Decoding the effects of synonymous variants

Genetic evidence for involvement of β2-adrenergic receptor in brown adipose tissue thermogenesis in humans

BACKGROUND

Sympathetic activation of brown adipose tissue (BAT) thermogenesis can ameliorate obesity and related metabolic abnormalities. However, crucial subtypes of the β-adrenergic receptor (AR), as well as effects of its genetic variants on functions of BAT, remains unclear in humans. We conducted association analyses of genes encoding β-ARs and BAT activity in human adults.

METHODS

Single nucleotide polymorphisms (SNPs) in β1-, β2-, and β3-AR genes (ADRB1, ADRB2, and ADRB3) were tested for the association with BAT activity under mild cold exposure (19 °C, 2 h) in 399 healthy Japanese adults. BAT activity was measured using fluorodeoxyglucose-positron emission tomography and computed tomography (FDG-PET/CT). To validate the results, we assessed the effects of SNPs in the two independent populations comprising 277 healthy East Asian adults using near-infrared time-resolved spectroscopy (NIRTRS) or infrared thermography (IRT). Effects of SNPs on physiological responses to intensive cold exposure were tested in 42 healthy Japanese adult males using an artificial climate chamber.

RESULTS

We found a significant association between a functional SNP (rs1042718) in ADRB2 and BAT activity assessed with FDG-PET/CT (p < 0.001). This SNP also showed an association with cold-induced thermogenesis in the population subset. Furthermore, the association was replicated in the two other independent populations; BAT activity was evaluated by NIRTRS or IRT (p < 0.05). This SNP did not show associations with oxygen consumption and cold-induced thermogenesis under intensive cold exposure, suggesting the irrelevance of shivering thermogenesis. The SNPs of ADRB1 and ADRB3 were not associated with these BAT-related traits.

CONCLUSIONS

The present study supports the importance of β2-AR in the sympathetic regulation of BAT thermogenesis in humans. The present collection of DNA samples is the largest to which information on the donor's BAT activity has been assigned and can serve as a reference for further in-depth understanding of human BAT function.

Effect Predictor of Driver Synonymous Mutations Based on Multi-Feature Fusion and Iterative Feature Representation Learning

Accurate identification of driver mutations is crucial in genetic studies of human cancers. While numerous cancer driver missense mutations have been identified, research into potential cancer drivers for synonymous mutations has shown limited success to date. Here, we developed a novel machine learning framework, epSMic, for predicting cancer driver synonymous mutations. epSMic employs an iterative feature representation scheme that facilitates the learning of discriminative features from various sequential models in a supervised iterative mode. We constructed the benchmark datasets and encoded the embedding sequence, physicochemical property, and basic information such as conservation and splicing feature. The evaluation results on benchmark test datasets demonstrate that epSMic outperforms existing methods, making it a valuable tool for researchers in identifying functional synonymous mutations in cancer. We hope epSMic can enable researchers to concentrate on synonymous mutations that have a functional impact on cancer.

Bioinformatics of germline variant discovery for rare disease diagnostics: current approaches and remaining challenges

Next-generation sequencing (NGS) has revolutionized the field of rare disease diagnostics. Whole exome and whole genome sequencing are now routinely used for diagnostic purposes; however, the overall diagnosis rate remains lower than expected. In this work, we review current approaches used for calling and interpretation of germline genetic variants in the human genome, and discuss the most important challenges that persist in the bioinformatic analysis of NGS data in medical genetics. We describe and attempt to quantitatively assess the remaining problems, such as the quality of the reference genome sequence, reproducible coverage biases, or variant calling accuracy in complex regions of the genome. We also discuss the prospects of switching to the complete human genome assembly or the human pan-genome and important caveats associated with such a switch. We touch on arguably the hardest problem of NGS data analysis for medical genomics, namely, the annotation of genetic variants and their subsequent interpretation. We highlight the most challenging aspects of annotation and prioritization of both coding and non-coding variants. Finally, we demonstrate the persistent prevalence of pathogenic variants in the coding genome, and outline research directions that may enhance the efficiency of NGS-based disease diagnostics.

Prioritizing de novo potential non-canonical splicing variants in neurodevelopmental disorders

BACKGROUND

Genomic variants outside of the canonical splicing site (±2) may generate abnormal mRNA splicing, which are defined as non-canonical splicing variants (NCSVs). However, the clinical interpretation of NCSVs in neurodevelopmental disorders (NDDs) is largely unknown.

