Kevin Brown

Mid-career Tenured Researcher, National Cancer Institute (NIH - NCI)

1 active project

Melanoma GWAS, Controled Access

Cutaneous melanoma has a complex etiology. Ultraviolet radiation (UVR) exposure is the strongest environmental risk factor. Family and twin studies both show a clear heritable component to melanoma risk, with twin studies suggesting melanoma has the highest heritability of any…

Scientific Questions Being Studied

Cutaneous melanoma has a complex etiology. Ultraviolet radiation (UVR) exposure is the strongest environmental risk factor. Family and twin studies both show a clear heritable component to melanoma risk, with twin studies suggesting melanoma has the highest heritability of any solid tumor. Multiple pigmentation characteristics as well as number of melanocytic nevi, both genetically determined, are also clearly associated with melanoma risk. Given a dramatic difference in survival between patients diagnosed with early and late-stage disease, strategies for applying genetics to identify at-risk individuals and to facilitate targeted prevention and early detection would likely make a significant impact on human health. Here we propose to identify associations between both common and rare variants and melanoma (and or melanoma-associated traits) in the AllofUs dataset and subsequently meta-analyze these data with those from other case-control studies.

Project Purpose(s)

  • Disease Focused Research (melanoma)

Scientific Approaches

We propose to assess associations of common variants and melanoma risk (or melanoma-associated traits) using SNP genotyping array data. We will impute ~10M SNP genotypes using directly genotyped SNPs and test for association in a case-control study as we have done previously in other datasets (for example Landi et al, 2020, Nature Genetics). Using summary data from this analysis, we will subsequently perform a meta-analysis of AllofUs data with those from multiple other melanoma case-control datasets (including those from Landi et al) to identify novel, genome-wide significant melanoma risk loci. We will similarly utilize whole-genome sequencing data to assess for associations between rare variants and melanoma risk, first in the AllofUs dataset with subsequent meta-analysis of summary level data with other WGS and WES data from melanoma cases and controls.

Anticipated Findings

The meta-analyses of both common and rare variants we plan to conduct including data from AllofUs represent the largest genetic studies of melanoma risk to data. We anticipate more than doubling the number of cases in our GWAS analysis over that previously published (>36,000 cases in 2020) and thus expect dramatically expand the list of known genome-wide significant loci. We anticipate applying these data in order to improve polygenic risk scores for application to identifying at-risk patients, as well as to identifying potential causal genes and genetic variants that may shed light on processes involved in melanoma development and progression. To date the is a dearth of large well-powered rare-variant studies of melanoma risk. We anticipate our meta-analysis will identify novel medium- or high-penetrance melanoma risk genes, with implications for genetic testing in high-risk families.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Kevin Brown - Mid-career Tenured Researcher, National Cancer Institute (NIH - NCI)

Collaborators:

  • Jianxin Shi - Mid-career Tenured Researcher, National Cancer Institute (NIH - NCI)
  • Xing Hua - Research Associate, National Cancer Institute (NIH - NCI)
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