Applications of Probabilistic Genotyping Method for Combining Evidence Across Microhaplotype DNA Mixture Profiles

In cases of serious crimes that involve challenging DNA samples from the perpetrator (e.g., a minor contributor to a mixture), there is justification to combine different mixture profiles. In our previous study, we developed a massively parallel sequencing (MPS)-based assay targeting 140 microhaplotype markers. In this study, we extended the use of the microhaplotype panel to common scenarios, such as determining the presence of a common contributor or relatedness between different mixture profiles when no reference source is available. Data interpretation was performed using the R package KinMix. Our findings revealed that correct assignments of a common contributor and relatedness were made between relatively balanced mixtures. However, when profiles suffered from allele imbalance, inclusive assignments were significantly associated with the suspect's mixture proportion. Additionally, our analysis showed zero false-positive rates in the studied scenarios. These results indicate that microhaplotype data can be reliably interpreted for identifying a common donor or related donors among different mixtures. Further research based on larger sample sizes may yield more reliable results, which could assist in solving issues related to complex scenarios where multiple mixed profiles were involved.

Authors

Institutions

Publication Details

Journal
Electrophoresis
Published
2024-10-14
DOI
https://doi.org/10.1002/elps.202400140
Citations
4
Primary Topic
Forensic and Genetic Research
Type
article
Field-Weighted Citation Impact
2.20

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Applications of Probabilistic Genotyping Method for Combining Evidence Across Microhaplotype DNA Mixture Profiles

Zhaochen Bai, Xueling Ou, Xiaohua Ling, Xinyi Lin et al.
4 citations
Electrophoresis
Forensic and Genetic Research
2.20
article

Applications of Probabilistic Genotyping Method for Combining Evidence Across Microhaplotype DNA Mixture Profiles

Zhaochen Bai, Xueling Ou, Xiaohua Ling, Xinyi Lin, Nan Zhang, Huan Wang, Shuang Han, Jiayue Li
article en
4 citations

Abstract

In cases of serious crimes that involve challenging DNA samples from the perpetrator (e.g., a minor contributor to a mixture), there is justification to combine different mixture profiles. In our previous study, we developed a massively parallel sequencing (MPS)-based assay targeting 140 microhaplotype markers. In this study, we extended the use of the microhaplotype panel to common scenarios, such as determining the presence of a common contributor or relatedness between different mixture profiles when no reference source is available. Data interpretation was performed using the R package KinMix. Our findings revealed that correct assignments of a common contributor and relatedness were made between relatively balanced mixtures. However, when profiles suffered from allele imbalance, inclusive assignments were significantly associated with the suspect's mixture proportion. Additionally, our analysis showed zero false-positive rates in the studied scenarios. These results indicate that microhaplotype data can be reliably interpreted for identifying a common donor or related donors among different mixtures. Further research based on larger sample sizes may yield more reliable results, which could assist in solving issues related to complex scenarios where multiple mixed profiles were involved.

ElectrophoresisVol. 46(15)
Sun Yat-sen University (CN), Third Affiliated Hospital of Sun Yat-sen University (CN)
National Natural Science Foundation of China
Peace, Justice and strong institutions
Openalex Percentile: Top 11%
Forensic and Genetic Research
2.20
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.