Biology subjects

Whiffin, N.

Publications and source records attributed to Whiffin, N..

17 recordsLinked to original sources

bioRxiv2025

Modulating splicing in five prime untranslated regions to treat rare haploinsufficient disease

Rare genetic disorders collectively impact over 300 million people worldwide, yet around 95% have no specific treatments. For the many rare disorders caused by haploinsufficiency, effective therapies need to upregulate protein expression. However, therapeutic upregulation is often not straightforward. Increasing protein translation of the wildtype allele through inhibiting repressive upstream open reading frames (uORFs) has been proposed as a therapeutic approach for a few specific genes. The widespread success of steric-block antisense oligonucleotides (ASOs) for this purpose is, however, debated. Here, we explore an alternative approach, using splice-switching to exclude uORF-containing exons from the mRNA. Through a genome-wide computational screening approach, we identified 79 uORF-containing 5UTR exons in haploinsufficient monogenic disease genes as candidate exon skipping targets. We demonstrate that removing the target exon significantly increased protein translation (between 1.4-5.5 fold) in a luciferase reporter assay for four of six prioritised target 5UTR exons in neurodevelopmental disorder genes (CTCF, GRIN2B, KRIT1, and TSC1). Overall, this work supports the widespread application of 5UTR exon skipping to boost translation of clinically relevant haploinsufficient genes.

genomics↗

bioRxiv2025

Accurately modelling RNase H-mediated antisense oligonucleotide efficacy

Antisense oligonucleotides (ASOs) are a powerful class of drugs with the potential to treat a wide range of human diseases. However, the prediction of ASO efficacy remains challenging, as large-scale and costly experimental screens are typically required to identify optimal candidates for a specific therapeutic target. To address this challenge, we compiled ASO Atlas, a database comprising 188,521 RNase H-mediated ASO sequences targeting 334 unique genes with corresponding knockdown efficacy measurements extracted from published patents. Using ASO Atlas, we trained OligoAI, a deep learning model capable of jointly modelling RNA target context, ASO sequence, sugar and backbone chemistries, and dosage to predict in vitro efficacy. We experimentally validated OligoAI by targeting KCNT2, achieving a 5.72-fold reduction in screening effort compared to random selection. ASO Atlas provides the first systematic resource to rigorously evaluate hypotheses regarding key parameters in ASO design, including sequence composition, chemical modifications, and target region selection. Both ASO Atlas and OligoAI have been made freely accessible through an online web-tool with the aim of facilitating the accelerated optimisation of ASO design.

bioinformatics↗

bioRxiv2025

Aberrant splicing prediction during human organ development

Developmental disorders constitute a major class of genetic diseases, yet tools to identify splicing-disruptive variants during development are lacking. To address this need we extended the AbSplice framework to incorporate splicing dynamics across developmental stages. Moreover, we introduced several improvements including a refined ground truth from the aberrant splicing caller FRASER2, a continuous representation of splice site usage, and integration of the rich set of predictions from the sequence-based model Pangolin. These advances improve the performance of the original model at predicting aberrant splicing events and enable predictions across embryonic, childhood, and adult tissues. Genome-wide scores for all single-nucleotide variants and a web interface to score indels are available to facilitate the exploration of the predictions. Our genome-wide predictions reveal a class of variants with splicing-disruptive effects confined to early development, particularly abundant in the brain (>18,000 variants). These variants are enriched in loss-of-function intolerant genes and neurodevelopmental disorder genes. Within the rare-disease cohort Solve-RD high-impact brain-specific predictions are significantly enriched in individuals affected by neurodevelopmental disorders (NDD) in NDD-linked genes. Furthermore, the predicted developmental timing of the splicing disruption correlates with the clinical age of onset. Analysis of genomes of individuals with a suspected Mendelian disorder from Genomics England identified 26 unique variants in disease-linked genes, with stronger predicted effects during development than in adulthood, including a candidate new diagnosis in the gene FGFR1. Altogether, these results improve the accuracy of splice-disruptive variant prediction and provide tissue and developmental context to aid interpretation in rare disease diagnostics.

genomics↗

bioRxiv2023

Modulation of prion protein expression through cryptic splice site manipulation

Lowering expression of prion protein (PrP) is a well-validated therapeutic strategy in prion disease, but additional modalities are urgently needed. In other diseases, small molecules have proven capable of modulating pre-mRNA splicing, sometimes by forcing inclusion of cryptic exons that reduce gene expression. Here, we characterize a cryptic exon located in human PRNPs sole intron and evaluate its potential to reduce PrP expression through incorporation into the 5 untranslated region (5UTR). This exon is homologous to exon 2 in non-primate species, but contains a start codon that would yield an upstream open reading frame (uORF) with a stop codon prior to a splice site if included in PRNP mRNA, potentially downregulating PrP expression through translational repression or nonsense-mediated decay. We establish a minigene transfection system and test a panel of splice site alterations, identifying mutants that reduce PrP expression by as much as 78%. Our findings nominate a new therapeutic target for lowering PrP.

