Validating multiplexes for use in conjunction with modern interpretation strategies

Abstract

In response to requests from the forensic community, commercial companies are generating larger, more sensitive, and more discriminating STR multiplexes. These multiplexes are now applied to a wider range of samples including complex multi-person mixtures. In parallel there is an overdue reappraisal of profile interpretation methodology. Aspects of this reappraisal include 1. The need for a quantitative understanding of allele and stutter peak heights and their variability, 2. An interest in reassessing the utility of smaller peaks below the often used analytical threshold, 3. A need to understand not just the occurrence of peak drop-in but also the height distribution of such peaks, and 4. A need to understand the limitations of the multiplex-interpretation strategy pair implemented. In this work we present a full scheme for validation of a new multiplex that is suitable for informing modern interpretation practice. We predominantly use GlobalFiler™ as an example multiplex but we suggest that the aspects investigated here are fundamental to introducing any multiplex in the modern interpretation environment.

Exploring STR signal in the single‐ and multicopy number regimes: Deductions from an in silico model of the entire DNA laboratory process

“…Recently, a number of studies have focused on characterizing noise generated during the forensic PCR process . This is typically performed in order to establish an AT, which is a signal threshold designed to filter noise.…”

Systematic evaluation of STRmix™ performance on degraded DNA profile data

“…Stutter artifacts one-half repeat smaller (i.e., half-back stutter), two full repeats smaller (i.e., double-back stutter) and two full repeats larger (i.e., double-forward stutter) than the parent peak are often observed in GlobalFiler™ data but are not modeled by STRmix™ v2.4 [15]. CAL DOJ addresses this issue with a "Phantom" approach to ambiguous alleles, which adds an additional assumed contributor to the analysis that has the ambiguous alleles at the loci of interest [16][17][18] and placeholder "1" alleles at all other loci.…”

Characterisation of artefacts and drop-in events using STR-validator and single-cell analysis

“…Replicate PCR amplification and in-15 terpretation using a consensus approach [8,9,10,11] or composite approach 16 [12] have been common ways to deal with drop-in and other low-template ite approaches were never recommended as preferred approaches compared 22 to probabilistic models [12]; currently both open source (e.g. LRmix [16] 23 and EuroForMix [17]) and commercial (e.g. TrueAllele R [18,19], STRmix TM 24 [20, 21, 22]) implementations exist that can handle replicate analyses without 25 wasting information.…”

“…TrueAllele R [18,19], STRmix TM 24 [20, 21, 22]) implementations exist that can handle replicate analyses without 25 wasting information. 26 To further improve existing models there is a need to understand the 27 occurrence of drop-in and their peak height distribution [23]. Drop-in can be 28 characterized by reference to negative controls [24].…”

“…Some choose to include PCR artefacts like stutters in the definition of drop-in [28, 10, 13] (although the proportion of alleles 48 in stutter positions are shown separately), especially to inform qualitative 49 continuous probabilistic DNA profile interpretation software [29,30]. Others 50 choose to exclude possible stutters and require that drop-in events need not 51 be reproducible [8,9,31,23]. 52 Here we agree with the latter definition that drop-in events arise from 53 random fragments of DNA present in the PCR reaction hence they need not 3 This is convenient for our example because we will analyse drop-in events from both kinds of controls collectively, although it would probably be more correct to analyse the two types of controls separately with different AT (in our data the difference was negligible: 53 RFU and 58 RFU for amplification and extraction negative controls respectively).…”