WisecondorFF
Improved Fetal Aneuploidy Detection from Shallow WGS through Fragment Length Analysis
Tom Mokveld (TU Delft - Pattern Recognition and Bioinformatics)
Zaid Al-Ars (TU Delft - Computer Engineering)
EA Sistermans (Vrije Universiteit Amsterdam)
Marcel J. T. Reinders (TU Delft - Pattern Recognition and Bioinformatics)
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Abstract
In prenatal diagnostics, NIPT screening utilizing read coverage-based profiles obtained from shallow WGS data is routinely used to detect fetal CNVs. From this same data, fragment size distributions of fetal and maternal DNA fragments can be derived, which are known to be different, and often used to infer fetal fractions. We argue that the fragment size has the potential to aid in the detection of CNVs. By integrating, in parallel, fragment size and read coverage in a within-sample normalization approach, it is possible to construct a reference set encompassing both data types. This reference then allows the detection of CNVs within queried samples, utilizing both data sources. We present a new methodology, WisecondorFF, which improves sensitivity, while maintaining specificity, relative to existing approaches. WisecondorFF increases robustness of detected CNVs, and can reliably detect even at lower fetal fractions (<2%).
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