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.

Abstract 288: Novel Mechanisms of Non-coding Genomic Regulation Identified in Cardiac Disease-in-a-dish Models

2016 ◽  
Vol 119 (suppl_1) ◽  
Author(s):  
Aditya Kumar ◽  
Stephanie Thomas ◽  
Kirsten Wong ◽  
Kevin Tenerelli ◽  
Valentina Lo Sardo ◽  
...  
Keyword(s):  
Heart Attack ◽  
Connexin 43 ◽  
Disease Risk ◽  
Association Studies ◽  
Correlation Coefficients ◽  
Induced Pluripotent Stem Cell ◽  
Genome Wide Association Studies ◽  
Isogenic Line ◽  
Nucleotide Polymorphisms ◽  
Increased Risk

Genome-wide association studies have identified single nucleotide polymorphisms (SNPs) at gene loci that affect cardiovascular function, and while mechanisms in protein-coding loci are obvious, those in non-coding loci are difficult to determine. 9p21 is a recently identified locus associated with increased risk of coronary artery disease (CAD) and myocardial infarction. Associations have implicated SNPs in altering smooth muscle and endothelial cell properties but have not identified adverse effects in cardiomyocytes (CMs) despite enhanced disease risk. Using induced pluripotent stem cell-derived CMs from patients that are homozygous risk/risk (R/R) and non-risk/non-risk (N/N) for 9p21 SNPs and either CAD positive or negative, we assessed CM function when cultured on hydrogels capable of mimicking the fibrotic stiffening associated with disease post-heart attack, i.e. “heart attack-in-a-dish” stiffening from 11 kiloPascals (kPa) to 50 kPa. While all CMs independent of genotype and disease beat synchronously on soft matrices, R/R CMs cultured on dynamically stiffened hydrogels exhibited asynchronous contractions and had significantly lower correlation coefficients versus N/N CMs in the same conditions. Dynamic stiffening reduced connexin 43 expression and gap junction assembly in R/R CMs but not N/N CMs. To eliminate patient-to-patient variability, we created an isogenic line by deleting the 9p21 gene locus from a R/R patient using TALEN-mediated gene editing, i.e. R/R KO. Deletion of the 9p21 locus restored synchronous contractility and organized connexin 43 junctions. As a non-coding locus, 9p21 appears to repress connexin transcription, leading to the phenotypes we observe, but only when the niche is stiffened as in disease. These data are the first to demonstrate that disease-specific niche remodeling, e.g. a “heart attack-in-a-dish” model, can differentially affect CM function depending on SNPs within a non-coding locus.

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 A selective inference approach for false discovery rate control using multiomics covariates yields insights into disease risk

2020 ◽  
Vol 117 (26) ◽  
pp. 15028-15035 ◽  
Author(s):  
Ronald Yurko ◽  
Max G’Sell ◽  
Kathryn Roeder ◽  
Bernie Devlin
Keyword(s):  
Gene Expression ◽  
Multiple Testing ◽  
Disease Risk ◽  
Association Studies ◽  
Auxiliary Information ◽  
Primary Data ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Hypothesis Tests ◽  
Selective Inference

To correct for a large number of hypothesis tests, most researchers rely on simple multiple testing corrections. Yet, new methodologies of selective inference could potentially improve power while retaining statistical guarantees, especially those that enable exploration of test statistics using auxiliary information (covariates) to weight hypothesis tests for association. We explore one such method, adaptiveP-value thresholding (AdaPT), in the framework of genome-wide association studies (GWAS) and gene expression/coexpression studies, with particular emphasis on schizophrenia (SCZ). Selected SCZ GWAS associationPvalues play the role of the primary data for AdaPT; single-nucleotide polymorphisms (SNPs) are selected because they are gene expression quantitative trait loci (eQTLs). This natural pairing of SNPs and genes allow us to map the following covariate values to these pairs: GWAS statistics from genetically correlated bipolar disorder, the effect size of SNP genotypes on gene expression, and gene–gene coexpression, captured by subnetwork (module) membership. In all, 24 covariates per SNP/gene pair were included in the AdaPT analysis using flexible gradient boosted trees. We demonstrate a substantial increase in power to detect SCZ associations using gene expression information from the developing human prefrontal cortex. We interpret these results in light of recent theories about the polygenic nature of SCZ. Importantly, our entire process for identifying enrichment and creating features with independent complementary data sources can be implemented in many different high-throughput settings to ultimately improve power.

scholarly journals Challenges and progress in interpretation of non-coding genetic variants associated with human disease

2017 ◽  
Vol 242 (13) ◽  
pp. 1325-1334 ◽  
Author(s):  
Yizhou Zhu ◽  
Cagdas Tazearslan ◽  
Yousin Suh
Keyword(s):  
Molecular Mechanisms ◽  
Target Genes ◽  
Disease Risk ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Functional Interpretation ◽  
Genome Wide ◽  
Coding Variants