METHODS

We investigated the contribution of NCSVs to NDDs from 345,787 de novo variants (DNVs) in 47,574 patients with NDDs. We performed functional enrichment and protein-protein interaction analysis to assess the association between genes carrying prioritised NCSVs and NDDs. Minigene was used to validate the impact of NCSVs on mRNA splicing.

FINDINGS

We observed significantly more NCSVs (p = 0.02, odds ratio [OR] = 2.05) among patients with NDD than in controls. Both canonical splicing variants (CSVs) and NCSVs contributed to an equal proportion of patients with NDD (0.76% vs. 0.82%). The candidate genes carrying NCSVs were associated with glutamatergic synapse and chromatin remodelling. Minigene successfully validated 59 of 79 (74.68%) NCSVs that led to abnormal splicing in 40 candidate genes, and 9 of the genes (ARID1B, KAT6B, TCF4, SMARCA2, SHANK3, PDHA1, WDR45, SCN2A, SYNGAP1) harboured recurrent NCSVs with the same variant present in more than two unrelated patients with NDD. Moreover, 36 of 59 (61.02%) NCSVs are novel clinically relevant variants, including 34 unreported and 2 clinically conflicting interpretations or of uncertain significance NCSVs in the ClinVar database.

INTERPRETATION

This study highlights the common pathology and clinical importance of NCSVs in unsolved patients with NDD.

FUNDING

The present study was funded by grants from the National Natural Science Foundation of China, China Postdoctoral Science Foundation, the Hunan Youth Science and Technology Innovation Talent Project, the Provincial Natural Science Foundation of Hunan, The Scientific Research Program of FuRong laboratory, and the Natural Science Project of the University of Anhui Province.

Populational pan-ethnic screening panel enabled by deep whole genome sequencing

Birth defect is a global threat to the public health systems. Mitigating neonatal anomalies is hampered by elusive molecular mechanisms of pathogenic mutations and poor subsequent translation into preventative measures. Applying appropriate strategies in China to promote reproductive health is particularly challenging, as the Chinese population compromises complex genomic diversity due to the inclusion of many ethnic groups with distinct genetic backgrounds. To investigate and evaluate the feasibility of implementing a pan-ethnic screening strategy, and guide future reproductive counselling, high-quality variants associated with autosome recessive (AR) diseases derived from the largest publicly available cohort of the Chinese population were re-analysed using a bottom-up approach. The analyses of gene carrier rates (GCRs) across distinct ethnic groups revealed that substantial heterogeneity existed potentially due to diverse evolutionary selection. The sampling population, sequencing coverage and underlying population structure contributed to the differential variants observed between ChinaMAP and the East Asian group in gnomAD. Beyond characteristics of GCR, potential druggable targets were additionally explored according to genomic features and functional roles of investigated genes, demonstrating that phase separation could be a therapeutic target for autosomal recessive diseases. A further examination of estimated GCR across ethnic groups indicated that most genes shared by at least two populations could be utilised to direct the design of a pan-ethnic screening application once sequencing and interpreting costs become negligible. To this end, a list of autosomal recessive disease genes is proposed based on the prioritised rank of GCR to formulate a tiered screening strategy.

Nonsynonymous Synonymous Variants Demand for a Paradigm Shift in Genetics

Synonymous (also known as silent) variations are by definition not considered to change the coded protein. Still many variations in this category affect either protein abundance or properties. As this situation is confusing, we have recently introduced systematics for synonymous variations and those that may on the surface look like synonymous, but these may affect the coded protein in various ways. A new category, unsense variation, was introduced to describe variants that do not introduce a stop codon into the variation site, but which lead to different types of changes in the coded protein. Many of these variations lead to mRNA degradation and missing protein. Here, consequences of the systematics are discussed from the perspectives of variation annotation and interpretation, evolutionary calculations, nonsynonymous-to-synonymous substitution rates, phylogenetics and other evolutionary inferences that are based on the principle of (nearly) neutral synonymous variations. It may be necessary to reassess published results. Further, databases for synonymous variations and prediction methods for such variations should consider unsense variations. Thus, there is a need to evaluate and reflect principles of numerous aspects in genetics, ranging from variation naming and classification to evolutionary calculations.

