The use of whole exome sequencing data to identify candidate genes involved in cancer and benign tumour predisposition
type: CreativeWork
description: The development of whole exome sequencing has transformed the study of disease predisposition. The sequencing of both large disease sets and smaller rare disease families enables the identification of new predisposition variants and potentially provide clinical insight into disease management. There is no standard protocol for analysing exome sequencing data. Outside of extremely large sequencing studies including thousands of individuals, statistical approaches are often underpowered to detect rare disease associated variants. Aggregation of variants into functionally related regions, including genes, gene clusters, and pathways, allows for the detection of biological processes that, when interrupted, may impact disease risk. In silico functional studies can also be utilised to further understand how variants disrupt biological processes and identify genotype-phenotype relationships. This study describes the exploration of sequencing datasets from cancers and benign tumour diseases including: i) hereditary diffuse gastric cancer, ii) sweat duct proliferation tumours, iii) adrenocortical carcinoma, and iv) breast cancer. Each set underwent germline whole exome sequencing followed by additional tumour or targeted sequencing to identify associated predisposition genes. Variants within a cluster of risk genes that are involved in double strand break repair were identified as associated with hereditary diffuse gastric cancer risk via gene ontology enrichment analysis. This cluster included PALB2 within which, using externally collated data, loss of function variants were identified as significantly associated with hereditary diffuse gastric cancer risk. Germline protein-affecting variants in the myosin gene MYH9 were identified in all individuals with a rare sweat duct proliferative syndrome, suggesting a role for MYH9 in skin development, regulation and tumorigenesis. These MYH9 variants were analysed in silico to identify a genotype-phenotype relationship between the …
abstract: The development of whole exome sequencing has transformed the study of disease predisposition. The sequencing of both large disease sets and smaller rare disease families enables the identification of new predisposition variants and potentially provide clinical insight into disease management. There is no standard protocol for analysing exome sequencing data. Outside of extremely large sequencing studies including thousands of individuals, statistical approaches are often underpowered to detect rare disease associated variants. Aggregation of variants into functionally related regions, including genes, gene clusters, and pathways, allows for the detection of biological processes that, when interrupted, may impact disease risk. In silico functional studies can also be utilised to further understand how variants disrupt biological processes and identify genotype-phenotype relationships. This study describes the exploration of sequencing datasets from cancers and benign tumour diseases including: i) hereditary diffuse gastric cancer, ii) sweat duct proliferation tumours, iii) adrenocortical carcinoma, and iv) breast cancer. Each set underwent germline whole exome sequencing followed by additional tumour or targeted sequencing to identify associated predisposition genes. Variants within a cluster of risk genes that are involved in double strand break repair were identified as associated with hereditary diffuse gastric cancer risk via gene ontology enrichment analysis. This cluster included PALB2 within which, using externally collated data, loss of function variants were identified as significantly associated with hereditary diffuse gastric cancer risk. Germline protein-affecting variants in the myosin gene MYH9 were identified in all individuals with a rare sweat duct proliferative syndrome, suggesting a role for MYH9 in skin development, regulation and tumorigenesis. These MYH9 variants were analysed in silico to identify a genotype-phenotype relationship between the …
author: Fewings, Eleanor Rose
language: en
keywords: Cancer, Genetics, Predisposition, Sequencing, Bioinformatics, Breast Cancer, Hereditary Diffuse Gastric Cancer
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/09e4c088-f203-46e1-9a61-ff10eb274a29/download
license: https://www.rioxx.net/licenses/all-rights-reserved/
date published: 2019-01-01
date modified: 2026-07-22T22:24:13Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.33290