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

BACKGROUND: The absence of heterozygosity (AOH) is a kind of genomic change characterized by a long contiguous region of homozygous alleles in a chromosome, which may cause human genetic disorders. However, no method of low-pass whole genome sequencing (LP-WGS) has been reported for the detection of AOH in a low-pass setting of less than onefold. We developed a method, termed CNVseq-AOH, for predicting the absence of heterozygosity using LP-WGS with ultra-low sequencing data, which overcomes the sparse nature of typical LP-WGS data by combing population-based haplotype information, adjustable sliding windows, and recurrent neural network (RNN). We tested the feasibility of CNVseq-AOH for the detection of AOH in 409 cases (11 AOH regions for model training and 863 AOH regions for validation) from the 1000 Genomes Project (1KGP). AOH detection using CNVseq-AOH was also performed on 6 clinical cases with previously ascertained AOHs by whole exome sequencing (WES). RESULTS: Using SNP-based microarray results as reference (AOHs detected by CNVseq-AOH with at least a 50% overlap with the AOHs detected by chromosomal microarray analysis), 409 samples (863 AOH regions) in the 1KGP were used for concordant analysis. For 784 AOHs on autosomes and 79 AOHs on the X chromosome, CNVseq-AOH can predict AOHs with a concordant rate of 96.23% and 59.49% respectively based on the analysis of 0.1-fold LP-WGS data, which is far lower than the current standard in the field. Using 0.1-fold LP-WGS data, CNVseq-AOH revealed 5 additional AOHs (larger than 10 Mb in size) in the 409 samples. We further analyzed AOHs larger than 10 Mb, which is recommended for reporting the possibility of UPD. For the 291 AOH regions larger than 10 Mb, CNVseq-AOH can predict AOHs with a concordant rate of 99.66% with only 0.1-fold LP-WGS data. In the 6 clinical cases, CNVseq-AOH revealed all 15 known AOH regions. CONCLUSIONS: Here we reported a method for analyzing LP-WGS data to accurately identify regions of AOH, which possesses great potential to improve genetic testing of AOH.

Test development, optimization and validation of a WGS pipeline for genetic disorders.

BACKGROUND: With advances in massive parallel sequencing (MPS) technology, whole-genome sequencing (WGS) has gradually evolved into the first-tier diagnostic test for genetic disorders. However, deployment practice and pipeline testing for clinical WGS are lacking. METHODS: In this study, we introduced a whole WGS pipeline for genetic disorders, which included the entire process from obtaining a sample to clinical reporting. All samples that underwent WGS were constructed using polymerase chain reaction (PCR)-free library preparation protocols and sequenced on the MGISEQ-2000 platform. Bioinformatics pipelines were developed for the simultaneous detection of various types of variants, including single nucleotide variants (SNVs), insertions and deletions (indels), copy number variants (CNVs) and balanced rearrangements, mitochondrial (MT) variants, and other complex variants such as repeat expansion, pseudogenes and absence of heterozygosity (AOH). A semiautomatic pipeline was developed for the interpretation of potential SNVs and CNVs. Forty-five samples (including 14 positive commercially available samples, 23 laboratory-held positive cell lines and 8 clinical cases) with known variants were used to validate the whole pipeline. RESULTS: In this study, a whole WGS pipeline for genetic disorders was developed and optimized. Forty-five samples with known variants (6 with SNVs and Indels, 3 with MT variants, 5 with aneuploidies, 1 with triploidy, 23 with CNVs, 5 with balanced rearrangements, 2 with repeat expansions, 1 with AOHs, and 1 with exon 7-8 deletion of SMN1 gene) validated the effectiveness of our pipeline. CONCLUSIONS: This study has been piloted in test development, optimization, and validation of the WGS pipeline for genetic disorders. A set of best practices were recommended using our pipeline, along with a dataset of positive samples for benchmarking.

Whole Genome Sequencing in the Evaluation of Fetal Structural Anomalies: A Parallel Test with Chromosomal Microarray Plus Whole Exome Sequencing.

Whole genome sequencing (WGS) is a powerful tool for postnatal genetic diagnosis, but relevant clinical studies in the field of prenatal diagnosis are limited. The present study aimed to prospectively evaluate the utility of WGS compared with chromosomal microarray (CMA) and whole exome sequencing (WES) in the prenatal diagnosis of fetal structural anomalies. We performed trio WGS (≈40-fold) in parallel with CMA in 111 fetuses with structural or growth anomalies, and sequentially performed WES when CMA was negative (CMA plus WES). In comparison, WGS not only detected all pathogenic genetic variants in 22 diagnosed cases identified by CMA plus WES, yielding a diagnostic rate of 19.8% (22/110), but also provided additional and clinically significant information, including a case of balanced translocations and a case of intrauterine infection, which might not be detectable by CMA or WES. WGS also required less DNA (100 ng) as input and could provide a rapid turnaround time (TAT, 18 ± 6 days) compared with that (31 ± 8 days) of the CMA plus WES. Our results showed that WGS provided more comprehensive and precise genetic information with a rapid TAT and less DNA required than CMA plus WES, which enables it as an alternative prenatal diagnosis test for fetal structural anomalies.

Identification of key mRNAs and microRNAs in the pathogenesis and progression of osteoarthritis using microarray analysis.

Osteoarthritis (OA) is a common type of disease affecting the joints that results from the breakdown of joint cartilage and the underlying bone; currently, its pathogenesis is still unclear. The aim of the present study was to identify key mRNAs and miRNAs involved in the pathogenesis and progression of OA using microarray analysis. The gene expression profile of GSE27492 was downloaded from the Gene Expressed Omnibus database, and included 49 arthritic mouse ankle samples collected at 6 time points (0, 1, 3, 7, 12 and 18 days) following the induction of arthritis via serum transfer. Differentially expressed genes (DEGs) were identified in ankle samples taken on days 1, 3, 7, 12 and 18 following serum transfer compared with day 0 samples, and overlapping DEGs in day 3, 7, 12 and 18 samples were identified. The Database for Annotation, Visualization and Integrated Discovery online tool was used to perform functional and pathway enrichment analyses of the overlapping DEGs. The miRWalk database was used to identify potential micro (mi) RNAs regulating the selected overlapping DEGs, and regulatory miRNA­target mRNA pairs were obtained. The Cytoscape platform was used to establish and visualize the miRNA­mRNA regulatory network. The present results revealed that 35, 103, 62 and 75 DEGs were identified in day 3, 7, 12 and 18 samples, respectively. A total of 17 overlapping DEGs were identified among the 4 sample sets, and revealed to be enriched in 14 gene ontology terms and 3 Kyoto Encyclopedia of Genes and Genomes pathways. miRWalk analysis identified 242 potential miRNA­mRNA regulatory pairs and 211 nodes were revealed to be involved in the miRNA­mRNA regulatory network. The present study identified potential genes, including C­type lectin domain family 4 member D, chemokine (C­X­C motif) ligand 1 and C­C motif chemokine ligand, and pathways, including chemokine signaling pathways, cytokine­cytokine receptor interactions and nucleotide­binding oligomerization domain­like receptor signaling pathways, which may be involved in the pathogenesis and progression of OA. These findings may help elucidate the molecular mechanisms underlying OA pathophysiology, and may be useful for the development of novel therapeutic targets for the treatment of patients with OA.