Case report: Autosomal recessive type 3 Stickler syndrome caused by compound heterozygous mutations in COL11A2

Background: Stickler syndrome (SS) is a group of hereditary collagenopathies caused by a variety of collagen and non-collagen genes. Affected patients have characteristic manifestations involving ophthalmic, articular, craniofacial and auditory disorders. SS is classified into several subtypes according to clinical and molecular features. Type 3 SS is an ultra-rare disease, known as non-ocular SS or otospondylomegaepiphyseal dysplasia (OSMED) with only a few pathogenic COL11A2 variants reported to date. Case presentation: A 29-year-old Chinese male was referred to our hospital for hearing loss and multiple joint pain. He presented a phenotype highly suggestive of OSMED, including progressive sensorineural deafness, spondyloepiphyseal dysplasia with large epiphyses, platyspondyly, degenerative osteoarthritis, and sunken nasal bridge. We detected compound heterozygous mutations in COL11A2, both of which were predicted to be splicing mutations. One is synonymous mutation c.3774C>T (p.Gly1258Gly) supposed to be a splice site mutation, the other is a novel intron mutation c.4750 + 5 G>A, which is a highly conservative site across several species. We also present a review of the current known pathogenic mutation spectrum of COL11A2 in patients with type 3 SS. Conclusion: Both synonymous extonic and intronic variants are easily overlooked by whole-exome sequencing. For patients with clinical manifestations suspected of SS syndrome, next-generation whole-genome sequencing is necessary for precision diagnosis and genetic counseling.

In silico methods for predicting functional synonymous variants

Single nucleotide variants (SNVs) contribute to human genomic diversity. Synonymous SNVs are previously considered to be "silent," but mounting evidence has revealed that these variants can cause RNA and protein changes and are implicated in over 85 human diseases and cancers. Recent improvements in computational platforms have led to the development of numerous machine-learning tools, which can be used to advance synonymous SNV research. In this review, we discuss tools that should be used to investigate synonymous variants. We provide supportive examples from seminal studies that demonstrate how these tools have driven new discoveries of functional synonymous SNVs.

Deleterious synonymous mutation identification based on selective ensemble strategy

Although previous studies have revealed that synonymous mutations contribute to various human diseases, distinguishing deleterious synonymous mutations from benign ones is still a challenge in medical genomics. Recently, computational tools have been introduced to predict the harmfulness of synonymous mutations. However, most of these computational tools rely on balanced training sets without considering abundant negative samples that could result in deficient performance. In this study, we propose a computational model that uses a selective ensemble to predict deleterious synonymous mutations (seDSM). We construct several candidate base classifiers for the ensemble using balanced training subsets randomly sampled from the imbalanced benchmark training sets. The diversity measures of the base classifiers are calculated by the pairwise diversity metrics, and the classifiers with the highest diversities are selected for integration using soft voting for synonymous mutation prediction. We also design two strategies for filling in missing values in the imbalanced dataset and constructing models using different pairwise diversity metrics. The experimental results show that a selective ensemble based on double fault with the ensemble strategy EKNNI for filling in missing values is the most effective scheme. Finally, using 40-dimensional biology features, we propose a novel model based on a selective ensemble for predicting deleterious synonymous mutations (seDSM). seDSM outperformed other state-of-the-art methods on the independent test sets according to multiple evaluation indicators, indicating that it has an outstanding predictive performance for deleterious synonymous mutations. We hope that seDSM will be useful for studying deleterious synonymous mutations and advancing our understanding of synonymous mutations. The source code of seDSM is freely accessible at https://github.com/xialab-ahu/seDSM.git.

Pathogenicity of Intronic and Synonymous Variants of ATP7B in Wilson Disease

Wilson disease (WD) is a hereditary disorder of copper metabolism, resulting from mutations within ATP7B. Early diagnosis is essential for affected individuals. However, there are still patients with clinically suspected WD who do not have detectable pathogenic variants, which makes diagnosis difficult and delays treatment. This study included such patients from the authors' center and screened for the full-length sequence of ATP7B by next-generation sequencing. Newly identified synonymous and intronic variants were then analyzed with in silico tools. A minigene system was constructed to determine the pathogenicity of these variants in terms of splicing and blood RNA extraction, and RT-PCR experiments were performed on several patients to verify the splicing alterations. The phenotypes of the patients were also analyzed. Fourteen suspected pathogenic variants, including nine synonymous and five intronic variants, were detected in 12 patients with clinically suspected WD. Among them, four synonymous variants (c.1050G>A, c.1122C>G, c.3243G>A, and c.4014T>A) and four intronic variants (c.1543 +40G>A, c.1707+6_1707+16del, c.1870-49A>G, and c.2731-67A>G) resulted in splicing changes in ATP7B. After the above analysis, the diagnosis of WD could be confirmed in eight clinically suspected patients with WD who showed a late age of onset.

