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Implementation and Validation of an Improved Allele Specific Stutter Filtering Method for Electropherogram Interpretation

NCJ Number
253278
Date Published
July 2018
Length
7 pages
Author(s)
Tim Kalafut; Curt Schuerman; Joel Sutton; Tom Faris; Luigi Armogido; Jo-Anne Bright; John Buckleton; Duncan Taylor
Agencies
NIJ-Sponsored
Annotation
This article presents a novel stutter filter model in the ArmedXpert software package that uses a linear model based on allele for back stutter and applies an additive filter for combined stutter; it is called the allele specific stutter model (AM).
Abstract
Modern probabilistic genotyping (PG) software is capable of modeling stutter as part of the profile weighting statistic. This allows for peaks in stutter positions to be considered as allelic or stutter or both; however, prior to running any sample through a PG calculator, the examiner must first interpret the sample, considering such things as artifacts and number of contributors (NOC or N). Stutter can play a major role both during the assignment of the number of contributors, and the assessment of inclusion and exclusion. If stutter peaks are not filtered when they should be, it can lead to the assignment of an additional contributor, causing N contributors to be assigned as N plus 1. If peaks in the stutter position of a major contributor are filtered using a threshold that is too high, true alleles of minor contributors can be lost. Until now, the software used to view the electropherogram stutter filters are based on a locus specific model. Combined stutter peaks occur when a peak could be the result of both back stutter (stutter one repeat shorter than the allele) and forward stutter (stutter one repeat unit larger than the allele). This can challenge existing filters. The current project compared the developed AM model with a traditional model based on locus specific stutter filters (termed LM). This improved stutter model has the benefit of 1) increased detection of minor contributor alleles that are in a stutter position of a major contributor (we term the event of missing such alleles "over filtering") and 2) fewer false assignments of alleles (termed "under filtering"). Instances of over filtering were reduced 78 percent, from 101 for a traditional model (LM) to 22 for the allele specific model (AM) when scored against each other. Instances of under filtering were reduced 80 percent from 85 (LM) to 17 (AM) when scored against ground truth mixtures. (publisher abstract modified)

Date Created: July 20, 2021