Objective: Absence of homozygosity (AOH) is a genetic characteristic known to cause human diseases mainly through autosomal recessive or imprinting mechanisms. The importance and necessity of accurate AOH detection has become more clinically significant in recent years. However, it remains a challenging task for sequencing-based methods thus far. Methods: In this study, we developed and optimized a new bioinformatic algorithm based on the assessment of minimum sequencing coverage, optimal bin size, the Z-score threshold of four types of allele count and the frequency for accurate genotyping using 28 AOH negative samples, and redefined the AOH detection cutoff value. We showed the performance of chromosome analysis by five-fold coverage whole genome sequencing (CMA-seq) for AOH identification in 27 typical prenatal/postnatal AOH positive samples, which were previously confirmed by chromosomal microarray analysis with single nucleotide polymorphism array (CMA/SNP array). Results: The blinded study indicated that for all three forms of AOH, including whole genomic AOH, single chromosomal AOH and segmental AOH, and all kinds of sample types, including chorionic villus sampling, amniotic fluid, cord blood, peripheral blood and abortive tissue, CMA-seq showed equivalent detection power to that of routine CMA/SNP arrays (750K). The subtle difference between the two methods is that CMA-seq is prone to detect small inconsecutive AOHs, while CMA/SNP array reports it as a whole. Conclusion: Based on our newly developed bioinformatic algorithm, it is feasible to detect clinically significant AOH using CMA-seq in prenatal diagnosis.

Background: Congenital adrenal hyperplasia (CAH) is an autosomal recessive disorder primarily caused by defects in adrenal steroidogenesis. Conventional genetic methods struggle to resolve complex structural variations and pseudogene interference in key genes like CYP21A2. Our study will evaluate the efficacy of Long-Read Sequencing (LRS) as a comprehensive diagnostic tool for CAH, demonstrating its ability to simultaneously detect large structural variations, single nucleotide variants (SNVs), and small insertions or deletions.Methods: Four probands with clinically diagnosed CAH underwent detailed biochemical profiling, including serum 17-hydroxyprogesterone, serum sodium and serum potassium. Genomic DNA was extracted from peripheral blood and subjected to LRS using Single-Molecule Real-Time (SMRT) Technologies (Pacifc Biosciences). A targeted panel covering the CYP21A2 and HSD3B2 genes, as well as other genes related to CAH was captured. Bioinformatic analysis included alignment with Minimap2, variant calling with Sniffles2 and Medaka, and phasing analysis to resolve pseudogene interference.Results: LRS identified compound heterozygous and homozygous variants in CYP21A2 (e.g., c.293-13C > G, c.518T > A, CH-1) and novel compound heterozygous variants in HSD3B2 (c.121G > T and c.757T > G). In combination with biochemical tests, clinical manifestations, and the ACMG guidelines, these gene mutations were the cause of the patient's disease. LRS resolved pseudogene interference and provided unambiguous cis/trans phasing.Conclusion: LRS is a robust diagnostic tool for CAH, offering comprehensive detection of genetic variants, including large deletions and SNVs in both cis and trans forms. Its ability to resolve pseudogenes and structural variations positions LRS as a first-tier diagnostic tool for CAH, improving accuracy, streamlining clinical workflows and ultimately benefits patients.