NEW YORK (GenomeWeb) – A new Genetics in Medicine study suggests low-coverage whole-genome sequencing may be a feasible alternative to chromosomal microarray analyses when searching for clinically relevant copy number variations.
Researchers from BGI-Shenzhen, the Chinese University of Hong Kong, Nanjing Medical University, and elsewhere did low-pass genome sequencing on samples from hundreds of prenatal and postnatal samples referred for chromosomal analyses, producing informative profiles for more than 96 percent of the samples.
The team's search led to 119 chromosomal abnormalities, along with 103 copy number gains or losses. The diagnostic yield differed depending on the source of the samples, peaking at more than 53 percent in the 198 miscarried fetuses considered and dropping to as low as 14.7 percent across the stillborn samples.
Based on these and other findings, the study's authors contended that the work "highlights the potential for using [next-generation sequencing] to facilitate genetic diagnoses in the prenatal and postnatal samples that have not been detected by conventional karyotyping and/or [chromosomal microarray] analysis."
Traditionally, methods such as array-comparative genomic hybridization and SNP arrays have been used to pick up on the presence of pathogenic CNVs behind conditions such as DiGeorge syndrome, Angelman syndrome, and so on, the team noted.
But there have been hints from retrospective studies suggesting next-generation sequencing might identify some genetic alterations that are missed by array-based methods alone.
After determining the high sensitivity and specificity of their low-coverage, sequencing-based method on dozens of prenatal and postnatal samples that had already been profiled by chromosomal microarray analysis, the researchers focused on 570 samples suspected of containing aneuploidies and/or pathogenic CNVs.
These included 186 postnatal blood samples, 37 stillborn tissue samples, samples from 198 fetuses miscarried early in pregnancy, and 149 other prenatal samples, all referred from centers in China or Hong Kong between early 2013 and the spring of 2015.
Using the Illumina HiSeq 2000 instrument, the team successfully generated informative read-depth-based data for 549 of the samples. The remaining samples produced low quality sequences, which the group attributed to poor DNA quality in the original samples.
With their sequence data, the researchers uncovered chromosomal aneuploidies in 119 of the samples. They also detected more than 100 pathogenic CNVs in 82 of the samples, including 74 losses and 29 gains.
The approach also led to mosaic aneuploidies in 11 samples — all but one occurring in fetuses miscarried during the first trimester.
The diagnostic yield was highest in this set of samples as well, coming in at more than 53 percent. In the group of 198 miscarried or aborted samples from early pregnancy, the team detected 72 samples with one aneuploidy, six samples with more than one chromosomal alteration, and a dozen samples containing 15 pathogenic CNVs.
On the other hand, when the team applied the method to stillborn fetal tissue or amniotic fluid samples, it diagnosed 14.7 percent of the 34 successfully sequenced cases. In the prenatal and postnatal groups, meanwhile, the group found diagnostically informative aneuploidies or CNVs in 28.5 percent and 30.1 percent of cases, respectively.
From their set of 549 successfully sequenced samples, the researchers randomly selected a validation set of 25 samples for chromosomal microarray analyses with an Agilent custom 44K Fetal DNA Chip v1.0 and an Illumina Human CytoSNP-12 BeadChip array.
Indeed, the sequencing results coincided with those generated using chromosomal microarrays, while also picking up 32 variants of uncertain significance that were missed with the array-based approach.
The study's authors said the work "highlights the potential for using [next-generation sequencing] to facilitate genetic diagnoses in the prenatal and postnatal samples that have not been detected by conventional karyotyping and/or [chromosomal microarray] analysis."