Fetal chromosome karyotype analysis has long been used in prenatal testing for high-risk pregnancies. In recent years, chromosomal microarray analysis (CMA) has become an important technology for detecting chromosomal structural variations, including microdeletions and microduplications.
Whole genome sequencing (WGS) offers a more comprehensive view of the genome, enabling the detection of single nucleotide variants, insertions and deletions, copy number variations and structural rearrangements. However, high-depth WGS remains relatively expensive and generates large amounts of data, which can limit its routine clinical use in prenatal diagnosis.
To address these challenges, researchers have explored the use of low-pass whole genome sequencing (low-pass WGS), which reduces sequencing depth while maintaining the ability to detect important genomic variations.
Research Published in Genetics in Medicine
A research team from the Chinese University of Hong Kong (CUHK) School of Medicine recently published a study in the journal Genetics in Medicine. The study compared low-pass WGS with chromosomal microarray analysis in prenatal diagnosis.
The researchers found that low-depth, high-throughput genome sequencing could provide clinically meaningful genetic information while maintaining high diagnostic performance.
Limitations of Traditional Prenatal Diagnostic Methods
Invasive prenatal diagnostic procedures are often recommended when ultrasound examinations detect abnormalities, when Down’s syndrome screening results are positive or when there is a history of miscarriage or genetic disease.
Traditional fetal karyotype analysis examines chromosome structures under a microscope. While useful, this method may miss subtle chromosomal abnormalities such as microdeletions or microduplications that are associated with many genetic disorders.
To overcome these limitations, researchers have increasingly adopted chromosomal microarray analysis, which allows the detection of known genomic deletions or duplications across the genome.
Comparing Low-Pass WGS and Chromosomal Microarray Analysis
The CUHK research team compared the performance of low-pass whole genome sequencing and chromosomal microarray analysis in a cohort of 1,023 pregnant women between 2016 and 2019.
Among the samples analysed, chromosomal microarray analysis detected 87 cases of aneuploidy and 37 cases of pathogenic or likely pathogenic copy number variations, identifying a total of 121 abnormalities.
Low-pass WGS successfully detected all abnormalities reported by CMA and additionally identified 17 extra clinically relevant chromosomal abnormalities.
The results demonstrated a sensitivity of 99.9% and a specificity of 87.7%, with an overall diagnostic detection rate of 13.5%.
Lower DNA Requirements and Improved Laboratory Efficiency
The study also examined the practical requirements of each testing method. Chromosomal microarray analysis required approximately 300 ng of DNA as starting material, whereas low-pass WGS required only 50 ng.
In addition, the repeat experiment rate for chromosomal microarray analysis was 4.6%, compared with only 0.5% for low-pass WGS, suggesting that low-pass sequencing may offer improved experimental stability.
Future Potential for Prenatal Genomics
The research team also developed a genome copy number analysis method called FetalSeq, which uses low-pass whole genome sequencing to detect chromosomal abnormalities and pathogenic DNA copy number variations.
According to Associate Professor Cai Guangwei from the Department of Obstetrics and Gynaecology at CUHK, whole genome sequencing provides comprehensive genomic information and may identify clinically relevant variations that traditional technologies cannot detect.
Researchers believe that as sequencing costs continue to decline, low-pass whole genome sequencing could play an increasingly important role in clinical prenatal diagnosis.
Sequencing Platform
The study was conducted using the MGISEQ-2000 sequencing platform. The platform supports high-throughput genomic analysis with flexible read lengths and large data output, enabling applications across research, clinical diagnostics, agriculture and forensic science.
This article was originally published in 2019 and has been updated for clarity and readability.
