CoverageMaster: comprehensive CNV detection and visualization from NGS short reads for genetic medicine applications

The genomic and epigenomic landscape of iridocorneal endothelial syndrome

Iridocorneal endothelial (ICE) syndrome is a rare, irreversibly blinding eye disease with an unknown etiology. Understanding its genomic and epigenomic landscape could aid in developing etiology-based therapies. In this study, we recruited 99 ICE patients and performed whole-genome sequencing (WGS) on 51 and genome-wide DNA methylation profiling on 48 of them. We conducted mutational burden testing on genes and noncoding regulatory regions, comparing the ICE cohort with control groups (9197 East Asians from the gnomAD database and 350 normal Chinese from our in-house cohort). Copy number variation (CNV) analysis and differential methylation of regions were also explored. We identified RP1L1 (27/51, 53%) with a significantly higher coding-altering mutational burden in the ICE cohort (p < 8.3×10-7), with mutations predominantly at chr8:10467637 (hg19). Additionally, 41 regions with significant CNVs were identified, including two regions at chr19:15783859-15791329 (hg19) and chr3:75786061-75790887 (hg19), showing copy number loss in 39 and 19 patients, respectively. We also identified 2,717 differentially methylated regions (DMRs), with hypomethylation prevalent in ICE syndrome (91.9% of DMRs). Among these, 45 recurrent hypomethylated regions (HMRs) in more than 10% of ICE patients showed differential methylation compared to normal controls. This study presents the first comprehensive genomic and epigenomic characterization of ICE syndrome, offering insights into its underlying etiology.

Genome-wide association testing beyond SNPs

Decades of genetic association testing in human cohorts have provided important insights into the genetic architecture and biological underpinnings of complex traits and diseases. However, for certain traits, genome-wide association studies (GWAS) for common SNPs are approaching signal saturation, which underscores the need to explore other types of genetic variation to understand the genetic basis of traits and diseases. Copy number variation (CNV) is an important source of heritability that is well known to functionally affect human traits. Recent technological and computational advances enable the large-scale, genome-wide evaluation of CNVs, with implications for downstream applications such as polygenic risk scoring and drug target identification. Here, we review the current state of CNV-GWAS, discuss current limitations in resource infrastructure that need to be overcome to enable the wider uptake of CNV-GWAS results, highlight emerging opportunities and suggest guidelines and standards for future GWAS for genetic variation beyond SNPs at scale.

Genetic landscape of Charcot-Marie-Tooth disease in Vietnam: A prospective multicenter study

BACKGROUND

In many developing regions, genetic data on Charcot-Marie-Tooth disease (CMT) remains scarce.

OBJECTIVE

This study aimed to investigate the genetic landscape of CMT in Vietnam to guide the development of cost-effective diagnostic algorithms for patients with suspected genetic neuropathies.

METHODS

We recruited 44 patients with a diagnosis of CMT from three tertiary centers between March 2021 and December 2023 and recorded their clinical and electrophysiological characteristics. All patients were analyzed for duplications or deletions of PMP22, GJB1, MPZ, and MFN2 via multiplex ligation-dependent probe amplification (MLPA) and for 94 genes via targeted next-generation sequencing (NGS). The identified variants were classified per the American College of Medical Genetics and Genomics 2015 guidelines using VarSome, a bioinformatics engine.

RESULTS

Among 44 patients, 24 carried a total of 26 variants. Of these 26 variants, 15 were (57.7%) pathogenic, 6 (23.1%) were likely pathogenic, and 5 (19.2%) were variants of uncertain significance (VUS). Excluding the VUS, the diagnostic yield of the targeted sequencing was 43.2% (19/44). Through MLPA, PMP22 duplications were identified in 10 patients with the demyelinating type of CMT and 1 patient with the unclassified CMT type. The combined yield of MLPA and gene panels was 68.2% (30/44). We detected three novel pathogenic/likely pathogenic variants in GJB1, INF2, and IGHMBP2, as well as three novel VUS in MPZ, PMP22, and INF2. IGHMBP2 may represent the most prevalent autosomal recessive gene associated with CMT in Vietnam.

CONCLUSIONS

We propose a sequential genetic testing approach for CMT in resource-limited settings, with the initial testing via MLPA for demyelinating CMT, followed by NGS for those who test negative. Our findings broaden the CMT genotype-phenotype profile of the Vietnamese population by identifying six novel candidate variants.

