scholarly journals Prioritization of genes driving congenital phenotypes of patients with de novo genomic structural variants

2019 ◽  
Author(s):  
Sjors Middelkamp ◽  
Judith M. Vlaar ◽  
Jacques Giltay ◽  
Jerome Korzelius ◽  
Nicolle Besselink ◽  
...  
Keyword(s):  
Molecular Diagnosis ◽  
Molecular Mechanisms ◽  
De Novo ◽  
Regulatory Elements ◽  
Chromatin Organization ◽  
Computational Method ◽  
Sequencing Analysis ◽  
Structural Variants ◽  
Driver Genes ◽  
Genomic Structural Variants

AbstractBackgroundGenomic structural variants (SVs) can affect many genes and regulatory elements. Therefore, the molecular mechanisms driving the phenotypes of patients with multiple congenital abnormalities and/or intellectual disability carrying de novo SVs are frequently unknown.ResultsWe applied a combination of systematic experimental and bioinformatic methods to improve the molecular diagnosis of 39 patients with de novo SVs and an inconclusive diagnosis after regular genetic testing. In seven of these cases (18%) whole genome sequencing analysis detected disease-relevant complexities of the SVs missed in routine microarray-based analyses. We developed a computational tool to predict effects on genes directly affected by SVs and on genes indirectly affected due to changes in chromatin organization and impact on regulatory mechanisms. By combining these functional predictions with extensive phenotype information, candidate driver genes were identified in 16/39 (41%) patients. In eight cases evidence was found for involvement of multiple candidate drivers contributing to different parts of the phenotypes. Subsequently, we applied this computational method to a collection of 382 patients with previously detected and classified de novo SVs and identified candidate driver genes in 210 cases (54%), including 32 cases whose SVs were previously not classified as pathogenic. Pathogenic positional effects were predicted in 25% of the cases with balanced SVs and in 8% of the cases with copy number variants.ConclusionsThese results show that driver gene prioritization based on integrative analysis of WGS data with phenotype association and chromatin organization datasets can improve the molecular diagnosis of patients with de novo SVs.

scholarly journals Prioritization of genes driving congenital phenotypes of patients with de novo genomic structural variants

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Sjors Middelkamp ◽  
Judith M. Vlaar ◽  
Jacques Giltay ◽  
Jerome Korzelius ◽  
Nicolle Besselink ◽  
...  
Keyword(s):  
Molecular Diagnosis ◽  
Molecular Mechanisms ◽  
De Novo ◽  
Regulatory Elements ◽  
Chromatin Organization ◽  
Sequencing Analysis ◽  
Structural Variants ◽  
Driver Genes ◽  
Position Effects ◽  
Genomic Structural Variants

Abstract Background Genomic structural variants (SVs) can affect many genes and regulatory elements. Therefore, the molecular mechanisms driving the phenotypes of patients carrying de novo SVs are frequently unknown. Methods We applied a combination of systematic experimental and bioinformatic methods to improve the molecular diagnosis of 39 patients with multiple congenital abnormalities and/or intellectual disability harboring apparent de novo SVs, most with an inconclusive diagnosis after regular genetic testing. Results In 7 of these cases (18%), whole-genome sequencing analysis revealed disease-relevant complexities of the SVs missed in routine microarray-based analyses. We developed a computational tool to predict the effects on genes directly affected by SVs and on genes indirectly affected likely due to the changes in chromatin organization and impact on regulatory mechanisms. By combining these functional predictions with extensive phenotype information, candidate driver genes were identified in 16/39 (41%) patients. In 8 cases, evidence was found for the involvement of multiple candidate drivers contributing to different parts of the phenotypes. Subsequently, we applied this computational method to two cohorts containing a total of 379 patients with previously detected and classified de novo SVs and identified candidate driver genes in 189 cases (50%), including 40 cases whose SVs were previously not classified as pathogenic. Pathogenic position effects were predicted in 28% of all studied cases with balanced SVs and in 11% of the cases with copy number variants. Conclusions These results demonstrate an integrated computational and experimental approach to predict driver genes based on analyses of WGS data with phenotype association and chromatin organization datasets. These analyses nominate new pathogenic loci and have strong potential to improve the molecular diagnosis of patients with de novo SVs.

