls from the database of Genotypes and Phenotypes (dbGaP). When we conduct the GWAS using these additional controls, we find large inflation of the test statistic that is properly corrected by using eigenvectors from principal components analysis as covariates. To deal with errors introduced due to different sources, we propose simultaneously genotyping a small number of controls along with cases and then comparing this group to the additional controls. We show that removing SNPs that show differences between these control groups reduces false-positive findings. Thus, through an empirical approach, this report provides practical guidance for using additional controls from publicly available datasets.

Though genome-wide association studies (GWAS) have identified numerous susceptibility loci for common diseases, their use is limited due to the expense of genotyping large cohorts of individuals. One potential solution is to use ‘additional controls’, or genotype data from control individuals deposited in public repositories. While this approach has been used by several groups, the genetically heterogeneous nature of the population of the United States makes this approach potentially problematic. We empirically investigated the utility of this approach in a US-based GWAS. In a small GWAS of pancreatic cancer in New York, we observed clear population structure differences relative to controls from the database of Genotypes and Phenotypes (dbGaP). When we conduct the GWAS using these additional controls, we find large inflation of the test statistic that is properly corrected by using eigenvectors from principal components analysis as covariates. To deal with errors introduced due to different sources, we propose simultaneously genotyping a small number of controls along with cases and then comparing this group to the additional controls. We show that removing SNPs that show differences between these control groups reduces false-positive findings. Thus, through an empirical approach, this report provides practical guidance for using additional controls from publicly available datasets.