Korean J Leg Med.  2019 Aug;43(3):97-105. 10.7580/kjlm.2019.43.3.97.

Classification of Common Relationships Based on Short Tandem Repeat Profiles Using Data Mining

Affiliations
  • 1Department of Statistics, Korea University, Seoul, Korea. jael@korea.ac.kr
  • 2Product Development HQ, Dong-A ST, Seoul, Korea.
  • 3Department of Forensic Medicine, Seoul National University College of Medicine, Seoul, Korea.
  • 4Forensic Science Division 2, Supreme Prosecutor's Office, Seoul, Korea.

Abstract

We reviewed past studies on the identification of familial relationships using 22 short tandem repeat markers. As a result, we can obtain a high discrimination power and a relatively accurate cut-off value in parent-child and full sibling relationships. However, in the case of pairs of uncle-nephew or cousin, we found a limit of low discrimination power of the likelihood ratio (LR) method. Therefore, we compare the LR ranking method and data mining techniques (e.g., logistic regression, linear discriminant analysis, diagonal linear discriminant analysis, diagonal quadratic discriminant analysis, K-nearest neighbor, classification and regression trees, support vector machines, random forest [RF], and penalized multivariate analysis) that can be applied to identify familial relationships, and provide a guideline for choosing the most appropriate model under a given situation. RF, one of the data mining techniques, was found to be more accurate than other methods. The accuracy of RF is 99.99% for parent-child, 99.44% for full siblings, 90.34% for uncle-nephew, and 79.69% for first cousins.

Keyword

Short tandem repeats; Kinship testing; Relationships; Likelihood ratio; Data mining

MeSH Terms

Classification*
Data Mining*
Discrimination (Psychology)
Forests
Humans
Logistic Models
Methods
Microsatellite Repeats*
Siblings
Support Vector Machine
Trees
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