Meta-analysis of genomic variants in power and endurance sports to decode the impact of genomics on athletic performance and success

Association between genomic variants and athletic performance has seen a high degree of controversy, as there is often conflicting data as far as the association of genomic variants with endurance, speed and strength is concerned. Here, findings from a thorough meta-analysis from 4228 articles exploring the association of genomic variants with athletic performance in power and endurance sports are summarized, aiming to confirm or overrule the association of genetic variants with athletic performance of all types. From the 4228 articles, only 107 were eligible for further analysis, including 37 different genes. From these, there were 21 articles for the ACE gene, 29 articles for the ACTN3 gene and 8 articles for both the ACE and ACTN3 genes, including 54,382 subjects in total, from which 11,501 were endurance and power athletes and 42,881 control subjects. These data show that there is no statistically significant association between genomic variants and athletic performance either for endurance or power sports, underlying the fact that it is highly risky and even unethical to make such genetic testing services for athletic performance available to the general public. Overall, a strict regulatory monitoring should be exercised by health and other legislative authorities to protect the public from such services from an emerging discipline that still lacks the necessary scientific evidence and subsequent regulatory approval.

Systematic SARS-CoV-2 S-gene sequencing in wastewater samples enables early lineage detection and uncovers rare mutations in Portugal

As the COVID-19 pandemic reached its peak, many countries implemented genomic surveillance systems to track the evolution and transmission of SARS-CoV-2. Transition from the pandemic to the endemic phase prioritized alternative testing strategies to maintain effective epidemic surveillance at the population level, with less intensive sequencing efforts. One such promising approach was Wastewater-Based Surveillance (WBS), which offers non-invasive, cost-effective means for analysing virus trends at the sewershed level. From 2020 onwards, wastewater has been recognized as an instrumental source of information for public health, with national and international authorities exploring options to implement national wastewater surveillance systems and increasingly relying on WBS as early warning of potential pathogen outbreaks. In Portugal, several pioneer projects joined the academia, water utilities and Public Administration around WBS. To validate WBS as an effective genomic surveillance strategy, it is crucial to collect long term performance data. In this work, we present one year of systematic SARS-CoV-2 wastewater surveillance in Portugal, representing 35 % of the mainland population. We employed two complementary methods for lineage determination - allelic discrimination by RT-PCR and S-gene sequencing. This combination allowed us to monitor variant evolution in near-real-time and identify low-frequency mutations. Over the course of this year-long study, spanning from May 2022 to April 2023, we successfully tracked the dominant Omicron sub-lineages, their progression and evolution, which aligned with concurrent clinical surveillance data. Our results underscore the effectiveness of WBS as a tracking system for virus variants, with the ability to unveil mutations undetected via massive sequencing of clinical samples from Portugal, demonstrating the ability of WBS to uncover new mutations and detect rare genetic variants. Our findings emphasize that knowledge of the genetic diversity of SARS-CoV-2 at the population level can be extended far beyond via the combination of routine clinical genomic surveillance with wastewater sequencing and genotyping.

Genomic insight into variations associated with flowering-time and early-maturity in pigeonpea mutant TAT-10 and its wild type parent T21

Pigeonpea [Cajanus cajan (L.) Millspaugh] is an important grain legume crop with a broad range of 90 to 300 days for maturity. To identify the genomic variations associated with the early maturity, we conducted whole-genome resequencing of an early-maturing pigeonpea mutant TAT-10 and its wild type parent T21. A total of 135.67 and 146.34 million sequencing reads were generated for T21 and TAT-10, respectively. From this resequencing data, 1,397,178 and 1,419,904 SNPs, 276,741 and 292,347 InDels, and 87,583 and 92,903 SVs were identified in T21 and TAT-10, respectively. We identified 203 genes in the pigeonpea genome that are homologs of flowering-related genes in Arabidopsis and found 791 genomic variations unique to TAT-10 linked to 94 flowering-related genes. We identified three candidate genes for early maturity in TAT-10; Suppressor of FRI 4 (SUF4), Early Flowering In Short Days (EFS), and Probable Lysine-Specific Demethylase ELF6. The variations in ELF6 were predicted to be possibly damaging and the expression profiles of EFS and ELF6 also supported their probable role during early flowering in TAT-10. The present study has generated information on genomic variations associated with candidate genes for early maturity, which can be further studied and exploited for developing the early-maturing pigeonpea cultivars.

Clinical-genetic analysis of selected genes involved in the development of the human skeleton in 128 Czech patients with suspected congenital skeletal abnormalities

BACKGROUND

Congenital skeletal abnormalities are a heterogeneous group of diseases most commonly associated with small or disproportionate growth, cranial and facial dysmorphisms, delayed bone maturation, etc. Nonetheless, no detailed genotype-phenotype correlation in patients with specific genetic variants is readily available. Ergo, this study focuses on the analysis of patient phenotypes with candidate variants in genes involved in bone growth as detected by molecular genetic analysis.

