2023 ACMG Annual Clinical Genetics Meeting Digital Edition: Poster Gallery-Estimating predispositional breast cancer risk in genic regions using population sequencing data

Pdf Summary

REGatta is a method introduced to improve the estimation of clinical risk in gene segments for breast cancer. The method defines gene regions using the density of pathogenic diagnostic reports and calculates the relative risk in each of these regions using population sequencing data from the UK Biobank. The method was applied to seven established breast cancer genes and identified regions in each gene with statistically significant differences in breast cancer incidence for rare missense variant carriers.<br /><br />The study found that even in genes with no significant difference at the gene level, the REGatta approach significantly separates rare missense variant carriers at higher or lower risk. The regional risk estimates from REGatta showed high concordance with high-throughput functional assays of variant impact. Comparisons with existing methods and the use of protein domains (Pfam) as regions showed that REGatta better identifies individuals at elevated or reduced risk.<br /><br />The study validated regional risk using functional assay data and observed that carriers of variants in high-risk regions have a significantly different risk of breast cancer compared to carriers of variants in low-risk regions in all seven genes analyzed. The study also found that regions of elevated risk cluster closely in three-dimensional space in several genes.<br /><br />The HRR and LRR regional assignments from REGatta aligned with in vitro measurement of variant impact and showed depleted scores in the high-risk regions of all genes considered.<br /><br />Overall, the findings suggest that REGatta can improve risk assessment and clinical management by providing useful priors for estimating clinical risk in gene segments for breast cancer.

Asset Subtitle

Presenting Author - James Fife, BS; Co-Author - Christopher A. Cassa, PhD;

Meta Tag

Bioinformatics

Cancer Syndromes

Population Genetics

Risk Assessment

Sequencing

Co-Author Christopher A. Cassa, PhD

Presenting Author James Fife, BS

Keywords

REGatta

clinical risk

gene segments

breast cancer

gene regions

rare missense variant carriers

high-throughput functional assays

protein domains

risk assessment

clinical management