scholarly journals Enriching human interactome with functional mutations to detect high-impact network modules underlying complex diseases

2019 ◽  
Author(s):  
Hongzhu Cui ◽  
Suhas Srinivasan ◽  
Dmitry Korkin
Keyword(s):  
Biological Networks ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Genetic Diseases ◽  
Detection Methods ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Human Interactome ◽  
Module Detection ◽  
Disease Module

AbstractProgress in high-throughput -omics technologies moves us one step closer to the datacalypse in life sciences. In spite of the already generated volumes of data, our knowledge of the molecular mechanisms underlying complex genetic diseases remains limited. Increasing evidence shows that biological networks are essential, albeit not sufficient, for the better understanding of these mechanisms. The identification of disease-specific functional modules in the human interactome can provide a more focused insight into the mechanistic nature of the disease. However, carving a disease network module from the whole interactome is a difficult task. In this paper, we propose a computational framework, DIMSUM, which enables the integration of genome-wide association studies (GWAS), functional effects of mutations, and protein-protein interaction (PPI) network to improve disease module detection. Specifically, our approach incorporates and propagates the functional impact of non-synonymous single nucleotide polymorphisms (nsSNPs) on PPIs to implicate the genes that are most likely influenced by the disruptive mutations, and to identify the module with the greatest impact. Comparison against state-of-the-art seed-based module detection methods shows that our approach could yield modules that are biologically more relevant and have stronger association with the studied disease. We expect for our method to become a part of the common toolbox for disease module analysis, facilitating discovery of new disease markers.

scholarly journals Enriching Human Interactome with Functional Mutations to Detect High-Impact Network Modules Underlying Complex Diseases

Genes ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 933 ◽  
Author(s):  
Cui ◽  
Srinivasan ◽  
Korkin
Keyword(s):  
Biological Networks ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Detection Methods ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Human Interactome ◽  
Functional Impact ◽  
Module Detection ◽  
Disease Module

Rapid progress in high-throughput -omics technologies moves us one step closer to the datacalypse in life sciences. In spite of the already generated volumes of data, our knowledge of the molecular mechanisms underlying complex genetic diseases remains limited. Increasing evidence shows that biological networks are essential, albeit not sufficient, for the better understanding of these mechanisms. The identification of disease-specific functional modules in the human interactome can provide a more focused insight into the mechanistic nature of the disease. However, carving a disease network module from the whole interactome is a difficult task. In this paper, we propose a computational framework, Discovering most IMpacted SUbnetworks in interactoMe (DIMSUM), which enables the integration of genome-wide association studies (GWAS) and functional effects of mutations into the protein–protein interaction (PPI) network to improve disease module detection. Specifically, our approach incorporates and propagates the functional impact of non-synonymous single nucleotide polymorphisms (nsSNPs) on PPIs to implicate the genes that are most likely influenced by the disruptive mutations, and to identify the module with the greatest functional impact. Comparison against state-of-the-art seed-based module detection methods shows that our approach could yield modules that are biologically more relevant and have stronger association with the studied disease. We expect for our method to become a part of the common toolbox for the disease module analysis, facilitating the discovery of new disease markers.

scholarly journals Diagnosis of Human Axillary Osmidrosis by Genotyping of the HumanABCC11Gene: Clinical Practice and Basic Scientific Evidence

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Yu Toyoda ◽  
Tsuneaki Gomi ◽  
Hiroshi Nakagawa ◽  
Makoto Nagakura ◽  
Toshihisa Ishikawa
Keyword(s):  
Genetic Polymorphisms ◽  
Molecular Mechanisms ◽  
New Technologies ◽  
Disease Risk ◽  
Association Studies ◽  
Diagnostic Strategy ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Drug Induced ◽  
Axillary Osmidrosis

