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

ABSTRACT

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.

KEYWORDS:

• DNA mixture
• linear regression
• massively parallel sequencing
• neural network

Acknowledgments

The authors acknowledge Mr Chun-Yen Lin, and Ms Tsun-Ying Huang for technical support on DNA analysis. The authors thank the National Center for Genome Medicine at Academia Sinica, Taiwan, for performance of MPS analysis and genome-wide analysis with Genome-Wide Human SNP Array 6.0. This Center was supported by grants from the National Core Facility Program for Biotechnology of National Science Council, Taiwan, R.O.C.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

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Additional information

Funding

This work was supported by the Ministry of Science and Technology, Taiwan, R.O.C. under Grants [numbers MOST 107-2320-B-002-044-MY2; MOST 109-2634-F-002-022]; and National Taiwan University Hospital Taiwan, R.O.C. under Grant [number NTUH108-S4303].