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The recent advances in next-generation sequencing (NGS) technologies have enabled the development of effective high-throughput noninvasive prenatal screening (NIPS) assays for fetal genetic abnormalities using maternal circulating cell-free DNA (ccfDNA). An important NIPS quality assurance is quantifying the fetal proportion of the sampled ccfDNA. For methods using allelic read count ratios from targeted sequencing of single-nucleotide polymorphisms (SNPs), systematic biases and errors may reduce accuracy and diminish assay performance. We collected ccfDNA NIPS MiSeq sequencing data from an amplicon-based 92 SNP panel along with complementary low-depth whole-genome sequencing (WGS) on 243 normal male fetus pregnancies along with additional 144 nonpregnant female donor samples. Using fetal fraction estimates based on X and Y chromosome WGS coverage as gold standard, we compared an existing SNP-based approach, FetalQuant, to a more flexible Bayesian hierarchical modeling strategy that borrows information across interrogated SNPs to character SNP-level error rates and biases to improve fetal fraction estimates. Posterior distributions for SNP-level model parameters indicate most SNPs exhibited modest to moderate extrabinomial variation and a consistent underrepresentation of fetal alleles, with some extreme outliers in both regards. Fetal fraction estimates using FetalQuant, naive to these SNP properties, were relatively poor (R2 = 0.14, root mean squared error [RMSE] = 0.050), particularly when the true fetal fraction was low (<5%). In contrast, by quantifying SNP-level biases and error rates, our proposed approach demonstrated improved performance by reducing the bias and variability in fetal fraction estimates (R2 = 0.794, RMSE = 0.025). Using high-depth targeted SNP sequencing data, we identified a high degree of variability in distributional properties across SNP allelic read counts. These results highlight the benefits of leveraging hierarchical modeling for SNP-based fetal quantification assays (FQAs) and the need to properly calibrate FQAs dependent on NGS allelic ratio data. [ABSTRACT FROM AUTHOR]

Mate-pair sequencing (MPseq), using long-insert, paired-end genomic libraries, is a powerful next-generation sequencing-based approach for the detection of genomic structural variants. SVAtools is a set of algorithms to detect both chromosomal rearrangements and large (>10 kb) copy number variants (CNVs) in genome-wide MPseq data. SVAtools can also predict gene disruptions and gene fusions, and characterize the genomic structure of complex rearrangements. To illustrate the power of SVAtools' junction detection methods to provide comprehensive molecular karyotypes, MPseq data were compared against a set of samples previously characterized by traditional cytogenetic methods. Karyotype, FISH and chromosomal microarray (CMA), performed for 29 patients in a clinical laboratory setting, collectively revealed 285 breakpoints in 87 rearrangements. The junction detection methods of SVAtools detected 87% of these breakpoints compared to 48%, 42% and 57% for karyotype, FISH and CMA respectively. Breakpoint resolution was also reported to 1 kb or less and additional genomic rearrangement complexities not appreciable by standard cytogenetic techniques were revealed. For example, 63% of CNVs detected by CMA were shown by SVAtools' junction detection to occur secondary to a rearrangement other than a simple deletion or tandem duplication. SVAtools with MPseq provides comprehensive and accurate whole-genome junction detection with improved breakpoint resolution, compared to karyotype, FISH, and CMA combined. This approach to molecular karyotyping offers considerable diagnostic potential for the simultaneous detection of both novel and recurrent genomic rearrangements in hereditary and neoplastic disorders. [ABSTRACT FROM AUTHOR]