molecular biology↗

bioRxiv2023

Ribosome Decision Graphs for the Representation of Eukaryotic RNA Translation Complexity

The application of ribosome profiling has revealed an unexpected abundance of translation in addition to that responsible for the synthesis of previously annotated protein-coding regions. Multiple short sequences have been found to be translated within single RNA molecules, both within annotated protein-coding and non-coding regions. The biological significance of this translation is a matter of intensive investigation. However, current schematic or annotation-based representations of mRNA translation generally do not account for the apparent multitude of translated regions within the same molecules. They also do not take into account the stochasticity of the process that allows alternative translations of the same RNA molecules by different ribosomes. There is a need for formal representations of mRNA complexity that would enable the analysis of quantitative information on translation and more accurate models for predicting the phenotypic effects of genetic variants affecting translation. To address this, we developed a conceptually novel abstraction that we term Ribosome Decision Graphs (RDGs). RDGs represent translation as multiple ribosome paths through untranslated and translated mRNA segments. We termed the later translons. Non-deterministic events, such as initiation, re-initiation, selenocysteine insertion or ribosomal frameshifting are then represented as branching points. This representation allows for an adequate representation of eukaryotic translation complexity and focuses on locations critical for translation regulation. We show how RDGs can be used for depicting translated regions, analysis of genetic variation and quantitative genome-wide data on translation for characterisation of regulatory modulators of translation.

genomics↗

bioRxiv2023

Variant annotation across homologous proteins (Paralogue Annotation) identifies disease-causing missense variants with high precision, and is widely applicable across protein families

BackgroundDistinguishing pathogenic variants from those that are rare but benign remains a key challenge in clinical genetics, especially for variants not previously observed and characterised in humans. In vitro and in vivo functional characterisation are typically resource intensive, and model systems may not accurately predict influence on human disease. Many in silico tools have been developed to predict which variants are disease-causing, but typically lack precision. Here we demonstrate the applicability of a framework, called Paralogue Annotation, that draws on information from previously-characterised variants in homologous proteins to predict whether variants in a gene of interest are likely disease causing. MethodsWe assessed the performance of Paralogue Annotation through three orthogonal approaches: (1) comparison to established in silico variant prediction tools using 47,016 missense variants from ClinVar across 3,524 genes representing a broad range of diverse protein classes, by calculating precision and sensitivity; (2) evaluation against large-scale functional assays of variant effect; and (3) comparing odd ratios calculated from case-control association tests for inherited cardiac arrhythmia syndromes, and neurodevelopmental disorders with epilepsy, stratifying variants by Paralogue Annotation. ResultsParalogue Annotation correctly annotates 4,328 ClinVar pathogenic variants, with 245 false positives, yielding a precision of 0.95. This increases to 0.99 with more stringent annotation parameters (requiring greater conservation of amino acids between annotated orthologues) at the expense of sensitivity. Compared to established tools, Paralogue Annotation has higher precision for the identification of pathogenic variants, albeit with lower sensitivity across diverse test sets. Extending the technique by transferring annotations between homologous protein domains, rather than full-length protein paralogues, increases sensitivity. Rare variants predicted pathogenic by Paralogue Annotation were more strongly disease-associated (increased odds ratio) than unstratified rare variants for six out of eight genes tested with case-control cohort approaches. ConclusionsWe demonstrate that Paralogue Annotation has high precision for predicting pathogenic missense variants, providing a useful line of evidence for clinical variant interpretation that is complementary to other approaches in use. As the number of characterised variants increases in reference datasets such as ClinVar, Paralogue Annotation will further increase in sensitivity and applicability.

bioinformatics↗

bioRxiv2023

Differences in 5'untranslated regions highlight the importance of translational regulation of dosage sensitive genes