METHODS

In this study we used molecular genetic methods to analyse the ACAN, COL2A1, FGFR3, IGFALS, IGF1, IGF1R, GHR, NPR2, STAT5B and SHOX genes in 128 Czech children with suspected congenital skeletal abnormalities. Pathogenic variants and variants of unclear clinical significance were identified and we compared their frequency in this study cohort to the European non-Finnish population. Furthermore, a prediction tool was utilised to determine their possible impact on the final protein. All clinical patient data was obtained during pre-test genetic counselling.

RESULTS

Pathogenic variants were identified in the FGFR3, GHR, COL2A1 and SHOX genes in a total of six patients. Furthermore, we identified 23 variants with unclear clinical significance and high allelic frequency in this cohort of patients with skeletal abnormalities. Five of them have not yet been reported in the scientific literature.

CONCLUSION

Congenital skeletal abnormalities may lead to a number of musculoskeletal, neurological, cardiovascular problems. Knowledge of specific pathogenic variants may help us in therapeutic procedures.

A study of genetic variants associated with skin traits in the Vietnamese population

BACKGROUND

Most skin-related traits have been studied in Caucasian genetic backgrounds. A comprehensive study on skin-associated genetic effects on underrepresented populations such as Vietnam is needed to fill the gaps in the field.

OBJECTIVES

We aimed to develop a computational pipeline to predict the effect of genetic factors on skin traits using public data (GWAS catalogs and whole-genome sequencing (WGS) data from the 1000 Genomes Project-1KGP) and in-house Vietnamese data (WGS and genotyping by SNP array). Also, we compared the genetic predispositions of 25 skin-related traits of Vietnamese population to others to acquire population-specific insights regarding skin health.

METHODS

Vietnamese cohorts of whole-genome sequencing (WGS) of 1008 healthy individuals for the reference and 96 genotyping samples (which do not have any skin cutaneous issues) by Infinium Asian Screening Array-24 v1.0 BeadChip were employed to predict skin-associated genetic variants of 25 skin-related and micronutrient requirement traits in population analysis and correlation analysis. Simultaneously, we compared the landscape of cutaneous issues of Vietnamese people with other populations by assessing their genetic profiles.

RESULTS

The skin-related genetic profile of Vietnamese cohorts was similar at most to East Asian cohorts (JPT: Fst = 0.036, CHB: Fst = 0.031, CHS: Fst = 0.027, CDX: Fst = 0.025) in the population study. In addition, we identified pairs of skin traits at high risk of frequent co-occurrence (such as skin aging and wrinkles (r = 0.45, p = 1.50e-5) or collagen degradation and moisturizing (r = 0.35, p = 1.1e-3)).

CONCLUSION

This is the first investigation in Vietnam to explore genetic variants of facial skin. These findings could improve inadequate skin-related genetic diversity in the currently published database.

An individualized Bayesian method for estimating genomic variants of hypertension

BACKGROUND

Genomic variants of the disease are often discovered nowadays through population-based genome-wide association studies (GWAS). Identifying genomic variations potentially underlying a phenotype, such as hypertension, in an individual is important for designing personalized treatment; however, population-level models, such as GWAS, may not capture all the important, individualized factors well. In addition, GWAS typically requires a large sample size to detect the association of low-frequency genomic variants with sufficient power. Here, we report an individualized Bayesian inference (IBI) algorithm for estimating the genomic variants that influence complex traits, such as hypertension, at the level of an individual (e.g., a patient). By modeling at the level of the individual, IBI seeks to find genomic variants observed in the individual's genome that provide a strong explanation of the phenotype observed in this individual.

RESULTS

We applied the IBI algorithm to the data from the Framingham Heart Study to explore the genomic influences of hypertension. Among the top-ranking variants identified by IBI and GWAS, there is a significant number of shared variants (intersection); the unique variants identified only by IBI tend to have relatively lower minor allele frequency than those identified by GWAS. In addition, IBI discovered more individualized and diverse variants that explain hypertension patients better than GWAS. Furthermore, IBI found several well-known low-frequency variants as well as genes related to blood pressure that GWAS missed in the same cohort. Finally, IBI identified top-ranked variants that predicted hypertension better than GWAS, according to the area under the ROC curve.

CONCLUSIONS

The results support IBI as a promising approach for complementing GWAS, especially in detecting low-frequency genomic variants as well as learning personalized genomic variants of clinical traits and disease, such as the complex trait of hypertension, to help advance precision medicine.

Evaluation of two Massive Open Online Courses (MOOCs) in genomic variant interpretation for the NHS workforce

BACKGROUND

The implementation of the National Genomic Medicine Service in the UK has increased patient access to germline genomic testing. Increased testing leads to more genetic diagnoses but does result in the identification of genomic variants of uncertain significance (VUS). The rigorous process of interpreting these variants requires multi-disciplinary, highly trained healthcare professionals (HCPs). To meet this training need, we designed two Massive Open Online Courses (MOOCs) for HCPs involved in germline genomic testing pathways: Fundamental Principles (FP) and Inherited Cancer Susceptibility (ICS).

METHODS

An evaluation cohort of HCPs involved in genomic testing were recruited, with additional data also available from anonymous self-registered learners to both MOOCs. Pre- and post-course surveys and in-course quizzes were used to assess learner satisfaction, confidence and knowledge gained in variant interpretation. In addition, granular feedback was collected on the complexity of the MOOCs to iteratively improve the resources.

