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. 2023 Jan 25;14(2):312.
doi: 10.3390/genes14020312.

NIPAT as Non-Invasive Prenatal Paternity Testing Using a Panel of 861 SNVs

Riccardo Giannico  1 , Luca Forlani  1 , Valentina Andrioletti  1 , Ettore Cotroneo  1 , Andrea Termine  2 , Carlo Fabrizio  2 , Raffaella Cascella  3   4 , Luca Salvaderi  1 , Pasquale Linarello  1 , Debora Varrone  1 , Laura Gigante  1 , Emiliano Giardina  3   5
Affiliations

NIPAT as Non-Invasive Prenatal Paternity Testing Using a Panel of 861 SNVs

Riccardo Giannico et al. Genes (Basel). .

Abstract

In 1997, it was discovered that maternal plasma contains Cell-Free Fetal DNA (cffDNA). cffDNA has been investigated as a source of DNA for non-invasive prenatal testing for fetal pathologies, as well as for non-invasive paternity testing. While the advent of Next Generation Sequencing (NGS) led to the routine use of Non-Invasive Prenatal Screening (NIPT or NIPS), few data are available regarding the reliability and reproducibility of Non-Invasive Prenatal Paternity Testing (NIPPT or NIPAT). Here, we present a non-invasive prenatal paternity test (NIPAT) analyzing 861 Single Nucleotide Variants (SNV) from cffDNA through NGS technology. The test, validated on more than 900 meiosis samples, generated log(CPI)(Combined Paternity Index) values for designated fathers ranging from +34 to +85, whereas log(CPI) values calculated for unrelated individuals were below -150. This study suggests that NIPAT can be used with high accuracy in real cases.

Keywords: NGS; NIPAT; cffDNA; paternity test.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Distribution assessment for log(CPI). (A) Skewness-Kurtosis plot of log(CPI). This plot was produced as a further contribution for the assessment of normality for the log(CPI) distribution after the Shapiro-Wilk test. This plot shows the distribution of log(CPI) as determined by square of skewness and kurtosis. The blue dot represents statistics for the distribution, while the yellow dots represent the statistics for the bootstrapped distribution. Based on the position of the blue dot with respect to the position of the theoretical distributions, it is clear to see that both the blue dot and the yellow dots are far from the normal distribution. This indicates the distribution of log(CPI) is non-normal. (B) Density curve of log(CPI) highlighting the shape of the distribution. This plot was produced as a visual assessment of log(CPI) distribution, which is non-normal.
Figure 2
Figure 2
Log(CPI) distribution in different groups. The boxplot shows the comparison among biological fathers, simulated brothers and unrelated fathers. Stars report Wilcoxon test p-value (*** is p ≤ 1.146 × 10−6).
See this image and copyright information in PMC

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