UNASSIGNED: Genetic diagnosis of Alport syndrome (AS), which results from pathogenic variants in COL4A3, COL4A4, or COL4A5 genes, is hindered by large numbers of unclassified variants detected using next-generation sequencing (NGS). We examined the impact on splicing of variants of uncertain significance in COL4A3 to COL4A5.
UNASSIGNED: Nine unrelated patients with clinical diagnosis or suspicion of AS were enrolled according to the criteria. Their clinical and genetic data were collected. Blood and urine samples were obtained from the patients and their family members. Sanger sequencing was used to confirm the 9 COL4A3 to COL4A5 unclassified variants identified by NGS. COL4A3 to COL4A5 mRNAs from urine were analyzed using targeted reverse transcription polymerase chain reaction and direct sequencing.
UNASSIGNED: Nine COL4A3 to COL4A5 unclassified variants were found to alter mRNAs splicing. Skipping of an exon or an exon fragment was induced by variants COL4A3 c.828+5G>A; COL4A4 c.3506-13_3528del; and COL4A5 c.451A>G (p. [Ile151Val]), c.2042-9 T>G, c.2689 G>C (p. [Glu897Gln]) and c.1033-10_1033-2delGGTAATAAA. Retention of an intron fragment was caused by variants COL4A3 c.3211-30G＞T, and COL4A5 c.4316-20T>A and c.1033-10 G>A, respectively. The 9 families in this study obtained genetic diagnosis of AS, including 3 with autosomal recessive AS and 6 with X-linked AS.
UNASSIGNED: Our findings demonstrate that urine mRNA analysis facilitates the identification of abnormal splicing of unclassified variants in Alport genes, which provides evidence of routine use of RNA analysis to improve genetic diagnosis of AS.

Recently, we demonstrated that the qualitative American College of Medical Genetics and Genomics/Association for Medical Pathology (ACMG/AMP) guidelines for evaluation of Mendelian disease gene variants are fundamentally compatible with a quantitative Bayesian formulation. Here, we show that the underlying ACMG/AMP \"strength of evidence categories\" can be abstracted into a point system. These points are proportional to Log(odds), are additive, and produce a system that recapitulates the Bayesian formulation of the ACMG/AMP guidelines. The strengths of this system are its simplicity and that the connection between point values and odds of pathogenicity allows empirical calibration of the strength of evidence for individual data types. Weaknesses include that a narrow range of prior probabilities is locked in and that the Bayesian nature of the system is inapparent. We conclude that a points-based system has the practical attribute of user-friendliness and can be useful so long as the underlying Bayesian principles are acknowledged.

Pathogenic mutation in BRCA1 gene is one of the most penetrant genetic predispositions towards cancer. Identification of the mutation provides important aspect in prevention and treatment of the mutation-caused cancer. Of the large quantity of genetic variants identified in human BRCA1, substantial portion is classified as Variant of Uncertain Significance (VUS) or unclassified variants due to the lack of functional evidence. In this study, we focused on the VUS and unclassified variants in BRCT repeat located at BRCA1 C-terminal. Utilizing the well-determined structure of BRCT repeats, we measured the influence of the variants on the structural conformations of BRCT repeats by using molecular dynamics simulation (MDS) consisting of RMSD (Root-mean-square-deviation), RMSF (Root-mean-square-fluctuations), Rg (Radius of gyration), SASA (Solvent accessible surface area), NH bond (hydrogen bond) and Covariance analysis. Using this approach, we analyzed 131 variants consisting of 89 VUS (Variant of Uncertain Significance) and 42 unclassified variants (unclassifiable by current methods) within BRCT repeats and were able to differentiate them into 78 Deleterious and 53 Tolerated variants. Comparing the results made by the saturation genome editing assay, multiple experimental assays, and BRCA1 reference databases shows that our approach provides high specificity, sensitivity and robust. Our study opens an avenue to classify VUS and unclassified variants in many cancer predisposition genes with known protein structure.

