1. A novel counting algorithm to detect common fetal trisomies
in non-invasive prenatal testing by massively parallel shotgun sequencing and cross-platform validation results
Ming Chen1,2,3, Gwo-Chin Ma1, Kuanting Chen2, Chen-Hsiang Yeang4
1Department of Genomic Medicine, Changhua Christian Hospital, Changhua, Taiwan
2Department of Obstetrics and Gynecology, Changhua Christian Hospital, Changhua, Taiwan
3Department of Obstetrics and Gynecology & Department of Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan
4Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
Introduction
Analyzing circulating cell free fetal DNA (ccfDNA) in the maternal plasma by massively parallel shotgun sequencing (MPSS) has enabled noninvasive prenatal testing (NIPT) for fetal aneuploidy. A remaining challenge is developing a counting algorithm with high sensitivity required to assess the genetic information contained within ccfDNA. Z-score, a popular algorithm for NIPT detecting trisomy 21 (T21) is described in Chiu et al. (2008). However, z-score depends solely on chromosome 21 read numbers (and the total read numbers) and thus are sensitive to variations on chromosome 21 reads. Sehnert et al. (2011) proposed an alternative algorithm to improve T21 detection by calculating the ratio of chromosome 21 read number to the read number of a reference chromosome 9. The reference chromosome undergoes no copy number alterations in both normal and pathological samples and should maintain a robust proportion of reads. The statistical score was termed as normalized chromosome value (NCV). NCV improves information utilization but still ignores information from all the remaining chromosomes. In this study, we propose an extension of existing methods to fully utilize information from all 22 autosomes and report the performance of this self-designed counting algorithm (genome-wide normalised score, namely GWNS) by comparison with the two existing methodologies (z-score and NCV) and its cross-platform validations.
We used theoretical approximations, computer simulations and real experimental data to compare the performance of GWNS with z-score and NCV in our own dataset, and we also did cross-platform comparisons on the different MPSS sequencer platforms (Miseq and GAIIx) manufactured by Illumina. We also looked into the minimal cell-free fetal DNA fraction as well as the lowest read numbers that can achieve a reliable clinical utility.
With a fixed level of significance and power, our method requires consistently smaller fetal DNA proportions and total reads compared to the two popular methods – z-scores and NCV. This superiority sustains in theoretic approximations, simulated reads and experimental data collected from maternal plasmas of normal and T21/T18/T13 fetuses. Cross-platform interchangeability between Illumina’s GAIIX and Miseq is also demonstrated in our setting. Approximately 4% and 4M reads are the safe lowest limit for safe clinical utility in our setting.
Methods
Results
Figure 1. Theoretical approximations of iso-quality curves of three detection methods for trisomy 21 (T21): GWNS, z-score, and NCV (not displayed as their approximations coincide with z-scores).
Figure 3. ROC curves of three detection methods on diluted data. The area under the ROC curve (AUCs) of z-score, NCV and GWNS are , and , respectively. 5
4.5 4
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Proportion
Simple index
Figure 5. The occurrences of high-scoring entries (top 10, 50 or 100 among the 22 x 112 GWNS matrix) in each of the 112 euploid samples.
Figure 4. T21 screening by GWNS for 128 disomy 21 (124 euploid plus 4 T18), and 25 T21 pregnancies and a serially diluted T21 plasma DNA (defined threshold is p-value < 0.05).
Figure 2. Iso-quality curves of three detection methods (z-score, NCV and GWNS) in simulated data. The significance level is fixed to 0.05 and two detection power level ranges are considered: (red curves) and (blue curves).
Figure 6. Comparison of the GWNS distributions among normal samples from illumina GAIIx and MiSeq data. No difference was found between the two MPSS platforms.
Our results demonstrate the benefits of incorporating whole-genome information and can potentially be utilised to detect aneuploidies of other autosomes. GWNS is comparable to the two prevalent counting algorithms in terms of test performance.
Conclusion