Perlin Mark W, Belrose Jamie L, Duceman Barry W
Cybergenetics Corp, 160 North Craig Street, Suite 210, Pittsburgh, PA, 15213.
J Forensic Sci. 2013 Nov;58(6):1458-66. doi: 10.1111/1556-4029.12223. Epub 2013 Jul 18.
DNA evidence can pose interpretation challenges, particularly with low-level or mixed samples. It would be desirable to make full use of the quantitative data, consider every genotype possibility, and objectively produce accurate and reproducible DNA match results. Probabilistic genotype computing is designed to achieve these goals. This validation study assessed TrueAllele(®) probabilistic computer interpretation on 368 evidence items in 41 test cases and compared the results with human review of the same data. Whenever there was a human result, the computer's genotype was concordant. Further, the computer produced a match statistic on 81 mixture items (for 87 inferred matching genotypes) in the test cases, while human review reported a statistic on 25 of these items (30.9%). Using match statistics to quantify information, probabilistic genotyping was shown to be sensitive, specific, and reproducible. These results demonstrate that objective probabilistic genotyping of biological evidence can reliably preserve DNA identification information.
DNA证据可能带来解释方面的挑战，尤其是对于低水平或混合样本。充分利用定量数据、考虑每种基因型可能性并客观地得出准确且可重复的DNA匹配结果将是很有必要的。概率基因型计算旨在实现这些目标。这项验证研究评估了TrueAllele(®)概率计算机解释在41个测试案例中的368个证据样本，并将结果与对相同数据的人工审查进行了比较。只要有人工审查结果，计算机得出的基因型就是一致的。此外，计算机在测试案例中对81个混合样本（涉及87个推断出的匹配基因型）生成了匹配统计数据，而人工审查仅报告了其中25个样本（30.9%）的统计数据。通过使用匹配统计数据来量化信息，概率基因分型被证明具有敏感性、特异性和可重复性。这些结果表明，对生物证据进行客观的概率基因分型能够可靠地保留DNA识别信息。
