scholarly journals Synonymous mutations reveal genome-wide driver mutation rates in healthy tissues

2020 ◽  
Author(s):  
Gladys Poon ◽  
Caroline J. Watson ◽  
Daniel S. Fisher ◽  
Jamie R. Blundell
Keyword(s):  
Cancer Risk ◽  
Allele Frequency ◽  
Genetic Alterations ◽  
Driver Mutation ◽  
Variant Allele ◽  
Driver Mutations ◽  
Total Rate ◽  
Variant Allele Frequency ◽  
Synonymous Mutations ◽  
Passenger Mutations

Genetic alterations that drive clonal expansions in ostensibly healthy tissues have implications for cancer risk. However, the total rate at which clonal expansions occur in healthy tissues remains unknown. Synonymous passenger mutations that hitchhike to high variant allele frequency due to a linked driver mutation can be used to estimate the total rate of positive selection across the genome. Because these synonymous hitchhikers are influenced by all mutations under selection, regardless of type or location, they can be used to estimate how many driver mutations are missed by narrow gene-focused sequencing panels. Here we analyse the variant allele frequency spectrum of synonymous passenger mutations to estimate the total rate at which mutations driving clonal expansions occur in healthy tissues. By applying our framework to data from physiologically healthy blood, we find that a large fraction of mutations driving clonal expansions occur outside of canonical cancer driver genes. In contrast, analysis of data from healthy oesophagus reveals little evidence for many driver mutations outside of those in NOTCH1 and TP53. Our framework, which generalizes to other tissues, sheds light on the fraction of drivers mutations that remain undiscovered and has implications for cancer risk prediction.

scholarly journals Examination of the predicted prevalence of Gitelman syndrome by ethnicity based on genome databases

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Atsushi Kondo ◽  
China Nagano ◽  
Shinya Ishiko ◽  
Takashi Omori ◽  
Yuya Aoto ◽  
...  
Keyword(s):  
Allele Frequency ◽  
Carrier Frequency ◽  
Japanese Population ◽  
Autosomal Recessive ◽  
Disease Prevalence ◽  
Gitelman Syndrome ◽  
Variant Allele ◽  
Variant Allele Frequency ◽  
Genome Databases ◽  
Heterozygous Carriers

AbstractGitelman syndrome is an autosomal recessive inherited salt-losing tubulopathy. It has a prevalence of around 1 in 40,000 people, and heterozygous carriers are estimated at approximately 1%, although the exact prevalence is unknown. We estimated the predicted prevalence of Gitelman syndrome based on multiple genome databases, HGVD and jMorp for the Japanese population and gnomAD for other ethnicities, and included all 274 pathogenic missense or nonsense variants registered in HGMD Professional. The frequencies of all these alleles were summed to calculate the total variant allele frequency in SLC12A3. The carrier frequency and the disease prevalence were assumed to be twice and the square of the total allele frequency, respectively, according to the Hardy–Weinberg principle. In the Japanese population, the total carrier frequencies were 0.0948 (9.5%) and 0.0868 (8.7%) and the calculated prevalence was 0.00225 (2.3 in 1000 people) and 0.00188 (1.9 in 1000 people) in HGVD and jMorp, respectively. Other ethnicities showed a prevalence varying from 0.000012 to 0.00083. These findings indicate that the prevalence of Gitelman syndrome in the Japanese population is higher than expected and that some other ethnicities also have a higher prevalence than has previously been considered.

Abstract 569: Correlation of variant allele frequency and mean tumor molecules with tumor burden in patients with solid tumors

2021 ◽  
Author(s):  
Antony Tin ◽  
Vasily Aushev ◽  
Ekaterina Kalashnikova ◽  
Raheleh Salari ◽  
Svetalana Shchegrova ◽  
...  
Keyword(s):  
Solid Tumors ◽  
Allele Frequency ◽  
Tumor Burden ◽  
Variant Allele ◽  
Variant Allele Frequency

scholarly journals Variant allele frequency enrichment analysis in vitro reveals sonic hedgehog pathway to impede sustained temozolomide response in GBM

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Nidhan K. Biswas ◽  
Vikas Chandra ◽  
Neeta Sarkar-Roy ◽  
Tapojyoti Das ◽  
Rabindra N. Bhattacharya ◽  
...  
Keyword(s):  
Allele Frequency ◽  
Sonic Hedgehog ◽  
Enrichment Analysis ◽  
Hedgehog Pathway ◽  
Variant Allele ◽  
Sonic Hedgehog Pathway ◽  
Variant Allele Frequency

Approaches for the identification of driver mutations in cancer: A tutorial from a computational perspective

2020 ◽  
Vol 18 (03) ◽  
pp. 2050016 ◽  
Author(s):  
Jorge Francisco Cutigi ◽  
Adriane Feijo Evangelista ◽  
Adenilso Simao
Keyword(s):  
Computational Methods ◽  
Complex Disease ◽  
Cancer Genomics ◽  
Genetic Alterations ◽  
Driver Mutations ◽  
Easy Access ◽  
Computational Perspective ◽  
High Level ◽  
Passenger Mutations ◽  
Cancer Bioinformatics

Cancer is a complex disease caused by the accumulation of genetic alterations during the individual’s life. Such alterations are called genetic mutations and can be divided into two groups: (1) Passenger mutations, which are not responsible for cancer and (2) Driver mutations, which are significant for cancer and responsible for its initiation and progression. Cancer cells undergo a large number of mutations, of which most are passengers, and few are drivers. The identification of driver mutations is a key point and one of the biggest challenges in Cancer Genomics. Many computational methods for such a purpose have been developed in Cancer Bioinformatics. Such computational methods are complex and are usually described in a high level of abstraction. This tutorial details some classical computational methods, from a computational perspective, with the transcription in an algorithmic format towards an easy access by researchers.

scholarly journals Clone decomposition based on mutation signatures provides novel insights into mutational processes

2021 ◽  
Author(s):  
Taro Matsutani ◽  
Michiaki Hamada
Keyword(s):  
Allele Frequency ◽  
Tumor Heterogeneity ◽  
Cytidine Deaminase ◽  
Lymphocytic Leukemia ◽  
Variant Allele ◽  
Variant Allele Frequency ◽  
Coding Regions ◽  
Activation Induced Cytidine Deaminase ◽  
Almost All ◽  
Mutational Processes

Intra-tumor heterogeneity is a phenomenon in which mutation profiles differ from cell to cell within the same tumor and is observed in almost all tumors. Understanding intra-tumor heterogeneity is essential from the clinical perspective. Numerous methods have been developed to predict this phenomenon based on variant allele frequency. Among the methods, CloneSig models the variant allele frequency and mutation signatures simultaneously and provides an accurate clone decomposition. However, this method has limitations in terms of clone number selection and modeling. We propose SigTracer, a novel hierarchical Bayesian approach for analyzing intra-tumor heterogeneity based on mutation signatures to tackle these issues. We show that SigTracer predicts more reasonable clone decompositions than the existing methods that use artificial data that mimic cancer genomes. We applied SigTracer to whole-genome sequences of blood cancer samples. The results were consistent with past findings that single base substitutions caused by a specific signature (previously reported as SBS9) related to the activation-induced cytidine deaminase intensively lie within immunoglobulin-coding regions for chronic lymphocytic leukemia samples. Furthermore, we showed that this signature mutates regions responsible for cell-cell adhesion. Accurate assignments of mutations to signatures by SigTracer can provide novel insights into signature origins and mutational processes.

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