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Computational framework for targeted high-coverage sequencing based NIPT.

Hindrek Teder1,2, Priit Paluoja1,3, Kadri Rekker1,4

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A new computational framework enhances non-invasive prenatal testing (NIPT) for fetal trisomies using targeted sequencing. It improves accuracy, especially with low fetal DNA fractions, enabling earlier and more reliable detection.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Non-invasive prenatal testing (NIPT) accurately detects fetal chromosomal trisomies.
  • Existing NIPT computational methods often require low-coverage whole-genome sequencing (WGS) data, limiting applicability to targeted, high-coverage sequencing of cell-free DNA.
  • Challenges exist in NIPT analysis with low fetal DNA fractions, common in early pregnancy or high maternal BMI.

Purpose of the Study:

  • To develop a novel computational framework for NIPT analysis using targeted high-coverage sequencing data.
  • To improve the accuracy and reliability of fetal trisomy detection, particularly in scenarios with low fetal fractions.
  • To enable earlier NIPT screening than currently possible.

Main Methods:

  • Developed a computational framework employing a hidden Markov model (HMM) combined with machine learning models (Decision Tree/Support Vector Machine).
  • Utilized simulated and experimental targeted sequencing datasets with varying chromosomal counts, parental origins, fetal DNA fractions, and sequencing depths.
  • Evaluated read count and allelic ratio-based models for trisomy detection.

Main Results:

  • The HMM achieved 0.89 classification accuracy for fetal euploidies and trisomies on simulated data.
  • Integrating Decision Tree and Support Vector Machine models boosted trisomy classification accuracy to 0.98 and 0.99, respectively.
  • High accuracy (up to 0.98) was achieved for fetal trisomy detection with fetal fractions as low as 2%, surpassing the 4% threshold of current commercial NIPT.

Conclusions:

  • The novel computational framework is functionally feasible for targeted high-coverage sequencing-based NIPT.
  • The HMM combined with machine learning models significantly enhances trisomy detection accuracy, especially in borderline cases with low fetal fractions.
  • This approach enables earlier NIPT screening (before 10 weeks) and expands its applicability to diverse maternal profiles.