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Pflugers Archiv : European Journal of... May 2019Mutations in cardiac myosin binding protein C (MYBPC3) represent the most frequent cause of familial hypertrophic cardiomyopathy (HCM), making up approximately 50% of... (Review)
Review
Mutations in cardiac myosin binding protein C (MYBPC3) represent the most frequent cause of familial hypertrophic cardiomyopathy (HCM), making up approximately 50% of identified HCM mutations. MYBPC3 is distinct among other sarcomere genes associated with HCM in that truncating mutations make up the vast majority, whereas nontruncating mutations predominant in other sarcomere genes. Several studies using myocardial tissue from HCM patients have found reduced abundance of wild-type MYBPC3 compared to control hearts, suggesting haploinsufficiency of full-length MYBPC3. Further, decreased mutant versus wild-type mRNA and lack of truncated mutant MYBPC3 protein has been demonstrated, highlighting the presence of allelic imbalance. In this review, we will begin by introducing allelic imbalance and haploinsufficiency, highlighting the broad role each plays within the spectrum of human disease. We will subsequently focus on the roles allelic imbalance and haploinsufficiency play within MYBPC3-linked HCM. Finally, we will explore the implications of these findings on future directions of HCM research. An improved understanding of allelic imbalance and haploinsufficiency may help us better understand genotype-phenotype relationships in HCM and develop novel targeted therapies, providing exciting future research opportunities.
Topics: Animals; Cardiomyopathy, Hypertrophic; Carrier Proteins; Haploinsufficiency; Humans
PubMed: 30456444
DOI: 10.1007/s00424-018-2226-9 -
Scientific Reports Aug 2019The patho-mechanism of somatic driver mutations in cancer usually involves transcription, but the proportion of mutations and wild-type alleles transcribed from DNA to...
The patho-mechanism of somatic driver mutations in cancer usually involves transcription, but the proportion of mutations and wild-type alleles transcribed from DNA to RNA is largely unknown. We systematically compared the variant allele frequencies of recurrently mutated genes in DNA and RNA sequencing data of 246 acute myeloid leukaemia (AML) patients. We observed that 95% of all detected variants were transcribed while the rest were not detectable in RNA sequencing with a minimum read-depth cut-off (10x). Our analysis focusing on 11 genes harbouring recurring mutations demonstrated allelic imbalance (AI) in most patients. GATA2, RUNX1, TET2, SRSF2, IDH2, PTPN11, WT1, NPM1 and CEBPA showed significant AIs. While the effect size was small in general, GATA2 exhibited the largest allelic imbalance. By pooling heterogeneous data from three independent AML cohorts with paired DNA and RNA sequencing (N = 253), we could validate the preferential transcription of GATA2-mutated alleles. Differential expression analysis of the genes with significant AI showed no significant differential gene and isoform expression for the mutated genes, between mutated and wild-type patients. In conclusion, our analyses identified AI in nine out of eleven recurrently mutated genes. AI might be a common phenomenon in AML which potentially contributes to leukaemogenesis.
Topics: Allelic Imbalance; Female; Gene Expression Regulation, Leukemic; High-Throughput Nucleotide Sequencing; Humans; Leukemia, Myeloid, Acute; Male; Mutation; Neoplasm Proteins; Neoplasm Recurrence, Local; Nucleophosmin; Prognosis
PubMed: 31409822
DOI: 10.1038/s41598-019-48167-4 -
Nature Genetics Jun 2022While many germline cancer risk variants have been identified through genome-wide association studies (GWAS), the mechanisms by which these variants operate remain...
While many germline cancer risk variants have been identified through genome-wide association studies (GWAS), the mechanisms by which these variants operate remain largely unknown. Here we used 406 cancer ATAC-Seq samples across 23 cancer types to identify 7,262 germline allele-specific accessibility QTLs (as-aQTLs). Cancer as-aQTLs had stronger enrichment for cancer risk heritability (up to 145 fold) than any other functional annotation across seven cancer GWAS. Most cancer as-aQTLs directly altered transcription factor (TF) motifs and exhibited differential TF binding and gene expression in functional screens. To connect as-aQTLs to putative risk mechanisms, we introduced the regulome-wide associations study (RWAS). RWAS identified genetically associated accessible peaks at >70% of known breast and prostate loci and discovered new risk loci in all examined cancer types. Integrating as-aQTL discovery, motif analysis and RWAS identified candidate causal regulatory elements and their probable upstream regulators. Our work establishes cancer as-aQTLs and RWAS analysis as powerful tools to study the genetic architecture of cancer risk.
