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A Noninvasive Hair Sampling Technique to Obtain High Quality DNA from Elusive Small Mammals

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Pedigree-based relationship inference from complex DNA mixtures.

Guro Dørum¹, Navreet Kaur², Mario Gysi³

¹Zurich Institute of Forensic Medicine, University of Zurich, Zurich, Switzerland. guro.dorum@irm.uzh.ch.

International Journal of Legal Medicine

|January 20, 2017

Summary

This summary is machine-generated.

This study introduces a new method for DNA mixture analysis involving relatives, accurately handling dropout, drop-in, and mutations. The developed software, relMix, aids in identifying contributors and determining relationships, including complex family structures.

Keywords:

DNA mixtures Drop-in Dropout Kinship Likelihood ratio Mutations NGS Non-invasive prenatal paternity testing

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Area of Science:

Forensic Genetics
Population Genetics
Bioinformatics

Background:

Analyzing DNA mixtures, especially those involving relatives, presents challenges due to factors like allele dropout, drop-in, mutations, and population substructure.
Accurate kinship analysis from DNA mixtures is crucial for various applications, including forensic investigations and reproductive genetics.

Purpose of the Study:

To develop a general method and software for analyzing DNA mixtures involving related individuals.
To address challenges in DNA mixture analysis, including dropout, drop-in, mutations, and population substructure.
To facilitate both the identification of mixture contributors and the determination of relationships between individuals.

Main Methods:

A novel statistical method was developed to analyze DNA mixtures, incorporating models for dropout, drop-in, mutations, and silent alleles.
Population substructure was accounted for in the analysis.
Relationships were represented using pedigrees, allowing for complex kinship scenarios involving multiple individuals.
A graphical user interface was implemented in the R package relMix for user-friendly application.

Main Results:

The presented method successfully analyzes DNA mixtures involving relatives, accounting for various sources of error and complexity.
The software, relMix, can be used to identify related contributors to a DNA mixture.
The method is particularly applicable to scenarios like non-invasive prenatal paternity testing, where a child's DNA profile is mixed with the mother's.

Conclusions:

The developed method provides a robust framework for DNA mixture analysis in the presence of relatedness and technical artifacts.
The relMix software offers a practical and accessible tool for researchers and practitioners in forensic and human genetics.
This approach enhances the accuracy and reliability of kinship determination from complex DNA mixtures.