Genome sequencing reveals novel causative structural and single nucleotide variants in Pakistani families with congenital hypogonadotropic hypogonadism

BACKGROUND/OBJECTIVES

This study aims to elucidate the genetic causes of congenital hypogonadotropic hypogonadism (CHH), a rare genetic disorder resulting in GnRH deficiency, in six families from Pakistan.

METHODS

Eighteen DNA samples from six families underwent genome sequencing followed by standard evaluation for pathogenic single nucleotide variants (SNVs) and small indels. All families were subsequently analyzed for pathogenic copy number variants (CNVs) using CoverageMaster.

RESULTS

Novel pathogenic homozygous SNVs in known CHH genes were identified in four families: two families with variants in GNRHR, and two others harboring KISS1R variants. Subsequent investigation of CNVs in the remaining two families identified novel unique large deletions in ANOS1.

CONCLUSION

A combined, systematic analysis of single nucleotide and CNVs helps to improve the diagnostic yield for variants in patients with CHH.

CopyVAE: a variational autoencoder-based approach for copy number variation inference using single-cell transcriptomics

MOTIVATION

Copy number variations (CNVs) are common genetic alterations in tumour cells. The delineation of CNVs holds promise for enhancing our comprehension of cancer progression. Moreover, accurate inference of CNVs from single-cell sequencing data is essential for unravelling intratumoral heterogeneity. However, existing inference methods face limitations in resolution and sensitivity.

RESULTS

To address these challenges, we present CopyVAE, a deep learning framework based on a variational autoencoder architecture. Through experiments, we demonstrated that CopyVAE can accurately and reliably detect CNVs from data obtained using single-cell RNA sequencing. CopyVAE surpasses existing methods in terms of sensitivity and specificity. We also discussed CopyVAE's potential to advance our understanding of genetic alterations and their impact on disease advancement.

AVAILABILITY AND IMPLEMENTATION

CopyVAE is implemented and freely available under MIT license at https://github.com/kurtsemih/copyVAE.

Genetic interrogation for sequence and copy number variants in systemic lupus erythematosus

Early-onset systemic lupus erythematosus presents with a more severe disease and is associated with a greater genetic burden, especially in patients from Black, Asian or Hispanic ancestries. Next-generation sequencing techniques, notably whole exome sequencing, have been extensively used in genomic interrogation studies to identify causal disease variants that are increasingly implicated in the development of autoimmunity. This Review discusses the known casual variants of polygenic and monogenic systemic lupus erythematosus and its implications under certain genetic disparities while suggesting an age-based sequencing strategy to aid in clinical diagnostics and patient management for improved patient care.

Recurrent human 16p11.2 microdeletions in type I Mayer-Rokitansky-Küster-Hauser (MRKH) syndrome patients in Chinese Han population

BACKGROUNDS

Mayer-Rokitansky-Küster-Hauser (MRKH) syndrome, a severe congenital malformation of the female genital tract, is a highly heterogeneous disease which has no clear etiology. Previous studies have suggested that copy number variations (CNVs) and single-gene mutations might contribute to the development of MRKH syndrome. In particular, deletions in 16p11.2, which are suggested to be involved in several congenital diseases, have been reported in Chinese type II MRKH patients and European MRKH patients. However, few CNVs including 16p11.2 microdeletions were identified in Chinese type I MRKH cases although it accounted for the majority of MRKH patients in China. Thus, we conducted a retrospective study to identify whether CNVs at human chromosome 16p11.2 are risk factors of type I MRKH syndrome in the Chinese Han population.

METHODS

We recruited 143 patients diagnosed with type I MRKH between 2012 and 2014. Five hundred unrelated Chinese without congenital malformation were enrolled in control group, consisting of 197 from the 1000 Genomes Project and 303 from Fudan University. Quantitative PCR, array comparative genomic hybridization, and sanger sequencing were conducted to screen and verify candidate variant.

RESULTS

Our study identified recurrent 16p11.2 microdeletions of approximately 600 kb in two out of the 143 type I MRKH syndrome patients using high-density array-based comparative genomic hybridization (aCGH), while no 16p11.2 deletion was found in the control group. We did not find any mutations in TBX6 gene in our samples.