We evaluated the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) variant pathogenicity guidelines for internal consistency and compatibility with Bayesian statistical reasoning.
The ACMG/AMP criteria were translated into a naive Bayesian classifier, assuming four levels of evidence and exponentially scaled odds of pathogenicity. We tested this framework with a range of prior probabilities and odds of pathogenicity.
We modeled the ACMG/AMP guidelines using biologically plausible assumptions. Most ACMG/AMP combining criteria were compatible. One ACMG/AMP likely pathogenic combination was mathematically equivalent to pathogenic and one ACMG/AMP pathogenic combination was actually likely pathogenic. We modeled combinations that include evidence for and against pathogenicity, showing that our approach scored some combinations as pathogenic or likely pathogenic that ACMG/AMP would designate as variant of uncertain significance (VUS).
By transforming the ACMG/AMP guidelines into a Bayesian framework, we provide a mathematical foundation for what was a qualitative heuristic. Only 2 of the 18 existing ACMG/AMP evidence combinations were mathematically inconsistent with the overall framework. Mixed combinations of pathogenic and benign evidence could yield a likely pathogenic, likely benign, or VUS result. This quantitative framework validates the approach adopted by the ACMG/AMP, provides opportunities to further refine evidence categories and combining rules, and supports efforts to automate components of variant pathogenicity assessments.

OBJECTIVE: Unclassified variants (UVs) of BRCA1 and BRCA2 genes are not defined as pathogenic for breast cancer, and their clinical significance currently remains undefined. Therefore, this study was conducted to identify potentially pathogenic UVs by comparing their prevalence between breast cancer patients and controls.
METHODS: A total of 328 breast cancer patients underwent BRCA1/2 genetic screening at the National Cancer Center of Korea. Genetic variants of BRCA genes that were categorized as unclassified according to the Breast Cancer Information Core database were selected based on allelic frequency, after which candidate variants were genotyped in 421 healthy controls. We also examined family members of the study participants. Finally, the effects of amino acid substitutions on protein structure and function were predicted in silico.
RESULTS: Genetic tests revealed 33 UVs in BRCA1 and 47 in BRCA2. Among 15 candidates genotyped in healthy controls, c.5339T>C in BRCA1 and c.6029T>G, c.7522G>A in BRCA2 were not detected. Moreover, the c.5339T>C variant in the BRCA1 gene was detected in four patients with a family history of breast cancer. This nonsynonymous variant (Leu1780Pro) in the BRCA1 C-terminal domain was predicted to have an effect on BRCA1 protein structure/function.
CONCLUSIONS: This study showed that comparison of genotype frequency between cases and controls could help identify UVs of BRCA genes that are potentially pathogenic. Moreover, ourfindings suggest that c.5339T>C in BRCA1 might be a pathogenic variant for patients and their families.

Numerous pathogenic DNA variants impair the splicing mechanism in human genetic diseases. Minigenes are optimal approaches to test variants under the splicing viewpoint without the need of patient samples. We aimed to design a robust minigene construct of the breast cancer gene BRCA2 in order to investigate the impact of variants on splicing. BRCA2 exons 19-27 (MGBR2_ex19-27) were cloned in the new vector pSAD. It produced a large transcript of the expected size (2,174 nucleotides) and exon structure (V1-ex19-27-V2). Splicing assays showed that 18 (17 splice-site and 1 silencer variants) out of 40 candidate DNA variants induced aberrant patterns. Twenty-four anomalous transcripts were accurately detected by fluorescent-RT-PCR that were generated by exon-skipping, alternative site usage, and intron-retention events. Fourteen variants induced major anomalies and were predicted to disrupt protein function so they could be classified as pathogenic. Furthermore, minigene mimicked previously reported patient RNA outcomes of seven variants supporting the reproducibility of minigene assays. Therefore, a relevant fraction of variants are involved in breast cancer through splicing alterations. MGBR2_ex19-27 is the largest reported BRCA2 minigene and constitutes a valuable tool for the functional and clinical classification of sequence variations.