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

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.

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

10.1101/433367 ◽

2018 ◽

Cited By ~ 3

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.

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Addressing the Missing Heritability Problem With the Help of Regulatory Features

Evolutionary Bioinformatics ◽

10.1177/1176934319860861 ◽

2019 ◽

Vol 15 ◽

pp. 117693431986086

Author(s):

Shan-Shan Dong ◽

Yan Guo ◽

Tie-Lin Yang

Keyword(s):

Target Genes ◽

Association Studies ◽

Complex Diseases ◽

Regulatory Elements ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Susceptibility Loci ◽

Missing Heritability ◽

Genome Wide ◽

Missing Heritability Problem

Genome-wide association studies (GWASs) have successfully identified thousands of susceptibility loci for human complex diseases. However, missing heritability is still a challenging problem. Considering most GWAS loci are located in regulatory elements, we recently developed a pipeline named functional disease-associated single-nucleotide polymorphisms (SNPs) prediction (FDSP), to predict novel susceptibility loci for complex diseases based on the interpretation of regulatory features and published GWAS results with machine learning. When applied to type 2 diabetes and hypertension, the predicted susceptibility loci by FDSP were proved to be capable of explaining additional heritability. In addition, potential target genes of the predicted positive SNPs were significantly enriched in disease-related pathways. Our results suggested that taking regulatory features into consideration might be a useful way to address the missing heritability problem. We hope FDSP could offer help for the identification of novel susceptibility loci for complex diseases.

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Varanto: variant enrichment analysis and annotation

Bioinformatics ◽

10.1093/bioinformatics/btz046 ◽

2019 ◽

Vol 35 (17) ◽

pp. 3154-3156 ◽

Cited By ~ 1

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

The Journal of Clinical Endocrinology & Metabolism ◽

10.1210/clinem/dgz270 ◽

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.

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Covariate Adaptive Family-wise Error Rate Control for Genome-Wide Association Studies

Biometrika ◽

10.1093/biomet/asaa098 ◽

2020 ◽

Author(s):

Huijuan Zhou ◽

Xianyang Zhang ◽

Jun Chen

Keyword(s):

Error Rate ◽

Rate Control ◽

Multiple Testing ◽

Statistical Power ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Family Wise Error Rate ◽

Genome Wide

Abstract The family-wise error rate (FWER) has been widely used in genome-wide association studies. With the increasing availability of functional genomics data, it is possible to increase the detection power by leveraging these genomic functional annotations. Previous efforts to accommodate covariates in multiple testing focus on the false discovery rate control while covariate-adaptive FWER-controlling procedures remain under-developed. Here we propose a novel covariate-adaptive FWER-controlling procedure that incorporates external covariates which are potentially informative of either the statistical power or the prior null probability. An efficient algorithm is developed to implement the proposed method. We prove its asymptotic validity and obtain the rate of convergence through a perturbation-type argument. Our numerical studies show that the new procedure is more powerful than competing methods and maintains robustness across different settings. We apply the proposed approach to the UK Biobank data and analyze 27 traits with 9 million single-nucleotide polymorphisms tested for associations. Seventy-five genomic annotations are used as covariates. Our approach detects more genome-wide significant loci than other methods in 21 out of the 27 traits.

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Pharmacogenomics of Lithium Response in Bipolar Disorder

Pharmaceuticals ◽

10.3390/ph14040287 ◽

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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Transcriptional Regulation of RUNX1: An Informatics Analysis

Genes ◽

10.3390/genes12081175 ◽

2021 ◽

Vol 12 (8) ◽

pp. 1175

Author(s):

Amarni L. Thomas ◽

Judith Marsman ◽

Jisha Antony ◽

William Schierding ◽

Justin M. O’Sullivan ◽

...

Keyword(s):

Association Studies ◽

Regulatory Elements ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Genome Wide ◽

Runx1 Gene ◽

Gene Regulatory Elements ◽

Important Regulator ◽

Aml1 Gene ◽

Regulatory Landscape

The RUNX1/AML1 gene encodes a developmental transcription factor that is an important regulator of haematopoiesis in vertebrates. Genetic disruptions to the RUNX1 gene are frequently associated with acute myeloid leukaemia. Gene regulatory elements (REs), such as enhancers located in non-coding DNA, are likely to be important for Runx1 transcription. Non-coding elements that modulate Runx1 expression have been investigated over several decades, but how and when these REs function remains poorly understood. Here we used bioinformatic methods and functional data to characterise the regulatory landscape of vertebrate Runx1. We identified REs that are conserved between human and mouse, many of which produce enhancer RNAs in diverse tissues. Genome-wide association studies detected single nucleotide polymorphisms in REs, some of which correlate with gene expression quantitative trait loci in tissues in which the RE is active. Our analyses also suggest that REs can be variant in haematological malignancies. In summary, our analysis identifies features of the RUNX1 regulatory landscape that are likely to be important for the regulation of this gene in normal and malignant haematopoiesis.

