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The landscape of the heritable cancer genome

Fanfani, Viola and Citi, Luca and Harris, Adrian L and Pezzella, Francesco and Stracquadanio, Giovanni (2021) 'The landscape of the heritable cancer genome.' Cancer Research, 81 (10). pp. 2588-2599. ISSN 0008-5472

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Abstract

Genome-wide association studies (GWAS) have found hundreds of single-nucleotide polymorphisms (SNP) associated with increased risk of cancer. However, the amount of heritable risk explained by SNPs is limited, leaving most of the cancer heritability unexplained. Tumor sequencing projects have shown that causal mutations are enriched in genic regions. We hypothesized that SNPs located in protein coding genes and nearby regulatory regions could explain a significant proportion of the heritable risk of cancer. To perform gene-level heritability analysis, we developed a new method, called Bayesian Gene Heritability Analysis (BAGHERA), to estimate the heritability explained by all genotyped SNPs and by those located in genic regions using GWAS summary statistics. BAGHERA was specifically designed for low heritability traits such as cancer and provides robust heritability estimates under different genetic architectures. BAGHERA-based analysis of 38 cancers reported in the UK Biobank showed that SNPs explain at least 10% of the heritable risk for 14 of them, including late onset malignancies. We then identified 1,146 genes, called cancer heritability genes (CHG), explaining a significant proportion of cancer heritability. CHGs were involved in hallmark processes controlling the transformation from normal to cancerous cells. Importantly, 60 of them also harbored somatic driver mutations, and 27 are tumor suppressors. Our results suggest that germline and somatic mutation information could be exploited to identify subgroups of individuals at higher risk of cancer in the broader population and could prove useful to establish strategies for early detection and cancer surveillance.

Item Type: Article
Uncontrolled Keywords: Humans; Neoplasms; Bayes Theorem; Genome-Wide Association Study
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 18 Oct 2021 13:09
Last Modified: 15 Jan 2022 01:36
URI: http://repository.essex.ac.uk/id/eprint/29832

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