Precise detection of de novo single nucleotide variants in human genomes

Recent advances in enzyme-free and enzyme-mediated single-nucleotide variation assay in vitro

Single-nucleotide variants (SNVs) are the most common type variation of sequence alterations at a specific location in the genome, thus involving significant clinical and biological information. The assay of SNVs has engaged great awareness, because many genome-wide association studies demonstrated that SNVs are highly associated with serious human diseases. Moreover, the investigation of SNV expression levels in single cells are capable of visualizing genetic information and revealing the complexity and heterogeneity of single-nucleotide mutation-related diseases. Thus, developing SNV assay approaches in vitro, particularly in single cells, is becoming increasingly in demand. In this review, we summarized recent progress in the enzyme-free and enzyme-mediated strategies enabling SNV assay transition from sensing interface to the test tube and single cells, which will potentially delve deeper into the knowledge of SNV functions and disease associations, as well as discovering new pathways to diagnose and treat diseases based on individual genetic profiles. The leap of SNV assay achievements will motivate observation and measurement genetic variations in single cells, even within living organisms, delve into the knowledge of SNV functions and disease associations, as well as open up entirely new avenues in the diagnosis and treatment of diseases based on individual genetic profiles.

SARS-CoV-2 variant detection with ADSSpike

The SARS-CoV-2 coronavirus pandemic has been an unprecedented challenge to global pandemic response and preparedness. With the continuous appearance of new SARS-CoV-2 variants, it is imperative to implement tools for genomic surveillance and diagnosis in order to decrease viral transmission and prevalence. The ADSSpike workflow was developed with the goal of identifying signature SNPs from the S gene associated with SARS-CoV-2 variants through amplicon deep sequencing. Seventy-two samples were sequenced, and 30 mutations were identified. Among those, signature SNPs were linked to 2 Zeta-VOI (P.2) samples and one to the Alpha-VOC (B.1.17). An average depth of 700 reads was found to properlycorrectly identify all SNPs and deletions pertinent to SARS-CoV-2 mutants. ADSSpike is the first workflow to provide a practical, cost-effective, and scalable solution to diagnose SARS-CoV-2 VOC/VOI in the clinical laboratory, adding a valuable tool to public health measures to fight the COVID-19 pandemic for approximately $41.85 USD/reaction.

Nebula: ultra-efficient mapping-free structural variant genotyper

Large scale catalogs of common genetic variants (including indels and structural variants) are being created using data from second and third generation whole-genome sequencing technologies. However, the genotyping of these variants in newly sequenced samples is a nontrivial task that requires extensive computational resources. Furthermore, current approaches are mostly limited to only specific types of variants and are generally prone to various errors and ambiguities when genotyping complex events. We are proposing an ultra-efficient approach for genotyping any type of structural variation that is not limited by the shortcomings and complexities of current mapping-based approaches. Our method Nebula utilizes the changes in the count of k-mers to predict the genotype of structural variants. We have shown that not only Nebula is an order of magnitude faster than mapping based approaches for genotyping structural variants, but also has comparable accuracy to state-of-the-art approaches. Furthermore, Nebula is a generic framework not limited to any specific type of event. Nebula is publicly available at https://github.com/Parsoa/Nebula.

KATK: Fast genotyping of rare variants directly from unmapped sequencing reads

KATK is a fast and accurate software tool for calling variants directly from raw next-generation sequencing reads. It uses predefined k-mers to retrieve only the reads of interest from the FASTQ file and calls genotypes by aligning retrieved reads locally. KATK does not use data about known polymorphisms and has NC (no call) as the default genotype. The reference or variant allele is called only if there is sufficient evidence for their presence in data. Thus it is not biased against rare variants or de-novo mutations. With simulated datasets, we achieved a false-negative rate of 0.23% (sensitivity 99.77%) and a false discovery rate of 0.19%. Calling all human exonic regions with KATK requires 1-2 h, depending on sequencing coverage.

Scleral HIF-1α is a prominent regulatory candidate for genetic and environmental interactions in human myopia pathogenesis

BACKGROUND

Myopia is a good model for understanding the interaction between genetics and environmental stimuli. Here we dissect the biological processes affecting myopia progression.

METHODS

Human Genetic Analyses: (1) gene set analysis (GSA) of new genome wide association study (GWAS) data for 593 individuals with high myopia (refraction ≤ -6 diopters [D]); (2) over-representation analysis (ORA) of 196 genes with de novo mutations, identified by whole genome sequencing of 45 high-myopia trio families, and (3) ORA of 284 previously reported myopia risk genes. Contributions of the enriched signaling pathways in mediating the genetic and environmental interactions during myopia development were investigated in vivo and in vitro.

RESULTS

All three genetic analyses showed significant enrichment of four KEGG signaling pathways, including amphetamine addiction, extracellular matrix (ECM) receptor interaction, neuroactive ligand-receptor interaction, and regulation of actin cytoskeleton pathways. In individuals with extremely high myopia (refraction ≤ -10 D), the GSA of GWAS data revealed significant enrichment of the HIF-1α signaling pathway. Using human scleral fibroblasts, silencing the key nodal genes within protein-protein interaction networks for the enriched pathways antagonized the hypoxia-induced increase in myofibroblast transdifferentiation. In mice, scleral HIF-1α downregulation led to hyperopia, whereas upregulation resulted in myopia. In human subjects, near work, a risk factor for myopia, significantly decreased choroidal blood perfusion, which might cause scleral hypoxia.

INTERPRETATION

Our study implicated the HIF-1α signaling pathway in promoting human myopia through mediating interactions between genetic and environmental factors.

FUNDING

National Natural Science Foundation of China grants; Natural Science Foundation of Zhejiang Province.

