pathogenicity prediction
Recently Published Documents

Next generation sequencing technologies both boost the discovery of variants in the human genome and exacerbate the challenges of pathogenic variant identification. In this study, we developed mvPPT (Pathogenicity Prediction Tool for missense variants), a highly sensitive and accurate missense variant classifier based on gradient boosting. MvPPT adopts high-confidence training sets with a wide spectrum of variant profiles, and extracts three categories of features, including scores from existing prediction tools, allele, amino acid and genotype frequencies, and genomic context. Compared with established predictors, mvPPT achieved superior performance in all test sets, regardless of data source. In addition, our study also provides guidance for training set and feature selection strategies, as well as reveals highly relevant features, which may further provide biological insights of variant pathogenicity.

Single nucleotide polymorphisms (SNPs) in the first intron of the FTO gene reported in 2007 continue to be the known variants with the greatest effect on adiposity in different human populations. Coding variants in the FTO gene, on the other hand, have been little explored, although data from complete sequencing of the exomes of various populations are available in public databases and provide an excellent opportunity to investigate potential functional variants in FTO. In this context, this study aimed to track nonsynonymous variants in the exons of the FTO gene in different population groups employing the gnomAD database and analyze the potential functional impact of these variants on the FTO protein using five publicly available pathogenicity prediction programs. The findings revealed 345 rare mutations, of which 321 are missense (93%), 19 are stop gained (5.6%) and five mutations are located in the splice region (1.4%). Of these, 134 (38.8%) were classified as pathogenic, 144 (41.7%) as benign and 67 (19.5%) as unknown. The available data, however, suggest that these variants are probably not associated with BMI and obesity, but instead, with other diseases. Functional studies are, therefore, required to identify the role of these variants in disease genesis.

Many proteins are causative for inherited partial lipodystrophies, including lamins, the essential constituents of the nuclear envelope scaffold called the lamina. By performing high throughput sequencing on a panel of genes involved in lipodystrophies, we identified a heterozygous mutation in LMNB2 gene (c.700C > T p.(Arg234Trp)) in a female patient presenting early onset type II diabetes, hypertriglyceridemia, and android fat distribution. This mutation is rare in the general population (frequency 0.013% in GnomAD) and was predicted pathogenic by a set of pathogenicity prediction software. Patient-derived fibroblasts showed nuclear shape abnormalities and premature senescence features, which are two typical cellular phenotypes associated with laminopathies. Moreover, we observed an atypical aggregation of lamin B2 in nucleoplasm, which co-distributes with emerin and lamin A/C, along with an abnormal distribution of lamin A/C at the nuclear envelope. Finally, reducing lamin B2 expression level by siRNA targeted toward LMNB2 transcripts resulted in decreased nuclear anomalies and senescence-associated beta-galactosidase, suggesting a role of the mutated protein in the occurrence of the observed cellular phenotype. Altogether, these results suggest that mutations in lamin B2 could produce premature senescence and partial lipodystrophy features as observed with certain mutants of lamin A/C.

Abstract Background Gene copy number variations (CNVs) contribute to genetic diversity and disease prevalence across populations. Substantial efforts have been made to decipher the relationship between CNVs and pathogenesis but with limited success. Results We have developed a novel computational framework X-CNV (www.unimd.org/XCNV), to predict the pathogenicity of CNVs by integrating more than 30 informative features such as allele frequency (AF), CNV length, CNV type, and some deleterious scores. Notably, over 14 million CNVs across various ethnic groups, covering nearly 93% of the human genome, were unified to calculate the AF. X-CNV, which yielded area under curve (AUC) values of 0.96 and 0.94 in training and validation sets, was demonstrated to outperform other available tools in terms of CNV pathogenicity prediction. A meta-voting prediction (MVP) score was developed to quantitively measure the pathogenic effect, which is based on the probabilistic value generated from the XGBoost algorithm. The proposed MVP score demonstrated a high discriminative power in determining pathogenetic CNVs for inherited traits/diseases in different ethnic groups. Conclusions The ability of the X-CNV framework to quantitatively prioritize functional, deleterious, and disease-causing CNV on a genome-wide basis outperformed current CNV-annotation tools and will have broad utility in population genetics, disease-association studies, and diagnostic screening.

Structural variants (SVs) are implicated in the etiology of Mendelian diseases but have been systematically underascertained owing to limitations of existing technology. Recent technological advances such as long-read sequencing (LRS) enable more comprehensive detection of SVs, but approaches for clinical prioritization of candidate SVs are needed. Existing computational approaches do not specifically target LRS data, thereby missing a substantial proportion of candidate SVs, and do not provide a unified computational model for assessing all types of SVs. Structural Variant Annotation and Analysis (SvAnna) assesses all classes of SV and their intersection with transcripts and regulatory sequences in the context of topologically associating domains, relating predicted effects on gene function with clinical phenotype data. We show with a collection of 182 published case reports with pathogenic SVs that SvAnna places over 90% of pathogenic SVs in the top ten ranks. The interpretable prioritizations provided by SvAnna will facilitate the widespread adoption of LRS in diagnostic genomics.

The present limitations in the pathogenicity prediction of BRCA1 and BRCA2 (BRCA1/2) missense variants constitute an important problem with negative consequences for the diagnosis of hereditary breast and ovarian cancer. However, it has been proposed that the use of endophenotype predictions, i.e., computational estimates of the outcomes of functional assays, can be a good option to address this bottleneck. The application of this idea to the BRCA1/2 variants in the CAGI 5-ENIGMA international challenge has shown promising results. Here, we developed this approach, exploring the predictive performances of the regression models applied to the BRCA1/2 variants for which the values of the homology-directed DNA repair and saturation genome editing assays are available. Our results first showed that we can generate endophenotype estimates using a few molecular-level properties. Second, we show that the accuracy of these estimates is enough to obtain pathogenicity predictions comparable to those of many standard tools. Third, endophenotype-based predictions are complementary to, but do not outperform, those of a Random Forest model trained using variant pathogenicity annotations instead of endophenotype values. In summary, our results confirmed the usefulness of the endophenotype approach for the pathogenicity prediction of the BRCA1/2 missense variants, suggesting different options for future improvements.

Signatures of DNA motifs associated with distinct mutagenic exposures have been defined for somatic variants, but little is known about the consequences different mutational processes pose to the cancer cell, particularly the distribution of the resulting variants in the implied proteins and their structural regions (surface, core, interacting interface). Here we first compare the protein-level consequences of six mutational signatures (Aging, APOBEC, POLE, UV, 5-FU and Platinum) characterised by clear DNA motif preferences. By mapping individual substitution events observed in tumours to three-dimensional protein structures, we show that these common somatic mutational signatures are biased against the protein core, consistent with the lower tolerability of substitutions at such structurally important regions. On the other hand, deep mutational scanning (DMS) data allow us to probe the "dark matter" of somatic mutational landscape, exploring variants which are otherwise removed in purifying selection. A computational DMS analysis identifies mutational contexts (5'-G/C[T>G]A/G-3') which are associated with damaging mutations, by altering physicochemical characteristics of amino acids at the protein core. We argue that comprehensive DMS analysis can contribute to classification of variants according to their true impact to the stability/activity of the affected protein, decoupling this from pathogenicity prediction offered by conventional variant impact classifiers.