Bayesian regression analysis of stutter in DNA mixtures

Date

2020

Authors

Alaeddini, Reza
Yang, Mo
Puza, Borek

Journal Title

Journal ISSN

Volume Title

Publisher

Taylor & Francis Group

Abstract

Probabilistic genotyping methods use a hierarchical probability model in deconvolution of DNA mixtures. The parameters of the model, including the stutter which are required to calculate the expected values of peak heights, are estimated in the validation process. Linear modeling of stutter, as a common artifact in DNA genotyping, has been reported previously. The typically right-skewed error distribution and non-negativeness of stutter to its allele peak heights ratios make generalized linear models preferable, especially Bayesian analogs, which allow even more flexibility. In this paper, we show how such models can be fitted and applied with the aid of Markov chain Monte Carlo methods.

Description

Keywords

Bayesian methods, DNA genotyping, generalized linear models, Markov chain Monte Carlo methods, stutter

Citation

Source

Communications in Statistics: Theory and Methods

Type

Journal article

Book Title

Entity type

Access Statement

License Rights

DOI

10.1080/03610926.2019.1710760

Restricted until

2099-12-31