BackgroundUntranslated regions (UTRs) are important mediators of post-transcriptional regulation. The length of UTRs and the composition of regulatory elements within them are known to vary substantially across genes, but little is known about the reasons for this variation in humans. Here, we set out to determine whether this variation, specifically in 5UTRs, correlates with gene dosage sensitivity. ResultsWe investigated 5UTR length, the number of alternative transcription start sites, the potential for alternative splicing, the number and type of upstream open reading frames (uORFs) and the propensity of 5UTRs to form secondary structures. We explored how these elements vary by gene tolerance to loss-of-function (LoF; using the LOEUF metric), and in genes where changes in dosage are known to cause disease. We show that LOEUF correlates with 5UTR length and complexity. Genes that are most intolerant to LoF have longer 5UTRs (P<1x10-15), greater TSS diversity (P<1x 10-15), and more upstream regulatory elements than their LoF tolerant counterparts. We show that these differences are evident in disease gene-sets, but not in recessive developmental disorder genes where LoF of a single allele is tolerated. ConclusionsOur results confirm the importance of post-transcriptional regulation through 5UTRs in tight regulation of mRNA and protein levels, particularly for genes where changes in dosage are deleterious and lead to disease. Finally, to support gene-based investigation we release a web-based browser tool, VuTR (https://vutr.rarediseasegenomics.org/), that supports exploration of the composition of individual 5UTRs and the impact of genetic variation within them.

genomics↗

bioRxiv2020

Annotating high-impact 5'untranslated region variants with the UTRannotator

SummaryCurrent tools to annotate the predicted effect of genetic variants are heavily biased towards protein-coding sequence. Variants outside of these regions may have a large impact on protein expression and/or structure and can lead to disease, but this effect can be challenging to predict. Consequently, these variants are poorly annotated using standard tools. We have developed a plugin to the Ensembl Variant Effect Predictor, the UTRannotator, that annotates variants in 5untranslated regions (5UTR) that create or disrupt upstream open reading frames (uORFs). We investigate the utility of this tool using the ClinVar database, providing an annotation for 30.8% of all 5UTR (likely) pathogenic variants, and highlighting 31 variants of uncertain significance as candidates for further follow-up. We will continue to update the UTR annotator as we gain new knowledge on the impact of variants in UTRs. Availability and implementationUTRannotator is freely available on Github: https://github.com/ImperialCardioGenetics/UTRannotator Supplementary informationSupplementary data are available at bioRxiv.

bioinformatics↗

bioRxiv2020

Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions

BackgroundAccurate discrimination of benign and pathogenic rare variation remains a priority for clinical genome interpretation. State-of-the-art machine learning tools are useful for genome-wide variant prioritisation but remain imprecise. Since the relationship between molecular consequence and likelihood of pathogenicity varies between genes with distinct molecular mechanisms, we hypothesised that a disease-specific classifier may outperform existing genome-wide tools. MethodsWe present a novel disease-specific variant classification tool, CardioBoost, that estimates the probability of pathogenicity for rare missense variants in inherited cardiomyopathies and arrhythmias, trained with variants of known clinical effect. To benchmark against state-of-the-art genome-wide pathogenicity classification tools, we assessed classification of hold-out test variants using both overall performance metrics, and metrics of high-confidence (>90%) classifications relevant to variant interpretation. We further evaluated the prioritisation of variants associated with disease and patient clinical outcomes, providing validations that are robust to potential mis-classification in gold-standard reference datasets. ResultsCardioBoost has higher discriminating power than published genome-wide variant classification tools in distinguishing between pathogenic and benign variants based on overall classification performance measures with the highest area under the Precision-Recall Curve as 91% for cardiomyopathies and as 96% for inherited arrhythmias. When assessed at high-confidence (>90%) classification thresholds, prediction accuracy is improved by at least 120% over existing tools for both cardiomyopathies and arrhythmias, with significantly improved sensitivity and specificity. Finally, CardioBoost improves prioritisation of variants significantly associated with disease, and stratifies survival of patients with cardiomyopathies, confirming biologically relevant variant classification. ConclusionsWe demonstrate that a disease-specific variant pathogenicity prediction tool outperforms state-of-the-art genome-wide tools for the classification of rare missense variants of uncertain significance for inherited cardiac conditions. To facilitate evaluation of CardioBoost, we provide pre-computed pathogenicity scores for all possible rare missense variants in genes associated with cardiomyopathies and arrhythmias (https://www.cardiodb.org/cardioboost/). Our results also highlight the need to develop and evaluate variant classification tools focused on specific diseases and clinical application contexts. Our proposed model for assessing variants in known disease genes, and the use of application-specific evaluations, is broadly applicable to improve variant interpretation across a wide range of Mendelian diseases.

genetics↗

bioRxiv2019

Genomics of Egyptian Healthy Volunteers: The EHVol Study

Comprehensive genomic databases offer unprecedented opportunities towards effective tailored strategies for the prevention and treatment of disease. The integration of genomic and phenotypic data from diverse ethnic populations is also key to advancements in precision medicine and novel diagnostic technologies. Current reference genomic databases, however, are not representative of the global human population, making variant interpretation challenging and uncertain, especially in underrepresented populations such as the North African population. To address this, a study of 391 Egyptian healthy volunteers (EHVols) was initiated as a milestone towards establishing the 1000 Egyptian Genomes project.