CONCLUSIONS

The results of the study identify 16p11.2 deletion in Chinese MRKH I patients for the first time, as well as support the contention that 16p11.2 microdeletions are associated with MRKH syndrome in both types across populations. It is suggested that 16p11.2 microdeletions should be included in molecular diagnosis and genetic counseling of female reproductive tract disorders.

A Bioinformatics Toolkit for Next-Generation Sequencing in Clinical Oncology

Next-generation sequencing (NGS) has taken on major importance in clinical oncology practice. With the advent of targeted therapies capable of effectively targeting specific genomic alterations in cancer patients, the development of bioinformatics processes has become crucial. Thus, bioinformatics pipelines play an essential role not only in the detection and in identification of molecular alterations obtained from NGS data but also in the analysis and interpretation of variants, making it possible to transform raw sequencing data into meaningful and clinically useful information. In this review, we aim to examine the multiple steps of a bioinformatics pipeline as used in current clinical practice, and we also provide an updated list of the necessary bioinformatics tools. This resource is intended to assist researchers and clinicians in their genetic data analyses, improving the precision and efficiency of these processes in clinical research and patient care.

Metagenomics next-generation sequencing for the diagnosis of central nervous system infection: A systematic review and meta-analysis

Objective

It is widely acknowledged that central nervous system (CNS) infection is a serious infectious disease accompanied by various complications. However, the accuracy of current detection methods is limited, leading to delayed diagnosis and treatment. In recent years, metagenomic next-generation sequencing (mNGS) has been increasingly adopted to improve the diagnostic yield. The present study sought to evaluate the value of mNGS in CNS infection diagnosis.

Methods

Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2022 guidelines, we searched relevant articles published in seven databases, including PubMed, Web of Science, and Cochrane Library, published from January 2014 to January 2022. High-quality articles related to mNGS applications in the CNS infection diagnosis were included. The comparison between mNGS and the gold standard of CNS infection, such as culture, PCR or serology, and microscopy, was conducted to obtain true positive (TP), true negative (TN), false positive (FP), and false negative (FN) values, which were extracted for sensitivity and specificity calculation.

Results

A total of 272 related studies were retrieved and strictly selected according to the inclusion and exclusion criteria. Finally, 12 studies were included for meta-analysis and the pooled sensitivity was 77% (95% CI: 70–82%, I² = 39.69%) and specificity was 96% (95% CI: 93–98%, I² = 72.07%). Although no significant heterogeneity in sensitivity was observed, a sub-group analysis was conducted based on the pathogen, region, age, and sample pretreatment method to ascertain potential confounders. The area under the curve (AUC) of the summary receiver operating characteristic curve (SROC) of mNGS for CNS infection was 0.91 (95% CI: 0.88–0.93). Besides, Deek's Funnel Plot Asymmetry Test indicated no publication bias in the included studies (Figure 3, p > 0.05).

Conclusion

Overall, mNGS exhibits good sensitivity and specificity for diagnosing CNS infection and diagnostic performance during clinical application by assisting in identifying the pathogen. However, the efficacy remains inconsistent, warranting subsequent studies for further performance improvement during its clinical application.

Study registration number

INPLASY202120002

PEcnv: accurate and efficient detection of copy number variations of various lengths

Copy number variation (CNV) is a class of key biomarkers in many complex traits and diseases. Detecting CNV from sequencing data is a substantial bioinformatics problem and a standard requirement in clinical practice. Although many proposed CNV detection approaches exist, the core statistical model at their foundation is weakened by two critical computational issues: (i) identifying the optimal setting on the sliding window and (ii) correcting for bias and noise. We designed a statistical process model to overcome these limitations by calculating regional read depths via an exponentially weighted moving average strategy. A one-run detection of CNVs of various lengths is then achieved by a dynamic sliding window, whose size is self-adopted according to the weighted averages. We also designed a novel bias/noise reduction model, accompanied by the moving average, which can handle complicated patterns and extend training data. This model, called PEcnv, accurately detects CNVs ranging from kb-scale to chromosome-arm level. The model performance was validated with simulation samples and real samples. Comparative analysis showed that PEcnv outperforms current popular approaches. Notably, PEcnv provided considerable advantages in detecting small CNVs (1 kb–1 Mb) in panel sequencing data. Thus, PEcnv fills the gap left by existing methods focusing on large CNVs. PEcnv may have broad applications in clinical testing where panel sequencing is the dominant strategy. Availability and implementation: Source code is freely available at https://github.com/Sherwin-xjtu/PEcnv