Allele-Specific Methylation Occurs at Genetic Variants Associated with Complex Disease

Abstract Motivation Genome Architecture Mapping (GAM) was recently introduced as a digestion- and ligation-free method to detect chromatin conformation. Orthogonal to existing approaches based on chromatin conformation capture (3C), GAM’s ability to capture both inter- and intra-chromosomal contacts from low amounts of input data makes it particularly well suited for allele-specific analyses in a clinical setting. Allele-specific analyses are powerful tools to investigate the effects of genetic variants on many cellular phenotypes including chromatin conformation, but require the haplotypes of the individuals under study to be known a-priori. So far however, no algorithm exists for haplotype reconstruction and phasing of genetic variants from GAM data, hindering the allele-specific analysis of chromatin contact points in non-model organisms or individuals with unknown haplotypes. Results We present GAMIBHEAR, a tool for accurate haplotype reconstruction from GAM data. GAMIBHEAR aggregates allelic co-observation frequencies from GAM data and employs a GAM-specific probabilistic model of haplotype capture to optimise phasing accuracy. Using a hybrid mouse embryonic stem cell line with known haplotype structure as a benchmark dataset, we assess correctness and completeness of the reconstructed haplotypes, and demonstrate the power of GAMIBHEAR to infer accurate genome-wide haplotypes from GAM data. Availability GAMIBHEAR is available as an R package under the open source GPL-2 license at https://bitbucket.org/schwarzlab/gamibhear Maintainer [email protected] Supplementary information Supplementary information is available at Bioinformatics online.

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Discovery of allele-specific protein-RNA interactions in human transcriptomes

10.1101/389205 ◽

2018 ◽

Author(s):

Emad Bahrami-Samani ◽

Yi Xing

Keyword(s):

Genetic Variants ◽

Rna Binding ◽

Rna Binding Proteins ◽

Specific Protein ◽

Computational Method ◽

Joint Analysis ◽

Transcriptional Level ◽

Rna Seq ◽

Allele Specific ◽

Rna Interaction

AbstractGene expression is tightly regulated at the post-transcriptional level through splicing, transport, translation, and decay. RNA-binding proteins (RBPs) play key roles in post-transcriptional gene regulation, and genetic variants that alter RBP-RNA interactions can affect gene products and functions. We developed a computational method ASPRIN (Allele-Specific Protein-RNA Interaction), that uses a joint analysis of CLIP-seq (cross-linking and immunoprecipitation followed by high-throughput sequencing) and RNA-seq data to identify genetic variants that alter RBP-RNA interactions by directly observing the allelic preference of RBP from CLIP-seq experiments as compared to RNA-seq. We used ASPRIN to systematically analyze CLIP-seq and RNA-seq data for 166 RBPs in two ENCODE (Encyclopedia of DNA Elements) cell lines. ASPRIN identified genetic variants that alter RBP-RNA interactions by modifying RBP binding motifs within RNA. Moreover, through an integrative ASPRIN analysis with population-scale RNA-seq data, we showed that ASPRIN can help reveal potential causal variants that affect alternative splicing via allele-specific protein-RNA interactions.

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Oxidative Stress Inside the Pathogenesis of Ascending Aorta Aneurysms: A Clearer Vision for Identifying Promising Biomarkers and Therapeutic Targets

10.20944/preprints202111.0346.v1 ◽

2021 ◽

Author(s):

Carmela Balistreri ◽

Calogera Pisano ◽

Giovanni Ruvolo

Keyword(s):

Oxidative Stress ◽

Risk Factors ◽

Genetic Variants ◽

Complex Disease ◽

Ascending Aorta ◽

Oxygen Species ◽

Aorta Aneurysm ◽

Independent Risk Factors ◽

The Impact ◽

And Oxidative Stress

Ascending aorta aneurysm (AsAA) is a complex disease, currently defined an inflammatory disease. In the sporadic form, AsAA has, indeed, a complex physiopathology with a strong inflammatory basis, significantly modulated by genetic variants in innate/inflammatory genes, acting as independent risk factors and as largely evidenced in our recent studies performed during the last 10 years. Based on these premises, here, we want to revise the impact of reactive oxygen species (ROS) and oxidative stress on AsAA pathophysiology and consequently on the onset and progression of sporadic AsAA. This might consent to add other important pieces in the intricate puzzle of the pathophysiology of this disease with the translational aim to identify biomarkers and targets to apply in the complex management of AsAA, by facilitating the AsAA diagnosis currently based only on imaging evaluations, and the treatment exclusively founded on surgery approaches.

