Evaluation and Analysis of Absence of Homozygosity (AOH) Using Chromosome Analysis by Medium Coverage Whole Genome Sequencing (CMA-seq) in Prenatal Diagnosis

Abstract

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.

Recurrent neural network for predicting absence of heterozygosity from low pass WGS with ultra-low depth

“…Only one AOH with an overlap of 47% was missed by CNVseq-AOH (Supplementary Table 1). The sensitivity of CNVseq-AOH reached 96.23% even with a depth of 0.1-fold, which is far lower than current studies, which need 4-to-fivefold depth [ 11 , 17 ]. For the 79 AOHs on the X chromosome, the sensitivity of CNVseq-AOH was 59.49% (47/79) with a depth of 0.1-fold.…”

Detection of Mosaic Absence of Heterozygosity (AOH) Using Low-Pass Whole Genome Sequencing in Prenatal Diagnosis: A Preliminary Report

“…One key improvement of AOH detection is the enhanced detection criteria for constitutional AOH. Previously, merely the AB allele and the B allele (i.e., two components of D1) were considered when confirming constitutional AOH, without implicating D2 [ 19 ]. Now, it is essential to consider both D1 and D2 jointly, an important optimization step.…”

“…The genotype status is used to determine whether a region has undergone AOH event to become homozygous. As we implicitly assumed in the previous study [ 19 ], normal individuals are expected to possess a quantity of heterozygous alleles for a given genomic region. If not, and concomitant with an abnormal increase in homozygous variants, then this region is considered as putative constitutional AOH.…”

“…The Affymetrix ® CytoScanTM 750 K Array (Affymetrix, Santa Clara, CA, USA) was used for AOH analyses for all samples in the benchmark cohort, training, and validation data sets, except for AOH-negative samples. See [ 19 ] for details. The threshold for reporting autosomal AOH is ≥5 Mb for terminal AOH and 10 Mb for interstitial AOH.…”

“…Previous studies [ 17 , 18 ] showed the feasibility of applying low-pass WGS in the identification of clinically significant AOHs; however, for detecting mosaic AOH, they only described broad principles, and no specific algorithm was provided. In our previous publication [ 19 ], we introduced ‘CMA-seq’ (a method of 5~8-fold short-read WGS) showing equivalent AOH detection power to that of routine CMA (750K), yet the performance of mosaic AOH detection was not considered. Currently, there are only a few such investigations because the pipeline for detecting AOH entails following a typical variant calling pipeline and then analyzing allele frequencies, whereas low-pass WGS is unable to accurately calculate the variant allele fraction (VAF), which results in a high likelihood of incorrect genotyping.…”