Denoising of microhaplotype MPS data using DADA2 and its application to two-person DNA mixture analysis

Abstract

With the advent of phase-known sequencing enabled by massively parallel sequencing (MPS), research on microhaplotypes (microhaps), multi-single nucleotide polymorphisms within short DNA fragments, has advanced significantly in forensic genetics. However, MPS data inherently contains PCR and sequencing errors, presenting challenges in distinguishing minor contributor alleles from background noise in DNA mixture analysis. Divisive Amplicon Denoising Algorithm 2 (DADA2) has been widely used in microbial research for inferring amplicon sequence variants (ASVs) through computational error correction. However, its potential applicability to forensic identity testing has not been fully explored. In this study, we redesigned an in-house MPS panel targeting 24 multipurpose microhaps and established a pipeline employing DADA2’s ASV inference algorithm to denoise microhap MPS data. Denoising performance was evaluated using 1 ng of DNA from 50 single-source samples. The average not suppressed noise level decreased from 1.2 % to 0.1 % after denoising, achieving a genotype concordance rate of 99.5 % with undenoised data. However, DADA2 had difficulty in distinguishing heterozygous alleles differing only by single indel. In two-person DNA mixture analysis, DADA2-denoising pipeline reduced the number of noise haplotypes by 10-fold across various ratios (1:10, 1:20, 1:50, and 1:100) using 1 ng of total DNA. Even at a 1:100 ratio with 10 pg of minor DNA, noise was detected in only two or fewer markers among the 24 microhaps. These findings highlight the potential of computational error correction for enhancing the accuracy of detecting minor alleles and estimating the number of contributors in forensic analyses.