Noninvasive Prenatal Diagnosis of Fetal Trisomy 18 and Trisomy 13 by Maternal Plasma DNA Sequencing

Abstract

Massively parallel sequencing of DNA molecules in the plasma of pregnant women has been shown to allow accurate and noninvasive prenatal detection of fetal trisomy 21. However, whether the sequencing approach is as accurate for the noninvasive prenatal diagnosis of trisomy 13 and 18 is unclear due to the lack of data from a large sample set. We studied 392 pregnancies, among which 25 involved a trisomy 13 fetus and 37 involved a trisomy 18 fetus, by massively parallel sequencing. By using our previously reported standard z-score approach, we demonstrated that this approach could identify 36.0% and 73.0% of trisomy 13 and 18 at specificities of 92.4% and 97.2%, respectively. We aimed to improve the detection of trisomy 13 and 18 by using a non-repeat-masked reference human genome instead of a repeat-masked one to increase the number of aligned sequence reads for each sample. We then applied a bioinformatics approach to correct GC content bias in the sequencing data. With these measures, we detected all (25 out of 25) trisomy 13 fetuses at a specificity of 98.9% (261 out of 264 non-trisomy 13 cases), and 91.9% (34 out of 37) of the trisomy 18 fetuses at 98.0% specificity (247 out of 252 non-trisomy 18 cases). These data indicate that with appropriate bioinformatics analysis, noninvasive prenatal diagnosis of trisomy 13 and trisomy 18 by maternal plasma DNA sequencing is achievable.

Next generation sequencing of SNPs for non-invasive prenatal diagnosis: challenges and feasibility as illustrated by an application to β-thalassaemia

“…9,10,15, 16 Palomaki et al 16 and Chiu et al 10 reported false-positive rates of 1.4% and 2.1%, respectively, which were improved in a subsequent study. 9 False-positive SNP calls could be derived from erroneous alignments of short reads. 30 Therefore, different bioinformatics parameters, as well as improvements, in statistical analysis have been investigated by these teams in order to eliminate the erroneous calls and decrease the observed false-positive rate.…”

“…7,8 As the advent of next generation sequencing (NGS), a lot of effort has been concentrated on exploiting this technology for the measurement of aneuploidies in maternal plasma. [9][10][11][12][13][14] Using this technology, non-invasive prenatal diagnostic (NIPD) for trisomy 21, 18 and 13 has recently reached the clinical setting by analysing the relative amount of chromosomes in circulating cell-free DNA from maternal plasma. 15,16 However, approaches permitting reliable detection of single-gene mutations or single-nucleotide polymorphisms (SNPs) using cell-free fetal DNA in maternal plasma are still under development.…”

Non-invasive prenatal testing (NIPT): limitations on the way to become diagnosis

“…This is particularly important at early gestational ages, because the fetal fractions rise with increasing gestational age, and in women with high BMI, as fetal fractions are inversely proportional to weight [7,24,25]. To distinguish these minor differences with high confidence a large number of reads is required; because MPSS sequences all chromosomes, approximately 6.3 million uniquely-mapped reads from the entire genome are required to ensure sufficient, e.g., chromosome 21 counts for accurate copy number calls [27,[38][39][40]. Additionally, as only approximately 25% of MPSS-generated reads are uniquely mapped, approximately 25 million raw sequencing reads are required per sample to generate sufficient data for accurate analysis [9].…”

Non-invasive prenatal testing for fetal aneuploidy by massively parallel DNA sequencing of maternal plasma: the future has arrived today/Nicht-invasiver Pränatal-Test auf fetale Aneuploidie mittels massiv-paralleler DNA-Sequenzanalyse im mütterlichen Plasma: in der Zukunft angekommen

“…We refer to this predetermined set of chromosomes as the " reference chromosomes " , and they are used in the denominator for the ratio calculations. This approach mitigates the need to perform additional corrections on the data [e.g., correction for guanine-cytosine (GC) content used by others] (Figure 1 ) [20,21] . The optimal set of reference chromosomes for each chromosome of interest is determined from results of data on a training set that includes only euploid samples (diploid karyotype).…”