scholarly journals Genetic and pharmacological causes of germline hypermutation

2021 ◽  
Author(s):  
Joanna Kaplanis ◽  
Benjamin Ide ◽  
Rashesh Sanghvi ◽  
Matthew Neville ◽  
Petr Danecek ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Genetic Disease ◽  
De Novo ◽  
Genetic Diseases ◽  
Chemotherapeutic Agents ◽  
Single Nucleotide Variants ◽  
Mutational Signatures ◽  
Rare Genetic Diseases ◽  
Primary Determinant ◽  
Clinical Records

Mutation in the germline is the source of all evolutionary genetic variation and a cause of genetic disease. Previous studies have shown parental age to be the primary determinant of the number of new germline mutations seen in an individual's genome. Here we analysed the genome-wide sequences of 21,879 families with rare genetic diseases and identified 12 hypermutated individuals with between two and seven times more de novo single nucleotide variants (dnSNVs) than expected. In most of these families (9/12) the excess mutations could be attributed to the father. We determined that two of these families had genetic drivers of germline hypermutation, with the fathers carrying damaging genetic variation in known DNA repair genes, causing distinctive mutational signatures. For five families, by analysing clinical records and mutational signatures, we determined that paternal exposure to chemotherapeutic agents prior to conception was a key driver of hypermutation. Our results suggest that the germline is well protected from mutagenic effects, hypermutation is rare and relatively modest in degree and that most hypermutated individuals will not have a genetic disease.

scholarly journals Parameters tuning boosts hyperSMURF predictions of rare deleterious non-coding genetic variants

2017 ◽  
Author(s):  
Alessandro Petrini ◽  
Max Schubach ◽  
Matteo Re ◽  
Marco Frasca ◽  
Marco Mesiti ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Genetic Variants ◽  
Regulatory Element ◽  
Genetic Diseases ◽  
Large Majority ◽  
Single Nucleotide Variants ◽  
Mendelian Diseases ◽  
Parameters Tuning ◽  
Rare Genetic Diseases ◽  
Coding Variants

The regulatory code that determines whether and how a given genetic variant affects the function of a regulatory element remains poorly understood for most classes of regulatory variation. Indeed the large majority of bioinformatics tools have been developed to predict the pathogenicity of genetic variants in coding sequences or conserved splice sites. Computational algorithms for the prediction of non-coding deleterious variants associated with rare genetic diseases are faced with special challenges owing to the rarity of confirmed pathogenic mutations. Indeed in this context classical machine learning methods are biased toward neutral variants that constitute the large majority of genetic variation, and are not able to detect the potential deleterious variants that constitute only a tiny minority of all known genetic variation. We recently proposed hyperSMURF, hyper-ensemble of SMOTE Undersampled Random Forests, an ensemble approach explicitly designed to deal with the huge imbalance between deleterious and neutral variants, and able to significantly outperform state-of-the-art methods for the prediction of non-coding variants associated with Mendelian diseases. Despite its successful application to the detection of deleterious single nucleotide variants (SNV) as well as to small insertions or deletions (indels), hyperSMURF is a method that depends on several learning parameters, that strongly influence its overall performances. In this work we experimentally show that by tuning hyperSMURF parameters we can significantly boost the performance of the method, thus predicting with significantly better precision and recall rare SNVs associated with Mendelian diseases.

scholarly journals Parameters tuning boosts hyperSMURF predictions of rare deleterious non-coding genetic variants

2017 ◽  
Author(s):  
Alessandro Petrini ◽  
Max Schubach ◽  
Matteo Re ◽  
Marco Frasca ◽  
Marco Mesiti ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Genetic Variants ◽  
Regulatory Element ◽  
Genetic Diseases ◽  
Large Majority ◽  
Single Nucleotide Variants ◽  
Mendelian Diseases ◽  
Parameters Tuning ◽  
Rare Genetic Diseases ◽  
Coding Variants