The importance of personalized medicine and healthcare is becoming increasingly recognized. Genetic polymorphisms associated with potential risks of various human genetic diseases as well as drug-induced adverse reactions have recently been well studied, and their underlying molecular mechanisms are being uncovered by functional genomics as well as genome-wide association studies. Knowledge of certain genetic polymorphisms is clinically important for our understanding of interindividual differences in drug response and/or disease risk. As such evidence accumulates, new clinical applications and practices are needed. In this context, the development of new technologies for simple, fast, accurate, and cost-effective genotyping is imperative. Here, we describe a simple isothermal genotyping method capable of detecting single nucleotide polymorphisms (SNPs) in the human ATP-binding cassette (ABC) transporterABCC11gene and its application to the clinical diagnosis of axillary osmidrosis. We have recently reported that axillary osmidrosis is linked with one SNP 538G>A in theABCC11gene. Our molecular biological and biochemical studies have revealed that this SNP greatly affects the protein expression level and the function of ABCC11. In this review, we highlight the clinical relevance and importance of this diagnostic strategy in axillary osmidrosis therapy.

scholarly journals Uncovering the complex genetics of human temperament

2018 ◽  
Vol 25 (10) ◽  
pp. 2275-2294 ◽  
Author(s):  
Igor Zwir ◽  
Javier Arnedo ◽  
Coral Del-Val ◽  
Laura Pulkki-Råback ◽  
Bettina Konte ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Association Studies ◽  
Experimental Studies ◽  
Long Term Memory ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Complex Genetics ◽  
Associative Conditioning ◽  
Discovery Sample ◽  
Character Inventory

Abstract Experimental studies of learning suggest that human temperament may depend on the molecular mechanisms for associative conditioning, which are highly conserved in animals. The main genetic pathways for associative conditioning are known in experimental animals, but have not been identified in prior genome-wide association studies (GWAS) of human temperament. We used a data-driven machine learning method for GWAS to uncover the complex genotypic–phenotypic networks and environmental interactions related to human temperament. In a discovery sample of 2149 healthy Finns, we identified sets of single-nucleotide polymorphisms (SNPs) that cluster within particular individuals (i.e., SNP sets) regardless of phenotype. Second, we identified 3 clusters of people with distinct temperament profiles measured by the Temperament and Character Inventory regardless of genotype. Third, we found 51 SNP sets that identified 736 gene loci and were significantly associated with temperament. The identified genes were enriched in pathways activated by associative conditioning in animals, including the ERK, PI3K, and PKC pathways. 74% of the identified genes were unique to a specific temperament profile. Environmental influences measured in childhood and adulthood had small but significant effects. We confirmed the replicability of the 51 Finnish SNP sets in healthy Korean (90%) and German samples (89%), as well as their associations with temperament. The identified SNPs explained nearly all the heritability expected in each sample (37–53%) despite variable cultures and environments. We conclude that human temperament is strongly influenced by more than 700 genes that modulate associative conditioning by molecular processes for synaptic plasticity and long-term memory.

scholarly journals From Genotype to Phenotype: Through Chromatin

Genes ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 76 ◽  
Author(s):  
Julia Romanowska ◽  
Anagha Joshi
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Genetic Diseases ◽  
Future Research ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Sequencing Technologies ◽  
Complementary Approach ◽  
Rare Genetic Diseases ◽  
Causal Genes

Advances in sequencing technologies have enabled the exploration of the genetic basis for several clinical disorders by allowing identification of causal mutations in rare genetic diseases. Sequencing technology has also facilitated genome-wide association studies to gather single nucleotide polymorphisms in common diseases including cancer and diabetes. Sequencing has therefore become common in the clinic for both prognostics and diagnostics. The success in follow-up steps, i.e., mapping mutations to causal genes and therapeutic targets to further the development of novel therapies, has nevertheless been very limited. This is because most mutations associated with diseases lie in inter-genic regions including the so-called regulatory genome. Additionally, no genetic causes are apparent for many diseases including neurodegenerative disorders. A complementary approach is therefore gaining interest, namely to focus on epigenetic control of the disease to generate more complete functional genomic maps. To this end, several recent studies have generated large-scale epigenetic datasets in a disease context to form a link between genotype and phenotype. We focus DNA methylation and important histone marks, where recent advances have been made thanks to technology improvements, cost effectiveness, and large meta-scale epigenome consortia efforts. We summarize recent studies unravelling the mechanistic understanding of epigenetic processes in disease development and progression. Moreover, we show how methodology advancements enable causal relationships to be established, and we pinpoint the most important issues to be addressed by future research.

