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Exploring Various Polygenic Risk Scores for Skin Cancer in the Phenomes of the Michigan Genomics Initiative and the UK Biobank with a Visual Catalog: PRSWeb

Abstract

Polygenic risk scores (PRS) are designed to serve as single summary measures that are easy to construct, condensing information from a large number of genetic variants associated with a disease. They have been used for stratification and prediction of disease risk. The primary focus of this paper is to demonstrate how we can combine PRS and electronic health records data to better understand the shared and unique genetic architecture and etiology of disease subtypes that may be both related and heterogeneous. PRS construction strategies often depend on the purpose of the study, the available data/summary estimates, and the underlying genetic architecture of a disease. We consider several choices for constructing a PRS using data obtained from various publicly-available sources including the UK Biobank and evaluate their abilities to predict not just the primary phenotype but also secondary phenotypes derived from electronic health records (EHR). This study was conducted using data from 30,702 unrelated, genotyped patients of recent European descent from the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort within Michigan Medicine. We examine the three most common skin cancer subtypes in the USA: basal cell carcinoma, cutaneous squamous cell carcinoma, and melanoma. Using these PRS for various skin cancer subtypes, we conduct a phenome-wide association study (PheWAS) within the MGI data to evaluate PRS associations with secondary traits. PheWAS results are then replicated using population-based UK Biobank data and compared across various PRS construction methods. We develop an accompanying visual catalog called PRSweb that provides detailed PheWAS results and allows users to directly compare different PRS construction methods.

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References
1.
Law M, Medland S, Zhu G, Yazar S, Vinuela A, Wallace L . Genome-Wide Association Shows that Pigmentation Genes Play a Role in Skin Aging. J Invest Dermatol. 2017; 137(9):1887-1894. DOI: 10.1016/j.jid.2017.04.026. View

2.
Adhikari K, Fontanil T, Cal S, Mendoza-Revilla J, Fuentes-Guajardo M, Chacon-Duque J . A genome-wide association scan in admixed Latin Americans identifies loci influencing facial and scalp hair features. Nat Commun. 2016; 7:10815. PMC: 4773514. DOI: 10.1038/ncomms10815. View

3.
Hernandez-Pacheco N, Flores C, Alonso S, Eng C, Mak A, Hunstman S . Identification of a novel locus associated with skin colour in African-admixed populations. Sci Rep. 2017; 7:44548. PMC: 5353593. DOI: 10.1038/srep44548. View

4.
Eriksson N, Macpherson J, Tung J, Hon L, Naughton B, Saxonov S . Web-based, participant-driven studies yield novel genetic associations for common traits. PLoS Genet. 2010; 6(6):e1000993. PMC: 2891811. DOI: 10.1371/journal.pgen.1000993. View

5.
Reisberg S, Iljasenko T, Lall K, Fischer K, Vilo J . Comparing distributions of polygenic risk scores of type 2 diabetes and coronary heart disease within different populations. PLoS One. 2017; 12(7):e0179238. PMC: 5497939. DOI: 10.1371/journal.pone.0179238. View