RESULTS

A cohort of 92 genomics HCPs, including clinical scientists, and non-genomics clinicians (clinicians working in specialties outside of genomics) participated in the evaluation cohort. Between baseline and follow-up, total confidence scores improved by 38% (15.2/40.0) (95% confidence interval [CI] 12.4-18.0) for the FP MOOC and 54% (18.9/34.9) (95%CI 15.5-22.5) for the ICS MOOC (p < 0.0001 for both). Of those who completed the knowledge assessment through six summative variant classification quizzes (V1-6), a mean of 79% of respondents classified the variants such that correct clinical management would be undertaken (FP: V1 (73/90) 81% Likely Pathogenic/Pathogenic [LP/P]; V2 (55/78) 70% VUS; V3 (59/75) 79% LP/P; V4 (62/72) 86% LP/LP. ICS: V5 (66/91) 73% VUS; V6 (76/88) 86% LP/P). A non-statistically significant higher attrition rate was seen amongst the non-genomics workforce when compared to genomics specialists for both courses. More participants from the non-genomics workforce rated the material as "Too Complex" (FP n = 2/7 [29%], ICS n = 1/5 [20%]) when compared to the specialist genomics workforce (FP n = 1/43 [2%], ICS n = 0/35 [0%]).

CONCLUSIONS

After completing one or both MOOCs, self-reported confidence in genomic variant interpretation significantly increased, and most respondents could correctly classify variants such that appropriate clinical management would be instigated. Genomics HCPs reported higher satisfaction with the level of content than the non-genomics clinicians. The MOOCs provided foundational knowledge and improved learner confidence, but should be adapted for different workforces to maximise the benefit for clinicians working in specialties outside of genetics.

Investigation of susceptibility genes for chickenpox disease across multiple continents

Chickenpox (varicella) is caused by infection with the varicella-zoster virus (VZV), a neurotropic alpha herpes virus with a double-stranded DNA genome. Chickenpox can cause life-threatening complications, including subsequent bacterial infections, central nervous system symptoms, and even death without any risk factors. Few studies have been reported to investigate genetic susceptibility implicated in chickenpox. Herein, our study identified global genetic variants that potentially contributed to chickenpox susceptibility by utilizing the established bioinformatic-based approach. We integrated several databases, such as genome-wide association studies (GWAS) catalog, GTEx portal, HaploReg version 4.1, and Ensembl databases analyses to investigate susceptibility genes associated with chickenpox. Notably, increased expression of HLA-S, HCG4P5, and ABHD16A genes underlie enhanced chickenpox susceptibility in the European, American, and African populations. As compared to the Asian population, Europeans, Americans, and Africans have higher allele frequencies of the extant variants rs9266089, rs10947050, and rs79501286 from the susceptibility genes. Our study suggested that these susceptibility genes and associated genetic variants might play a critical role in chickenpox progression based on host genetics with clinical implications.

Integration of genomic variants and bioinformatic-based approach to drive drug repurposing for multiple sclerosis

Multiple sclerosis (MS) is a chronic autoimmune disease in the central nervous system (CNS) marked by inflammation, demyelination, and axonal loss. Currently available MS medication is limited, thereby calling for a strategy to accelerate new drug discovery. One of the strategies to discover new drugs is to utilize old drugs for new indications, an approach known as drug repurposing. Herein, we first identified 421 MS-associated SNPs from the Genome-Wide Association Study (GWAS) catalog (p-value < 5 × 10-8), and a total of 427 risk genes associated with MS using HaploReg version 4.1 under the criterion r 2  > 0.8. MS risk genes were then prioritized using bioinformatics analysis to identify biological MS risk genes. The prioritization was performed based on six defined categories of functional annotations, namely missense mutation, cis-expression quantitative trait locus (cis-eQTL), molecular pathway analysis, protein-protein interaction (PPI), genes overlap with knockout mouse phenotype, and primary immunodeficiency (PID). A total of 144 biological MS risk genes were found and mapped into 194 genes within an expanded PPI network. According to the DrugBank and the Therapeutic Target Database, 27 genes within the list targeted by 68 new candidate drugs were identified. Importantly, the power of our approach is confirmed with the identification of a known approved drug (dimethyl fumarate) for MS. Based on additional data from ClinicalTrials.gov, eight drugs targeting eight distinct genes are prioritized with clinical evidence for MS disease treatment. Notably, CD80 and CD86 pathways are promising targets for MS drug repurposing. Using in silico drug repurposing, we identified belatacept as a promising MS drug candidate. Overall, this study emphasized the integration of functional genomic variants and bioinformatic-based approach that reveal important biological insights for MS and drive drug repurposing efforts for the treatment of this devastating disease.

The use of genomic variants to drive drug repurposing for chronic hepatitis B

Background

One of the main challenges in personalized medicine is to establish and apply a large number of variants from genomic databases into clinical diagnostics and further facilitate genome-driven drug repurposing. By utilizing biological chronic hepatitis B infection (CHB) risk genes, our study proposed a systematic approach to use genomic variants to drive drug repurposing for CHB.

