Transcription Factor Enrichment Analysis in Enhancers Identifies EZH2 as a Susceptibility Gene for Osteoporosis

2019 ◽  
Vol 105 (4) ◽  
pp. e1152-e1161
Author(s):  
Meng Li ◽  
Shi Yao ◽  
Yuan-Yuan Duan ◽  
Yu-Jie Zhang ◽  
Yan Guo ◽  
...  
Keyword(s):  
Chinese Population ◽  
Multiple Testing ◽  
Association Studies ◽  
Susceptibility Gene ◽  
Enrichment Analysis ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Mineral Density ◽  
Association Analyses ◽  
Hip Bmd

Abstract Purpose Genome-wide association studies (GWASs) have identified hundreds of single nucleotide polymorphisms (SNPs) associated with osteoporosis. Most of these SNPs are noncoding variants and could be mapped to enhancers. Transcription factors (TFs) play important roles in gene regulation via enhancers harboring these SNPs; thus, we aimed to identify common regulatory TFs binding to enhancers associated with osteoporosis. Methods We first annotated all the osteoporosis-related SNPs identified by GWASs to enhancers and conducted TF enrichment analyses to identify common TFs binding to osteoporosis-associated enhancers. We further conducted genetic association analyses between the identified TFs and bone mineral density (BMD) in a Han Chinese population. Results After functional annotation, a total of 5081 osteoporosis-related SNPs were mapped to enhancers. TF enrichment analyses identified 2 significant TFs after multiple testing adjustments, which are EZH2 (Padj = .028) and NRSF (Padj = .038). We also found 1 SNP, rs111851041, in EZH2 was significantly associated with BMD both at the hip and spine after multiple testing adjustments (hip BMD: P = 4.32 × 10–4; spine BMD: P = 2.72 × 10–3). The expression of EZH2 decreased significantly from 12 to 48 hours of osteogenic differentiation. And functional validation showed that EZH2 was associated with osteoporosis-related phenotypes in knockout mice. Conclusions By conducting TF enrichment analyses, we identified EZH2 as a common TF binding to osteoporosis-associated enhancers, and EZH2 was also associated with BMD in a Chinese population. EZH2 is functionally related to bone phenotypes. The identified gene could provide new insight into osteoporosis pathophysiology and highlight opportunities for future clinical and pharmacological research on osteoporosis.

scholarly journals An Enrichment Analysis for Cardiometabolic Traits Suggests Non-Random Assignment of Genes to microRNAs

2018 ◽  
Vol 19 (11) ◽  
pp. 3666 ◽  
Author(s):  
Rima Mustafa ◽  
Mohsen Ghanbari ◽  
Marina Evangelou ◽  
Abbas Dehghan
Keyword(s):  
Multiple Testing ◽  
Target Genes ◽  
Association Studies ◽  
Enrichment Analysis ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Random Assignment ◽  
Genome Wide ◽  
Cardiometabolic Disorders ◽  
Cardiometabolic Traits

MicroRNAs (miRNAs) regulate the expression of the majority of genes. However, it is not known whether they regulate genes in random or are organized according to their function. To this end, we chose cardiometabolic disorders as an example and investigated whether genes associated with cardiometabolic disorders are regulated by a random set of miRNAs or a limited number of them. Single-nucleotide polymorphisms (SNPs) reaching genome-wide level significance were retrieved from most recent genome-wide association studies on cardiometabolic traits, which were cross-referenced with Ensembl to identify related genes and combined with miRNA target prediction databases (TargetScan, miRTarBase, or miRecords) to identify miRNAs that regulate them. We retrieved 520 SNPs, of which 355 were intragenic, corresponding to 304 genes. While we found a higher proportion of genes reported from all GWAS that were predicted targets for miRNAs in comparison to all protein-coding genes (75.1%), the proportion was even higher for cardiometabolic genes (80.6%). Enrichment analysis was performed within each database. We found that cardiometabolic genes were over-represented in target genes for 29 miRNAs (based on TargetScan) and 3 miRNAs (miR-181a, miR-302d and miR-372) (based on miRecords) after Benjamini-Hochberg correction for multiple testing. Our work provides evidence for non-random assignment of genes to miRNAs and supports the idea that miRNAs regulate sets of genes that are functionally related.

