General Formulas for Calculating Commonly Used Kinship Index / 法医学杂志

OBJECTIVES@#To derive general formulas for calculating commonly used kinship index (KI).@*METHODS@#By introducing the Kronecker symbol, the formulas used to calculate the same KI under different genotype combinations were summarized into a unified expression.@*RESULTS@#The general formulas were successfully derived for KI in various case situations, including the paternity index, full sibling index, half sibling index, avuncular index, grandpaternity index, first-cousin index, and second-cousin index between two individuals without or with the mother being involved; grandpaternity index between grandparents and a grandchild without or with the mother being involved; half sibling index between two children with two mothers being involved; full sibling index among three children; and half sibling index among three children with no, one, or two mothers being involved.@*CONCLUSIONS@#The general formulas given in this study simplify the calculation of KIs and facilitate fast and accurate calculation through programming.

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

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.