scholarly journals De novo characterization of the Camellia sinensis transcriptome and comprehensive analysis of the diploid and triploid leaf morphological differences

2020 ◽  
Author(s):  
yong Qi ◽  
Xinzhuan Yao ◽  
Degang Zhao ◽  
Litang Lu
Keyword(s):  
Molecular Mechanisms ◽  
De Novo ◽  
Morphological Structure ◽  
Sponge Tissue ◽  
Sequencing Analysis ◽  
Stomatal Development ◽  
Specific Expression ◽  
Hormone Synthesis ◽  
Morphological Differences ◽  
Tea Plants

Abstract Background: Polyploidization has undergone a series of significant changes in the morphology and physiology of tea plants as plants multiply, especially in terms of increased growth rate and genetic gains Result: In this study, we found that the leaves of triploid tea had obvious growth advantages compared with diploid tea leaves, which was 59.81% higher than that of diploid leaves areas. The morphological structure of the triploid leaves showed obvious changes, the xylem of the veins was more developed, the cell-to-cell gap between the palisade tissue and the sponge tissue became larger, and the stomata of the triploid leaves were enlarged. Transcriptome sequencing analysis showed that after the triploidization of tea, the changes of leaf morphology and physiological characteristics were affected by the specific expression of some key regulatory genes. we identified a large number of transcripts and genes that might play important roles in leaf development, especially those involved in cell division, photosynthesis, hormone synthesis, and stomatal development.Conclusion: This study will improve our understanding of the molecular mechanisms of tea leaf and stomatal development and provide the basis for molecular breeding of high quality and yield tea varieties. Furthermore, it gives information that may enhance understanding of triploid physiology.

scholarly journals De novo characterization of the Camellia sinensis transcriptome and comprehensive analysis of the diploid and triploid leaf morphology and physiological differences

2019 ◽  
Author(s):  
Yong QI ◽  
Xinzhuan YAO ◽  
Degang ZHAO ◽  
Litang Lu
Keyword(s):  
Leaf Morphology ◽  
Molecular Mechanisms ◽  
De Novo ◽  
Morphological Structure ◽  
Sponge Tissue ◽  
Sequencing Analysis ◽  
Stomatal Development ◽  
Tea Leaves ◽  
Specific Expression ◽  
Hormone Synthesis

Abstract Background Polyploidization has undergone a series of significant changes in the morphology and physiology of tea plants as plants multiply, especially in terms of increased growth rate and genetic gainsResult In this study, we found that the leaves of triploid tea had obvious growth advantages compared with diploid tea leaves, which was 59.81% higher than that of diploid leaves areas. The morphological structure of the triploid leaves showed obvious changes, the xylem of the veins was more developed, the cell-to-cell gap between the palisade tissue and the sponge tissue became larger, and the stomata of the triploid leaves were enlarged. After the polyploidy of tea, the content of secondary metabolites in tea leaves also changed significantly. Transcriptome sequencing analysis showed that after the triploidization of tea, the changes of leaf morphology and physiological characteristics were affected by the specific expression of some key regulatory genes. we identified a large number of transcripts and genes that might play important roles in leaf development, especially those involved in cell division, photosynthesis, hormone synthesis, and stomatal development.Conclusion This study will improve our understanding of the molecular mechanisms of tea leaf and stomatal development and provide the basis for molecular breeding of high quality and yield tea varieties. Furthermore, it gives information that may enhance understanding of triploid physiology.

scholarly journals De novo characterization of the Camellia sinensis transcriptome and comprehensive analysis of the diploid and triploid leaf morphological differences

2020 ◽  
Author(s):  
yong Qi ◽  
Xinzhuan Yao ◽  
Degang Zhao ◽  
Litang lu
Keyword(s):  
Molecular Mechanisms ◽  
De Novo ◽  
Morphological Structure ◽  
Sponge Tissue ◽  
Sequencing Analysis ◽  
Stomatal Development ◽  
Specific Expression ◽  
Hormone Synthesis ◽  
Morphological Differences ◽  
Tea Plants

