Biology subjects

Gomez, K.

Publications and source records attributed to Gomez, K..

23 records · Page 2Linked to original sources

bioRxiv2020

Mutation bias can shape adaptation in large asexual populations experiencing clonal interference

The Extended Evolutionary Synthesis invokes a role for development in shaping adaptive evolution, which in population genetics terms corresponds to mutation-biased adaptation. Critics have claimed that clonal interference makes mutation-biased adaptation rare. We consider the behavior of two simultaneously adapting traits, one with larger mutation rate U, the other with larger selection coefficient s, using asexual traveling wave models. We find that adaptation is dominated by whichever trait has the faster rate of adaptation v in isolation, with the other trait subject to evolutionary stalling. Reviewing empirical claims for mutation-biased adaptation, we find that not all occur in the "origin-fixation" regime of population genetics where v is only twice as sensitive to s as to U. In some cases, differences in U are at least ten to twelve times larger than differences in s, as needed to cause mutation-biased adaptation even in the "multiple mutations" regime. Surprisingly, when U > s in the "diffusive-mutation" regime, the required sensitivity ratio is also only two, despite pervasive clonal interference. Given two traits with identical v, the benefit of having higher s is surprisingly small, occurring largely when one trait is at the boundary between the origin-fixation and multiple mutations regimes.

evolutionary biology↗

bioRxiv2018

Diagnostic high-throughput sequencing of 2,390 patients with bleeding, thrombotic and platelet disorders

A targeted high-throughput sequencing (HTS) panel test for clinical diagnostics requires careful consideration of the inclusion of appropriate diagnostic-grade genes, the ability to detect multiple types of genomic variation with high levels of analytic sensitivity and reproducibility, and variant interpretation by a multi-disciplinary team (MDT) in the context of the clinical phenotype. We have sequenced 2,390 index patients using the ThromboGenomics HTS panel test of diagnostic-grade genes known to harbour variants associated with rare bleeding, thrombotic or platelet disorders (BPD). The diagnostic rate was determined by the clinical phenotype, with an overall rate of 50.4% for all thrombotic, coagulation, platelet count and function disorder patients and a rate of 6.2% for patients with unexplained bleeding disorders characterized by normal hemostasis test results. The MDT classified 756 unique variants, including copy number and intronic variants, as Pathogenic, Likely Pathogenic or Variants of Uncertain Significance. Almost half (49.7%) of these variants are novel and 41 unique variants were identified in 7 genes recently found to be implicated in BPD. Inspection of canonical hemostasis pathways identified 29 patients with evidence of oligogenic inheritance. A molecular diagnosis has been reported for 897 index patients providing evidence that introducing a HTS genetic test for BPD patients is meeting an important unmet clinical need.\n\nKey pointsO_LIHigh-throughput sequencing (HTS) test reveals a molecular diagnosis for 38% of 2,390 patients with bleeding, thrombotic and platelet disorders.\nC_LIO_LIThromboGenomics HTS test validates recent gene discoveries and detects copy number and intronic variants.\nC_LI

genetics↗

bioRxiv2018

In rapidly adapting asexuals, the orientation of G can reflect selection rather than functional constraints

Genetic covariances represent a combination of pleiotropy and linkage disequilibrium, shaped by the populations history. Observed genetic covariance is most often interpreted in pleiotropic terms. In particular, functional constraints restricting which phenotypes are physically possible can lead to a stable G matrix with high genetic variance in fitness-associated traits and high pleiotropic negative covariance along the phenotypic curve of constraint. In contrast, population genetic models of relative fitness assume endless adaptation without constraint, through a series of selective sweeps that are well described by recent traveling wave models. We describe the implications of such population genetic models for the G matrix when pleiotropy is excluded by design, such that all covariance comes from linkage disequilibrium. The G matrix is far less stable than has previously been found, fluctuating over the timescale of selective sweeps. However, its orientation is relatively stable, corresponding to high genetic variance in fitness-associated traits and strong negative covariance - the same pattern often interpreted in terms of pleiotropic constraints but caused instead by linkage disequilibrium. We find that different mechanisms drive the instabilities along versus perpendicular to the fitness gradient. The origin of linkage disequilibrium is not drift, but small amounts of linkage disequilibrium are instead introduced by mutation and then amplified during competing selective sweeps. This illustrates the need to integrate a broader range of population genetic phenomena into quantitative genetics.

evolutionary biology↗

bioRxiv2018

Predicting clone genotypes from tumor bulk sequencing of multiple samples

MotivationAnalyses of data generated from bulk sequencing of tumors have revealed extensive genomic heterogeneity within patients. Many computational methods have been developed to enable the inference of genotypes of tumor cell populations (clones) from bulk sequencing data. However, the relative and absolute accuracy of available computational methods in estimating clone counts and clone genotypes is not yet known.\n\nResultsWe have assessed the performance of nine methods, including eight previously-published and one new method (CloneFinder), by analyzing computer simulated datasets. CloneFinder, LICHeE, CITUP, and cloneHD inferred clone genotypes with low error (<5% per clone) for a majority of datasets in which the tumor samples contained evolutionarily-related clones. Computational methods did not perform well for datasets in which tumor samples contained mixtures of clones from different clonal lineages. Generally, the number of clones was underestimated by cloneHD and overestimated by Phy-loWGS, and BayClone2, Canopy, and Clomial required prior information regarding the number of clones. AncesTree and Canopy did not produce results for a large number of datasets.\n\nConclusionsDeconvolution of clone genotypes from single nucleotide variant (SNV) frequency differences among tumor samples remains challenging, so there is a need to develop more accurate computational methods and robust software for clone genotype inference.\n\nAvailability and ImplementationCloneFinder is implemented in Python and is available from https://github.com/gstecher/CloneFinderAPI.\n\nContacts.kumar@temple.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online

cancer biology↗

bioRxiv2018

Computational enhancement of single-cell sequences for inferring tumor evolution

Motivation: Tumor sequencing has entered an exciting phase with the advent of single-cell techniques that are revolutionizing the assessment of single nucleotide variation (SNV) at the highest cellular resolution. However, state-of-the-art single-cell sequencing technologies produce data with many missing bases (MBs) and incorrect base designations that lead to false-positive (FP) and false-negative (FN) detection of somatic mutations. While computational methods are available to make biological inferences in the presence of these errors, the accuracy of the imputed MBs and corrected FPs and FNs remains unknown.\n\nResults: Using computer simulated datasets, we assessed the robustness performance of four existing methods (OncoNEM, SCG, SCITE, and SiFit) and one new method (BEAM). BEAM is a Bayesian evolution-aware method that improves the quality of single-cell sequences by using the intrinsic evolutionary information in the single-cell data in a molecular phylogenetic framework. Overall, BEAM and SCITE performed the best. Most of the methods imputed MBs with high accuracy, but effective detection and correction of FPs and FNs require sampling a large number of SNVs. Analysis of an empirical dataset shows that computational methods can improve both the quality of tumor single-cell sequences and their utility for biological inference.\n\nConclusions: Tumor cells descend from pre-existing cells, which creates evolutionary continuity in single-cell sequencing datasets. This information enables BEAM and other methods to correctly impute missing data and incorrect base assignments, but correction of FPs and FNs remains challenging when the number of SNVs sampled is small relative to the number of cells sequenced.\n\nAvailability: BEAM is available on the web at https://github.com/SayakaMiura/BEAM.\n\nContact: s.kumar@temple.edu

cancer biology↗