BATCAVE: calling somatic mutations with a tumor- And site-specific prior

Detecting somatic mutations withins tumors is key to understanding treatment resistance, patient prognosis and tumor evolution. Mutations at low allelic frequency, those present in only a small portion of tumor cells, are particularly difficult to detect. Many algorithms have been developed to detect such mutations, but none models a key aspect of tumor biology. Namely, every tumor has its own profile of mutation types that it tends to generate. We present BATCAVE (Bayesian Analysis Tools for Context-Aware Variant Evaluation), an algorithm that first learns the individual tumor mutational profile and mutation rate then uses them in a prior for evaluating potential mutations. We also present an R implementation of the algorithm, built on the popular caller MuTect. Using simulations, we show that adding the BATCAVE algorithm to MuTect improves variant detection. It also improves the calibration of posterior probabilities, enabling more principled tradeoff between precision and recall. We also show that BATCAVE performs well on real data. Our implementation is computationally inexpensive and straightforward to incorporate into existing MuTect pipelines. More broadly, the algorithm can be added to other variant callers, and it can be extended to include additional biological features that affect mutation generation. © The Author(s) 2020. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Shifts in Mutation Bias Promote Mutators by Altering the Distribution of Fitness Effects

Recent experimental evidence demonstrates that shifts in mutational biases—for example, increases in transversion frequency— can change the distribution of fitness effects of mutations (DFE). In particular, reducing or reversing a prevailing bias can increase the probability that a de novo mutation is beneficial. It has also been shown that mutator bacteria are more likely to emerge if the beneficial mutations they generate have a larger effect size than observed in the wild type. Here, we connect these two results, demonstrating that mutator strains that reduce or reverse a prevailing bias have a positively shifted DFE, which in turn can dramatically increase their emergence probability. Since changes in mutation rate and bias are often coupled through the gain and loss of DNA repair enzymes, our results predict that the invasion of mutator strains will be facilitated by shifts in mutation bias that offer improved access to previously undersampled beneficial mutations. © 2023 The University of Chicago.12 month embargo; first published 23 August 2023This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

On the prospect of achieving accurate joint estimation of selection with population history

As both natural selection and population history can affect genome-wide patterns of variation, disentangling the contributions of each has remained as a major challenge in population genetics. We here discuss historical and recent progress towards this goal-highlighting theoretical and computational challenges that remain to be addressed, as well as inherent difficulties in dealing with model complexity and model violations-and offer thoughts on potentially fruitful next steps. © The Author(s) 2022. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Deep connections: Divergence histories with gene flow in mesophotic Agaricia corals

Largely understudied, mesophotic coral ecosystems lie below shallow reefs (at >30 m depth) and comprise ecologically distinct communities. Brooding reproductive modes appear to predominate among mesophotic-specialist corals and may limit genetic connectivity among populations. Using reduced representation genomic sequencing, we assessed spatial population genetic structure at 50 m depth in an ecologically important mesophotic-specialist species Agaricia grahamae, among locations in the Southern Caribbean. We also tested for hybridisation with the closely related (but depth-generalist) species Agaricia lamarcki, within their sympatric depth zone (50 m). In contrast to our expectations, no spatial genetic structure was detected between the reefs of Curaçao and Bonaire (~40 km apart) within A. grahamae. However, cryptic taxa were discovered within both taxonomic species, with those in A. lamarcki (incompletely) partitioned by depth and those in A. grahamae occurring sympatrically (at the same depth). Hybrid analyses and demographic modelling identified contemporary and historical gene flow among cryptic taxa, both within and between A. grahamae and A. lamarcki. These results (1) indicate that spatial connectivity and subsequent replenishment may be possible between islands of moderate geographic distances for A. grahamae, an ecologically important mesophotic species, (2) that cryptic taxa occur in the mesophotic zone and environmental selection along shallow to mesophotic depth gradients may drive divergence in depth-generalists such as A. lamarcki, and (3) highlight that gene flow links taxa within this relativity diverse Caribbean genus.Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Genomic landscape of lymphatic malformations: a case series and response to the PI3Kα inhibitor alpelisib in an N-of-1 clinical trial

Background: Lymphatic malformations (LMs) often pose treatment challenges due to a large size or a critical location that could lead to disfigurement, and there are no standardized treatment approaches for either refractory or unresectable cases. Methods: We examined the genomic landscape of a patient cohort of LMs (n = 30 cases) that underwent comprehensive genomic profiling using a large-panel next-generation sequencing assay. Immunohistochemical analyses were completed in parallel. Results: These LMs had low mutational burden with hotspot PIK3CA mutations (n = 20) and NRAS (n = 5) mutations being most frequent, and mutually exclusive. All LM cases with Kaposi sarcoma-like (kaposiform) histology had NRAS mutations. One index patient presented with subacute abdominal pain and was diagnosed with a large retroperitoneal LM harboring a somatic PIK3CA gain-of-function mutation (H1047R). The patient achieved a rapid and durable radiologic complete response, as defined in RECIST1.1, to the PI3Kα inhibitor alpelisib within the context of a personal-ized N-of-1 clinical trial (NCT03941782). In translational correlative studies, canonical PI3Kα pathway activation was confirmed by immunohistochemistry and human LM-derived lymphatic endothelial cells carrying an allele with an activating mutation at the same locus were sensitive to alpelisib treatment in vitro, which was demonstrated by a concentration-dependent drop in measurable imped-ance, an assessment of cell status. Conclusions: Our findings establish that LM patients with conventional or kaposiform histology have distinct, yet targetable, driver mutations. © Shaheen, Tse et al.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

A map of human genome variation from population-scale sequencing

The 1000 Genomes Project aims to provide a deep characterization of human genome sequence variation as a foundation for investigating the relationship between genotype and phenotype. Here we present results of the pilot phase of the project, designed to develop and compare different strategies for genome-wide sequencing with high-throughput platforms. We undertook three projects: low-coverage whole-genome sequencing of 179 individuals from four populations; high-coverage sequencing of two mother-father-child trios; and exon-targeted sequencing of 697 individuals from seven populations. We describe the location, allele frequency and local haplotype structure of approximately 15 million single nucleotide polymorphisms, 1 million short insertions and deletions, and 20,000 structural variants, most of which were previously undescribed. We show that, because we have catalogued the vast majority of common variation, over 95% of the currently accessible variants found in any individual are present in this data set. On average, each person is found to carry approximately 250 to 300 loss-of-function variants in annotated genes and 50 to 100 variants previously implicated in inherited disorders. We demonstrate how these results can be used to inform association and functional studies. From the two trios, we directly estimate the rate of de novo germline base substitution mutations to be approximately 10(-8) per base pair per generation. We explore the data with regard to signatures of natural selection, and identify a marked reduction of genetic variation in the neighbourhood of genes, due to selection at linked sites. These methods and public data will support the next phase of human genetic research