Abstract Background: Polyploidization has undergone a series of significant changes in the morphology and physiology of tea plants as plants multiply, especially in terms of increased growth rate and genetic gains Result: In this study, we found that the leaves of triploid tea had obvious growth advantages compared with diploid tea leaves, which was 59.81% higher than that of diploid leaves areas. The morphological structure of the triploid leaves showed obvious changes, the xylem of the veins was more developed, the cell-to-cell gap between the palisade tissue and the sponge tissue became larger, and the stomata of the triploid leaves were enlarged. Transcriptome sequencing analysis showed that after the triploidization of tea, the changes of leaf morphology and physiological characteristics were affected by the specific expression of some key regulatory genes. We identified a large number of transcripts and genes that might play important roles in leaf development, especially those involved in cell division, photosynthesis, hormone synthesis, and stomatal development. Conclusion: This study will improve our understanding of the molecular mechanisms of tea leaf and stomatal development and provide the basis for molecular breeding of tea varieties with high quality and yield. Furthermore, it gives information to improve understanding of triploid physiology.

scholarly journals Genome wide efficiency profiling reveals modulation of maintenance and de novo methylation by Tets

2020 ◽  
Author(s):  
Pascal Giehr ◽  
Charalampos Kyriakopoulos ◽  
Karl Nordström ◽  
Abduhlrahman Salhab ◽  
Fabian Müller ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Molecular Mechanisms ◽  
De Novo ◽  
Epigenetic Modification ◽  
Embryonic Stem ◽  
Regulatory Elements ◽  
Sequencing Data ◽  
Reduced Representation ◽  
Genome Wide ◽  
Global And Local

AbstractBackgroundDNA methylation is an essential epigenetic modification which is set and maintained by DNA methyl transferases (Dnmts) and removed via active and passive mechanisms involving Tet mediated oxidation. While the molecular mechanisms of these enzymes are well studied, their interplay on shaping cell specific methylomes remains less well understood. In our work we model the activities of Tets and Dnmts at single CpGs across the genome using a novel type of high resolution sequencing data.ResultsTo accurately measure 5mC and 5hmC levels at single CpGs we developed RRHPoxBS, a reduced representation hairpin oxidative bisulfite sequencing approach. Using this method we mapped the methylomes and hydroxymethylomes of wild type and Tet triple knockout mouse embryonic stem cells. These comprehensive datasets were then used to develop an extended Hidden Markov model allowing us i) to determine the symmetrical methylation and hydroxymethylation state at millions of individual CpGs, ii) infer the maintenance and de novo methylation efficiencies of Dnmts and the hydroxylation efficiencies of Tets at individual CpG positions. We find that Tets exhibit their highest activity around unmethylated regulatory elements, i.e. active promoters and enhancers. Furthermore, we find that Tets’ presence has a profound effect on the global and local maintenance and de novo methylation activities by the Dnmts, not only substantially contributing to a universal demethylation of the genome but also shaping the overall methylation landscape.ConclusionsOur analysis demonstrates that a fine tuned and locally controlled interplay between Tets and Dnmts is important to modulate de novo and maintenance activities of Dnmts across the genome. Tet activities contribute to DNA methylation patterning in the following ways: They oxidize 5mC, they locally shield DNA from accidental de novo methylation and at the same time modulate maintenance and de novo methylation efficiencies of Dnmts across the genome.

scholarly journals ModulOmics: Integrating Multi-Omics Data to Identify Cancer Driver Modules

2018 ◽  
Author(s):  
Dana Silverbush ◽  
Simona Cristea ◽  
Gali Yanovich ◽  
Tamar Geiger ◽  
Niko Beerenwinkel ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
De Novo ◽  
Optimization Procedure ◽  
Biological Information ◽  
Data Types ◽  
Driver Genes ◽  
Cancer Subtypes ◽  
Cancer Driver