Method

The genomic variants were retrieved from the Genome-Wide Association Study (GWAS) and Phenome-Wide Association Study (PheWAS) databases. Then, the biological CHB risk genes crucial for CHB progression were prioritized based on the scoring system devised with five strict functional annotation criteria. A score of ≥ 2 were categorized as the biological CHB risk genes and further shed light on drug target genes for CHB treatments. Overlapping druggable targets were identified using two drug databases (DrugBank and Drug-Gene Interaction Database (DGIdb)).

Results

A total of 44 biological CHB risk genes were screened based on the scoring system from five functional annotation criteria. Interestingly, we found 6 druggable targets that overlapped with 18 drugs with status of undergoing clinical trials for CHB, and 9 druggable targets that overlapped with 20 drugs undergoing preclinical investigations for CHB. Eight druggable targets were identified, overlapping with 25 drugs that can potentially be repurposed for CHB. Notably, CD40 and HLA-DPB1 were identified as promising targets for CHB drug repurposing based on the target scores.

Conclusion

Through the integration of genomic variants and a bioinformatic approach, our findings suggested the plausibility of CHB genomic variant-driven drug repurposing for CHB.

Whole genome re-sequencing of indian wheat genotypes for identification of genomic variants for grain iron and zinc content

BACKGROUND

Whole-genome sequencing information which is of abundant significance for genetic evolution, and breeding of crops. Wheat (Triticum spp) is most widely grown and consumed crops globally. Micronutrients are very essential for healthy development of human being and their sufficient consumption in diet is essential for various metabolic functions. Biofortification of wheat grains with iron (Fe) and zinc (Zn) has proved the most reliable and effective way to combat micronutrient associated deficiency. Genetic variability for grain micronutrient could provide insight to dissect the traits.

METHODS AND RESULTS

In the current study, 1300 wheat lines were screened for grain Fe and Zn content, out of which only five important Indian wheat genotypes were selected on the basis of Fe and Zn contents. These lines were multiplied during at the National Agri-Food Biotechnology Institute (NABI) and re-sequenced to identify genomic variants in candidate genes for Fe and Zn between the genotypes. Whole genome sequencing generated ̴ 12 Gb clean data. Comparative genome analysis identified 254 genomic variants in the candidate genes associated with deleterious effect on protein function.

CONCLUSIONS

The present study demonstrated the fundamental in understanding the genomic variations for Fe and Zn enrichment to generate healthier wheat grains.

GeneTerpret: a customizable multilayer approach to genomic variant prioritization and interpretation

Background

Variant interpretation is the main bottleneck in medical genomic sequencing efforts. This usually involves genome analysts manually searching through a multitude of independent databases, often with the aid of several, mostly independent, computational tools. To streamline variant interpretation, we developed the GeneTerpret platform which collates data from current interpretation tools and databases, and applies a phenotype-driven query to categorize the variants identified in the genome(s). The platform assigns quantitative validity scores to genes by query and assembly of the genotype–phenotype data, sequence homology, molecular interactions, expression data, and animal models. It also uses the American College of Medical Genetics and Genomics (ACMG) criteria to categorize variants into five tiers of pathogenicity. The final output is a prioritized list of potentially causal variants/genes.

Results

We tested GeneTerpret by comparing its performance to expert-curated genes (ClinGen’s gene-validity database) and variant pathogenicity reports (DECIPHER database). Output from GeneTerpret was 97.2% and 83.5% concordant with the expert-curated sources, respectively. Additionally, similar concordance was observed when GeneTerpret’s performance was compared with our internal expert-interpreted clinical datasets.

Conclusions

GeneTerpret is a flexible platform designed to streamline the genome interpretation process, through a unique interface, with improved ease, speed and accuracy. This modular and customizable system allows the user to tailor the component-programs in the analysis process to their preference. GeneTerpret is available online at https://geneterpret.com.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01166-3.

Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes

Background

Genes implicated in tumorigenesis often exhibit diverse sets of genomic variants in the tumor cohorts within which they are frequently mutated. For many genes, neither the transcriptomic effects of these variants nor their relationship to one another in cancer processes have been well-characterized. We sought to identify the downstream expression effects of these mutations and to determine whether this heterogeneity at the genomic level is reflected in a corresponding heterogeneity at the transcriptomic level.

Results

By applying a novel hierarchical framework for organizing the mutations present in a cohort along with machine learning pipelines trained on samples’ expression profiles we systematically interrogated the signatures associated with combinations of mutations recurrent in cancer. This allowed us to catalogue the mutations with discernible downstream expression effects across a number of tumor cohorts as well as to uncover and characterize over a hundred cases where subsets of a gene’s mutations are clearly divergent in their function from the remaining mutations of the gene. These findings successfully replicated across a number of disease contexts and were found to have clear implications for the delineation of cancer processes and for clinical decisions.

Conclusions

The results of cataloguing the downstream effects of mutation subgroupings across cancer cohorts underline the importance of incorporating the diversity present within oncogenes in models designed to capture the downstream effects of their mutations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04147-y.

