WisecondorFF: Improved Fetal Aneuploidy Detection from Shallow WGS through Fragment Length Analysis

Area of Science:

• Genetics
• Bioinformatics
• Genomic Medicine

Background:

• Non-invasive prenatal testing (NIPT) commonly uses shallow whole genome sequencing (WGS) data for fetal copy number variation (CNV) detection.
• Fetal fraction estimation in NIPT often relies on DNA fragment size distributions, which differ between maternal and fetal DNA.
• The potential of fragment size data to directly aid CNV detection in NIPT has been underexplored.

Purpose of the Study:

• To introduce a novel methodology, WisecondorFF, that integrates both read coverage and DNA fragment size data for improved CNV detection in NIPT.
• To demonstrate that combining these data types within a within-sample normalization framework enhances CNV detection.
• To evaluate the performance of WisecondorFF in terms of sensitivity, specificity, and robustness, particularly at low fetal fractions.

Main Methods:

• Developed WisecondorFF, a method integrating DNA fragment size and read coverage profiles from shallow WGS data.
• Implemented a within-sample normalization approach to construct a reference set incorporating both data types.
• Applied the WisecondorFF methodology to query samples for CNV detection.

Main Results:

• WisecondorFF demonstrates improved sensitivity for fetal CNV detection compared to existing methods.
• The method maintains high specificity while enhancing detection capabilities.
• WisecondorFF shows increased robustness in CNV detection and is effective even with fetal fractions below 2%.

Conclusions:

• Integrating DNA fragment size with read coverage offers a powerful approach for enhancing NIPT.
• WisecondorFF provides a more sensitive and robust method for detecting fetal CNVs, expanding NIPT's utility.
• The methodology is particularly valuable for cases with low fetal fractions, improving diagnostic yield.