genomics↗

bioRxiv2019

Human loss-of-function variants suggest that partial LRRK2 inhibition is a safe therapeutic strategy for Parkinsons disease

Human genetic variants causing loss-of-function (LoF) of protein-coding genes provide natural in vivo models of gene inactivation, which are powerful indicators of gene function and the potential toxicity of therapeutic inhibitors targeting these genes1,2. Gain-of-kinase-function variants in LRRK2 are known to significantly increase the risk of Parkinsons disease3,4, suggesting that inhibition of LRRK2 kinase activity is a promising therapeutic strategy. Whilst preclinical studies in model organisms have raised some on-target toxicity concerns5-8, the biological consequences of LRRK2 inhibition have not been well characterized in humans. Here we systematically analyse LoF variants in LRRK2 observed across 141,456 individuals sequenced in the Genome Aggregation Database (gnomAD)9 and over 4 million participants in the 23andMe genotyped dataset, to assess their impact at a molecular and phenotypic level. After thorough variant curation, we identify 1,358 individuals with high-confidence predicted LoF variants in LRRK2, several with experimental validation. We show that heterozygous LoF of LRRK2 reduces LRRK2 protein level by ~50% but is not associated with reduced life expectancy, or with any specific phenotype or disease state. These data suggest that therapeutics that downregulate LRRK2 levels or kinase activity by up to 50% are unlikely to have major on-target safety liabilities. Our results demonstrate the value of large scale genomic databases and phenotyping of human LoF carriers for target validation in drug discovery.

genomics↗

bioRxiv2019

Characterising the loss-of-function impact of 5' untranslated region variants in whole genome sequence data from 15,708 individuals

Upstream open reading frames (uORFs) are important tissue-specific cis-regulators of protein translation. Although isolated case reports have shown that variants that create or disrupt uORFs can cause disease, genetic sequencing approaches typically focus on protein-coding regions and ignore these variants. Here, we describe a systematic genome-wide study of variants that create and disrupt human uORFs, and explore their role in human disease using 15,708 whole genome sequences collected by the Genome Aggregation Database (gnomAD) project. We show that 14,897 variants that create new start codons upstream of the canonical coding sequence (CDS), and 2,406 variants disrupting the stop site of existing uORFs, are under strong negative selection. Furthermore, variants creating uORFs that overlap the CDS show signals of selection equivalent to coding loss-of-function variants, and uORF-perturbing variants are under strong selection when arising upstream of known disease genes and genes intolerant to loss-of-function variants. Finally, we identify specific genes where perturbation of uORFs is likely to represent an important disease mechanism, and report a novel uORF frameshift variant upstream of NF2 in families with neurofibromatosis. Our results highlight uORF-perturbing variants as an important and under-recognised functional class that can contribute to penetrant human disease, and demonstrate the power of large-scale population sequencing data to study the deleteriousness of specific classes of non-coding variants.

genomics↗

bioRxiv2019

Evaluating potential drug targets through human loss-of-function genetic variation

Human genetics has informed the clinical development of new drugs, and is beginning to influence the selection of new drug targets. Large-scale DNA sequencing studies have created a catalogue of naturally occurring genetic variants predicted to cause loss of function in human genes, which in principle should provide powerful in vivo models of human genetic "knockouts" to complement model organism knockout studies and inform drug development. Here, we consider the use of predicted loss-of-function (pLoF) variation catalogued in the Genome Aggregation Database (gnomAD) for the evaluation of genes as potential drug targets. Many drug targets, including the targets of highly successful inhibitors such as aspirin and statins, are under natural selection at least as extreme as known haploinsufficient genes, with pLoF variants almost completely depleted from the population. Thus, metrics of gene essentiality should not be used to eliminate genes from consideration as potential targets. The identification of individual humans harboring "knockouts" (biallelic gene inactivation), followed by individual recall and deep phenotyping, is highly valuable to study gene function. In most genes, pLoF alleles are sufficiently rare that ascertainment will be largely limited to heterozygous individuals in outbred populations. Sampling of diverse bottlenecked populations and consanguineous individuals will aid in identification of total "knockouts". Careful filtering and curation of pLoF variants in a gene of interest is necessary in order to identify true LoF individuals for follow-up, and the positional distribution or frequency of true LoF variants may reveal important disease biology. Our analysis suggests that the value of pLoF variant data for drug discovery lies in deep curation informed by the nature of the drug and its indication, as well as the biology of the gene, followed by recall-by-genotype studies in targeted populations.

