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TrueAllele ® Genotype Identification on DNA Mixtures Containing up to Five Unknown Contributors
Author(s) -
Perlin Mark W.,
Hornyak Jennifer M.,
Sugimoto Garett,
Miller Kevin W.P.
Publication year - 2015
Publication title -
journal of forensic sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.715
H-Index - 96
eISSN - 1556-4029
pISSN - 0022-1198
DOI - 10.1111/1556-4029.12788
Subject(s) - genotype , reproducibility , dna , covariance , dna profiling , computational biology , variance (accounting) , statistics , identification (biology) , analysis of variance , analysis of covariance , data mining , computer science , mathematics , algorithm , biology , genetics , gene , botany , accounting , business
Computer methods have been developed for mathematically interpreting mixed and low‐template DNA . The genotype modeling approach computationally separates out the contributors to a mixture, with uncertainty represented through probability. Comparison of inferred genotypes calculates a likelihood ratio ( LR ), which measures identification information. This study statistically examined the genotype modeling performance of Cybergenetics TrueAllele ® computer system. High‐ and low‐template DNA mixtures of known randomized composition containing 2, 3, 4, and 5 contributors were tested. Sensitivity, specificity, and reproducibility were established through LR quantification in each of these eight groups. Covariance analysis found LR behavior to be relatively invariant to DNA amount or contributor number. Analysis of variance found that consistent solutions were produced, once a sufficient number of contributors were considered. This study demonstrates the reliability of TrueAllele interpretation on complex DNA mixtures of representative casework composition. The results can help predict an information outcome for a DNA mixture analysis.