The regulatory code that determines whether and how a given genetic variant affects the function of a regulatory element remains poorly understood for most classes of regulatory variation. Indeed the large majority of bioinformatics tools have been developed to predict the pathogenicity of genetic variants in coding sequences or conserved splice sites. Computational algorithms for the prediction of non-coding deleterious variants associated with rare genetic diseases are faced with special challenges owing to the rarity of confirmed pathogenic mutations. Indeed in this context classical machine learning methods are biased toward neutral variants that constitute the large majority of genetic variation, and are not able to detect the potential deleterious variants that constitute only a tiny minority of all known genetic variation. We recently proposed hyperSMURF, hyper-ensemble of SMOTE Undersampled Random Forests, an ensemble approach explicitly designed to deal with the huge imbalance between deleterious and neutral variants, and able to significantly outperform state-of-the-art methods for the prediction of non-coding variants associated with Mendelian diseases. Despite its successful application to the detection of deleterious single nucleotide variants (SNV) as well as to small insertions or deletions (indels), hyperSMURF is a method that depends on several learning parameters, that strongly influence its overall performances. In this work we experimentally show that by tuning hyperSMURF parameters we can significantly boost the performance of the method, thus predicting with significantly better precision and recall rare SNVs associated with Mendelian diseases.

scholarly journals New Diagnostic Approaches for Undiagnosed Rare Genetic Diseases

2020 ◽  
Vol 21 (1) ◽  
pp. 351-372 ◽  
Author(s):  
Taila Hartley ◽  
Gabrielle Lemire ◽  
Kristin D. Kernohan ◽  
Heather E. Howley ◽  
David R. Adams ◽  
...  
Keyword(s):  
Molecular Diagnosis ◽  
Genetic Disease ◽  
New Technologies ◽  
Genetic Diseases ◽  
Well Being ◽  
Accurate Diagnosis ◽  
Diagnostic Odyssey ◽  
Rare Genetic Disease ◽  
Rare Genetic Diseases ◽  
Diagnostic Approaches

Accurate diagnosis is the cornerstone of medicine; it is essential for informed care and promoting patient and family well-being. However, families with a rare genetic disease (RGD) often spend more than five years on a diagnostic odyssey of specialist visits and invasive testing that is lengthy, costly, and often futile, as 50% of patients do not receive a molecular diagnosis. The current diagnostic paradigm is not well designed for RGDs, especially for patients who remain undiagnosed after the initial set of investigations, and thus requires an expansion of approaches in the clinic. Leveraging opportunities to participate in research programs that utilize new technologies to understand RGDs is an important path forward for patients seeking a diagnosis. Given recent advancements in such technologies and international initiatives, the prospect of identifying a molecular diagnosis for all patients with RGDs has never been so attainable, but achieving this goal will require global cooperation at an unprecedented scale.

scholarly journals Reference exome data for a Northern Brazilian population

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Alexia L. Weeks ◽  
Richard W. Francis ◽  
Joao I. C. F. Neri ◽  
Nathaly M. C. Costa ◽  
Nivea M. R. Arrais ◽  
...  
Keyword(s):  
Rare Diseases ◽  
Sequence Data ◽  
Genetic Diseases ◽  
Specific Reference ◽  
Single Nucleotide Variants ◽  
Data Set ◽  
Single Nucleotide ◽  
Rare Genetic Diseases ◽  
Genetics And Genomics ◽  
Brazilian Cohort

Abstract Exome sequencing is widely used in the diagnosis of rare genetic diseases and provides useful variant data for analysis of complex diseases. There is not always adequate population-specific reference data to assist in assigning a diagnostic variant to a specific clinical condition. Here we provide a catalogue of variants called after sequencing the exomes of 45 babies from Rio Grande do Nord in Brazil. Sequence data were processed using an ‘intersect-then-combine’ (ITC) approach, using GATK and SAMtools to call variants. A total of 612,761 variants were identified in at least one individual in this Brazilian Cohort, including 559,448 single nucleotide variants (SNVs) and 53,313 insertion/deletions. Of these, 58,111 overlapped with nonsynonymous (nsSNVs) or splice site (ssSNVs) SNVs in dbNSFP. As an aid to clinical diagnosis of rare diseases, we used the American College of Medicine Genetics and Genomics (ACMG) guidelines to assign pathogenic/likely pathogenic status to 185 (0.32%) of the 58,111 nsSNVs and ssSNVs. Our data set provides a useful reference point for diagnosis of rare diseases in Brazil. (169 words).

scholarly journals Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases.