A comprehensive profile of genomic variations in the SARS-CoV-2 isolates from the state of Telangana, India

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing COVID-19 has rapidly turned into a pandemic, infecting millions and causing 1 157 509 (as of 27 October 2020) deaths across the globe. In addition to studying the mode of transmission and evasion of host immune system, analysing the viral mutational landscape constitutes an area under active research. The latter is expected to impart knowledge on the emergence of different clades, subclades, viral protein functions and protein-protein and protein-RNA interactions during replication/transcription cycle of virus and response to host immune checkpoints. In this study, we have attempted to bring forth the viral genomic variants defining the major clade(s) as identified from samples collected from the state of Telangana, India. We further report a comprehensive draft of all genomic variations (including unique mutations) present in SARS-CoV-2 strain in the state of Telangana. Our results reveal the presence of two mutually exclusive subgroups defined by specific variants within the dominant clade present in the population. This work attempts to bridge the critical gap regarding the genomic landscape and associate mutations in SARS-CoV-2 from a highly infected southern region of India, which was lacking to date.

Large-scale sequencing and comparative analysis of oenological Saccharomyces cerevisiae strains supported by nanopore refinement of key genomes

Saccharomyces cerevisiae has long been part of human activities related to the production of food and wine. The industrial demand for fermented beverages with well-defined and stable characteristics boosted the isolation and selection of strains conferring a distinctive aroma profile to the final product. To uncover variants characterizing oenological strains, the sequencing of 65 new S. cerevisiae isolates, and the comparison with other 503 publicly available genomes were performed. A hybrid approach based on short Illumina and long Oxford Nanopore reads allowed the in-depth investigation of eleven genomes and the identification of putative laterally transferred regions and structural variants. A comparative analysis between clusters of strains belonging to different datasets allowed the identification of novel relevant genetic features including single nucleotide polymorphisms, insertions and structural variants. Detection of oenological single nucleotide variants shed light on the existence of different levels of modulation for the mevalonate pathway relevant for the biosynthesis of aromatic compounds.

Exploring the genomic and proteomic variations of SARS-CoV-2 spike glycoprotein: A computational biology approach

The newly identified SARS-CoV-2 has now been reported from around 185 countries with more than a million confirmed human cases including more than 120,000 deaths. The genomes of SARS-COV-2 strains isolated from different parts of the world are now available and the unique features of constituent genes and proteins need to be explored to understand the biology of the virus. Spike glycoprotein is one of the major targets to be explored because of its role during the entry of coronaviruses into host cells. We analyzed 320 whole-genome sequences and 320 spike protein sequences of SARS-CoV-2 using multiple sequence alignment. In this study, 483 unique variations have been identified among the genomes of SARS-CoV-2 including 25 nonsynonymous mutations and one deletion in the spike (S) protein. Among the 26 variations detected in S, 12 variations were located at the N-terminal domain (NTD) and 6 variations at the receptor-binding domain (RBD) which might alter the interaction of S protein with the host receptor angiotensin-converting enzyme 2 (ACE2). Besides, 22 amino acid insertions were identified in the spike protein of SARS-CoV-2 in comparison with that of SARS-CoV. Phylogenetic analyses of spike protein revealed that Bat coronavirus have a close evolutionary relationship with circulating SARS-CoV-2. The genetic variation analysis data presented in this study can help a better understanding of SARS-CoV-2 pathogenesis. Based on results reported herein, potential inhibitors against S protein can be designed by considering these variations and their impact on protein structure.

Predicting the effect of variants on splicing using Convolutional Neural Networks

Mutations that cause an error in the splicing of a messenger RNA (mRNA) can lead to diseases in humans. Various computational models have been developed to recognize the sequence pattern of the splice sites. In recent studies, Convolutional Neural Network (CNN) architectures were shown to outperform other existing models in predicting the splice sites. However, an insufficient effort has been put into extending the CNN model to predict the effect of the genomic variants on the splicing of mRNAs. This study proposes a framework to elaborate on the utility of CNNs to assess the effect of splice variants on the identification of potential disease-causing variants that disrupt the RNA splicing process. Five models, including three CNN-based and two non-CNN machine learning based, were trained and compared using two existing splice site datasets, Genome Wide Human splice sites (GWH) and a dataset provided at the Deep Learning and Artificial Intelligence winter school 2018 (DLAI). The donor sites were also used to test on the HSplice tool to evaluate the predictive models. To improve the effectiveness of predictive models, two datasets were combined. The CNN model with four convolutional layers showed the best splice site prediction performance with an AUPRC of 93.4% and 88.8% for donor and acceptor sites, respectively. The effects of variants on splicing were estimated by applying the best model on variant data from the ClinVar database. Based on the estimation, the framework could effectively differentiate pathogenic variants from the benign variants (p = 5.9 × 10⁻⁷). These promising results support that the proposed framework could be applied in future genetic studies to identify disease causing loci involving the splicing mechanism. The datasets and Python scripts used in this study are available on the GitHub repository at https://github.com/smiile8888/rna-splice-sites-recognition.