scholarly journals Learning Gene Networks Underlying Clinical Phenotypes Using SNP Perturbations

2018 ◽  
Author(s):  
Calvin McCarter ◽  
Judie Howrylak ◽  
Seyoung Kim
Keyword(s):  
Gene Networks ◽  
Gene Network ◽  
Molecular Mechanisms ◽  
Graphical Model ◽  
Sequence Data ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Clinical Phenotypes ◽  
Genome Wide

AbstractRecent technologies are generating an abundance of genome sequence data and molecular and clinical phenotype data, providing an opportunity to understand the genetic architecture and molecular mechanisms underlying diseases. Previous approaches have largely focused on the co-localization of single-nucleotide polymorphisms (SNPs) associated with clinical and expression traits, each identified from genome-wide association studies and expression quantitative trait locus (eQTL) mapping, and thus have provided only limited capabilities for uncovering the molecular mechanisms behind the SNPs influencing clinical phenotypes. Here we aim to extract rich information on the functional role of trait-perturbing SNPs that goes far beyond this simple co-localization. We introduce a computational framework called Perturb-Net for learning the gene network that modulates the influence of SNPs on phenotypes, using SNPs as naturally occurring perturbation of a biological system. Perturb-Net uses a probabilistic graphical model to directly model both the cascade of perturbation from SNPs to the gene network to the phenotype network and the network at each layer of molecular and clinical phenotypes. Perturb-Net learns the entire model by solving a single optimization problem with an extremely fast algorithm that can analyze human genome-wide data within a few hours. In our analysis of asthma data, for a locus that was previously implicated in asthma susceptibility but for which little is known about the molecular mechanism underlying the association, Perturb-Net revealed the gene network modules that mediate the influence of the SNP on asthma phenotypes. Many genes in this network module were well supported in the literature as asthma-related.

scholarly journals Dissecting genome-wide studies for microbiome-related metabolic diseases

2020 ◽  
Vol 29 (R1) ◽  
pp. R73-R80
Author(s):  
Denis Awany ◽  
Imane Allali ◽  
Emile R Chimusa
Keyword(s):  
Ethnic Groups ◽  
Molecular Mechanisms ◽  
Metabolic Diseases ◽  
Association Studies ◽  
Whole Genome Sequence ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Host Genetics ◽  
Research Areas ◽  
Genome Wide

Abstract Despite the meteoric rise in genome-wide association studies for metabolic diseases (MetD) over the last few years, our understanding of the pathogenesis of these diseases is still far from complete. Recent developments have established that MetD arises from complex interactions between host genetics, the gut microbiome and the environment. However, our knowledge of the genetic and microbiome components involved and the underlying molecular mechanisms remains limited. Here, we review and summarize recent studies investigating the genetic and microbiome basis of MetD. Then, given the critical importance of study-individual’s ancestry in these studies, we leverage 4932 whole-genome sequence samples from 18 worldwide ethnic groups to examine genetic diversity in currently reported variants associated with MetD. The analyses show marked differences in gene-specific proportion of pathogenic single-nucleotide polymorphisms (SNPs) and gene-specific SNPs MAFs across ethnic groups, highlighting the importance of population- and ethnic-specific investigations in pinpointing the causative factors for MetD. We conclude with a discussion of research areas where further investigation on interactions between host genetics, microbiome and the environment is needed.

