HNF1A is an essential component of the transcription factor network that controls pancreatic β-cell differentiation, maintenance, and glucose stimulated insulin secretion (GSIS). A continuum of protein malfunction is caused by variations in the HNF1A gene, from severe loss-of-function (LOF) variants that cause the highly penetrant Maturity Onset Diabetes of the Young (MODY) to milder LOF variants that are far less penetrant but impart a population-wide risk of type 2 diabetes that is up to five times higher. Before classifying and reporting the discovered variations as relevant in clinical diagnosis, a critical review is required. Functional investigations offer substantial support for classifying a variant as pathogenic, or otherwise as advised by the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) ACMG/AMP criteria for variant interpretation.
To determine the molecular basis for the variations in the HNF1A gene found in patients with monogenic diabetes in India.
We performed functional protein analyses such as transactivation, protein expression, DNA binding, nuclear localization, and glucose stimulated insulin secretion (GSIS) assay, along with structural prediction analysis for 14 HNF1A variants found in 20 patients with monogenic diabetes.
Of the 14 variants, 4 (28.6%) were interpreted as pathogenic, 6 (42.8%) as likely pathogenic, 3 (21.4%) as variants of uncertain significance, and 1 (7.14%) as benign. Patients harboring the pathogenic/likely pathogenic variants were able to successfully switch from insulin to sulfonylureas (SU) making these variants clinically actionable.
Our findings are the first to show the need of using additive scores during molecular characterization for accurate pathogenicity evaluations of HNF1A variants in precision medicine.

Implementation of next-generation sequencing (NGS) for the genetic analysis of hereditary diseases has resulted in a vast number of genetic variants identified daily, leading to inadequate variant interpretation and, consequently, a lack of useful clinical information for treatment decisions. Herein, we present MARGINAL 1.0.0, a machine learning (ML)-based software for the interpretation of rare BRCA1 and BRCA2 germline variants. MARGINAL software classifies variants into three categories, namely, (likely) pathogenic, of uncertain significance and (likely) benign, implementing the criteria established by the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG-AMP). We first annotated BRCA1 and BRCA2 variants using various sources. Then, we automatically implemented the ACMG-AMP criteria, and we finally constructed the ML model for variant classification. To maximize accuracy, we compared the performance of eight different ML algorithms in a classification scheme based on a serial combination of two classifiers. The model showed high predictive abilities with maximum accuracy of 92% and 98%, recall of 92% and 98% and specificity of 90% and 98% for the first and second classifiers, respectively. Our results indicate that using a gene and disease-specific ML automated software for clinical variant evaluation can minimize conflicting interpretations.

Till now not many studies have been conducted to classify PAH gene variants according to American College of Medical Genetics and Genomics (ACMG-AMP) guidelines. The aim of this study was to collect all PAH gene variants reported among Iranian population and investigate their pathogenicity based on ACMG-AMP guidelines. Systematic collection of PAH gene variants, verification of variants, in silico analysis, and application of ACMG-AMP guidelines were the main steps in performing the present study. A total of 267 unique variants were identified; according to ACMG-AMP guidelines, 90, 40, 71, 14, and 52 variants were classified as pathogenic (P), likely pathogenic (LP), variant of uncertain significance (VUS), likely benign (LB), and benign (B), respectively. The need to pay more attention to synonymous and missense variants with low or no impact on protein function as well as intronic variants, whether they are deep or are close to intron/exon boundaries, was a highlight of this study. Due to the fact that few functional studies are performed on these variants, it is suggested that they be analyzed first using bioinformatics tools, and if positive results are obtained, then functional studies can be designed.

Amyotrophic lateral sclerosis (ALS) is the most common type of motor neuron disease whose causes are unclear. The first ALS gene associated with the autosomal dominant form of the disease was SOD1. This gene has a high rate of rare variants, and an appropriate classification is essential for a correct ALS diagnosis. In this study, we re-evaluated the classification of all previously reported SOD1 variants (n = 202) from ALSoD, project MinE, and in-house databases by applying the ACMG-AMP criteria to ALS. New bioinformatics analysis, frequency rating, and a thorough search for functional studies were performed. We also proposed adjusting criteria strength describing how to apply them to SOD1 variants. Most of the previously reported variants have been reclassified as likely pathogenic and pathogenic based on the modified weight of the PS3 criterion, highlighting how in vivo or in vitro functional studies are determining their interpretation and classification. Furthermore, this study reveals the concordance and discordance of annotations between open databases, indicating the need for expert review to adapt the study of variants to a specific disease. Indeed, in complex diseases, such as ALS, the oligogenic inheritance, the presence of genes that act as risk factors and the reduced penetration must be considered. Overall, the diagnosis of ALS remains clinical, and improving variant classification could support genetic data as diagnostic criteria.