Multiple assays in a real-time RT-PCR SARS-CoV-2 panel can mitigate the risk of loss of sensitivity by new genomic variants during the COVID-19 outbreak

•

Publicly available RT-PCR Panels detect SARS-CoV-2 targeting more than one genomic region.

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Genetic variability observed until week 21 is predicted to have no effect on panel sensitivity.

•

The QIAstat-Dx SARS-CoV-2 Panel remains highly sensitive despite the nucleotide variations.

•

Combination of multiple assays in RT-PCR SARS-CoV-2 panels mitigate possible sensitivity loss.

•

Genetic variability assessment is critical to monitor sensitivity and specificity of the assays.

Objectives

In this study, five SARS-CoV-2 PCR assay panels were evaluated against the accumulated genetic variability of the virus to assess the effect on sensitivity of the individual assays.

Design or methods

As of week 21, 2020, the complete set of available SARS-CoV-2 genomes from GISAID and GenBank databases were used in this study. SARS-CoV-2 primer sequences from publicly available panels (WHO, CDC, NMDC, and HKU) and QIAstat-Dx were included in the alignment, and accumulated genetic variability affecting any oligonucleotide annealing was annotated.

Results

A total of 11,627 (34.38%) genomes included single mutations affecting annealing of any PCR assay. Variations in 8,773 (25.94%) genomes were considered as high risk, whereas additional 2,854 (8.43%) genomes presented low frequent single mutations and were predicted to yield no impact on sensitivity. In case of the QIAstat-Dx SARS-CoV-2 Panel, 99.11% of the genomes matched with a 100% coverage all oligonucleotides, and critical variations were tested in vitro corroborating no loss of sensitivity.

Conclusions

This analysis stresses the importance of targeting more than one region in the viral genome for SARS-CoV-2 detection to mitigate the risk of loss of sensitivity due to the unknown mutation rate during this SARS-CoV-2 outbreak.

Clinical application of medical exome sequencing for prenatal diagnosis of fetal structural anomalies

OBJECTIVE

To evaluate the clinical application of medical exome sequencing (MES) for prenatal diagnosis of genetic diseases related to fetal structural anomalies detected by prenatal ultrasound examination.

STUDY DESIGN

A total of 105 fetuses with structural anomalies were negative results in both Quantitative fluorescent polymerase chain reaction (QF-PCR) and chromosomal microarray analysis (CMA). Then trio-based MES was further used for identifying the potential monogenic diseases in these fetuses. Coding regions and known pathogenic non-coding regions of over 4000 disease-related genes were interrogated, and variants were classified following the guidelines of American College of Medical Genetics (ACMG).

RESULTS

The 105 fetuses with structural anomalies were categorized into 12 phenotypic groups. A definitive diagnosis was achieved in 19% (20/105) of the cases, with the identification of 21 pathogenic or likely pathogenic variants in 14 genes. The proportion of patients with diagnostic genetic variants varied between the phenotypic groups, with the highest diagnostic yield in the cardiovascular abnormalities (44%), followed by the skeletal and limb abnormalities (38%) and brain structural abnormalities (25%). In addition, 12 fetuses were detected variants of unknown significance (VOUS), while the relevance of phenotypes and variants would further evaluated.

CONCLUSION

MES can identify the underlying genetic cause in fetal structural anomalies. It can further assist the management of pregnancy and genetic counseling. It was demonstrated the importance of translating prenatal MES into clinical practice.

Functional DNA methylation signatures for autism spectrum disorder genomic risk loci: 16p11.2 deletions and CHD8 variants

Background

Autism spectrum disorder (ASD) is a common and etiologically heterogeneous neurodevelopmental disorder. Although many genetic causes have been identified (> 200 ASD-risk genes), no single gene variant accounts for > 1% of all ASD cases. A role for epigenetic mechanisms in ASD etiology is supported by the fact that many ASD-risk genes function as epigenetic regulators and evidence that epigenetic dysregulation can interrupt normal brain development. Gene-specific DNAm profiles have been shown to assist in the interpretation of variants of unknown significance. Therefore, we investigated the epigenome in patients with ASD or two of the most common genomic variants conferring increased risk for ASD. Genome-wide DNA methylation (DNAm) was assessed using the Illumina Infinium HumanMethylation450 and MethylationEPIC arrays in blood from individuals with ASD of heterogeneous, undefined etiology (n = 52), and individuals with 16p11.2 deletions (16p11.2del, n = 9) or pathogenic variants in the chromatin modifier CHD8 (CHD8^+/−, n = 7).

Results

DNAm patterns did not clearly distinguish heterogeneous ASD cases from controls. However, the homogeneous genetically-defined 16p11.2del and CHD8^+/− subgroups each exhibited unique DNAm signatures that distinguished 16p11.2del or CHD8^+/− individuals from each other and from heterogeneous ASD and control groups with high sensitivity and specificity. These signatures also classified additional 16p11.2del (n = 9) and CHD8 (n = 13) variants as pathogenic or benign. Our findings that DNAm alterations in each signature target unique genes in relevant biological pathways including neural development support their functional relevance. Furthermore, genes identified in our CHD8^+/− DNAm signature in blood overlapped differentially expressed genes in CHD8^+/− human-induced pluripotent cell-derived neurons and cerebral organoids from independent studies.

