Quantifying the cumulative effect of low-penetrance genetic variants on breast cancer risk

Health influenced by genetics: A first comprehensive analysis of breast cancer high and moderate penetrance susceptibility genes in the Tunisian population

Significant advances have been made to understand the genetic basis of breast cancer. High, moderate and low penetrance variants have been identified with inter-ethnic variability in mutation frequency and spectrum. Genome wide association studies (GWAS) are widely used to identify disease-associated SNPs. Understanding the functional impact of these risk-SNPs will help the translation of GWAS findings into clinical interventions. Here we aim to characterize the genetic patterns of high and moderate penetrance breast cancer susceptibility genes and to assess the functional impact of non-coding SNPs. We analyzed BRCA1/2, PTEN, STK11, TP53, ATM, BRIP1, CHEK2 and PALB2 genotype data obtained from 135 healthy participants genotyped using Affymetrix Genome-Wide Human SNP-Array 6.0. Haplotype analysis was performed using Haploview.V4.2 and PHASE.V2.1. Population structure and genetic differentiation were assessed using principal component analysis (PCA) and fixation index (FST). Functional annotation was performed using In Silico web-based tools including RegulomeDB and VARAdb. Haplotype analysis showed distinct LD patterns with high levels of recombination and haplotype blocks of moderate to small size. Our findings revealed also that the Tunisian population tends to have a mixed origin with European, South Asian and Mexican footprints. Functional annotation allowed the selection of 28 putative regulatory variants. Of special interest were BRCA1_ rs8176318 predicted to alter the binding sites of a tumor suppressor miRNA hsa-miR-149 and PALB2_ rs120963 located in tumorigenesis-associated enhancer and predicted to strongly affect the binding of P53. Significant differences in allele frequencies were observed with populations of African and European ancestries for rs8176318 and rs120963 respectively. Our findings will help to better understand the genetic basis of breast cancer by guiding upcoming genome wide studies in the Tunisian population. Putative functional SNPs may be used to develop an efficient polygenic risk score to predict breast cancer risk leading to better disease prevention and management.