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Common genetic variants with fetal effects on birth weight are enriched for proximity to genes implicated in rare developmental disorders

Human Molecular Genetics ◽

10.1093/hmg/ddab060 ◽

2021 ◽

Author(s):

Robin N Beaumont ◽

Isabelle K Mayne ◽

Rachel M Freathy ◽

Caroline F Wright

Keyword(s):

Birth Weight ◽

Statistical Power ◽

Developmental Disorders ◽

Association Studies ◽

Later Life ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Genome Wide ◽

Common Genetic Variants ◽

Causal Genes

Abstract Birth weight is an important factor in newborn survival; both low and high birth weights are associated with adverse later-life health outcomes. Genome-wide association studies (GWAS) have identified 190 loci associated with maternal or fetal effects on birth weight. Knowledge of the underlying causal genes is crucial to understand how these loci influence birth weight and the links between infant and adult morbidity. Numerous monogenic developmental syndromes are associated with birth weights at the extreme ends of the distribution. Genes implicated in those syndromes may provide valuable information to prioritize candidate genes at the GWAS loci. We examined the proximity of genes implicated in developmental disorders (DDs) to birth weight GWAS loci using simulations to test whether they fall disproportionately close to the GWAS loci. We found birth weight GWAS single nucleotide polymorphisms (SNPs) fall closer to such genes than expected both when the DD gene is the nearest gene to the birth weight SNP and also when examining all genes within 258 kb of the SNP. This enrichment was driven by genes causing monogenic DDs with dominant modes of inheritance. We found examples of SNPs in the intron of one gene marking plausible effects via different nearby genes, highlighting the closest gene to the SNP not necessarily being the functionally relevant gene. This is the first application of this approach to birth weight, which has helped identify GWAS loci likely to have direct fetal effects on birth weight, which could not previously be classified as fetal or maternal owing to insufficient statistical power.

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Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2016.0569 ◽

2016 ◽

Vol 283 (1835) ◽

pp. 20160569 ◽

Cited By ~ 52

Author(s):

M. E. Goddard ◽

K. E. Kemper ◽

I. M. MacLeod ◽

A. J. Chamberlain ◽

B. J. Hayes

Keyword(s):

Complex Traits ◽

Genetic Architecture ◽

Quantitative Traits ◽

Association Studies ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Crop Breeding ◽

Single Nucleotide ◽

Genome Wide ◽

Phenotype Identification

Complex or quantitative traits are important in medicine, agriculture and evolution, yet, until recently, few of the polymorphisms that cause variation in these traits were known. Genome-wide association studies (GWAS), based on the ability to assay thousands of single nucleotide polymorphisms (SNPs), have revolutionized our understanding of the genetics of complex traits. We advocate the analysis of GWAS data by a statistical method that fits all SNP effects simultaneously, assuming that these effects are drawn from a prior distribution. We illustrate how this method can be used to predict future phenotypes, to map and identify the causal mutations, and to study the genetic architecture of complex traits. The genetic architecture of complex traits is even more complex than previously thought: in almost every trait studied there are thousands of polymorphisms that explain genetic variation. Methods of predicting future phenotypes, collectively known as genomic selection or genomic prediction, have been widely adopted in livestock and crop breeding, leading to increased rates of genetic improvement.

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Are there monogenic hereditary forms of bladder cancer or only genetic susceptibilities?

Pharmacogenomics ◽

10.2217/pgs-2020-0165 ◽

2021 ◽

Author(s):

Tarek Souaid ◽

Joya-Rita Hindy ◽

Ernest Diab ◽

Hampig Raphael Kourie

Keyword(s):

Bladder Cancer ◽

Association Studies ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Significance Level ◽

Dna Mismatch ◽

Common Cancer ◽

Genome Wide ◽

Dna Mismatch Repair Genes ◽

Mutyh Associated Polyposis

Bladder cancer (BC) is the most common cancer involving the urinary system and the ninth most common cancer worldwide. Tobacco smoking is the most important environmental risk factor of BC. Several single nucleotide polymorphisms have been validated by genome-wide association studies as genetic risk factors for BC. However, the identification of DNA mismatch-repair genes, including MSH2 in Lynch syndrome and MUTYH in MUTYH-associated polyposis, raises the possibility of monogenic hereditary forms of BC. Moreover, other genetic mutations may play a key role in familial and hereditary transmissions of BC. Therefore, the aim of this review is to focus on the major hereditary syndromes involved in the development of BC and to report BC genetic susceptibilities established with genome-wide significance level.

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