scholarly journals Chromosome-specific polymorphic SSR markers in tropical eucalypt species using low coverage whole genome sequences: systematic characterization and validation

2021 ◽  
Vol 19 (3) ◽  
pp. e33
Author(s):  
Maheswari Patturaj ◽  
Aiswarya Munusamy ◽  
Nithishkumar Kannan ◽  
Ulaganathan Kandasamy ◽  
Yasodha Ramasamy
Keyword(s):  
Molecular Mechanisms ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Nucleotide Polymorphisms ◽  
Economic Traits ◽  
Genome Wide ◽  
Plantation Species ◽  
Ssr Loci ◽  
Low Coverage

Eucalyptus is one of the major plantation species with wide variety of industrial uses. Polymorphic and informative simple sequence repeats (SSRs) have broad range of applications in genetic analysis. In this study, two individuals of Eucalyptus tereticornis (ET217 and ET86), one individual each from E. camaldulensis (EC17) and E. grandis (EG9) were subjected to whole genome resequencing. Low coverage (10×) genome sequencing was used to find polymorphic SSRs between the individuals. Average number of SSR loci identified was 95,513 and the density of SSRs per Mb was from 157.39 in EG9 to 155.08 in EC17. Among all the SSRs detected, the most abundant repeat motifs were di-nucleotide (59.6%–62.5%), followed by tri- (23.7%–27.2%), tetra- (5.2%–5.6%), penta- (5.0%–5.3%) and hexa-nucleotide (2.7%–2.9%). The predominant SSR motif units were AG/CT and AAG/TTC. Computational genome analysis predicted the SSR length variations between the individuals and identified the gene functions of SSR containing sequences. Selected subset of polymorphic markers was validated in a full-sib family of eucalypts. Additionally, genome-wide characterization of single nucleotide polymorphisms, InDels and transcriptional regulators were carried out. These variations will find their utility in genome-wide association studies as well as understanding of molecular mechanisms involved in key economic traits. The genomic resources generated in this study would provide an impetus to integrate genomics in marker-trait associations and breeding of tropical eucalypts.

scholarly journals Molecular Mechanisms of ZC3H12C/Reg-3 Biological Activity and Its Involvement in Psoriasis Pathology

2021 ◽  
Vol 22 (14) ◽  
pp. 7311
Author(s):  
Mateusz Wawro ◽  
Jakub Kochan ◽  
Weronika Sowinska ◽  
Aleksandra Solecka ◽  
Karolina Wawro ◽  
...  
Keyword(s):  
Family Member ◽  
Cellular Localization ◽  
Molecular Mechanisms ◽  
Inflammatory Diseases ◽  
Association Studies ◽  
Psoriasis Patient ◽  
Genome Wide Association Studies ◽  
Mrna Levels ◽  
Transcript Levels

The members of the ZC3H12/MCPIP/Regnase family of RNases have emerged as important regulators of inflammation. In contrast to Regnase-1, -2 and -4, a thorough characterization of Regnase-3 (Reg-3) has not yet been explored. Here we demonstrate that Reg-3 differs from other family members in terms of NYN/PIN domain features, cellular localization pattern and substrate specificity. Together with Reg-1, the most comprehensively characterized family member, Reg-3 shared IL-6, IER-3 and Reg-1 mRNAs, but not IL-1β mRNA, as substrates. In addition, Reg-3 was found to be the only family member which regulates transcript levels of TNF, a cytokine implicated in chronic inflammatory diseases including psoriasis. Previous meta-analysis of genome-wide association studies revealed Reg-3 to be among new psoriasis susceptibility loci. Here we demonstrate that Reg-3 transcript levels are increased in psoriasis patient skin tissue and in an experimental model of psoriasis, supporting the immunomodulatory role of Reg-3 in psoriasis, possibly through degradation of mRNA for TNF and other factors such as Reg-1. On the other hand, Reg-1 was found to destabilize Reg-3 transcripts, suggesting reciprocal regulation between Reg-3 and Reg-1 in the skin. We found that either Reg-1 or Reg-3 were expressed in human keratinocytes in vitro. However, in contrast to robustly upregulated Reg-1 mRNA levels, Reg-3 expression was not affected in the epidermis of psoriasis patients. Taken together, these data suggest that epidermal levels of Reg-3 are negatively regulated by Reg-1 in psoriasis, and that Reg-1 and Reg-3 are both involved in psoriasis pathophysiology through controlling, at least in part different transcripts.

