Biology subjects

Oza, A.

Publications and source records attributed to Oza, A..

4 recordsLinked to original sources

bioRxiv2018

Recommendations for Interpreting the Loss of Function PVS1 ACMG/AMP Variant Criteria

The 2015 ACMG/AMP sequence variant interpretation guideline provided a framework for classifying variants based on several benign and pathogenic evidence criteria. This guideline includes a pathogenic criterion (PVS1) for predicted loss of function variants. However, the guideline did not elaborate on the specific considerations for the different types of loss of function variants, nor did it provide decision-making pathways assimilating information about the variant type, its location within the gene, or any additional evidence for the likelihood of a true null effect. Furthermore, the ACMG/AMP guideline did not take into account the relative strengths for each evidence type and the final outcome of their combinations with respect to PVS1 strength. Finally, criteria specifying the genes for which PVS1 can be used are still missing. Here, as part of the Clinical Genomic Resource (ClinGen) Sequence Variant Interpretation (SVI) Working Groups goal of refining ACMG/AMP criteria, we provide recommendations for applying the PVS1 rule using detailed guidance addressing all the above-mentioned gaps. We evaluate the performance of the refined rule using heterogeneous types of loss of function variants (n = 56) curated by seven disease-specific groups across ten genes. Our recommendations will facilitate consistent and accurate interpretation of predicted loss of function variants.\n\nGRANT NUMBERSResearch reported in this publication was supported by the National Human Genome Research Institute (NHGRI) under award number U41HG006834. LGB was supported by the Intramural Research Program of the NHGRI grant number HG200359 09.

genomics↗

bioRxiv2018

Expert Specification of the ACMG/AMP Variant Interpretation Guidelines for Genetic Hearing Loss

Due to the high genetic heterogeneity of hearing loss, current clinical testing includes sequencing large numbers of genes, which often yields a significant number of novel variants. Therefore, the standardization of variant interpretation is crucial to provide consistent and accurate diagnoses. The Hearing Loss Variant Curation Expert Panel was created within the Clinical Genome Resource to provide expert guidance for standardized genomic interpretation in the context of hearing loss. As one of its major tasks, our Expert Panel has adapted the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) standards and guidelines for the interpretation of sequence variants in hearing loss genes. Here, we provide a comprehensive illustration of the newly specified ACMG/AMP hearing loss rules. Three rules remained unchanged, four rules were removed, and the remaining twenty-one rules were specified. Of the specified rules, four had general recommendations, seven were gene/disease considerations, seven had strength-level specifications, and three rules had both gene/disease and strength-level specifications. These rules were further validated and refined using a pilot set of 51 variants assessed by curators. These hearing loss-specific ACMG/AMP rules will help standardize variant interpretation, ultimately leading to better care for individuals with hearing loss.\n\nGRANT NUMBERSResearch reported in this publication was supported by the National Human Genome Research Institute (NHGRI) under award number U41HG006834.

genetics↗

bioRxiv2018

Curating clinically relevant transcripts for the interpretation of sequence variants

Variant interpretation depends on accurate annotations using biologically relevant transcripts. We have developed a systematic strategy for designating primary transcripts, and applied it to 109 hearing loss-associated genes that were divided into 3 categories. Category 1 genes (n=38) had a single transcript, Category 2 genes (n=32) had multiple transcripts, but a single transcript was sufficient to represent all exons, and Category 3 genes (n=38) had multiple transcripts with unique exons. Transcripts were curated with respect to gene expression reported in the literature and the Genotype-Tissue Expression Project. In addition, high frequency loss of function variants in the Genome Aggregation Database, and disease-causing variants in ClinVar and the Human Gene Mutation Database across the 109 genes were queried. These data were used to classify exons as \"clinically relevant\", \"uncertain significance\", or \"clinically insignificant\". Interestingly, 7% of all exons, containing >124 \"clinically significant\" variants, were of \"uncertain significance\". Finally, we used exon-level next generation sequencing quality metrics generated at two clinical labs, and identified a total of 43 technically challenging exons in 20 different genes that had inadequate coverage and/or homology issues which might lead to false variant calls. We have demonstrated that transcript analysis plays a critical role in accurate clinical variant interpretation.

genomics↗

bioRxiv2017

Overlap among spatial memories triggers divergence of hippocampal representations

Across the domains of spatial navigation and episodic memory, the hippocampus is thought to play a critical role in disambiguating (pattern separating) representations of overlapping events. However, the mechanisms underlying hippocampal pattern separation are not fully understood. Here, using a naturalistic route-learning paradigm and spatiotemporal pattern analysis of human fMRI data, we found that hippocampal representations of overlapping routes gradually diverged with learning to the point that they became less similar than representations of non-overlapping events. This representational reversal of the objective route similarity (a) was selective to the hippocampus, (b) only occurred for the specific route segments that were shared across routes, and (c) was predicted by the degree to which individual hippocampal voxels were initially shared across route representations. These findings indicate that event overlap triggers a repulsion of hippocampal representations--a finding that provides critical mechanistic insight into how and why hippocampal representations become separated.

neuroscience↗