Limited Clinical Utility of Non-invasive Prenatal Testing for Subchromosomal Abnormalities

Abstract

The use of massively parallel sequencing of maternal cfDNA for non-invasive prenatal testing (NIPT) of aneuploidy is widely available. Recently, the scope of testing has increased to include selected subchromosomal abnormalities, but the number of samples reported has been small. We developed a calling pipeline based on a segmentation algorithm for the detection of these rearrangements in maternal plasma. The same read depth used in our standard pipeline for aneuploidy NIPT detected 15/18 (83%) samples with pathogenic rearrangements > 6 Mb but only 2/10 samples with rearrangements < 6 Mb, unless they were maternally inherited. There were two false-positive calls in 534 samples with no known subchromosomal abnormalities (specificity 99.6%). Using higher read depths, we detected 29/31 fetal subchromosomal abnormalities, including the three samples with maternally inherited microduplications. We conclude that test sensitivity is a function of the fetal fraction, read depth, and size of the fetal CNV and that at least one of the two false negatives is due to a low fetal fraction. The lack of an independent method for determining fetal fraction, especially for female fetuses, leads to uncertainty in test sensitivity, which currently has implications for this technique's future as a clinical diagnostic test. Furthermore, to be effective, NIPT must be able to detect chromosomal rearrangements across the whole genome for a very low false-positive rate. Because standard NIPT can only detect the majority of larger (>6 Mb) chromosomal rearrangements and requires knowledge of fetal fraction, we consider that it is not yet ready for routine clinical implementation.

Figure 1

Plots of Count Ratios Illustrate a Microduplication and a Microdeletion in Sample 1144 Dots are the counts divided by the expected counts of the reference set in 100 kb bins; the solid line is the output from the segmentation algorithm. (A) Microdeletion event in 18q23. (B) Microduplication event in 12p13.1.

Figure 2

Plots of Count Ratios for Sample 12295 Illustrate How Variance Decreases as Read Depth Increases The three different read depths are 7 million (A), 32 million (B), and 71 million (C).

Figure 3

Comparison of the CNV Positions Derived from Microarray and Our Segmentation Algorithm (A) Microduplication event of 3.7 Mb in 17p11.2. (B) Microdeletion event of 4.8 Mb in 8q24.3.

Figure 4

The Fetal-Fraction Estimates from CNVs Called by Our Pipeline Are Closely Correlated with the Fetal-Fraction Estimates from Chromosome Y

Figure 5

Test Sensitivity for Deletions of Different Sizes Deletions of 3 Mb (A) and 10 Mb (B). The dotted line assumes that the counts follow a binomial distribution, which is the theoretical optimal sensitivity that can be achieved with the read-counting method; the solid line assumes that the counts follow a beta-binomial distribution with $\varphi$ = 10⁻⁸ for the 3 Mb deletion and $\varphi$ = 3 × 10⁻⁸ for the 10 Mb deletion. $\varphi$ is the over-dispersion parameter, and it captures the additional variance in the counts. The $\varphi$ we used in the simulation was derived from our dataset.

Figure 6

Number of Reads Required for Detecting Each CNV According to Size and Fetal Fraction Fetal fraction were estimated from chromosome Y for male fetuses or from the CNV detected for female fetuses. Missed detection for female fetuses is not shown in this plot because we were unable to estimate the fetal fraction.