Biomedical subjects

Guy N Brock

Publications and source records attributed to Guy N Brock.

3 recordsLinked to original sources

PubMed2026

The Clinical Application of Refined Risk Estimates Study in BRCA1 and BRCA2 Pathogenic Variant Carriers: A Randomized Controlled Trial.

UNLABELLED: Individuals with germline BRCA1 or BRCA2 pathogenic variants (PV) may struggle with risk management decision-making. Advancements in technology could provide more specific risk information to patients, but the impact of this information is unknown. The Clinical Application of Refined Risk Estimates Study is a two-arm randomized controlled trial in women with a BRCA1/BRCA2 PV. The primary objective was to determine whether genotype-informed personalized cancer risk estimates (GRE) compared with standard lifetime cancer risk estimates (SRE) decreased decisional conflict related to cancer risk management decision-making. Women were recruited following the disclosure of their PV results. Participants completed a baseline survey and were randomized 1:1 to receive a GRE or SRE. After receiving their results, participants completed a follow-up survey. Likert and continuous data measures were analyzed using linear regression. There were no differences in decisional conflict between study arms at follow-up. However, individuals in the SRE arm showed an increased need for personal structure compared with those in the GRE arm (P = 0.02). Compared with baseline, individuals within the SRE arm showed decreased decisional conflict (P = 0.003) and increased perceived stress (P = 0.02) at follow-up. A more personalized cancer risk estimate did not decrease decisional conflict in women with BRCA1/BRCA2 PVs. Future studies will determine whether a GRE affects actual decision-making behaviors. PREVENTION RELEVANCE: Women with a germline PV in BRCA1 or BRCA2 have significantly elevated risks of developing breast and ovarian cancers. This randomized controlled trial evaluates the impact of polygenic risk scores on decisional conflict related to breast and ovarian cancer prevention and risk management in those with BRCA1/BRCA2 PVs.

Humans↗

PubMed2005

Methods for detecting gene x gene interaction in multiplex extended pedigrees.

Complex diseases are multifactorial in nature and can involve multiple loci with gene x gene and gene x environment interactions. Research on methods to uncover the interactions between those genes that confer susceptibility to disease has been extensive, but many of these methods have only been developed for sibling pairs or sibships. In this report, we assess the performance of two methods for finding gene x gene interactions that are applicable to arbitrarily sized pedigrees, one based on correlation in per-family nonparametric linkage scores and another that incorporates candidate loci genotypes as covariates into an affected relative pair linkage analysis. The power and type I error rate of both of these methods was addressed using the simulated Genetic Analysis Workshop 14 data. In general, we found detection of the interacting loci to be a difficult problem, and though we experienced some modest success there is a clear need to continue developing new methods and approaches to the problem.

Epistasis, Genetic↗

PubMed2005

Approaches to detecting gene x gene interaction in Genetic Analysis Workshop 14 pedigrees.

Whether driven by the general lack of success in finding single-gene contributions to complex disease, by increased knowledge about the potential involvement of specific biological interactions in complex disease, or by recent dramatic increases in computational power, a large number of approaches to detect locus x locus interactions were recently proposed and implemented. The six Genetic Analysis Workshop 14 (GAW14) papers summarized here each applied either existing or refined approaches with the goal of detecting gene x gene, or locus x locus, interactions in the GAW14 data. Five of six papers analyzed the simulated data; the other analyzed the Collaborative Study on the Genetics of Alcoholism data. The analytic strategies implemented for detecting interactions included multifactor dimensionality reduction, conditional linkage analysis, nonparametric linkage correlation, two-locus parametric linkage analysis, and a joint test of linkage and association. Overall, most of the groups found limited success in consistently detecting all of the simulated interactions due, in large part, to the nature of the generating model.

Alcoholism↗