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Getseva, V.

Publications and source records attributed to Getseva, V..

2 recordsLinked to original sources

A sibling study of variation in parental mutation rates

People are born with variable numbers of de novo germline mutations (DNMs), depending primarily on the ages of their parents. To explore additional causes, we developed an approach to call DNMs from nucleotide differences between siblings in genomic regions inherited identical by descent from both parents. Applying it to whole genome sequences from 28,985 sibling pairs of diverse genetic ancestries present in the UK Biobank and All of Us datasets, as well as 2,330 trios, we identified >800K autosomal DNMs and characterized mutation phenotypes in 27,645 sets of parents. We found subtle shifts in the mutation spectrum but no differences in total DNM rates among genetic ancestry groups, or between smokers and non-smokers. Testing for associations between parental mutation phenotypes and their burden of loss-of-function and deleterious missense variants in a set of 180 DNA repair and maintenance genes, we discovered that disruptions in REV1 and LIG1 increase germline mutation rates, and thus that rare mutator alleles segregate in population cohorts.

genomics↗

Weakly-Supervised Tumor Purity Prediction FromFrozen H&E Stained Slides

Estimating tumor purity is especially important in the age of precision medicine. Purity estimates have been shown to be critical for correction of tumor sequencing results, and higher purity samples allow for more accurate interpretations from next-generation sequencing results. In addition, tumor purity has been shown to be correlated with survival outcomes for several diseases. Molecular-based purity estimates using computational approaches require sequencing of tumors, which is both time-consuming and expensive. Here we propose an approach, weakly-supervised purity (wsPurity), which can accurately quantify tumor purity within a slide, using multiple and different types of cancer. This approach allows for a flexible analysis of tumors from whole slide imaging (WSI) of histology hematoxylin and eosin (H&E) slides. Our model predicts tumor type with high accuracy (greater than 80% on an independent test cohort), and tumor purity at a higher accuracy compared to a comparable fully-supervised approach (0.1335 MAE on an independent test cohort). In addition to tumor purity prediction, our approach can identify high resolution tumor regions within a slide, to enrich tumor cell selection for downstream analyses. This model could also be used in a clinical setting, to stratify tumors into high and low tumor purity, using different thresholds, in a cancer-dependent manner, depending on what purity levels correlate with worse disease outcomes. In addition, this approach could be used in clinical practice to select the best tissue block for sequencing. Overall, this approach can be used in several different ways to analyze WSIs of tumor H&E sections.

bioinformatics↗