Genetic Burden Analyses of Phenotypes Relevant to Aging in the Berlin Aging Study II (BASE-II)

Gerontology ◽  
2016 ◽  
Vol 62 (3) ◽  
pp. 316-322 ◽  
Author(s):  
Christina M. Lill ◽  
Tian Liu ◽  
Kristina Norman ◽  
Antje Meyer ◽  
Elisabeth Steinhagen-Thiessen ◽  
...  
Keyword(s):  
Telomere Length ◽  
Association Studies ◽  
Receiver Operating Curve ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Mineral Density ◽  
Association Analyses ◽  
Genome Wide ◽  
Aging Study

Background: Body mass index (BMI), bone mineral density (BMD), and telomere length are phenotypes that modulate the course of aging. Over 40% of their phenotypic variance is determined by genetics. Genome-wide association studies (GWAS) have recently uncovered >100 independent single-nucleotide polymorphisms (SNPs) showing genome-wide significant (p < 5 × 10-8) association with these traits. Objective: To test the individual and combined impact of previously reported GWAS SNPs on BMI, BMD, and relative leukocyte telomere length (rLTL) in ∼1,750 participants of the Berlin Aging Study II (BASE-II), a cohort consisting predominantly of individuals >60 years of age. Methods: Linear regression analyses were performed on a total of 101 SNPs and BMI, BMD measurements of the femoral neck (FN) and lumbar spine (LS), and rLTL. The combined effect of all trait-specific SNPs was evaluated by generating a weighted genomic profile score (wGPS) used in the association analyses. The predictive capability of the wGPS was estimated by determining the area under the receiver operating curve (AUC) for osteoporosis status (determined by BMD) with and without the wGPS. Results: Five loci showed experiment-wide significant association with BMI (FTO rs1558902, p = 1.80 × 10-5) or BMD (MEPE rs6532023, pFN = 5.40 × 10-4, pLS = 1.09 × 10-4; TNFRSF11B rs2062377, pLS = 8.70 × 10-4; AKAP11 rs9533090, pLS = 1.05 × 10-3; SMG6 rs4790881, pFN = 3.41 × 10-4) after correction for multiple testing. Several additional loci showed nominally significant (p < 0.05) association with BMI and BMD. The trait-specific wGPS was highly significantly associated with BMD (p < 2 × 10-16) and BMI (p = 1.10 × 10-6). No significant association was detected for rLTL in either single-SNP or wGPS-based analyses. The AUC for osteoporosis improved modestly from 0.762 (95% CI 0.733-0.800) to 0.786 (95% CI 0.756-0.823) and 0.785 (95% CI 0.757-0.824) upon inclusion of the FN- and LS-BMD wGPS, respectively. Conclusion: Our study provides an independent validation of previously reported genetic association signals for BMI and BMD in the BASE-II cohort. Additional studies are needed to pinpoint the factors underlying the proportion of phenotypic variance that remains unexplained by the current models.

scholarly journals Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions

2018 ◽  
Author(s):  
David M. Howard ◽  
Mark J. Adams ◽  
Toni-Kim Clarke ◽  
Jonathan D. Hafferty ◽  
Jude Gibson ◽  
...  
Keyword(s):  
Multiple Testing ◽  
Drug Repositioning ◽  
Association Studies ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Brain Regions ◽  
Genome Wide Association Studies ◽  
Multiple Testing Correction ◽  
Synaptic Structure ◽  
Genome Wide

AbstractMajor depression is a debilitating psychiatric illness that is typically associated with low mood, anhedonia and a range of comorbidities. Depression has a heritable component that has remained difficult to elucidate with current sample sizes due to the polygenic nature of the disorder. To maximise sample size, we meta-analysed data on 807,553 individuals (246,363 cases and 561,190 controls) from the three largest genome-wide association studies of depression. We identified 102 independent variants, 269 genes, and 15 gene-sets associated with depression, including both genes and gene-pathways associated with synaptic structure and neurotransmission. Further evidence of the importance of prefrontal brain regions in depression was provided by an enrichment analysis. In an independent replication sample of 1,306,354 individuals (414,055 cases and 892,299 controls), 87 of the 102 associated variants were significant following multiple testing correction. Based on the putative genes associated with depression this work also highlights several potential drug repositioning opportunities. These findings advance our understanding of the complex genetic architecture of depression and provide several future avenues for understanding aetiology and developing new treatment approaches.

scholarly journals Associations of Two Obesity-Related Single-Nucleotide Polymorphisms with Adiponectin in Chinese Children

2017 ◽  
Vol 2017 ◽  
pp. 1-5 ◽  
Author(s):  
Lijun Wu ◽  
Liwang Gao ◽  
Xiaoyuan Zhao ◽  
Meixian Zhang ◽  
Jianxin Wu ◽  
...  
Keyword(s):  
Single Nucleotide Polymorphisms ◽  
Multiple Testing ◽  
Association Studies ◽  
Statistical Significance ◽  
Additive Model ◽  
Recessive Model ◽  
Chinese Children ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide

Purpose. Genome-wide association studies have found two obesity-related single-nucleotide polymorphisms (SNPs), rs17782313 near the melanocortin-4 receptor (MC4R) gene and rs6265 near the brain-derived neurotrophic factor (BDNF) gene, but the associations of both SNPs with other obesity-related traits are not fully described, especially in children. The aim of the present study is to investigate the associations between the SNPs and adiponectin that has a regulatory role in glucose and lipid metabolism. Methods. We examined the associations of the SNPs with adiponectin in Beijing Child and Adolescent Metabolic Syndrome (BCAMS) study. A total of 3503 children participated in the study. Results. The SNP rs6265 was significantly associated with adiponectin under an additive model (P=0.02 and 0.024, resp.) after adjustment for age, gender, and BMI or obesity statuses. The SNP rs17782313 was significantly associated with low adiponectin under a recessive model. No statistical significance was found between the two SNPs and low adiponectin after correction for multiple testing. Conclusion. We demonstrate for the first time that the SNP rs17782313 near MC4R and the SNP rs6265 near BDNF are associated with adiponectin in Chinese children. These novel findings provide important evidence that adiponectin possibly mediates MC4R and BDNF involved in obesity.

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 Generalized Structured Component Analysis in candidate gene association studies: applications and limitations

2020 ◽  
Vol 4 ◽  
pp. 142
Author(s):  
Paul A. Thompson ◽  
Dorothy V. M. Bishop ◽  
Else Eising ◽  
Simon E. Fisher ◽  
Dianne F. Newbury
Keyword(s):  
Structural Equation ◽  
Multiple Testing ◽  
Association Studies ◽  
Component Analysis ◽  
Candidate Snps ◽  
Nucleotide Polymorphisms ◽  
Association Analyses ◽  
Generalized Structured Component Analysis ◽  
Replication Studies ◽  
Candidate Gene Association

Background: Generalized Structured Component Analysis (GSCA) is a component-based alternative to traditional covariance-based structural equation modelling. This method has previously been applied to test for association between candidate genes and clinical phenotypes, contrasting with traditional genetic association analyses that adopt univariate testing of many individual single nucleotide polymorphisms (SNPs) with correction for multiple testing. Methods: We first evaluate the ability of the GSCA method to replicate two previous findings from a genetics association study of developmental language disorders. We then present the results of a simulation study to test the validity of the GSCA method under more restrictive data conditions, using smaller sample sizes and larger numbers of SNPs than have previously been investigated. Finally, we compare GSCA performance against univariate association analysis conducted using PLINK v1.9. Results: Results from simulations show that power to detect effects depends not just on sample size, but also on the ratio of SNPs with effect to number of SNPs tested within a gene. Inclusion of many SNPs in a model dilutes true effects. Conclusions: We propose that GSCA is a useful method for replication studies, when candidate SNPs have been identified, but should not be used for exploratory analysis.

scholarly journals Unravelling the shared genetic mechanisms underlying 18 autoimmune diseases using a systems approach

2021 ◽  
Author(s):  
Sreemol Gokuladhas ◽  
William Schierding ◽  
Evgeniia Golovina ◽  
Tayaza Fadason ◽  
Justin M. O'Sullivan
Keyword(s):  
Autoimmune Diseases ◽  
Systems Approach ◽  
Association Studies ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Protein Protein Interaction ◽  
Disease Specific ◽  
Shared Genetic

Autoimmune diseases (AiDs) are complex heterogeneous diseases characterized by hyperactive immune responses against self. Genome-wide association studies have identified thousands of single nucleotide polymorphisms (SNPs) associated with several AiDs. While these studies have identified a handful of pleiotropic loci that confer risk to multiple AiDs, they lack the power to detect shared genetic factors residing outside of these loci. Here, we integrated chromatin contact, expression quantitative trait loci and protein-protein interaction (PPI) data to identify genes that are regulated by both pleiotropic and non-pleiotropic SNPs. The PPI analysis revealed complex interactions between the shared and disease-specific genes. Furthermore, pathway enrichment analysis demonstrated that the shared genes co-occur with disease-specific genes within the same biological pathways. In conclusion, our results are consistent with the hypothesis that genetic risk loci associated with multiple AiDs converge on a core set of biological processes that potentially contribute to the emergence of polyautoimmunity.

scholarly journals Generalized Structured Component Analysis in candidate gene association studies: applications and limitations