Genome-wide association studies have shown that the far majority of disease-associated variants reside in the non-coding regions of the genome, suggesting that gene regulatory changes contribute to disease risk. To identify truly causal non-coding variants and their affected target genes remains challenging but is a critical step to translate the genetic associations to molecular mechanisms and ultimately clinical applications. Here we review genomic/epigenomic resources and in silico tools that can be used to identify causal non-coding variants and experimental strategies to validate their functionalities. Impact statement Most signals from genome-wide association studies (GWASs) map to the non-coding genome, and functional interpretation of these associations remained challenging. We reviewed recent progress in methodologies of studying the non-coding genome and argued that no single approach allows one to effectively identify the causal regulatory variants from GWAS results. By illustrating the advantages and limitations of each method, our review potentially provided a guideline for taking a combinatorial approach to accurately predict, prioritize, and eventually experimentally validate the causal variants.

scholarly journals DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning

2016 ◽  
Author(s):  
Janine Arloth ◽  
Gökcen Eraslan ◽  
Till F.M. Andlauer ◽  
Jade Martins ◽  
Stella Iurato ◽  
...  
Keyword(s):  
Deep Learning ◽  
Disease Risk ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Cell Type ◽  
Individual Snps ◽  
Multivariate Gwas ◽  
Regulatory Effects ◽  
Joint Contribution

AbstractGenome-wide association studies (GWAS) identify genetic variants associated with quantitative traits or disease. Thus, GWAS never directly link variants to regulatory mechanisms, which, in turn, are typically inferred during post-hoc analyses. In parallel, a recent deep learning-based method allows for prediction of regulatory effects per variant on currently up to 1,000 cell type-specific chromatin features. We here describe “DeepWAS”, a new approach that directly integrates predictions of these regulatory effects of single variants into a multivariate GWAS setting. As a result, single variants associated with a trait or disease are, by design, coupled to their impact on a chromatin feature in a cell type. Up to 40,000 regulatory single-nucleotide polymorphisms (SNPs) were associated with multiple sclerosis (MS, 4,888 cases and 10,395 controls), major depressive disorder (MDD, 1,475 cases and 2,144 controls), and height (5,974 individuals) to each identify 43-61 regulatory SNPs, called deepSNPs, which are shown to reach at least nominal significance in large GWAS. MS- and height-specific deepSNPs resided in active chromatin and introns, whereas MDD-specific deepSNPs located mostly to intragenic regions and repressive chromatin states. We found deepSNPs to be enriched in public or cohort-matched expression and methylation quantitative trait loci and demonstrate the potential of the DeepWAS method to directly generate testable functional hypotheses based on genotype data alone. DeepWAS is an innovative GWAS approach with the power to identify individual SNPs in non-coding regions with gene regulatory capacity with a joint contribution to disease risk. DeepWAS is available at https://github.com/cellmapslab/DeepWAS.

scholarly journals Unravelling the Roles Of susceptibility Loci for Autoimmune Diseases in the Post-GWAS Era

2018 ◽  
Author(s):  
Jody Ye ◽  
Kathleen Gillespie ◽  
Santiago Rodriguez
Keyword(s):  
Autoimmune Diseases ◽  
Disease Risk ◽  
Association Studies ◽  
Copy Number Variations ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Susceptibility Loci ◽  
Single Nucleotide ◽  
Environmental Interactions ◽  
Genome Wide

Although genome-wide association studies (GWAS) have identified several hundred loci associated with autoimmune diseases, their mechanistic insights are still poorly understood. The human genome is more complex than common single nucleotide polymorphisms (SNPs) that are interrogated by GWAS arrays. Some structural variants such as insertions-deletions, copy number variations, and minisatellites that are not very well tagged by SNPs cannot be fully explored by GWAS. Therefore, it is possible that some of these loci may have large effects on autoimmune disease risk. In addition, other layers of regulations such as gene-gene interactions, epigenetic-determinants, gene and environmental interactions also contribute to the heritability of autoimmune diseases. This review focuses on discussing why studying these elements may allow us to gain a more comprehensive understanding of the aetiology of complex autoimmune traits.

scholarly journals Functional Testing of Thousands of Osteoarthritis-Associated Variants for Regulatory Activity

2018 ◽  
Author(s):  
Jason Chesler Klein ◽  
Aidan Keith ◽  
Sarah J. Rice ◽  
Colin Shepherd ◽  
Vikram Agarwal ◽  
...  
Keyword(s):  
Cell Line ◽  
Disease Risk ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Osteosarcoma Cell ◽  
Nucleotide Polymorphisms ◽  
Regulatory Activity ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Massively Parallel Reporter Assay

