Bivariate Poisson models with varying offsets: An application to the paired mitochondrial DNA dataset

Pei Fang Su, Yu Lin Mau, Yan Guo, Chung I. Li, Qi Liu, John D. Boice, Yu Shyr

Research output: Contribution to journalArticle

Abstract

To assess the effect of chemotherapy on mitochondrial genome mutations in cancer survivors and their offspring, a study sequenced the full mitochondrial genome and determined the mitochondrial DNA heteroplasmic (mtDNA) mutation rate. To build a model for counts of heteroplasmic mutations in mothers and their offspring, bivariate Poisson regression was used to examine the relationship between mutation count and clinical information while accounting for the paired correlation. However, if the sequencing depth is not adequate, a limited fraction of the mtDNA will be available for variant calling. The classical bivariate Poisson regression model treats the offset term as equal within pairs; thus, it cannot be applied directly. In this research, we propose an extended bivariate Poisson regression model that has a more general offset term to adjust the length of the accessible genome for each observation. We evaluate the performance of the proposed method with comprehensive simulations, and the results show that the regression model provides unbiased parameter estimations. The use of the model is also demonstrated using the paired mtDNA dataset.

Original languageEnglish
Pages (from-to)47-58
Number of pages12
JournalStatistical Applications in Genetics and Molecular Biology
Volume16
Issue number1
DOIs
Publication statusPublished - 2017 Mar 1

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Molecular Biology
  • Genetics
  • Computational Mathematics

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