Biology subjects

Ellard, S.

Publications and source records attributed to Ellard, S..

6 recordsLinked to original sources

bioRxiv2020

Functional interpretation of ATAD3A variants in neuro-mitochondrial phenotypes

BackgroundThe ATPase family AAA-domain containing protein 3A (ATAD3A) is a nuclear-encoded mitochondrial membrane anchored protein involved in diverse processes including mitochondrial dynamics, mitochondrial DNA organization, and cholesterol metabolism. Biallelic deletions (null), recessive missense variants (hypomorph), and heterozygous missense variants or duplications (antimorph) in ATAD3A lead to neurological syndromes in humans. ObjectiveTo expand the mutational spectrum of ATAD3A variants and to provide functional interpretation of missense alleles in trans to deletion alleles. MethodsExome sequencing was used to identify single nucleotide variants (SNVs) and copy number variants (CNVs) in ATAD3A in individuals with neurological and mitochondrial phenotypes. A Drosophila Atad3A Gal4 trap null allele was generated using CRISPR-Cas9 genome editing technology to aid interpretation of variants. ResultsWe report 13 individuals from 8 unrelated families with biallelic ATAD3A variants. Four of the identified missense variants, p.(Leu77Val), p.(Phe50Leu), p.(Arg170Trp), p.(Gly236Val), were inherited in trans to loss-of-function alleles. A fifth missense variant, p.(Arg327Pro), was homozygous. Affected individuals exhibited findings previously associated with ATAD3A pathogenic variation, including developmental delay, hypotonia, congenital cataracts, hypertrophic cardiomyopathy, and cerebellar atrophy. Drosophila studies indicated that Phe50Leu, Gly236Val, and Arg327Pro are severe loss-of-function alleles leading to early developmental lethality and neurogenesis defects, whereas Leu77Val and Arg170Trp are partial loss of function alleles that cause progressive locomotion defects. Moreover, Leu77Val and Arg170Trp expression leads to an increase in autophagy and mitophagy in adult muscles. ConclusionOur findings expand the allelic spectrum of ATAD3A variants, and exemplify the use of a functional assay in Drosophila to aid variant interpretation.

genetics↗

bioRxiv2020

Assessing performance of pathogenicity predictors using clinically-relevant variant datasets

PurposePathogenicity predictors are an integral part of genomic variant interpretation but, despite their widespread usage, an independent validation of performance using a clinically-relevant dataset has not been undertaken. MethodsWe derive two validation datasets: an "open" dataset containing variants extracted from publicly-available databases, similar to those commonly applied in previous benchmarking exercises, and a "clinically-representative" dataset containing variants identified through research/diagnostic exome and diagnostic panel sequencing. Using these datasets, we evaluate the performance of three recently developed meta-predictors, REVEL, GAVIN and ClinPred, and compare their performance against two commonly used in silico tools, SIFT and PolyPhen-2. ResultsAlthough the newer meta-predictors outperform the older tools, the performance of all pathogenicity predictors is substantially lower in the clinically-representative dataset. Using our clinically-relevant dataset, REVEL performed best with an area under the ROC of 0.81. Using a concordance-based approach based on a consensus of multiple tools reduces the performance due to both discordance between tools and false concordance where tools make common misclassification. Analysis of tool feature usage may give an insight into the tool performance and misclassification. ConclusionOur results support the adoption of meta-predictors over traditional in silico tools, but do not support a consensus-based approach as recommended by current variant classification guidelines.

genomics↗

bioRxiv2019

Using structural analysis to assess the impact of missense variants in MEN1

Despite the rapid expansion in recent years of databases reporting either benign or pathogenic genetic variation, the interpretation of novel missense variants can remain challenging, particularly for clinical or genetic testing laboratories where functional analysis is often unfeasible. Previous studies have shown that thermodynamic analysis of protein structure in silico can discriminate between groups of benign and pathogenic missense variants. However, although structures exist for many human disease-associated proteins, such analysis remains largely unexploited in clinical laboratories. Here, we analysed the predicted effect of 338 known missense variants on the structure of Menin, the MEN1 gene product. Results provided strong discrimination between pathogenic and benign variants, with a threshold of >4 kcal/mol for the predicted change in stability providing a strong indicator of pathogenicity. Subsequent analysis of 7 novel missense variants identified during clinical testing of MEN1 patients showed that all 7 were predicted to destabilise Menin by >4 kcal/mol. We conclude that structural analysis provides a useful tool in understanding the impact of missense variants in MEN1, and that integration of proteomic with genomic data could potentially contribute to the classification of novel variants in this disease.

genetics↗

bioRxiv2019

SavvyCNV: genome-wide CNV calling from off-target reads

Identifying copy number variants (CNVS) can provide diagnoses to patients and provide important biological insights into human health and disease. Current exome and targeted sequencing approaches cannot detect clinically and biologically-relevant CNVs outside their target area. We present SavvyCNV, a tool which uses off-target read data to call CNVs genome-wide. Up to 70% of sequencing reads from exome and targeted sequencing fall outside the targeted regions - SavvyCNV exploits this free data.\n\nWe benchmarked SavvyCNV using truth sets generated from genome sequencing data and Multiplex Ligation-dependent Probe Amplification assays. SavvyCNV called CNVs with high precision and recall, outperforming five state-of-the-art CNV callers at calling CNVs genome-wide using off-target or on-target reads from targeted panel and exome sequencing. Furthermore SavvyCNV was able to call previously undetected clinically-relevant CNVs from targeted panel data highlighting the utility of this tool within the diagnostic setting. SavvyCNV is freely available.

bioinformatics↗

bioRxiv2018

Comprehensive screening shows that mutations in the known syndromic genes are rare in individuals presenting with hyperinsulinaemic hypoglycaemia

ObjectiveHyperinsulinaemic hypoglycaemia (HH) can occur in isolation or more rarely feature as part of a syndrome. Screening for mutations in the syndromic HH genes is guided by phenotype with genetic testing used to confirm the clinical diagnosis. As HH can be the presenting feature of a syndrome it is possible that mutations will be missed as these genes are not routinely screened in all newly diagnosed individuals. We investigated the frequency of pathogenic variants in syndromic genes in individuals with HH who had not been clinically diagnosed with a syndromic disorder at referral for genetic testing.\n\nDesignWe used genome sequencing data to assess the prevalence of mutations in syndromic HH genes in an international cohort of patients with HH of unknown genetic cause.\n\nMethodsWe undertook genome sequencing in 82 individuals with HH without a clinical diagnosis of a known syndrome at referral for genetic testing. Within this cohort we searched for the genetic aetiologies causing 20 different syndromes where HH had been reported as a feature.\n\nResultsWe identified a pathogenic KMT2D variant in a patient with HH diagnosed at birth, confirming a genetic diagnosis of Kabuki syndrome. Clinical data received following the identification of the mutation highlighted additional features consistent with the genetic diagnosis. Pathogenic variants were not identified in the remainder of the cohort.\n\nConclusionsPathogenic variants in the syndromic HH genes are rare but should be considered in newly diagnosed individuals as HH may be the presenting feature.

genetics↗

bioRxiv2017

Heterozygous RFX6 protein truncating variants cause Maturity-Onset Diabetes of the Young (MODY) with reduced penetrance

Finding new genetic causes of monogenic diabetes can help to understand development and function of the human pancreas. We aimed to find novel protein-truncating variants causing Maturity-Onset Diabetes of the Young (MODY), a subtype of monogenic diabetes. We used a combination of next-generation sequencing of MODY cases with unknown aetiology along with comparisons to the ExAC database to identify new MODY genes. In the discovery cohort of 36 European patients, we identified two probands with novel RFX6 heterozygous nonsense variants. RFX6 protein truncating variants were enriched in the MODY discovery cohort compared to the European control population within ExAC (odds ratio, OR=131, P=lxl0-4). We found similar results in non-Finnish European (n=348, OR=43, P=5xl0-5) and Finnish (n=80, OR=22, P=1xl0-6) replication cohorts. The overall meta-analysis OR was 34 (P=lxl0-16). RFX6 heterozygotes had reduced penetrance of diabetes compared to common HNF1A and HNF4A-MODY mutations (27%, 70% and 55% at 25 years of age, respectively). The hyperglycaemia resulted from beta-cell dysfunction and was associated with lower fasting and stimulated gastric inhibitory polypeptide (GIP) levels. Our study demonstrates that heterozygous RFX6 protein truncating variants are associated with MODY with reduced penetrance.

genetics↗