Conclusions

DNAm signatures can provide clinical utility complementary to next-generation sequencing in the interpretation of variants of unknown significance. Our study constitutes a novel approach for ASD risk-associated molecular classification that elucidates the vital cross-talk between genetics and epigenetics in the etiology of ASD.

Electronic supplementary material

The online version of this article (10.1186/s13148-019-0684-3) contains supplementary material, which is available to authorized users.

EAGLE: Explicit Alternative Genome Likelihood Evaluator

Background

Reliable detection of genome variations, especially insertions and deletions (indels), from single sample DNA sequencing data remains challenging, partially due to the inherent uncertainty involved in aligning sequencing reads to the reference genome. In practice a variety of ad hoc quality filtering methods are employed to produce more reliable lists of putative variants, but the resulting lists typically still include numerous false positives. Thus it would be desirable to be able to rigorously evaluate the degree to which each putative variant is supported by the data. Unfortunately, users who wish to do this, e.g. for the purpose of prioritizing validation experiments, have been faced with limited options.

Results

Here we present EAGLE, a method for evaluating the degree to which sequencing data supports a given candidate genome variant. EAGLE incorporates candidate variants into explicit hypotheses about the individual’s genome, and then computes the probability of the observed data (the sequencing reads) under each hypothesis. In comparison with methods which rely heavily on a particular alignment of the reads to the reference genome, EAGLE readily accounts for uncertainties that may arise from multi-mapping or local misalignment and uses the entire length of each read. We compared the scores assigned by several well-known variant callers to EAGLE for the task of ranking true putative variants on both simulated data and real genome sequencing based benchmarks. For indels, EAGLE obtained marked improvement on simulated data and a whole genome sequencing benchmark, and modest but statistically significant improvement on an exome sequencing benchmark.

Conclusions

EAGLE ranked true variants higher than the scores reported by the callers and can used to improve specificity in variant calling. EAGLE is freely available at https://github.com/tony-kuo/eagle.

Electronic supplementary material

The online version of this article (10.1186/s12920-018-0342-1) contains supplementary material, which is available to authorized users.

PeachVar-DB: A Curated Collection of Genetic Variations for the Interactive Analysis of Peach Genome Data

Applying next-generation sequencing (NGS) technologies to species of agricultural interest has the potential to accelerate the understanding and exploration of genetic resources. The storage, availability and maintenance of huge quantities of NGS-generated data remains a major challenge. The PeachVar-DB portal, available at http://hpc-bioinformatics.cineca.it/peach, is an open-source catalog of genetic variants present in peach (Prunus persica L. Batsch) and wild-related species of Prunus genera, annotated from 146 samples publicly released on the Sequence Read Archive (SRA). We designed a user-friendly web-based interface of the database, providing search tools to retrieve single nucleotide polymorphism (SNP) and InDel variants, along with useful statistics and information. PeachVar-DB results are linked to the Genome Database for Rosaceae (GDR) and the Phytozome database to allow easy access to other external useful plant-oriented resources. In order to extend the genetic diversity covered by the PeachVar-DB further, and to allow increasingly powerful comparative analysis, we will progressively integrate newly released data.

Genomic variants in the FTO gene are associated with sporadic amyotrophic lateral sclerosis in Greek patients

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a devastating disease whose complex pathology has been associated with a strong genetic component in the context of both familial and sporadic disease. Herein, we adopted a next-generation sequencing approach to Greek patients suffering from sporadic ALS (together with their healthy counterparts) in order to explore further the genetic basis of sporadic ALS (sALS).

RESULTS

Whole-genome sequencing analysis of Greek sALS patients revealed a positive association between FTO and TBC1D1 gene variants and sALS. Further, linkage disequilibrium analyses were suggestive of a specific disease-associated haplotype for FTO gene variants. Genotyping for these variants was performed in Greek, Sardinian, and Turkish sALS patients. A lack of association between FTO and TBC1D1 variants and sALS in patients of Sardinian and Turkish descent may suggest a founder effect in the Greek population. FTO was found to be highly expressed in motor neurons, while in silico analyses predicted an impact on FTO and TBC1D1 mRNA splicing for the genomic variants in question.

CONCLUSIONS

To our knowledge, this is the first study to present a possible association between FTO gene variants and the genetic etiology of sALS. In addition, the next-generation sequencing-based genomics approach coupled with the two-step validation strategy described herein has the potential to be applied to other types of human complex genetic disorders in order to identify variants of clinical significance.

Efficient and secure outsourcing of genomic data storage

Background

Cloud computing is becoming the preferred solution for efficiently dealing with the increasing amount of genomic data. Yet, outsourcing storage and processing sensitive information, such as genomic data, comes with important concerns related to privacy and security. This calls for new sophisticated techniques that ensure data protection from untrusted cloud providers and that still enable researchers to obtain useful information.