Pathogenic variants (PVs) in BRCA genes have been mainly associated with an increasing risk of triple negative breast cancer (TNBC). The contribution of PVs in non-BRCA genes to TNBC seems likely since the processing of homologous recombination repair of double-strand DNA breaks involves several genes. Here, we investigate the susceptibility of genetic variation of the BRCA and non-BRCA genes in 30 early-onset Moroccan women with TNBC. Methods: Targeted capture-based next generation sequencing (NGS) method was performed with a multigene panel testing (MGPT) for variant screening. Panel sequencing was performed with genes involved in hereditary predisposition to cancer and candidate genes whose involvement remains unclear using Illumina MiSeq platform. Interpretation was conducted by following the American College of Medical Genetics and Genomics-Association for Molecular Pathology (ACMG-AMP) criteria. Results: PVs were identified in 20% (6/30) of patients with TNBC. Of these, 16.7% (5/30) carried a BRCA PV [10% (3/30) in BRCA1, 6.7% (2/30) in BRCA2] and 6.6% (2/30) carried a non-BRCA PV. The identified PVs in BRCA genes (BRCA1 c.798_799delTT, BRCA1 c.3279delC, BRCA2 c.1310_1313del, and BRCA2 c.1658T>G) have been reported before and were classified as pathogenic. The identified founder PVs BRCA1 c.798_799del and BRCA2 c.1310_1313delAAGA represented 10% (3/30). Our MGPT allowed identification of several sequence variations in most investigated genes, among which we found novel truncating variations in PALB2 and BARD1 genes. The PALB2 c.3290dup and BARD1 c.1333G>T variants are classified as pathogenic. We also identified 42 variants of unknown/uncertain significance (VUS) in 70% (21/30) of patients with TNBC, including 50% (21/42) missense variants. The highest VUS rate was observed in ATM (13%, 4/30). Additionally, 35.7% (15/42) variants initially well-known as benign, likely benign or conflicting interpretations of pathogenicity have been reclassified as VUS according to ACMG-AMP. Conclusions: PALB2 and BARD1 along with BRCA genetic screening could be helpful for a larger proportion of early-onset TNBC in Morocco.

The strategies of classifying APP, PSEN1, and PSEN2 variants varied substantially in the previous studies. We aimed to re-evaluate these variants systematically according to the American college of medical genetics and genomics and the association for molecular pathology (ACMG-AMP) guidelines. In our study, APP, PSEN1, and PSEN2 variants were collected by searching Alzforum and PubMed database with keywords \"PSEN1,\" \"PSEN2,\" and \"APP.\" These variants were re-evaluated based on the ACMG-AMP guidelines. We compared the number of pathogenic/likely pathogenic variants of APP, PSEN1, and PSEN2. In total, 66 APP variants, 323 PSEN1 variants, and 63 PSEN2 variants were re-evaluated in our study. 94.91% of previously reported pathogenic variants were re-classified as pathogenic/likely pathogenic variants, while 5.09% of them were variants of uncertain significance (VUS). PSEN1 carried the most prevalent pathogenic/likely pathogenic variants, followed by APP and PSEN2. Significant statistically difference was identified among these three genes when comparing the number of pathogenic/likely pathogenic variants (P < 2.2 × 10-16). Most of the previously reported pathogenic variants were re-classified as pathogenic/likely pathogenic variants while the others were re-evaluated as VUS, highlighting the importance of interpreting APP, PSEN1, and PSEN2 variants with caution according to ACMG-AMP guidelines.

Cancer is one of the most important health issues globally and the accuracy of interpretation of cancer-related variants is critical for the clinical management of hereditary cancer. ClinGen Sequence Variant Interpretation Working Groups have developed many adaptations of American College of Medical Genetics and Genomics and the Association of Molecular Pathologists guidelines to improve the consistency of interpretation. We combined the most recent adaptations to expand the number of the criteria from 28 to 48 and developed a tool called Cancer SIGVAR to help genetic counselors interpret the clinical significance of cancer germline variants. Our tool can accept VCF files as input and realize fully automated interpretation based on 21 criteria and semiautomated interpretation based on 48 criteria. We validated the performance of our tool with the ClinVar and CLINVITAE benchmark databases, achieving an average consistency for pathogenic and benign assessment up to 93.71% and 79.38%, respectively. We compared Cancer SIGVAR with two similar tools, InterVar and PathoMAN, and analyzed the main differences in criteria and implementation. Furthermore, we selected 911 variants from another two in-house benchmark databases, and semiautomated interpretation reached an average classification consistency of 98.35%. Our findings highlight the need to optimize automated interpretation tools based on constantly updated guidelines. Cancer SIGVAR is publicly available at http://cancersigvar.bgi.com/.

PALB2 monoallelic loss-of-function germ-line variants confer a breast cancer risk comparable to the average BRCA2 pathogenic variant. Recommendations for risk reduction strategies in carriers are similar. Elaborating robust criteria to identify loss-of-function variants in PALB2-without incurring overprediction-is thus of paramount clinical relevance. Towards this aim, we have performed a comprehensive characterisation of alternative splicing in PALB2, analysing its relevance for the classification of truncating and splice site variants according to the 2015 American College of Medical Genetics and Genomics-Association for Molecular Pathology guidelines.
Alternative splicing was characterised in RNAs extracted from blood, breast and fimbriae/ovary-related human specimens (n=112). RNAseq, RT-PCR/CE and CloneSeq experiments were performed by five contributing laboratories. Centralised revision/curation was performed to assure high-quality annotations. Additional splicing analyses were performed in PALB2 c.212-1G>A, c.1684+1G>A, c.2748+2T>G, c.3113+5G>A, c.3350+1G>A, c.3350+4A>C and c.3350+5G>A carriers. The impact of the findings on PVS1 status was evaluated for truncating and splice site variant.
We identified 88 naturally occurring alternative splicing events (81 newly described), including 4 in-frame events predicted relevant to evaluate PVS1 status of splice site variants. We did not identify tissue-specific alternate gene transcripts in breast or ovarian-related samples, supporting the clinical relevance of blood-based splicing studies.
PVS1 is not necessarily warranted for splice site variants targeting four PALB2 acceptor sites (exons 2, 5, 7 and 10). As a result, rare variants at these splice sites cannot be assumed pathogenic/likely pathogenic without further evidences. Our study puts a warning in up to five PALB2 genetic variants that are currently reported as pathogenic/likely pathogenic in ClinVar.