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Bioinformatics. 2016 Jun 1;32(11):1662-9. doi: 10.1093/bioinformatics/btw178. Epub 2016 Apr 5.

Cell-free DNA fragment-size distribution analysis for non-invasive prenatal CNV prediction.

Author information

1
Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada Centre for Computational Medicine, Hospital for Sick Children, Toronto, ON M5G 1L7, Canada and.
2
Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada Genetics and Genome Biology, Hospital for Sick Children, Toronto, ON M5G 1L7, Canada.
3
Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada Centre for Computational Medicine, Hospital for Sick Children, Toronto, ON M5G 1L7, Canada and Genetics and Genome Biology, Hospital for Sick Children, Toronto, ON M5G 1L7, Canada.

Abstract

BACKGROUND:

Non-invasive detection of aneuploidies in a fetal genome through analysis of cell-free DNA circulating in the maternal plasma is becoming a routine clinical test. Such tests, which rely on analyzing the read coverage or the allelic ratios at single-nucleotide polymorphism (SNP) loci, are not sensitive enough for smaller sub-chromosomal abnormalities due to sequencing biases and paucity of SNPs in a genome.

RESULTS:

We have developed an alternative framework for identifying sub-chromosomal copy number variations in a fetal genome. This framework relies on the size distribution of fragments in a sample, as fetal-origin fragments tend to be smaller than those of maternal origin. By analyzing the local distribution of the cell-free DNA fragment sizes in each region, our method allows for the identification of sub-megabase CNVs, even in the absence of SNP positions. To evaluate the accuracy of our method, we used a plasma sample with the fetal fraction of 13%, down-sampled it to samples with coverage of 10X-40X and simulated samples with CNVs based on it. Our method had a perfect accuracy (both specificity and sensitivity) for detecting 5 Mb CNVs, and after reducing the fetal fraction (to 11%, 9% and 7%), it could correctly identify 98.82-100% of the 5 Mb CNVs and had a true-negative rate of 95.29-99.76%.

AVAILABILITY AND IMPLEMENTATION:

Our source code is available on GitHub at https://github.com/compbio-UofT/FSDA CONTACT: : brudno@cs.toronto.edu.

PMID:
27153615
DOI:
10.1093/bioinformatics/btw178
[Indexed for MEDLINE]

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