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Quantifying the Polygenic Contribution to Cutaneous Squamous Cell Carcinoma Risk.


ABSTRACT: Genetic factors play an important role in cutaneous squamous cell carcinoma risk. Genome-wide association studies have identified 21 single nucleotide polymorphisms associated with cutaneous squamous cell carcinoma risk. Yet no studies have attempted to quantify the contribution of heritability to cutaneous squamous cell carcinoma risk by calculating the population attributable risk using a combination of all discovered genetic variants. Using an additive multi-locus linear logistic model, we determined the cumulative association of these 21 genetic regions to cutaneous squamous cell carcinoma population attributable risk. We computed a multi-locus population attributable risk of 62%, suggesting that if the effects of all the risk alleles were removed from a population, the cutaneous squamous cell carcinoma risk would drop by 62%. Using stratified analysis, we also examined the impact of sex on polygenic risk score, and found that men have an increased relative risk throughout the spectrum of the polygenic risk score. Quantifying the impact of genetic predisposition on the proportion of cancer cases can guide future research decisions and public health policy planning.

SUBMITTER: Sordillo JE 

PROVIDER: S-EPMC6019610 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Quantifying the Polygenic Contribution to Cutaneous Squamous Cell Carcinoma Risk.

Sordillo Joanne E JE   Kraft Peter P   Wu Ann Chen AC   Asgari Maryam M MM  

The Journal of investigative dermatology 20180213 7


Genetic factors play an important role in cutaneous squamous cell carcinoma risk. Genome-wide association studies have identified 21 single nucleotide polymorphisms associated with cutaneous squamous cell carcinoma risk. Yet no studies have attempted to quantify the contribution of heritability to cutaneous squamous cell carcinoma risk by calculating the population attributable risk using a combination of all discovered genetic variants. Using an additive multi-locus linear logistic model, we de  ...[more]

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