AbstractThe identification of molecular pathways driving cancer progression is a fundamental unsolved problem in tumorigenesis, which can substantially further our understanding of cancer mechanisms and inform the development of targeted therapies. Most current approaches to address this problem use primarily somatic mutations, not fully exploiting additional layers of biological information. Here, we describe ModulOmics, a method to de novo identify cancer driver pathways, or modules, by integrating multiple data types (protein-protein interactions, mutual exclusivity of mutations or copy number alterations, transcriptional co-regulation, and RNA co-expression) into a single probabilistic model. To efficiently search the exponential space of candidate modules, ModulOmics employs a two-step optimization procedure that combines integer linear programming with stochastic search. Across several cancer types, ModulOmics identifies highly functionally connected modules enriched with cancer driver genes, outperforming state-of-the-art methods. For breast cancer subtypes, the inferred modules recapitulate known molecular mechanisms and suggest novel subtype-specific functionalities. These findings are supported by an independent patient cohort, as well as independent proteomic and phosphoproteomic datasets.

scholarly journals PopDel identifies medium-size deletions simultaneously in tens of thousands of genomes

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Sebastian Niehus ◽  
Hákon Jónsson ◽  
Janina Schönberger ◽  
Eythór Björnsson ◽  
Doruk Beyter ◽  
...  
Keyword(s):  
Large Scale ◽  
De Novo ◽  
Sequence Data ◽  
Whole Genome Sequence ◽  
Medium Size ◽  
Phenotypic Traits ◽  
Structural Variants ◽  
High Confidence ◽  
Sequencing Studies ◽  
Genomic Structural Variants

AbstractThousands of genomic structural variants (SVs) segregate in the human population and can impact phenotypic traits and diseases. Their identification in whole-genome sequence data of large cohorts is a major computational challenge. Most current approaches identify SVs in single genomes and afterwards merge the identified variants into a joint call set across many genomes. We describe the approach PopDel, which directly identifies deletions of about 500 to at least 10,000 bp in length in data of many genomes jointly, eliminating the need for subsequent variant merging. PopDel scales to tens of thousands of genomes as we demonstrate in evaluations on up to 49,962 genomes. We show that PopDel reliably reports common, rare and de novo deletions. On genomes with available high-confidence reference call sets PopDel shows excellent recall and precision. Genotype inheritance patterns in up to 6794 trios indicate that genotypes predicted by PopDel are more reliable than those of previous SV callers. Furthermore, PopDel’s running time is competitive with the fastest tested previous tools. The demonstrated scalability and accuracy of PopDel enables routine scans for deletions in large-scale sequencing studies.

scholarly journals Multi-tissue network analysis for drug prioritization in knee osteoarthritis

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Michael Neidlin ◽  
Smaragda Dimitrakopoulou ◽  
Leonidas G. Alexopoulos
Keyword(s):  
Drug Discovery ◽  
Network Analysis ◽  
Knee Osteoarthritis ◽  
Molecular Mechanisms ◽  
Complex Disease ◽  
Pharmaceutical Compounds ◽  
Computational Method ◽  
Joint Disease ◽  
Driver Genes ◽  
Gene Modules

Abstract Knee osteoarthritis (OA) is a joint disease that affects several tissues: cartilage, synovium, meniscus and subchondral bone. The pathophysiology of this complex disease is still not completely understood and existing pharmaceutical strategies are limited to pain relief treatments. Therefore, a computational method was developed considering the diverse mechanisms and the multi-tissue nature of OA in order to suggest pharmaceutical compounds. Specifically, weighted gene co-expression network analysis (WGCNA) was utilized to identify gene modules that were preserved across four joint tissues. The driver genes of these modules were selected as an input for a network-based drug discovery approach. WGCNA identified two preserved modules that described functions related to extracellular matrix physiology and immune system responses. Compounds that affected various anti-inflammatory pathways and drugs targeted at coagulation pathways were suggested. 9 out of the top 10 compounds had a proven association with OA and significantly outperformed randomized approaches not including WGCNA. The method presented herein is a viable strategy to identify overlapping molecular mechanisms in multi-tissue diseases such as OA and employ this information for drug discovery and compound prioritization.

scholarly journals PopDel identifies medium-size deletions jointly in tens of thousands of genomes