Methods

We present a novel privacy-preserving algorithm for fully outsourcing the storage of large genomic data files to a public cloud and enabling researchers to efficiently search for variants of interest. In order to protect data and query confidentiality from possible leakage, our solution exploits optimal encoding for genomic variants and combines it with homomorphic encryption and private information retrieval. Our proposed algorithm is implemented in C++ and was evaluated on real data as part of the 2016 iDash Genome Privacy-Protection Challenge.

Results

Results show that our solution outperforms the state-of-the-art solutions and enables researchers to search over millions of encrypted variants in a few seconds.

Conclusions

As opposed to prior beliefs that sophisticated privacy-enhancing technologies (PETs) are unpractical for real operational settings, our solution demonstrates that, in the case of genomic data, PETs are very efficient enablers.

Genotypic distribution of single nucleotide polymorphisms in oral cancer: global scene

Globocan 2012 reports the global oral cancer incidence of 300,373 new oral cancer cases annually, contributing to 2.1 % of the world cancer burden. The major well-established risk factors for oral cancer include tobacco, betel/areca nut, alcohol and high-risk oncogenic human papilloma virus (HPV) 16/18. However, only 5-10 % of individuals with high-risk lifestyle develop oral cancer. Thus, genomic variants in individuals represented as single nucleotide polymorphisms (SNPs) influence susceptibility to oral cancer. With a view to understanding the role of genomic variants in oral cancer, we reviewed SNPs in case-control studies with a minimum of 100 cases and 100 controls. PubMed and HuGE navigator search engines were used to obtain data published from 1990 to 2015, which identified 67 articles investigating the role of SNPs in oral cancer. Single publications reported 93 SNPs in 55 genes, with 34 SNPs associated with a risk of oral cancer. Meta-analysis of data in multiple studies defined nine SNPs associated with a risk of oral cancer. The genes were associated with critical functions deregulated in cancers, including cell proliferation, immune function, inflammation, transcription, DNA repair and xenobiotic metabolism.

Novel genetic risk variants for pediatric celiac disease

BACKGROUND

Celiac disease is a complex chronic immune-mediated disorder of the small intestine. Today, the pathobiology of the disease is unclear, perplexing differential diagnosis, patient stratification, and decision-making in the clinic.

METHODS

Herein, we adopted a next-generation sequencing approach in a celiac disease trio of Greek descent to identify all genomic variants with the potential of celiac disease predisposition.

RESULTS

Analysis revealed six genomic variants of prime interest: SLC9A4 c.1919G>A, KIAA1109 c.2933T>C and c.4268_4269delCCinsTA, HoxB6 c.668C>A, HoxD12 c.418G>A, and NCK2 c.745_746delAAinsG, from which NCK2 c.745_746delAAinsG is novel. Data validation in pediatric celiac disease patients of Greek (n = 109) and Serbian (n = 73) descent and their healthy counterparts (n = 111 and n = 32, respectively) indicated that HoxD12 c.418G>A is more prevalent in celiac disease patients in the Serbian population (P < 0.01), while NCK2 c.745_746delAAinsG is less prevalent in celiac disease patients rather than healthy individuals of Greek descent (P = 0.03). SLC9A4 c.1919G>A and KIAA1109 c.2933T>C and c.4268_4269delCCinsTA were more abundant in patients; nevertheless, they failed to show statistical significance.

CONCLUSIONS

The next-generation sequencing-based family genomics approach described herein may serve as a paradigm towards the identification of novel functional variants with the aim of understanding complex disease pathobiology.

Possible role of intronic polymorphisms in the PHACTR1 gene on the development of cardiovascular disease

Cardiovascular disease (CVD) is a complex multifactorial and polygenetic disease in which the interaction of numerous genes, genetic variants, and environmental factors plays a major role in its development. In an attempt to demonstrate the association between certain genetic variants and CVD, researchers have run large genomic wild association studies (GWAS) in recent decades. These studies have correlated several genomic variants with the presence of CVD. Recently, certain polymorphisms in the phosphatase and actin regulator 1 (PHACTR1) gene have been shown to be associated with CVD (i.e., coronary artery disease, coronary artery calcification, early onset myocardial infarction, cervical artery dissection and hypertension) in different ethnic groups. It is important to state that all of the described PHACTR1 genetic variants associated with CVD are located in non-translating gene regions known as introns. Thus, the purpose of this article is to hypothesize the effect of certain intronic polymorphisms in the PHACTR1 gene on pathological processes in the cardiovascular system. In addition, we present compelling evidence that supports this hypothesis as well as a methodology that could be used to assess the allelic effect using in vitro and in vivo models, which will ultimately demonstrate the pathophysiological contribution of PHACTR1 intronic polymorphisms to the development of CVD.

Genomic variants and variations in malformations of cortical development

Malformations of cortical development (MCDs) are a common cause of neurodevelopmental delay and epilepsy and are caused by disruptions in the normal development of the cerebral cortex. Several causative genes have been identified in patients with MCD. There is increasing evidence of role of de novo mutations, including those occurring post fertilization, in MCD. These somatic mutations may not be detectable by traditional methods of genetic testing performed on blood DNA. Identification of the genetic cause can help in guiding families in future pregnancies. Research has highlighted how elucidation of key molecular pathways can also allow for targeted therapeutic interventions.