Literature DB >> 29779563

Association of Polygenic Risk Scores for Multiple Cancers in a Phenome-wide Study: Results from The Michigan Genomics Initiative.

Lars G Fritsche1, Stephen B Gruber2, Zhenke Wu3, Ellen M Schmidt4, Matthew Zawistowski5, Stephanie E Moser6, Victoria M Blanc7, Chad M Brummett8, Sachin Kheterpal8, Gonçalo R Abecasis5, Bhramar Mukherjee9.   

Abstract

Health systems are stewards of patient electronic health record (EHR) data with extraordinarily rich depth and breadth, reflecting thousands of diagnoses and exposures. Measures of genomic variation integrated with EHRs offer a potential strategy to accurately stratify patients for risk profiling and discover new relationships between diagnoses and genomes. The objective of this study was to evaluate whether polygenic risk scores (PRS) for common cancers are associated with multiple phenotypes in a phenome-wide association study (PheWAS) conducted in 28,260 unrelated, genotyped patients of recent European ancestry who consented to participate in the Michigan Genomics Initiative, a longitudinal biorepository effort within Michigan Medicine. PRS for 12 cancer traits were calculated using summary statistics from the NHGRI-EBI catalog. A total of 1,711 synthetic case-control studies was used for PheWAS analyses. There were 13,490 (47.7%) patients with at least one cancer diagnosis in this study sample. PRS exhibited strong association for several cancer traits they were designed for, including female breast cancer, prostate cancer, melanoma, basal cell carcinoma, squamous cell carcinoma, and thyroid cancer. Phenome-wide significant associations were observed between PRS and many non-cancer diagnoses. To differentiate PRS associations driven by the primary trait from associations arising through shared genetic risk profiles, the idea of "exclusion PRS PheWAS" was introduced. Further analysis of temporal order of the diagnoses improved our understanding of these secondary associations. This comprehensive PheWAS used PRS instead of a single variant.
Copyright © 2018 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  electronic health records; genetic variation; genome-wide association study; hospitals; humans; multifactorial inheritance; neoplasms; phenome-wide association study; phenotype; risk

Mesh:

Year:  2018        PMID: 29779563      PMCID: PMC5992124          DOI: 10.1016/j.ajhg.2018.04.001

Source DB:  PubMed          Journal:  Am J Hum Genet        ISSN: 0002-9297            Impact factor:   11.025


  60 in total

1.  A powerful subset-based method identifies gene set associations and improves interpretation in UK Biobank.

Authors:  Diptavo Dutta; Peter VandeHaar; Lars G Fritsche; Sebastian Zöllner; Michael Boehnke; Laura J Scott; Seunggeun Lee
Journal:  Am J Hum Genet       Date:  2021-03-16       Impact factor: 11.025

2.  Assessing thyroid cancer risk using polygenic risk scores.

Authors:  Sandya Liyanarachchi; Julius Gudmundsson; Egil Ferkingstad; Huiling He; Jon G Jonasson; Vinicius Tragante; Folkert W Asselbergs; Li Xu; Lambertus A Kiemeney; Romana T Netea-Maier; Jose I Mayordomo; Theo S Plantinga; Hannes Hjartarson; Jon Hrafnkelsson; Erich M Sturgis; Pamela Brock; Fadi Nabhan; Gudmar Thorleifsson; Matthew D Ringel; Kari Stefansson; Albert de la Chapelle
Journal:  Proc Natl Acad Sci U S A       Date:  2020-03-04       Impact factor: 11.205

Review 3.  Methods for the Analysis and Interpretation for Rare Variants Associated with Complex Traits.

Authors:  J Dylan Weissenkampen; Yu Jiang; Scott Eckert; Bibo Jiang; Bingshan Li; Dajiang J Liu
Journal:  Curr Protoc Hum Genet       Date:  2019-03-08

4.  The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities.

Authors:  Lauren J Beesley; Maxwell Salvatore; Lars G Fritsche; Anita Pandit; Arvind Rao; Chad Brummett; Cristen J Willer; Lynda D Lisabeth; Bhramar Mukherjee
Journal:  Stat Med       Date:  2019-12-20       Impact factor: 2.373

5.  A Polygenic and Phenotypic Risk Prediction for Polycystic Ovary Syndrome Evaluated by Phenome-Wide Association Studies.

Authors:  Yoonjung Yoonie Joo; Ky'Era Actkins; Jennifer A Pacheco; Anna O Basile; Robert Carroll; David R Crosslin; Felix Day; Joshua C Denny; Digna R Velez Edwards; Hakon Hakonarson; John B Harley; Scott J Hebbring; Kevin Ho; Gail P Jarvik; Michelle Jones; Tugce Karaderi; Frank D Mentch; Cindy Meun; Bahram Namjou; Sarah Pendergrass; Marylyn D Ritchie; Ian B Stanaway; Margrit Urbanek; Theresa L Walunas; Maureen Smith; Rex L Chisholm; Abel N Kho; Lea Davis; M Geoffrey Hayes
Journal:  J Clin Endocrinol Metab       Date:  2020-06-01       Impact factor: 5.958

6.  Estimation of DNA contamination and its sources in genotyped samples.

Authors:  Gregory J M Zajac; Lars G Fritsche; Joshua S Weinstock; Susan L Dagenais; Robert H Lyons; Chad M Brummett; Gonçalo R Abecasis
Journal:  Genet Epidemiol       Date:  2019-08-26       Impact factor: 2.135

7.  Sex-Stratified Polygenic Risk Score Identifies Individuals at Increased Risk of Basal Cell Carcinoma.

Authors:  Michelle R Roberts; Joanne E Sordillo; Peter Kraft; Maryam M Asgari
Journal:  J Invest Dermatol       Date:  2019-11-01       Impact factor: 8.551

8.  Penetrance and Pleiotropy of Polygenic Risk Scores for Schizophrenia in 106,160 Patients Across Four Health Care Systems.

Authors:  Amanda B Zheutlin; Jessica Dennis; Richard Karlsson Linnér; Arden Moscati; Nicole Restrepo; Peter Straub; Douglas Ruderfer; Victor M Castro; Chia-Yen Chen; Tian Ge; Laura M Huckins; Alexander Charney; H Lester Kirchner; Eli A Stahl; Christopher F Chabris; Lea K Davis; Jordan W Smoller
Journal:  Am J Psychiatry       Date:  2019-08-16       Impact factor: 18.112

Review 9.  Keratinocyte Carcinomas: Current Concepts and Future Research Priorities.

Authors:  Priyadharsini Nagarajan; Maryam M Asgari; Adele C Green; Samantha M Guhan; Sarah T Arron; Charlotte M Proby; Dana E Rollison; Catherine A Harwood; Amanda Ewart Toland
Journal:  Clin Cancer Res       Date:  2018-12-06       Impact factor: 12.531

Review 10.  Genome-wide association studies and polygenic risk scores for skin cancer: clinically useful yet?

Authors:  M R Roberts; M M Asgari; A E Toland
Journal:  Br J Dermatol       Date:  2019-07-07       Impact factor: 9.302

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