2019 ◽  
Author(s):  
Sebastian Niehus ◽  
Hákon Jónsson ◽  
Janina Schönberger ◽  
Eythór Björnsson ◽  
Doruk Beyter ◽  
...  
Keyword(s):  
Large Scale ◽  
De Novo ◽  
Sequence Data ◽  
Whole Genome Sequence ◽  
Medium Size ◽  
Phenotypic Traits ◽  
Structural Variants ◽  
Novel Approach ◽  
Sequencing Studies ◽  
Genomic Structural Variants

AbstractThousands of genomic structural variants segregate in the human population and can impact phenotypic traits and diseases. Their identification in whole-genome sequence data of large cohorts is a major computational challenge. We describe a novel approach, PopDel, which jointly identifies deletions of about 500 to at least 10,000 bp in length in many genomes together. PopDel scales to tens of thousands of genomes as we demonstrate in evaluations on up to 49,962 genomes. We show that PopDel reliably reports common, rare and de novo deletions. On genomes with available high-confidence reference call sets PopDel shows excellent recall and precision. Genotype inheritance patterns in up to 6,794 trios indicate that genotypes predicted by PopDel are more reliable than those of previous SV callers. Furthermore, PopDel’s running time is competitive with the fastest tested previous tools. The demonstrated scalability and accuracy of PopDel enables routine scans for deletions in large-scale sequencing studies.

scholarly journals A cross-disorder dosage sensitivity map of the human genome

2021 ◽  
Author(s):  
Ryan L. Collins ◽  
Joseph T. Glessner ◽  
Eleonora Porcu ◽  
Lisa-Marie Niestroj ◽  
Jacob Ulirsch ◽  
...  
Keyword(s):  
Human Genome ◽  
Molecular Mechanisms ◽  
De Novo ◽  
Association Studies ◽  
Disease Genes ◽  
Driver Genes ◽  
Genome Wide ◽  
A Genome ◽  
The Impact ◽  
Dosage Sensitivity

SUMMARYRare deletions and duplications of genomic segments, collectively known as rare copy number variants (rCNVs), contribute to a broad spectrum of human diseases. To date, most disease-association studies of rCNVs have focused on recognized genomic disorders or on the impact of haploinsufficiency caused by deletions. By comparison, our understanding of duplications in disease remains rudimentary as very few individual genes are known to be triplosensitive (i.e., duplication intolerant). In this study, we meta-analyzed rCNVs from 753,994 individuals across 30 primarily neurological disease phenotypes to create a genome-wide catalog of rCNV association statistics across disorders. We discovered 114 rCNV-disease associations at 52 distinct loci surpassing genome-wide significance (P=3.72×10−6), 42% of which involve duplications. Using Bayesian fine-mapping methods, we further prioritized 38 novel triplosensitive disease genes (e.g., GMEB2 in brain abnormalities), including three known haploinsufficient genes that we now reveal as bidirectionally dosage sensitive (e.g., ANKRD11 in growth abnormalities). By integrating our results with prior literature, we found that disease-associated rCNV segments were enriched for genes constrained against damaging coding variation and identified likely dominant driver genes for about one-third (32%) of rCNV segments based on de novo mutations from exome sequencing studies of developmental disorders. However, while the presence of constrained driver genes was a common feature of many pathogenic large rCNVs across disorders, most of the rCNVs showing genome-wide significant association were incompletely penetrant (mean odds ratio=11.6) and we also identified two examples of noncoding disease-associated rCNVs (e.g., intronic CADM2 deletions in behavioral disorders). Finally, we developed a statistical model to predict dosage sensitivity for all genes, which defined 3,006 haploinsufficient and 295 triplosensitive genes where the effect sizes of rCNVs were comparable to deletions of genes constrained against truncating mutations. These dosage sensitivity scores classified disease genes across molecular mechanisms, prioritized pathogenic de novo rCNVs in children with autism, and revealed features that distinguished haploinsufficient and triplosensitive genes, such as insulation from other genes and local cis-regulatory complexity. Collectively, the cross-disorder rCNV maps and metrics derived in this study provide the most comprehensive assessment of dosage sensitive genomic segments and genes in disease to date and set the foundation for future studies of dosage sensitivity throughout the human genome.

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