A Novel Synonymous Variant of PHEX in a Patient with X-Linked Hypophosphatemia

X-linked dominant hypophosphatemia (XLH), the most common form of hereditary hypophosphatemic rickets/osteomalacia, is caused by loss-of-function phosphate-regulating endopeptidase homolog X-linked gene (PHEX) variants. However, synonymous PHEX variants are rare in XLH. We report a 7-year-old boy with hypophosphatemia, short stature, and lower limb deformity. Whole-exome sequencing, reverse transcription-polymerase chain reaction, and Sanger sequencing were performed to identify the pathogenicity of the variant. A novel synonymous PHEX variant (NM_000444.4:c.1530 C&gt;T, p.Arg510Arg) was detected in the proband. Further analysis revealed a 58-bp deletion at the 5' site of exon 14 during splicing. This study extends the genetic spectrum of XLH and confirms the rarity and significance of synonymous PHEX variants.

A catalog of the genetic causes of hereditary angioedema in the Canary Islands (Spain)

Hereditary angioedema (HAE) is a rare disease where known causes involve C1 inhibitor dysfunction or dysregulation of the kinin cascade. The updated HAE management guidelines recommend performing genetic tests to reach a precise diagnosis. Unfortunately, genetic tests are still uncommon in the diagnosis routine. Here, we characterized for the first time the genetic causes of HAE in affected families from the Canary Islands (Spain). Whole-exome sequencing data was obtained from 41 affected patients and unaffected relatives from 29 unrelated families identified in the archipelago. The Hereditary Angioedema Database Annotation (HADA) tool was used for pathogenicity classification and causal variant prioritization among the genes known to cause HAE. Manual reclassification of prioritized variants was used in those families lacking known causal variants. We detected a total of eight different variants causing HAE in this patient series, affecting essentially SERPING1 and F12 genes, one of them being a novel SERPING1 variant (c.686-12A&gt;G) with a predicted splicing effect which was reclassified as likely pathogenic in one family. Altogether, the diagnostic yield by assessing previously reported causal genes and considering variant reclassifications according to the American College of Medical Genetics guidelines reached 66.7% (95% Confidence Interval [CI]: 30.1-91.0) in families with more than one affected member and 10.0% (95% CI: 1.8-33.1) among cases without family information for the disease. Despite the genetic causes of many patients remain to be identified, our results reinforce the need of genetic tests as first-tier diagnostic tool in this disease, as recommended by the international WAO/EAACI guidelines for the management of HAE.

Performance evaluation of differential splicing analysis methods and splicing analytics platform construction

A proportion of previously defined benign variants or variants of uncertain significance in humans, which are challenging to identify, may induce an abnormal splicing process. An increasing number of methods have been developed to predict splicing variants, but their performance has not been completely evaluated using independent benchmarks. Here, we manually sourced ∼50 000 positive/negative splicing variants from > 8000 studies and selected the independent splicing variants to evaluate the performance of prediction methods. These methods showed different performances in recognizing splicing variants in donor and acceptor regions, reminiscent of different weight coefficient applications to predict novel splicing variants. Of these methods, 66.67% exhibited higher specificities than sensitivities, suggesting that more moderate cut-off values are necessary to distinguish splicing variants. Moreover, the high correlation and consistent prediction ratio validated the feasibility of integration of the splicing prediction method in identifying splicing variants. We developed a splicing analytics platform called SPCards, which curates splicing variants from publications and predicts splicing scores of variants in genomes. SPCards also offers variant-level and gene-level annotation information, including allele frequency, non-synonymous prediction and comprehensive functional information. SPCards is suitable for high-throughput genetic identification of splicing variants, particularly those located in non-canonical splicing regions.

Genome interpretation using in silico predictors of variant impact

Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.

Inferring Potential Cancer Driving Synonymous Variants

Synonymous single nucleotide variants (sSNVs) are often considered functionally silent, but a few cases of cancer-causing sSNVs have been reported. From available databases, we collected four categories of sSNVs: germline, somatic in normal tissues, somatic in cancerous tissues, and putative cancer drivers. We found that screening sSNVs for recurrence among patients, conservation of the affected genomic position, and synVep prediction (synVep is a machine learning-based sSNV effect predictor) recovers cancer driver variants (termed proposed drivers) and previously unknown putative cancer genes. Of the 2.9 million somatic sSNVs found in the COSMIC database, we identified 2111 proposed cancer driver sSNVs. Of these, 326 sSNVs could be further tagged for possible RNA splicing effects, RNA structural changes, and affected RBP motifs. This list of proposed cancer driver sSNVs provides computational guidance in prioritizing the experimental evaluation of synonymous mutations found in cancers. Furthermore, our list of novel potential cancer genes, galvanized by synonymous mutations, may highlight yet unexplored cancer mechanisms.