Frequency, phenotypic spectrum, and penetrance of pathogenic variation in cancer susceptibility genes in the general population
In our study, we will investigate genes that increase risk for cancer. To do this, we will count the number of mutations in genes known (or suspected) to increase the risk of cancer in the UK Biobank dataset. We will do this using well-established genetic and epidemiologic methods. Our group has many years of experience of doing this type of analysis, although in smaller datasets. Once we identify participants with mutations in the UK Biobank dataset, we will study the medical problems, especially cancer, that may arise with that damaging variation. Since cancer-associated genes may also cause problems in human development, we will examine medical problems affecting learning, the heart, and growth, among others. We will compare the frequency of these problems in people with mutations ("cases") to those without mutations ("controls"). We will use statistics whenever possible to be certain of our observations. We will first focus on genes that are well known to increase the risk for cancer. We will also study genes that are suspected to cause cancer. In this way, new cancer-associated genes may be discovered. By studying non-cancer medical problems (such as heart defects, or growth issues), we may identify new ways for doctors to diagnosis disease. We expect our analysis of the first 50,000 exomes will take two years; additional releases of UK Biobank data will take additional time. Findings from our study may benefit public health from 1) more accurate counts of cancer-associated mutations in the population, 2) better understanding of how often those mutations cause cancer, 3) better understanding of how often those mutations cause other medical problems, 4) description of new rare diseases.
We aim to investigate the following:
1) Frequency of pathogenic germline variants in cancer susceptibility genes (CSG).
2) Penetrance of phenotype (focused on cancer and benign neoplasms) in people with pathogenic germline variation in cancer susceptibility genes.
3) Identification of novel phenotypes associated with pathogenic germline variants in CSGs.
4) Quantification of gene-specific cancer risk in individuals with and without family history of cancer in addition to cancer risk modifiers including smoking, BMI, physical activity, sun exposure, and others.
5) Investigate copy-number variation (CNV) in known and suspected CSG
6) Explore pathogenic germline variants in suspected CSG
7) Genome-wide association study and polygenic risk score to better understand genetic variation with cancer-risk.
8) Using artificial intelligence (AI), identify germline signatures of risk, predict disease causing variants outside of exonic regions when genome data is available.