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A Statistical Method for Observing Personal Diploid Methylomes and Transcriptomes with Single-Molecule Real-Time Sequencing

Genes ◽

10.3390/genes9090460 ◽

2018 ◽

Vol 9 (9) ◽

pp. 460 ◽

Cited By ~ 1

Author(s):

Yuta Suzuki ◽

Yunhao Wang ◽

Kin Au ◽

Shinichi Morishita

Keyword(s):

Statistical Model ◽

Real Time ◽

Single Molecule ◽

Error Rate ◽

Methylation Pattern ◽

Specific Expression ◽

Long Reads ◽

Allele Specific ◽

Complex Locus ◽

Allele Specific Methylation

We address the problem of observing personal diploid methylomes, CpG methylome pairs of homologous chromosomes that are distinguishable with respect to phased heterozygous variants (PHVs), which is challenging due to scarcity of PHVs in personal genomes. Single molecule real-time (SMRT) sequencing is promising as it outputs long reads with CpG methylation information, but a serious concern is whether reliable PHVs are available in erroneous SMRT reads with an error rate of ∼15%. To overcome the issue, we propose a statistical model that reduces the error rate of phasing CpG site to 1%, thereby calling CpG hypomethylation in each haplotype with >90% precision and sensitivity. Using our statistical model, we examined GNAS complex locus known for a combination of maternally, paternally, or biallelically expressed isoforms, and observed allele-specific methylation pattern almost perfectly reflecting their respective allele-specific expression status, demonstrating the merit of elucidating comprehensive personal diploid methylomes and transcriptomes.

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ANALYSIS OF PATERNAL ALLELE-SPECIFIC METHYLATION AT MOUSE Gtl2 DURING EARLY EMBRYOGENESIS

Biology of Reproduction ◽

10.1093/biolreprod/77.s1.165 ◽

2007 ◽

Vol 77 (Suppl_1) ◽

pp. 165-165

Author(s):

Kamila Nowak ◽

Geneva Stein ◽

Lu Mei He ◽

Elizabeth Powell ◽

Tamara Davis

Keyword(s):

Early Embryogenesis ◽

Paternal Allele ◽

Allele Specific ◽

Allele Specific Methylation

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Improved haplotype inference by exploiting long-range linking and allelic imbalance in RNA-seq datasets

Nature Communications ◽

10.1038/s41467-020-18320-z ◽

2020 ◽

Vol 11 (1) ◽

Cited By ~ 2

Author(s):

Emily Berger ◽

Deniz Yorukoglu ◽

Lillian Zhang ◽

Sarah K. Nyquist ◽

Alex K. Shalek ◽

...

Keyword(s):

Long Range ◽

Genetic Variants ◽

Read Length ◽

Rna Seq ◽

Sequencing Data ◽

Specific Expression ◽

Integrative Framework ◽

Whole Exome ◽

Allele Specific ◽

Diverse Data

Abstract Haplotype reconstruction of distant genetic variants remains an unsolved problem due to the short-read length of common sequencing data. Here, we introduce HapTree-X, a probabilistic framework that utilizes latent long-range information to reconstruct unspecified haplotypes in diploid and polyploid organisms. It introduces the observation that differential allele-specific expression can link genetic variants from the same physical chromosome, thus even enabling using reads that cover only individual variants. We demonstrate HapTree-X’s feasibility on in-house sequenced Genome in a Bottle RNA-seq and various whole exome, genome, and 10X Genomics datasets. HapTree-X produces more complete phases (up to 25%), even in clinically important genes, and phases more variants than other methods while maintaining similar or higher accuracy and being up to 10× faster than other tools. The advantage of HapTree-X’s ability to use multiple lines of evidence, as well as to phase polyploid genomes in a single integrative framework, substantially grows as the amount of diverse data increases.

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