2021 ◽  
Author(s):  
Francisco M. De La Vega ◽  
Shimul Chowdhury ◽  
Barry Moore ◽  
Erwin Frise ◽  
Jeanette McCarthy ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Genetic Variants ◽  
Genetic Disease ◽  
Genetic Diseases ◽  
Disease Diagnosis ◽  
Case Review ◽  
Support Tool ◽  
Rare Genetic Diseases ◽  
New Findings

Clinical interpretation of genetic variants in the context of the patient's phenotype is becoming the largest component of cost and time expenditure for genome-based diagnosis of rare genetic diseases. Artificial intelligence (AI) holds promise to greatly simplify and speed interpretation by comprehensively evaluating genetic variants for pathogenicity in the context of the growing knowledge of genetic disease. We assess the diagnostic performance of GEM, a new, AI-based, clinical decision support tool, compared with expert manual interpretation. We benchmarked GEM in a retrospective cohort of 119 probands, mostly NICU infants, diagnosed with rare genetic diseases, who received whole genome sequencing (WGS) at Rady Children's Hospital. We also performed a replication study in a separate cohort of 60 cases diagnosed at five additional academic medical centers. For comparison, we also analyzed these cases with commonly used variant prioritization tools (Phevor, Exomiser, and VAAST). Included in the comparisons were WGS and whole exome sequencing (WES) as trios, duos, and singletons. Variants underpinning diagnoses spanned diverse modes of inheritance and types, including structural variants (SVs). Patient phenotypes were extracted either manually or by automated clinical natural language processing (CNLP) from clinical notes. Finally, 14 previously unsolved cases were re-analyzed. GEM ranked >90% of causal genes among the top or second candidate, using manually curated or CNLP derived phenotypes, and prioritized a median of 3 genes for review per case. Ranking of trios and duos was unchanged when analyzed as singletons. In 17 of 20 cases with diagnostic SVs, GEM identified the causal SVs as the top or second candidate irrespective of whether SV calls where provided or inferred ab initio by GEM when absent. Analysis of 14 previously unsolved cases provided novel findings in one, candidates ultimately not advanced in 3, and no new findings in 10, demonstrating the utility of GEM for reanalysis. GEM enables automated diagnostic interpretation of WES and WGS for all types of variants, including SVs, nominating a very short list of candidate genes and disorders for final review and reporting. In combination with deep phenotyping by CNLP, GEM enables substantial automation of genetic disease diagnosis, potentially decreasing the cost and speeding case review.

scholarly journals Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Francisco M. De La Vega ◽  
Shimul Chowdhury ◽  
Barry Moore ◽  
Erwin Frise ◽  
Jeanette McCarthy ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Candidate Genes ◽  
Language Processing ◽  
Genetic Disease ◽  
Genetic Diseases ◽  
Disease Diagnosis ◽  
Case Review ◽  
Genome Interpretation ◽  
Rare Genetic Diseases ◽  
New Findings

Abstract Background Clinical interpretation of genetic variants in the context of the patient’s phenotype is becoming the largest component of cost and time expenditure for genome-based diagnosis of rare genetic diseases. Artificial intelligence (AI) holds promise to greatly simplify and speed genome interpretation by integrating predictive methods with the growing knowledge of genetic disease. Here we assess the diagnostic performance of Fabric GEM, a new, AI-based, clinical decision support tool for expediting genome interpretation. Methods We benchmarked GEM in a retrospective cohort of 119 probands, mostly NICU infants, diagnosed with rare genetic diseases, who received whole-genome or whole-exome sequencing (WGS, WES). We replicated our analyses in a separate cohort of 60 cases collected from five academic medical centers. For comparison, we also analyzed these cases with current state-of-the-art variant prioritization tools. Included in the comparisons were trio, duo, and singleton cases. Variants underpinning diagnoses spanned diverse modes of inheritance and types, including structural variants (SVs). Patient phenotypes were extracted from clinical notes by two means: manually and using an automated clinical natural language processing (CNLP) tool. Finally, 14 previously unsolved cases were reanalyzed. Results GEM ranked over 90% of the causal genes among the top or second candidate and prioritized for review a median of 3 candidate genes per case, using either manually curated or CNLP-derived phenotype descriptions. Ranking of trios and duos was unchanged when analyzed as singletons. In 17 of 20 cases with diagnostic SVs, GEM identified the causal SVs as the top candidate and in 19/20 within the top five, irrespective of whether SV calls were provided or inferred ab initio by GEM using its own internal SV detection algorithm. GEM showed similar performance in absence of parental genotypes. Analysis of 14 previously unsolved cases resulted in a novel finding for one case, candidates ultimately not advanced upon manual review for 3 cases, and no new findings for 10 cases. Conclusions GEM enabled diagnostic interpretation inclusive of all variant types through automated nomination of a very short list of candidate genes and disorders for final review and reporting. In combination with deep phenotyping by CNLP, GEM enables substantial automation of genetic disease diagnosis, potentially decreasing cost and expediting case review.

