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Activity Number: 501
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #319719 View Presentation
Title: Analysis of Paired Mitochondrial DNA Data Set by Using Bivariate Poisson Models
Author(s): Pei-Fang Su* and Yan Guo and John D. Boice and Yu Shyr
Companies: National Cheng Kung University and Vanderbilt University and National Council on Radiation Protection and Measurements and Vanderbilt University
Keywords: Bivariate Poisson ; Next-generation sequencing ; heteroplasmic mutations
Abstract:

Next-generation sequencing technology (NGS) has provided a reliable and cost-effective approach to detect and study mitochondrial DNA (mtDNA) heteroplasmy. To assess the effect of chemotherapy or radiation therapy as part of cancer treatment on mitochondrial genome mutation in cancer survivors and their offspring, a recent study sequenced the full mitochondrial genome and determined the heteroplasmic mutation rate. Because the number of heteroplasmic mutations in mothers and their offspring is naturally correlated within each pair, a bivariate Poisson regression analysis approach is used to address both the paired correlation and clinical information. In addition, the correlation of the number of heteroplasmic mutations between mothers and that of their offspring can be estimated by the model adjusted for interested covariates. This study is the first to introduce bivariate Poisson regression into the paired sequencing data analysis field. The results illustrate that the bivariate Poisson regression is an appropriate model for investigating the relationship between the number of paired heteroplasmic mutations and clinical information. The average estimated correlation adjusted for


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

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