DNA mixture interpretation using linear regression and neural networks on massively parallel sequencing data of single nucleotide polymorphisms

Massively parallel sequencing (MPS) enables concurrent analysis of multiple single nucleotide polymorphisms (SNPs) and detection of alleles from minor contributors to extremely imbalanced DNA mixtures. To interpret the complex MPS data obtained from DNA mixtures, EuroForMix, linear regression, and neural network models were employed. Data of 960 autosomal SNPs sequenced through MPS of 10 single-source DNA samples, 26 nondegraded DNA mixtures from nonrelative (mixture ratio 1:29-1:99), 16 nondegraded DNA mixtures from relatives (1:29-1:99), 8 degraded DNA mixtures from nonrelatives (1:29-1:99), and 16 degraded DNA mixtures of relatives (1:29-1:99) were analysed. In total, 89.4% (59/66), 93.9% (62/66), and 93.9% (62/66) of the minor contributors to DNA mixtures could be correctly inferred using EuroForMix, linear regression, and a neural network, respectively. In conclusion, the linear regression and neural network models outperformed EuroForMix in determining the minor contributor to DNA mixtures from MPS data.

Automated summary

Massively parallel sequencing (MPS) allows for analysis of minor contributors in DNA mixtures, with EuroForMix, linear regression, and neural network models used to interpret the data. The study found that linear regression and neural network models outperformed EuroForMix in determining the minor contributors in DNA mixtures from MPS data.