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Activity Number: 366 - SPEED: Recent Advances in Statistical Genomics and Genetics
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 11:15 AM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #332644
Title: A Bayesian Gene-Based GWAS Analysis of Osteosarcoma Trio Data Using a Hierarchically Structured Prior
Author(s): Yi Yang* and SAONLI BASU and Lisa Mirabello and Logan Spector and Lin Zhang
Companies: University of Minnesota and University of Minnesota and National Institutes of Health and University of Minnesota and University of Minnesota
Keywords: Bayesian HSVS; Fused lasso; Gene-based GWAS; Multiple testing; Trio data

Osteosarcoma is considered to be the most common primary malignant bone cancer among children and young adults. Previous studies suggest growth spurts and height to be risk factors for osteosarcoma. However, studies on the genetic etiology are still limited given the rare occurrence of the disease. In this study, we investigated in a family trio dataset that consists of 209 patients and their unaffected parents, and conducted a genome-wide association study (GWAS) to identify genetic risk factors for osteosarcoma. We performed a Bayesian gene-based GWAS based on the SNP-level summary statistics obtained from a likelihood ratio test of the trio data, utilizing a hierarchically structured prior that incorporates the SNP-gene hierarchical structure. The Bayesian approach has higher power than SNP-level GWAS analysis due to the reduced number of tests, and is robust by accounting for the correlations between SNPs so that it borrows information across SNPs within a gene. We identified 217 genes that achieved genome-wide significance. Ingenuity pathway analysis indicated that osteosarcoma is potentially related to TP53, estrogen receptor signaling, and xenobiotic metabolism signaling.

Authors who are presenting talks have a * after their name.

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