Single nucleotide variant discrimination by toehold exchange spherical nucleic acids modulated on hierarchical molybdenum disulfide acanthospheres

Toehold exchange spherical nucleic acids (TESNA) modulated on molybdenum disulfide acanthospheres are proposed for the discrimination of single nucleotide variants (SNVs) with significantly improved sensitivity and specificity.

Toward Clinical Implementation of Next-Generation Sequencing-Based Genetic Testing in Rare Diseases: Where Are We?

Next-generation sequencing (NGS) technologies have changed the landscape of genetic testing in rare diseases. However, the rapid evolution of NGS technologies has outpaced its clinical adoption. Here, we re-evaluate the critical steps in the clinical application of NGS-based genetic testing from an informatics perspective. We suggest a 'fit-for-purpose' triage of current NGS technologies. We also point out potential shortcomings in the clinical management of genetic variants and offer ideas for potential improvement. We specifically emphasize the importance of ensuring the accuracy and reproducibility of NGS-based genetic testing in the context of rare disease diagnosis. We highlight the role of artificial intelligence (AI) in enhancing understanding and prioritization of variance in the clinical setting and propose deep learning frameworks for further investigation.

Kevlar: A Mapping-Free Framework for Accurate Discovery of De Novo Variants

De novo genetic variants are an important source of causative variation in complex genetic disorders. Many methods for variant discovery rely on mapping reads to a reference genome, detecting numerous inherited variants irrelevant to the phenotype of interest. To distinguish between inherited and de novo variation, sequencing of families (parents and siblings) is commonly pursued. However, standard mapping-based approaches tend to have a high false-discovery rate for de novo variant prediction. Kevlar is a mapping-free method for de novo variant discovery, based on direct comparison of sequences between related individuals. Kevlar identifies high-abundance k-mers unique to the individual of interest. Reads containing these k-mers are partitioned into disjoint sets by shared k-mer content for variant calling, and preliminary variant predictions are sorted using a probabilistic score. We evaluated Kevlar on simulated and real datasets, demonstrating its ability to detect both de novo single-nucleotide variants and indels with high accuracy.

Comparative Analysis for the Performance of Variant Calling Pipelines on Detecting the de novo Mutations in Humans

Despite of the low occurrence rate in the entire genomes, de novo mutation is proved to be deleterious and will lead to severe genetic diseases via impacting on the gene function. Considering the fact that the traditional family based linkage approaches and the genome-wide association studies are unsuitable for identifying the de novo mutations, in recent years, several pipelines have been proposed to detect them based on the whole-genome or whole-exome sequencing data and were used for calling them in the rare diseases. However, how the performance of these variant calling pipelines on detecting the de novo mutations is still unexplored. For the purpose of facilitating the appropriate choice of the pipelines and reducing the false positive rate, in this study, we thoroughly evaluated the performance of the commonly used trio calling methods on the detection of the de novo single-nucleotide variants (DNSNVs) by conducting a comparative analysis for the calling results. Our results exhibited that different pipelines have a specific tendency to detect the DNSNVs in the genomic regions with different GC contents. Additionally, to refine the calling results for a single pipeline, our proposed filter achieved satisfied results, indicating that the read coverage at the mutation positions can be used as an effective index to identify the high-confidence DNSNVs. Our findings should be good support for the committees to choose an appropriate way to explore the de novo mutations for the rare diseases.

Allele balance bias identifies systematic genotyping errors and false disease associations

In recent years, next‐generation sequencing (NGS) has become a cornerstone of clinical genetics and diagnostics. Many clinical applications require high precision, especially if rare events such as somatic mutations in cancer or genetic variants causing rare diseases need to be identified. Although random sequencing errors can be modeled statistically and deep sequencing minimizes their impact, systematic errors remain a problem even at high depth of coverage. Understanding their source is crucial to increase precision of clinical NGS applications. In this work, we studied the relation between recurrent biases in allele balance (AB), systematic errors, and false positive variant calls across a large cohort of human samples analyzed by whole exome sequencing (WES). We have modeled the AB distribution for biallelic genotypes in 987 WES samples in order to identify positions recurrently deviating significantly from the expectation, a phenomenon we termed allele balance bias (ABB). Furthermore, we have developed a genotype callability score based on ABB for all positions of the human exome, which detects false positive variant calls that passed state‐of‐the‐art filters. Finally, we demonstrate the use of ABB for detection of false associations proposed by rare variant association studies. Availability: https://github.com/Francesc-Muyas/ABB.

Population sequencing reveals clonal diversity and ancestral inbreeding in the grapevine cultivar Chardonnay

Chardonnay is the basis of some of the world's most iconic wines and its success is underpinned by a historic program of clonal selection. There are numerous clones of Chardonnay available that exhibit differences in key viticultural and oenological traits that have arisen from the accumulation of somatic mutations during centuries of asexual propagation. However, the genetic variation that underlies these differences remains largely unknown. To address this knowledge gap, a high-quality, diploid-phased Chardonnay genome assembly was produced from single-molecule real time sequencing, and combined with re-sequencing data from 15 different Chardonnay clones. There were 1620 markers identified that distinguish the 15 clones. These markers were reliably used for clonal identification of independently sourced genomic material, as well as in identifying a potential genetic basis for some clonal phenotypic differences. The predicted parentage of the Chardonnay haplomes was elucidated by mapping sequence data from the predicted parents of Chardonnay (Gouais blanc and Pinot noir) against the Chardonnay reference genome. This enabled the detection of instances of heterosis, with differentially-expanded gene families being inherited from the parents of Chardonnay. Most surprisingly however, the patterns of nucleotide variation present in the Chardonnay genome indicate that Pinot noir and Gouais blanc share an extremely high degree of kinship that has resulted in the Chardonnay genome displaying characteristics that are indicative of inbreeding.