AbstractTo date, genome-wide association studies have implicated at least 35 loci in osteoarthritis, but due to linkage disequilibrium, we have yet to pinpoint the specific variants that underlie these associations, nor the mechanisms by which they contribute to disease risk. Here we functionally tested 1,605 single nucleotide variants associated with osteoarthritis for regulatory activity using a massively parallel reporter assay. We identified six single nucleotide polymorphisms (SNPs) with differential regulatory activity between the major and minor alleles. We show that our most significant hit, rs4730222, drives increased expression of an alternative isoform of HBP1 in a heterozygote chondrosarcoma cell line, a CRISPR-edited osteosarcoma cell line, and in chondrocytes derived from osteoarthritis patients.

scholarly journals Establishing gene regulatory networks from Parkinson's disease risk loci

2021 ◽  
Author(s):  
Sophie L Farrow ◽  
William Schierding ◽  
Sreemol Gokuladhas ◽  
Evgeniia Golovina ◽  
Tayaza M. Fadason ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Disease Risk ◽  
Association Studies ◽  
Adult Brain ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Gene Regulatory

The latest meta-analysis of genome wide association studies (GWAS) identified 90 independent single nucleotide polymorphisms (SNPs) across 78 genomic regions associated with Parkinson's disease (PD), yet the mechanisms by which these variants influence the development of the disease remains largely elusive. To establish the functional gene regulatory networks associated with PD-SNPs, we utilised an approach combining spatial (chromosomal conformation capture) and functional (expression quantitative trait loci; eQTL) data. We identified 518 genes subject to regulation by 76 PD-SNPs across 49 tissues, that encompass 36 peripheral and 13 CNS tissues. Notably, one third of these genes were regulated via trans- acting mechanisms (distal; risk locus-gene separated by > 1Mb, or on different chromosomes). Of particular interest is the identification of a novel trans-eQTL-gene connection between rs10847864 and SYNJ1 in the adult brain cortex, highlighting a convergence between familial studies and PD GWAS loci for SYNJ1 (PARK20) for the first time. Furthermore, we identified 16 neuro-development specific eQTL-gene regulatory connections within the foetal cortex, consistent with hypotheses suggesting a neurodevelopmental involvement in the pathogenesis of PD. Through utilising Louvain clustering we extracted nine significant and highly intra-connected clusters within the entire gene regulatory network. The nine clusters are enriched for specific biological processes and pathways, some of which have not previously been associated with PD. Together, our results not only contribute to an overall understanding of the mechanisms and impact of specific combinations of PD-SNPs, but also highlight the potential impact gene regulatory networks may have when elucidating aetiological subtypes of PD.

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 Genetic combination risk for schizophrenia

2018 ◽  
Author(s):  
Kengo Oishi ◽  
Tomihisa Niitsu ◽  
Nobuhisa Kanahara ◽  
Tasuku Hashimoto ◽  
Hideki Komatsu ◽  
...  
Keyword(s):  
Large Scale ◽  
Disease Risk ◽  
Mental Disease ◽  
Association Studies ◽  
Polygenic Risk Score ◽  
Genome Wide Association Studies ◽  
Potential Candidate ◽  
Nucleotide Polymorphisms ◽  
Functional Snps ◽  
Dopamine Signaling

Summary ParagraphSchizophrenia is a highly hereditary mental disease1 related to abnormal dopaminergic activities.2,3 To elucidate the mechanisms underlying schizophrenia’s development, genomic studies have sought to identify the pathogenic genetic polymorphisms. Large-scale genome-wide association studies (GWAS) have reported potential candidate loci that contribute to schizophrenia’s development.4,5 The risk genetic profiles are not yet established. Here we show that the combination of three functional single nucleotide polymorphisms (SNPs) related to the key factors of dopaminergic signaling can be used to predict the risk of schizophrenia’s development, though none of the SNPs is known to be associated by itself. These functional SNPs were reported to demonstrate directional influences in their parent gene activity, perhaps characterizing the integrated properties of dopaminergic signaling. Interestingly, the risk combination presented here included the major genotype as well as the minor polymorphisms, suggesting a possible association of unaffected activities of some dopamine-related genes with the disease development. The phenotype speculated based on the allelic status seemed consistent with the conventional pathophysiological hypotheses, although recently developed predictive methods, such as the polygenic risk score, could miss this potent pathogenic role of carrying a normal genotype by evaluating only minor polymorphisms. Our results demonstrate the presence of a subtype in schizophrenia with the favored genetic background related to dopamine signaling. Our findings indicate the possibility that the combinations could characterize integrated biological functions (including neurotransmission) and therefore identify individuals with a disease risk. The biological microenvironment indicated by the functional SNPs could bring an insight to elucidate the pathogenic mechanisms of developing schizophrenia. Furthermore, we believe that our approach will contribute to the development of innovative means to predict disease risks even for other multi-factorial diseases and then, the following preventive medicine.

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