The application of machine learning to predict genetic relatedness using human mtDNA hypervariable region I sequences.

ABSTRACT: Human identification of unknown samples following disaster and mass casualty events is essential, especially to bring closure to family and friends of the deceased. Unfortunately, victim identification is often challenging for forensic investigators as analysis becomes complicated when biological samples are degraded or of poor quality as a result of exposure to harsh environmental factors. Mitochondrial DNA becomes the ideal option for analysis, particularly for determining the origin of the samples. In such events, the estimation of genetic parameters plays an important role in modelling and predicting genetic relatedness and is useful in assigning unknown individuals to an ethnic group. Various techniques exist for the estimation of genetic relatedness, but the use of Machine learning (

SUBMITTER: Govender P 

PROVIDER: S-EPMC8856515 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature