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BackgroundPathogenicity predictors are integral to 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 panel sequencing. Using these datasets, we evaluate the performance of three recent 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 receiver operating characteristic curve of 0.82. 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 in current practice.

Purpose Diagnosis of genetic disorders is hampered by large numbers of variants of uncertain significance (VUSs) identified through next-generation sequencing. Many such variants may disrupt normal RNA splicing. We examined effects on splicing of a large cohort of clinically identified variants and compared performance of bioinformatic splicing prediction tools commonly used in diagnostic laboratories. Methods Two hundred fifty-seven variants (coding and noncoding) were referred for analysis across three laboratories. Blood RNA samples underwent targeted reverse transcription polymerase chain reaction (RT-PCR) analysis with Sanger sequencing of PCR products and agarose gel electrophoresis. Seventeen samples also underwent transcriptome-wide RNA sequencing with targeted splicing analysis based on Sashimi plot visualization. Bioinformatic splicing predictions were obtained using Alamut, HSF 3.1, and SpliceAI software. Results Eighty-five variants (33%) were associated with abnormal splicing. The most frequent abnormality was upstream exon skipping (39/85 variants), which was most often associated with splice donor region variants. SpliceAI had greatest accuracy in predicting splicing abnormalities (0.91) and outperformed other tools in sensitivity and specificity. Conclusion Splicing analysis of blood RNA identifies diagnostically important splicing abnormalities and clarifies functional effects of a significant proportion of VUSs. Bioinformatic predictions are improving but still make significant errors. RNA analysis should therefore be routinely considered in genetic disease diagnostics.

Background Genetic variants that severely alter protein products (e.g. nonsense, frameshift) are often associated with disease. For some genes, these predicted loss-of-function variants (pLoFs) are observed throughout the gene, whilst in others, they occur only at specific locations. We hypothesised that, for genes linked with monogenic diseases that display incomplete penetrance, pLoF variants present in apparently unaffected individuals may be limited to regions where pLoFs are tolerated. To test this, we investigated whether pLoF location could explain instances of incomplete penetrance of variants expected to be pathogenic for Mendelian conditions. Methods We used exome sequence data in 454,773 individuals in the UK Biobank (UKB) to investigate the locations of pLoFs in a population cohort. We counted numbers of unique pLoF, missense, and synonymous variants in UKB in each quintile of the coding sequence (CDS) of all protein-coding genes and clustered the variants using Gaussian mixture models. We limited the analyses to genes with ≥ 5 variants of each type (16,473 genes). We compared the locations of pLoFs in UKB with all theoretically possible pLoFs in a transcript, and pathogenic pLoFs from ClinVar, and performed simulations to estimate the false-positive rate of non-uniformly distributed variants. Results For most genes, all variant classes fell into clusters representing broadly uniform variant distributions, but genes in which haploinsufficiency causes developmental disorders were less likely to have uniform pLoF distribution than other genes ( P < 2.2 × 10 −6 ). We identified a number of genes, including ARID1B and GATA6 , where pLoF variants in the first quarter of the CDS were rescued by the presence of an alternative translation start site and should not be reported as pathogenic. For other genes, such as ODC1 , pLoFs were located approximately uniformly across the gene, but pathogenic pLoFs were clustered only at the end, consistent with a gain-of-function disease mechanism. Conclusions Our results suggest the potential benefits of localised constraint metrics and that the location of pLoF variants should be considered when interpreting variants.

Background The widespread clinical application of genome-wide sequencing has resulted in many new diagnoses for rare genetic conditions, but testing regularly identifies variants of uncertain significance (VUS). The remarkable rise in the amount of genomic data has been paralleled by a rise in the number of protein structures that are now publicly available, which may have clinical utility for the interpretation of missense and in-frame insertions or deletions. Methods Within a UK National Health Service genomic medicine diagnostic laboratory, we investigated the number of VUS over a 5-year period that were evaluated using protein structural analysis and how often this analysis aided variant classification. Results We found 99 novel missense and in-frame variants across 67 genes that were initially classified as VUS by our diagnostic laboratory using standard variant classification guidelines and for which further analysis of protein structure was requested. Evidence from protein structural analysis was used in the re-assessment of 64 variants, of which 47 were subsequently reclassified as pathogenic or likely pathogenic and 17 remained as VUS. We identified several case studies where protein structural analysis aided variant interpretation by predicting disease mechanisms that were consistent with the observed phenotypes, including loss-of-function through thermodynamic destabilisation or disruption of ligand binding, and gain-of-function through de-repression or escape from proteasomal degradation. Conclusions We have shown that using in silico protein structural analysis can aid classification of VUS and give insights into the mechanisms of pathogenicity. Based on our experience, we propose a generic evidence-based workflow for incorporating protein structural information into diagnostic practice to facilitate variant classification.

Background 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. Methods To expand the mutational spectrum of ATAD3A variants and to provide functional interpretation of missense alleles in trans to deletion alleles, we performed exome sequencing for identification of single nucleotide variants (SNVs) and copy number variants (CNVs) in ATAD3A in individuals with neurological and mitochondrial phenotypes. A Drosophila Atad3a Gal4 knockin-null allele was generated using CRISPR-Cas9 genome editing technology to aid the interpretation of variants. Results We report 13 individuals from 8 unrelated families with biallelic ATAD3A variants. The variants included four missense variants inherited in trans to loss-of-function alleles (p.(Leu77Val), p.(Phe50Leu), p.(Arg170Trp), p.(Gly236Val)), a homozygous missense variant p.(Arg327Pro), and a heterozygous non-frameshift indel p.(Lys568del). 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, Arg327Pro, and Lys568del are severe loss-of-function alleles leading to early developmental lethality. Further, we showed that Phe50Leu, Gly236Val, and Arg327Pro cause neurogenesis defects. On the contrary, Leu77Val and Arg170Trp are partial loss-of-function alleles that cause progressive locomotion defects and whose expression leads to an increase in autophagy and mitophagy in adult muscles. Conclusion Our findings expand the allelic spectrum of ATAD3A variants and exemplify the use of a functional assay in Drosophila to aid variant interpretation.

Spermatogenesis is a highly complex process involving several thousand genes, only a minority of which have been studied in infertile men. In a previous study, we identified a number of Copy Number Variants (CNVs) by high-resolution array-Comparative Genomic Hybridization (a-CGH) analysis of the X chromosome, including 16 patient-specific X chromosome-linked gains. Of these, five gains (DUP1A, DUP5, DUP20, DUP26 and DUP40) were selected for further analysis to evaluate their clinical significance. The copy number state of the five selected loci was analyzed by quantitative-PCR on a total of 276 idiopathic infertile patients and 327 controls in a conventional case-control setting (199 subjects belonged to the previous a-CGH study). For one interesting locus (intersecting DUP1A) additional 338 subjects were analyzed. All gains were confirmed as patient-specific and the difference in duplication load between patients and controls is significant (p = 1.65 × 10(-4)). Two of the CNVs are private variants, whereas 3 are found recurrently in patients and none of the controls. These CNVs include, or are in close proximity to, genes with testis-specific expression. DUP1A, mapping to the PAR1, is found at the highest frequency (1.4%) that was significantly different from controls (0%) (p = 0.047 after Bonferroni correction). Two mechanisms are proposed by which DUP1A may cause spermatogenic failure: i) by affecting the correct regulation of a gene with potential role in spermatogenesis; ii) by disturbing recombination between PAR1 regions during meiosis. This study allowed the identification of novel spermatogenesis candidate genes linked to the 5 CNVs and the discovery of the first recurrent, X-linked gain with potential clinical relevance.

