NGS-based likelihood ratio for identifying contributors in two- and three-person DNA mixtures. (June 2018)

• Record Type: Journal Article
• Authors: Chan Mun Wei, Joshua; Zhao, Zicheng; Li, Shuai Cheng; Ng, Yen Kaow
• Is Part Of: Computational biology and chemistry. Volume 74(2018)
• Page Start: 428
• Page End: 433
• Publication Date: 2018-06
• Subjects: Forensics -- Mixture interpretation -- Statistics; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2018.03.010
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: DNA fingerprinting, also known as DNA profiling, serves as a standard procedure in forensics to identify a person by the short tandem repeat (STR) loci in their DNA. By comparing the STR loci between DNA samples, practitioners can calculate a probability of match to identity the contributors of a DNA mixture. Most existing methods are based on 13 core STR loci which were identified by the Federal Bureau of Investigation (FBI). Analyses based on these loci of DNA mixture for forensic purposes are highly variable in procedures, and suffer from subjectivity as well as bias in complex mixture interpretation. With the emergence of next-generation sequencing (NGS) technologies, the sequencing of billions of DNA molecules can be parallelized, thus greatly increasing throughput and reducing the associated costs. This allows the creation of new techniques that incorporate more loci to enable complex mixture interpretation. In this paper, we propose a computation for likelihood ratio that uses NGS (next generation sequencing) data for DNA testing on mixed samples. We have applied the method to 4480 simulated DNA mixtures, which consist of various mixture proportions of 8 unrelated whole-genome sequencing data. The results confirm the feasibility of utilizing NGS data in DNA mixture interpretations. We observed an average likelihood ratio as high as 285, 978 for two-person mixtures. Using our method, all 224 identity tests for two-person mixtures and three-person mixtures were correctly identified.