scholarly journals Recurrence of de novo mutations in families

2017 ◽  
Author(s):  
Hákon Jónsson ◽  
Patrick Sulem ◽  
Gudny A. Arnadottir ◽  
Gunnar Pálsson ◽  
Hannes P. Eggertsson ◽  
...  
Keyword(s):  
Germ Cells ◽  
De Novo ◽  
Large Fraction ◽  
Genetic Diseases ◽  
De Novo Mutations ◽  
Sibling Pairs ◽  
Rare Genetic Diseases ◽  
Early Embryos ◽  
Parent Of Origin ◽  
Serious Disease

ABSTRACTDe novo mutations (DNMs) cause a large fraction of severe rare diseases of childhood. DNMs that occur in early embryos may result in mosaicism of both somatic and germ cells. Such early mutations may be transmitted to more than one offspring and cause recurrence of serious disease. We scanned 1,007 sibling pairs from 251 families and identified 885 DNMs shared by siblings (ssDNMs) at 451 genomic sites. We estimated the probability of DNM recurrence based on presence in the blood of the parent, sharing by other siblings, parent-of-origin, mutation type, and genomic position. We detected 52.1% of ssDNMs in the parental blood. The probability of a DNM being shared goes down by 2.28% per year for paternal DNMs and 1.82% for maternal DNMs. Shared paternal DNMs are more likely to be T>C mutations than maternal ones, but less likely to be C>T mutations. Depending on DNM properties, the probability of recurrence in a younger sibling ranges from 0.013% to 29.6%. We have launched an online DNM recurrence probability calculator, to use in genetic counselling in cases of rare genetic diseases.

scholarly journals Considerations for Home-Based Treatment of Fabry Disease in Poland during the COVID-19 Pandemic and Beyond

2021 ◽  
Vol 18 (16) ◽  
pp. 8242
Author(s):  
Michał Nowicki ◽  
Stanisława Bazan-Socha ◽  
Mariusz Kłopotowski ◽  
Beata Błażejewska-Hyżorek ◽  
Mariusz Kusztal ◽  
...  
Keyword(s):  
Fabry Disease ◽  
Genetic Diseases ◽  
Healthcare Providers ◽  
Treatment Programs ◽  
Current Therapy ◽  
Term Care ◽  
Pharmacological Chaperone ◽  
Enzyme Replacement ◽  
Home Based ◽  
Rare Genetic Diseases

Current therapy for Anderson–Fabry disease in Poland includes hospital or clinic-based intravenous enzyme replacement therapy with recombinant agalsidase alpha or beta, or oral pharmacological chaperone therapy with migalastat. Some countries around the world offer such treatment to patients in the comfort of their own homes. The 2020–2021 COVID-19 pandemic has pushed global healthcare providers to evolve their services so as to minimize the risk of COVID-19 exposure to both patients and providers; this has led to advances in telemedicine services and the increasing availability of at-home treatment for various procedures including parenteral drug administration. A total of 80% of surveyed Anderson–Fabry disease patients in Poland would prefer home-based treatment, which would be a safe and convenient alternative to clinic-based treatment if patient selection is based on our proposed algorithm. Our recommendations for home-based treatments appear feasible for the long term care of Anderson–Fabry disease patients during the COVID-19 pandemic and beyond. This may also serve as a basis for home-based treatment programs in other rare and ultra-rare genetic diseases.

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