2019 ◽  
Vol 4 ◽  
pp. 142 ◽  
Author(s):  
Paul A. Thompson ◽  
Dorothy V. M. Bishop ◽  
Else Eising ◽  
Simon E. Fisher ◽  
Dianne F. Newbury
Keyword(s):  
Structural Equation ◽  
Multiple Testing ◽  
Association Studies ◽  
Component Analysis ◽  
Candidate Snps ◽  
Nucleotide Polymorphisms ◽  
Association Analyses ◽  
Generalized Structured Component Analysis ◽  
Replication Studies ◽  
Candidate Gene Association

Background: Generalized Structured Component Analysis (GSCA) is a component-based alternative to traditional covariance-based structural equation modelling. This method has previously been applied to test for association between candidate genes and clinical phenotypes, contrasting with traditional genetic association analyses that adopt univariate testing of many individual single nucleotide polymorphisms (SNPs) with correction for multiple testing. Methods: We first evaluate the ability of the GSCA method to replicate two previous findings from a genetics association study of developmental language disorders. We then present the results of a simulation study to test the validity of the GSCA method under more restrictive data conditions, using smaller sample sizes and larger numbers of SNPs than have previously been investigated. Finally, we compare GSCA performance against univariate association analysis conducted using PLINK v1.9. Results: Results from simulations show that power to detect effects depends not just on sample size, but also on the ratio of SNPs with effect to number of SNPs tested within a gene. Inclusion of many SNPs in a model dilutes true effects. Conclusions: We propose that GSCA is a useful method for replication studies, when candidate SNPs have been identified, but should not be used for exploratory analysis.

scholarly journals Association ofTNFAIP3Polymorphism with Susceptibility to Systemic Lupus Erythematosus in a Japanese Population

2010 ◽  
Vol 2010 ◽  
pp. 1-5 ◽  
Author(s):  
Aya Kawasaki ◽  
Ikue Ito ◽  
Satoshi Ito ◽  
Taichi Hayashi ◽  
Daisuke Goto ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Lupus Erythematosus ◽  
Japanese Population ◽  
Association Studies ◽  
Susceptibility Gene ◽  
Allelic Association ◽  
Genome Wide Association Studies ◽  
Mrna Levels ◽  
Nucleotide Polymorphisms ◽  
Systemic Lupus

Recent genome-wide association studies demonstrated association of single nucleotide polymorphisms (SNPs) in theTNFAIP3region at 6q23 with systemic lupus erythematosus (SLE) in European-American populations. In this study, we investigated whether SNPs in theTNFAIP3region are associated with SLE also in a Japanese population. A case-control association study was performed on the SNPs rs13192841, rs2230926, and rs6922466 in 318 Japanese SLE patients and 444 healthy controls. Association of rs2230926 G allele with SLE was replicated in Japanese (allelic associationP=.033, odds ratio [OR] 1.47, recessive modelP=.023, OR 8.52). The association was preferentially observed in the SLE patients with nephritis. When theTNFAIP3mRNA levels of the HapMap samples were examined using GENEVAR database, the presence ofTNFAIP3rs2230926 G allele was associated with lower mRNA expression ofTNFAIP3(P=.013). These results indicated thatTNFAIP3is a susceptibility gene to SLE both in the Caucasian and Asian populations.

Varanto: variant enrichment analysis and annotation

2019 ◽  
Vol 35 (17) ◽  
pp. 3154-3156 ◽  
Author(s):  
Oskari Timonen ◽  
Mikko Särkkä ◽  
Tibor Fülöp ◽  
Anton Mattsson ◽  
Juha Kekäläinen ◽  
...  
Keyword(s):  
Association Studies ◽  
Enrichment Analysis ◽  
Genetic Variations ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genome Wide ◽  
Shiny App ◽  
Specific Trait ◽  
Diverse Data

Abstract Summary Genome-wide association studies (GWAS) aim to identify associations of genetic variations such as single-nucleotide polymorphisms (SNPs) to a specific trait or a disease. Identifying common themes such as pathways, biological processes and diseases associations is needed to further explore and interpret these results. Varanto is a novel web tool for annotating, visualizing and analyzing human genetic variations using diverse data sources. Varanto can be used to query a set of input variations, retrieve their associated variation and gene level annotations, perform annotation enrichment analysis and visualize the results. Availability and implementation Varanto web app is developed with R and implemented as Shiny app with PostgreSQL database and is freely available at http://bioinformatics.uef.fi/varanto. Source code for the tool is available at https://github.com/oqe/varanto. Supplementary information Supplementary data are available at Bioinformatics online.

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