Utilization of Benchtop Next Generation Sequencing Platforms Ion Torrent PGM and MiSeq in Noninvasive Prenatal Testing for Chromosome 21 Trisomy and Testing of Impact of In Silico and Physical Size Selection on Its Analytical Performance

OBJECTIVES: The aims of this study were to test the utility of benchtop NGS platforms for NIPT for trisomy 21 using previously published z score calculation methods and to optimize the sample preparation and data analysis with use of in silico and physical size selection methods. METHODS: Samples from 130 pregnant women were analyzed by whole genome sequencing on benchtop NGS systems Ion Torrent PGM and MiSeq. The targeted yield of 3 million raw reads on each platform was used for z score calculation. The impact of in silico and physical size selection on analytical performance of the test was studied. RESULTS: Using a z score value of 3 as the cut-off, 98.11% - 100% (104-106/106) specificity and 100% (24/24) sensitivity and 99.06% - 100% (105-106/106) specificity and 100% (24/24) sensitivity were observed for Ion Torrent PGM and MiSeq, respectively. After in silico based size selection both platforms reached 100% specificity and sensitivity. Following the physical size selection z scores of tested trisomic samples increased significantly-p = 0.0141 and p = 0.025 for Ion Torrent PGM and MiSeq, respectively. CONCLUSIONS: Noninvasive prenatal testing for chromosome 21 trisomy with the utilization of benchtop NGS systems led to results equivalent to previously published studies performed on high-to-ultrahigh throughput NGS systems. The in silico size selection led to higher specificity of the test. Physical size selection performed on isolated DNA led to significant increase in z scores. The observed results could represent a basis for increasing of cost effectiveness of the test and thus help with its penetration worldwide

Non-invasive prenatal diagnosis of beta-thalassemia by semiconductor sequencing: A feasibility study in the Sardinian population

β-Thalassemia is the most common autosomal recessive single-gene disorder in Sardinia, where approximately 10.3% of the population is a carrier. Prenatal diagnosis is carried out at 12 weeks of gestation via villocentesis and is commonly aimed at ascertaining the presence or absence of the HBB variant c.118C>T, which is the most common in Sardinia. In this study, we describe for the first time the application of semiconductor sequencing to the non-invasive prenatal diagnosis of β-thalassemia in 37 couples at risk for this variant. In particular, by using a haplotyping-based approach with a hidden Markov model (HMM) and a dedicated pipeline, the two parental haplotypes most likely inherited by the foetus could be established in 30 out of 37 cffDNA samples. Specifically, the paternally inherited haplotype was correctly determined in all 30 of the samples, while the maternal haplotype was incorrectly predicted in six of the 30 genotyped samples. The lack of informative SNPs hampered the inference of both parental haplotypes in the remaining seven samples. As shown in previous studies, we have confirmed that the haplotyping-based approach represents a valuable resource, as it improves the detection of both parental haplotypes inherited by the foetus. In general, our results are encouraging, as we have proven that NIPD is also feasible in couples who are at risk for a monogenic disorder and share the same variant

Informative priors on fetal fraction increase power of the noninvasive prenatal screen

Noninvasive prenatal screening (NIPS) sequences a mixture of the maternal and fetal cell-free DNA. Fetal trisomy can be detected by examining chromosomal dosages estimated from sequencing reads. The traditional method uses the Z-test, which compares a subject against a set of euploid controls, where the information of fetal fraction is not fully utilized. Here we present a Bayesian method that leverages informative priors on the fetal fraction. Our Bayesian method combines the Z-test likelihood and informative priors of the fetal fraction, which are learned from the sex chromosomes, to compute Bayes factors. Bayesian framework can account for nongenetic risk factors through the prior odds, and our method can report individual positive/negative predictive values. Our Bayesian method has more power than the Z-test method. We analyzed 3,405 NIPS samples and spotted at least 9 (of 51) possible Z-test false positives. Bayesian NIPS is more powerful than the Z-test method, is able to account for nongenetic risk factors through prior odds, and can report individual positive/negative predictive values