Background Classification of rare missense variants remains an ongoing challenge in genomic medicine. Evidence of pathogenicity is often sparse, and decisions about how to weigh different evidence classes may be subjective. We used a Bayesian variant classification framework to investigate the performance of variant co-localisation, missense constraint, and aggregating data across paralogous protein domains (“meta-domains”). Methods We constructed a database of all possible coding single nucleotide variants in the human genome and used PFam predictions to annotate structurally-equivalent positions across protein domains. We counted the number of pathogenic and benign missense variants at these equivalent positions in the ClinVar database, calculated a regional constraint score for each meta-domain, and assessed this approach versus existing missense constraint metrics for classifying variant pathogenicity and benignity. Results Alternative pathogenic missense variants at the same amino acid position in the same protein provide strong evidence of pathogenicity (positive likelihood ratio, LR+  = 85). Additionally, clinically annotated pathogenic or benign missense variants at equivalent positions in different proteins can provide moderate evidence of pathogenicity (LR+  = 7) or benignity (LR+  = 5), respectively. Applying these approaches sequentially (through PM5) increases sensitivity for classifying pathogenic missense variants from 27 to 41%. Missense constraint can also provide strong evidence of pathogenicity for some variants, but its absence provides no evidence of benignity. Conclusions We propose using structurally equivalent positions across related protein domains from different genes to augment evidence for variant co-localisation when classifying novel missense variants. Additionally, we advocate adopting a numerical evidence-based approach to integrating diverse data in variant interpretation.

Despite the rapid expansion in recent years of databases reporting either benign or pathogenic genetic variations, the interpretation of novel missense variants remains 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 analyzed 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 seven novel missense variants identified during clinical testing of patients with MEN1 showed that all seven were predicted to destabilize menin by >4 kcal/mol. We conclude that structural analysis provides a useful tool in understanding the effect 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.

To date, penetrance figures for medullary thyroid cancer (MTC) for variants in rearranged during transfection (RET) have been estimated from families ascertained because of the presence of MTC. To gain estimates of penetrance, unbiased by ascertainment, we analyzed 61 RET mutations assigned as disease causing by the American Thyroid Association (ATA) in population whole-exome sequencing data. For the 61 RET mutations, we used analyses of the observed allele frequencies in ∼51,000 individuals from the Exome Aggregation Consortium (ExAC) database that were not contributed via The Cancer Genome Atlas (TCGA; non-TCGA ExAC), assuming lifetime penetrance for MTC of 90%, 50%, and unbounded. Population-based. Ten of 61 ATA disease-causing RET mutations were present in the non-TCGA ExAC population with observed frequency consistent with penetrance for MTC of >90%. For p.Val804Met, the lifetime penetrance for MTC, estimated from the allele frequency observed, was 4% [95% confidence interval (CI), 0.9% to 8%]. Based on penetrance analysis in carrier relatives of p.Val804Met-positive cases of MTC, p.Val804Met is currently understood to have high-lifetime penetrance for MTC (87% by age 70), albeit of later onset of MTC than other RET mutations. Given our unbiased estimate of penetrance for RET p.Val804Met of 4% (95% CI, 0.9% to 8%), the current recommendation by the ATA of prophylactic thyroidectomy as standard for all RET mutation carriers is likely inappropriate.

Isolated mitochondrial complex II deficiency is a rare cause of mitochondrial respiratory chain disease. To date biallelic variants in three genes encoding mitochondrial complex II molecular components have been unequivocally associated with mitochondrial disease (SDHA/SDHB/SDHAF1). Additionally, variants in one further complex II component (SDHD) have been identified as a candidate cause of isolated mitochondrial complex II deficiency in just two unrelated affected individuals with clinical features consistent with mitochondrial disease, including progressive encephalomyopathy and lethal infantile cardiomyopathy. We present clinical and genomic investigations in four individuals from an extended Palestinian family with clinical features consistent with an autosomal recessive mitochondrial complex II deficiency, in which our genomic studies identified a homozygous NM_003002.3:c.[205 G > A];[205 G > A];p.[(Glu69Lys)];[(Glu69Lys)] SDHD variant as the likely cause. Reviewing previously published cases, these findings consolidate disruption of SDHD function as a cause of mitochondrial complex II deficiency and further define the phenotypic spectrum associated with SDHD gene variants.