scholarly journals Causal Association between Periodontitis and Parkinson’s Disease: A Bidirectional Mendelian Randomization Study

Genes ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 772
Author(s):  
João Botelho ◽  
Vanessa Machado ◽  
José João Mendes ◽  
Paulo Mascarenhas
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Instrumental Variables ◽  
Mendelian Randomization ◽  
Association Studies ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genetic Liability ◽  
Bidirectional Association

The latest evidence revealed a possible association between periodontitis and Parkinson’s disease (PD). We explored the causal relationship of this bidirectional association through two-sample Mendelian randomization (MR) in European ancestry populations. To this end, we used openly accessible data of genome-wide association studies (GWAS) on periodontitis and PD. As instrumental variables for periodontitis, seventeen single-nucleotide polymorphisms (SNPs) from a GWAS of periodontitis (1817 periodontitis cases vs. 2215 controls) and eight non-overlapping SNPs of periodontitis from an additional GWAS for validation purposes. Instrumental variables to explore for the reverse causation included forty-five SNPs from a GWAS of PD (20,184 cases and 397,324 controls). Multiple approaches of MR were carried-out. There was no evidence of genetic liability of periodontitis being associated with a higher risk of PD (B = −0.0003, Standard Error [SE] 0.0003, p = 0.26). The eight independent SNPs (B = −0.0000, SE 0.0001, p = 0.99) validated this outcome. We also found no association of genetically primed PD towards periodontitis (B = −0.0001, SE 0.0001, p = 0.19). These MR study findings do not support a bidirectional causal genetic liability between periodontitis and PD. Further GWAS studies are needed to confirm the consistency of these results.

scholarly journals Pharmacogenomics of Lithium Response in Bipolar Disorder

2021 ◽  
Vol 14 (4) ◽  
pp. 287
Author(s):  
Courtney M. Vecera ◽  
Gabriel R. Fries ◽  
Lokesh R. Shahani ◽  
Jair C. Soares ◽  
Rodrigo Machado-Vieira
Keyword(s):  
Bipolar Disorder ◽  
Association Studies ◽  
Mood Stabilizer ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Non Coding Rna ◽  
Genome Wide ◽  
Lithium Response ◽  
Genetic Loading ◽  
Long Non Coding Rna

Despite being the most widely studied mood stabilizer, researchers have not confirmed a mechanism for lithium’s therapeutic efficacy in Bipolar Disorder (BD). Pharmacogenomic applications may be clinically useful in the future for identifying lithium-responsive patients and facilitating personalized treatment. Six genome-wide association studies (GWAS) reviewed here present evidence of genetic variations related to lithium responsivity and side effect expression. Variants were found on genes regulating the glutamate system, including GAD-like gene 1 (GADL1) and GRIA2 gene, a mutually-regulated target of lithium. In addition, single nucleotide polymorphisms (SNPs) discovered on SESTD1 may account for lithium’s exceptional ability to permeate cell membranes and mediate autoimmune and renal effects. Studies also corroborated the importance of epigenetics and stress regulation on lithium response, finding variants on long, non-coding RNA genes and associations between response and genetic loading for psychiatric comorbidities. Overall, the precision medicine model of stratifying patients based on phenotype seems to derive genotypic support of a separate clinical subtype of lithium-responsive BD. Results have yet to be expounded upon and should therefore be interpreted with caution.

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