genomics↗

bioRxiv2019

Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes

Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes critical for an organisms function will be depleted for such variants in natural populations, while non-essential genes will tolerate their accumulation. However, predicted loss-of-function (pLoF) variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes1. Here, we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence pLoF variants in this cohort after filtering for sequencing and annotation artifacts. Using an improved human mutation rate model, we classify human protein-coding genes along a spectrum representing tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve gene discovery power for both common and rare diseases.

genomics↗

bioRxiv2018

Response to Shah et al: Using high-resolution variant frequencies empowers clinical genome interpretation and enables investigation of genetic architecture

Main text Main text Supplemental Data description Declaration of Interests Web Resources References Recent work by Shah and colleagues1 demonstrated that many variants in the ClinVar database2 are misclassified, and that disease-specific allele frequency (AF) thresholds can identify wrongly classified alleles by flagging variants that are too prevalent in the population to be causative of rare penetrant disease. While we agree with the main conclusions of this work, the authors compare their AF filtering approach to our recently published method3, concluding that the method we advanced \"may be prone to removing potentially pathogenic variants\". This is incorrect. Here we demonstrate that our approach is robust, and further illustrate the power of di ...

genomics↗

bioRxiv2018

Quantitative approaches to variant classification increase the yield and precision of genetic testing in Mendelian diseases: The case of hypertrophic cardiomyopathy

BackgroundInternational guidelines for variant interpretation in Mendelian disease set stringent criteria to report a variant as (likely) pathogenic, prioritising control of false positive rate over test sensitivity and diagnostic yield. Genetic testing is also more likely informative in individuals with well-characterised variants from extensively studied European-ancestry populations. Inherited cardiomyopathies are relatively common Mendelian diseases that allow empirical calibration and assessment of this framework.\n\nResultsWe compared rare variants in large hypertrophic cardiomyopathy (HCM) cohorts to reference populations to identify variant classes with high prior likelihoods of pathogenicity, as defined by etiological fraction (EF). Analysis of variant distribution identified regions in which variants are significantly enriched in cases and variant location was a better discriminator of pathogenicity than generic computational functional prediction algorithms. Non-truncating variant classes with an EF[≥]0.95, and therefore clinically actionable, were identified in 5 established HCM genes. Applying this approach leads to an estimated 14-20% increase in cases with actionable HCM variants.\n\nConclusionsWhen found in a patient confirmed to have disease, novel variants in some genes and regions are empirically shown to have a sufficiently high probability of pathogenicity to support a \"likely pathogenic\" classification, even without additional segregation or functional data. This could increase the yield of high confidence actionable variants, consistent with the framework and recommendations of current guidelines. The techniques outlined offer a consistent, unbiased and equitable approach to variant interpretation for Mendelian disease genetic testing. We propose adaptations to ACMG/AMP guidelines to incorporate such evidence in a quantitative and transparent manner.

genetics↗

bioRxiv2017

CardioClassifier: demonstrating the power of disease- and gene-specific computational decision support for clinical genome interpretation

PurposeInternationally-adopted variant interpretation guidelines from the American College of Medical Genetics and Genomics (ACMG) are generic and require disease-specific refinement. Here we developed CardioClassifier (www.cardioclassifier.org), a semi-automated decision-support tool for inherited cardiac conditions (ICCs).\n\nMethodsCardioClassifier integrates data retrieved from multiple sources with user-input case-specific information, through an interactive interface, to support variant interpretation. Combining disease- and gene-specific knowledge with variant observations in large cohorts of cases and controls, we refined 14 computational ACMG criteria and created three ICC-specific rules.\n\nResultsWe benchmarked CardioClassifier on 57 expertly-curated variants and show full retrieval of all computational data, concordantly activating 87.3% of rules. A generic annotation tool identified fewer than half as many clinically-actionable variants (64/219 vs 156/219, Fishers P=1.1x10-18), with important false positives; illustrating the critical importance of disease and gene-specific annotations.\n\nCardioClassifier identified putatively disease-causing variants in 33.7% of 327 cardiomyopathy cases, comparable with leading ICC laboratories. Through addition of manually-curated data, variants found in over 40% of cardiomyopathy cases are fully annotated, without requiring additional user-input data.\n\nConclusionCardioClassifier is an ICC-specific decision-support tool that integrates expertly curated computational annotations with case-specific data to generate fast, reproducible and interactive variant pathogenicity reports, according to best practice guidelines.

genomics↗