Topics: Allelic Imbalance; Chromatin; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Male; Neoplasms; Polymorphism, Single Nucleotide
PubMed: 35697866
DOI: 10.1038/s41588-022-01075-2 -
Genome Biology Jul 2023Detecting allelic imbalance at the isoform level requires accounting for inferential uncertainty, caused by multi-mapping of RNA-seq reads. Our proposed method, SEESAW,...
Detecting allelic imbalance at the isoform level requires accounting for inferential uncertainty, caused by multi-mapping of RNA-seq reads. Our proposed method, SEESAW, uses Salmon and Swish to offer analysis at various levels of resolution, including gene, isoform, and aggregating isoforms to groups by transcription start site. The aggregation strategies strengthen the signal for transcripts with high uncertainty. The SEESAW suite of methods is shown to have higher power than other allelic imbalance methods when there is isoform-level allelic imbalance. We also introduce a new test for detecting imbalance that varies across a covariate, such as time.
Topics: Uncertainty; Allelic Imbalance; Protein Isoforms; RNA-Seq; Transcription Initiation Site
PubMed: 37438847
DOI: 10.1186/s13059-023-03003-x -
Bioinformatics (Oxford, England) Jul 2019Genetic analysis of cancer regularly includes two or more samples from the same patient. Somatic copy number alterations leading to allelic imbalance (AI) play a...
MOTIVATION
Genetic analysis of cancer regularly includes two or more samples from the same patient. Somatic copy number alterations leading to allelic imbalance (AI) play a critical role in cancer initiation and progression. Directional analysis and visualization of the alleles in imbalance in multi-sample settings allow for inference of recurrent mutations, providing insights into mutation rates, clonality and the genomic architecture and etiology of cancer.
RESULTS
The REpeat Chromosomal changes Uncovered by Reflection (RECUR) is an R application for the comparative analysis of AI profiles derived from SNP array and next-generation sequencing data. The algorithm accepts genotype calls and 'B allele' frequencies (BAFs) from at least two samples derived from the same individual. For a predefined set of genomic regions with AI, RECUR compares BAF values among samples. In the presence of AI, the expected value of a BAF can shift in two possible directions, reflecting an increased or decreased abundance of the maternal haplotype, relative to the paternal. The phenomenon of opposite haplotype shifts, or 'mirrored subclonal allelic imbalance', is a form of heterogeneity, and has been linked to clinico-pathological features of cancer. RECUR detects such genomic segments of opposite haplotypes in imbalance and plots BAF values for all samples, using a two-color scheme for intuitive visualization.
AVAILABILITY AND IMPLEMENTATION
RECUR is available as an R application. Source code and documentation are available at scheet.org.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
Topics: Alleles; Allelic Imbalance; DNA Copy Number Variations; Haplotypes; Humans; Polymorphism, Single Nucleotide
PubMed: 30462146
DOI: 10.1093/bioinformatics/bty885 -
PLoS Genetics Dec 2019Many disease risk loci identified in genome-wide association studies are present in non-coding regions of the genome. Previous studies have found enrichment of...
Many disease risk loci identified in genome-wide association studies are present in non-coding regions of the genome. Previous studies have found enrichment of expression quantitative trait loci (eQTLs) in disease risk loci, indicating that identifying causal variants for gene expression is important for elucidating the genetic basis of not only gene expression but also complex traits. However, detecting causal variants is challenging due to complex genetic correlation among variants known as linkage disequilibrium (LD) and the presence of multiple causal variants within a locus. Although several fine-mapping approaches have been developed to overcome these challenges, they may produce large sets of putative causal variants when true causal variants are in high LD with many non-causal variants. In eQTL studies, there is an additional source of information that can be used to improve fine-mapping called allelic imbalance (AIM) that measures imbalance in gene expression on two chromosomes of a diploid organism. In this work, we develop a novel statistical method that leverages both AIM and total expression data to detect causal variants that regulate gene expression. We illustrate through simulations and application to 10 tissues of the Genotype-Tissue Expression (GTEx) dataset that our method identifies the true causal variants with higher specificity than an approach that uses only eQTL information. Across all tissues and genes, our method achieves a median reduction rate of 11% in the number of putative causal variants. We use chromatin state data from the Roadmap Epigenomics Consortium to show that the putative causal variants identified by our method are enriched for active regions of the genome, providing orthogonal support that our method identifies causal variants with increased specificity.
