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Accurate Predictions of Postmortem Interval Using Linear Regression Analyses of Gene Meter Expression Data

Colby M. Hunter, View ORCID ProfileAlex E. Pozhitkov, View ORCID ProfilePeter A. Noble
doi: https://doi.org/10.1101/058370
Colby M. Hunter
1Ph.D. Microbiology Program, Department of Biological Sciences, Alabama State University, Montgomery, Alabama, USA 36104
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  • For correspondence: cmooghww@gmail.com
Alex E. Pozhitkov
2Department of Oral Health Sciences, University of Washington, Box 357444, Seattle, WA, USA 98195
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Peter A. Noble
1Ph.D. Microbiology Program, Department of Biological Sciences, Alabama State University, Montgomery, Alabama, USA 36104
2Department of Oral Health Sciences, University of Washington, Box 357444, Seattle, WA, USA 98195
3Department of Periodontics, School of Dentistry, Box 355061, University of Washington, Seattle, Washington, USA 98195
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  • For correspondence: cmooghww@gmail.com pozhit@uw.edu
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Abstract

In criminal and civil investigations, postmortem interval is used as evidence to help sort out circumstances at the time of human death. Many biological, chemical, and physical indicators can be used to determine the postmortem interval, but most are not accurate. Here, we sought to validate an experimental design to accurately predict the time of death by analyzing the expression of hundreds of upregulated genes in two model organisms, the zebrafish and mouse. In a previous study, the death of healthy adults was conducted under strictly controlled conditions to minimize the effects of confounding factors such as lifestyle and temperature. A total of 74,179 microarray probes were calibrated using the Gene Meter approach and the transcriptional profiles of 1,063 significantly upregulated genes were assembled into a time series spanning from life to 48 or 96 h postmortem. In this study, the experimental design involved splitting the gene profiles into training and testing datasets, randomly selecting groups of profiles, determining the modeling parameters of the genes to postmortem time using over- and/or perfectly- defined linear regression analyses, and calculating the fit (R2) and slope of predicted versus actual postmortem times. This design was repeated several thousand to million times to find the top predictive groups of gene transcription profiles. A group of eleven zebrafish genes yielded R2 of 1 and a slope of 0.99, while a group of seven mouse liver genes yielded a R2 of 0.98 and a slope of 0.97, and seven mouse brain genes yielded a R2 of 0.93 and a slope of 0.85. In all cases, groups of gene transcripts yielded better postmortem time predictions than individual gene transcripts. The significance of this study is two-fold: selected groups of upregulated genes provide accurate prediction of postmortem time, and the successfully validated experimental design can now be used to accurately predict postmortem time in cadavers.

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  • * The work was supported by funds from the Max-Planck-Society.

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Posted June 12, 2016.
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Accurate Predictions of Postmortem Interval Using Linear Regression Analyses of Gene Meter Expression Data
Colby M. Hunter, Alex E. Pozhitkov, Peter A. Noble
bioRxiv 058370; doi: https://doi.org/10.1101/058370
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Accurate Predictions of Postmortem Interval Using Linear Regression Analyses of Gene Meter Expression Data
Colby M. Hunter, Alex E. Pozhitkov, Peter A. Noble
bioRxiv 058370; doi: https://doi.org/10.1101/058370

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