Topics: Allelic Imbalance; Chromatin; Chromosome Mapping; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Linkage Disequilibrium; Multifactorial Inheritance; Polymorphism, Single Nucleotide; Quantitative Trait Loci
PubMed: 31834882
DOI: 10.1371/journal.pgen.1008481 -
Medicine Jul 2015Allelic imbalance of thymidylate synthase (TYMS) is attributed to polymorphisms in the 5'- and 3'-untranslated region (UTR). These polymorphisms have been related to the...
Allelic imbalance of thymidylate synthase (TYMS) is attributed to polymorphisms in the 5'- and 3'-untranslated region (UTR). These polymorphisms have been related to the risk of suffering different cancers, for example leukemia, breast or gastric cancer, and response to different drugs, among which are methotrexate glutamates, stavudine, and specifically 5-fluorouracil (5-FU), as TYMS is its direct target. A vast literature has been published in relation to 5-FU, even suggesting the sole use of these polymorphisms to effectively manage 5-FU dosage. Estimates of the extent to which these polymorphisms influence in TYMS expression have in the past been based on functional analysis by luciferase assays and quantification of TYMS mRNA, but both these studies, as the association studies with cancer risk or with toxicity or response to 5-FU, are very contradictory. Regarding functional assays, the artificial genetic environment created in luciferase assay and the problems derived from quantitative polymerase chain reactions (qPCRs), for example the use of a reference gene, may have distorted the results. To avoid these sources of interference, we have analyzed the allelic imbalance of TYMS by allelic-specific analysis in peripheral blood mononuclear cells (PBMCs) from patients.Allelic imbalance in PBMCs, taken from 40 patients with suspected myeloproliferative haematological diseases, was determined by fluorescent fragment analysis (for the 3'-UTR polymorphism), Sanger sequencing and allelic-specific qPCR in multiplex (for the 5'-UTR polymorphisms).For neither the 3'- nor the 5'-UTR polymorphisms did the observed allelic imbalance exceed 1.5 fold. None of the TYMS polymorphisms is statistically associated with allelic imbalance.The results acquired allow us to deny the previously established assertion of an influence of 2 to 4 fold of the rs45445694 and rs2853542 polymorphisms in the expression of TYMS and narrow its allelic imbalance to 1.5 fold, in our population. These data circumscribe the influence of these polymorphisms in the clinical outcome of 5-FU and question their use for establishing 5-FU dosage, above all when additional genetic factors are not considered.
Topics: 3' Untranslated Regions; 5' Untranslated Regions; Adult; Aged; Aged, 80 and over; Alleles; Allelic Imbalance; Female; Fluorouracil; Genotype; Humans; Leukocytes, Mononuclear; Male; Middle Aged; Neoplasms; Polymorphism, Genetic; Thymidylate Synthase
PubMed: 26166093
DOI: 10.1097/MD.0000000000001091 -
Clinical Cancer Research : An Official... Aug 2016BRCA1/2-mutated and some sporadic triple-negative breast cancers (TNBC) have DNA repair defects and are sensitive to DNA-damaging therapeutics. Recently, three...
PURPOSE
BRCA1/2-mutated and some sporadic triple-negative breast cancers (TNBC) have DNA repair defects and are sensitive to DNA-damaging therapeutics. Recently, three independent DNA-based measures of genomic instability were developed on the basis of loss of heterozygosity (LOH), telomeric allelic imbalance (TAI), and large-scale state transitions (LST).
EXPERIMENTAL DESIGN
We assessed a combined homologous recombination deficiency (HRD) score, an unweighted sum of LOH, TAI, and LST scores, in three neoadjuvant TNBC trials of platinum-containing therapy. We then tested the association of HR deficiency, defined as HRD score ≥42 or BRCA1/2 mutation, with response to platinum-based therapy.
RESULTS
In a trial of neoadjuvant platinum, gemcitabine, and iniparib, HR deficiency predicted residual cancer burden score of 0 or I (RCB 0/I) and pathologic complete response (pCR; OR = 4.96, P = 0.0036; OR = 6.52, P = 0.0058). HR deficiency remained a significant predictor of RCB 0/I when adjusted for clinical variables (OR = 5.86, P = 0.012). In two other trials of neoadjuvant cisplatin therapy, HR deficiency predicted RCB 0/I and pCR (OR = 10.18, P = 0.0011; OR = 17.00, P = 0.0066). In a multivariable model of RCB 0/I, HR deficiency retained significance when clinical variables were included (OR = 12.08, P = 0.0017). When restricted to BRCA1/2 nonmutated tumors, response was higher in patients with high HRD scores: RCB 0/I P = 0.062, pCR P = 0.063 in the neoadjuvant platinum, gemcitabine, and iniparib trial; RCB 0/I P = 0.0039, pCR P = 0.018 in the neoadjuvant cisplatin trials.
CONCLUSIONS
HR deficiency identifies TNBC tumors, including BRCA1/2 nonmutated tumors more likely to respond to platinum-containing therapy. Clin Cancer Res; 22(15); 3764-73. ©2016 AACR.
Topics: Allelic Imbalance; Antineoplastic Combined Chemotherapy Protocols; Biomarkers, Tumor; Female; Genes, BRCA1; Genes, BRCA2; Homologous Recombination; Humans; Loss of Heterozygosity; Mutation; Neoplasm Staging; Odds Ratio; Platinum; Prognosis; Telomere; Treatment Outcome; Triple Negative Breast Neoplasms
PubMed: 26957554
DOI: 10.1158/1078-0432.CCR-15-2477 -
BMC Research Notes Nov 2021Allelic imbalance (AI) is the differential expression of the two alleles in a diploid. AI can vary between tissues, treatments, and environments. Methods for testing AI...
OBJECTIVE
Allelic imbalance (AI) is the differential expression of the two alleles in a diploid. AI can vary between tissues, treatments, and environments. Methods for testing AI exist, but methods are needed to estimate type I error and power for detecting AI and difference of AI between conditions. As the costs of the technology plummet, what is more important: reads or replicates?
RESULTS
We find that a minimum of 2400, 480, and 240 allele specific reads divided equally among 12, 5, and 3 replicates is needed to detect a 10, 20, and 30%, respectively, deviation from allelic balance in a condition with power > 80%. A minimum of 960 and 240 allele specific reads divided equally among 8 replicates is needed to detect a 20 or 30% difference in AI between conditions with comparable power. Higher numbers of replicates increase power more than adding coverage without affecting type I error. We provide a Python package that enables simulation of AI scenarios and enables individuals to estimate type I error and power in detecting AI and differences in AI between conditions.
Topics: Alleles; Allelic Imbalance; Bayes Theorem; Computer Simulation; Humans
PubMed: 34838135
DOI: 10.1186/s13104-021-05851-x -
Scientific Reports May 2017Somatic mutations in cancer genomes often show allelic imbalance (AI) of mutation abundance between the genome and transcriptome, but there is not yet a systematic...
Somatic mutations in cancer genomes often show allelic imbalance (AI) of mutation abundance between the genome and transcriptome, but there is not yet a systematic understanding of AI. In this study, we performed large-scale DNA and RNA AI analyses of >100,000 somatic mutations in >2,000 cancer specimens across five tumor types using the exome and transcriptome sequencing data of the Cancer Genome Atlas consortium. First, AI analysis of nonsense mutations and frameshift indels revealed that nonsense-mediated decay is typical in cancer genomes, and we identified the relationship between the extent of AI and the location of mutations in addition to the well-recognized 50-nt rules. Second, the AI with splice site mutations may reflect the extent of intron retention and is frequently observed in known tumor suppressor genes. For missense mutations, we observed that mutations frequently subject to AI are enriched to genes related to cancer, especially those of apoptosis and the extracellular matrix, and C:G > A:T transversions. Our results suggest that mutations in known cancer-related genes and their transcripts are subjected to different levels of transcriptional or posttranscriptional regulation compared to wildtype alleles and may add an additional regulatory layer to the functions of cancer-relevant genes.
Topics: Allelic Imbalance; Codon, Nonsense; Databases, Genetic; Gene Frequency; Genome; Humans; Mutation; Mutation, Missense; Neoplasms; Nonsense Mediated mRNA Decay; Nucleotides; RNA Splice Sites; Transcriptome; Exome Sequencing
PubMed: 28490743
DOI: 10.1038/s41598-017-01966-z