scholarly journals FUMA: Functional mapping and annotation of genetic associations

2017 ◽  
Author(s):  
Kyoko Watanabe ◽  
Erdogan Taskesen ◽  
Bochoven Arjen van ◽  
Bochoven Arjen van ◽  
Danielle Posthuma
Keyword(s):  
Functional Annotation ◽  
Association Studies ◽  
Integrative Approach ◽  
Genome Wide Association Studies ◽  
Biological Databases ◽  
Genetic Associations ◽  
Web Based ◽  
Main Challenge ◽  
Genome Wide ◽  
Multiple State

ABSTRACTA main challenge in genome-wide association studies (GWAS) is to prioritize genetic variants and identify potential causal mechanisms of human diseases. Although multiple bioinformatics resources are available for functional annotation and prioritization, a standard, integrative approach is lacking. We developed FUMA: a web-based platform to facilitate functional annotation of GWAS results, prioritization of genes and interactive visualization of annotated results by incorporating information from multiple state-of-the-art biological databases.

scholarly journals haploR: an R-package for querying web-based annotation tools

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 97 ◽  
Author(s):  
Ilya Y. Zhbannikov ◽  
Konstantin Arbeev ◽  
Anatoliy I. Yashin
Keyword(s):  
Genetic Variants ◽  
Genome Annotation ◽  
Association Studies ◽  
R Package ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Web Based ◽  
Genome Wide

There exists a set of web-based tools for integration and exploring information linked to annotated genetic variants. We developed haploR, an R-package for querying such web-based genome annotation tools (currently implementing on HaploReg and RegulomeDB) and gathering information in a format suitable for downstream bioinformatic analyses. This will facilitate post-genome wide association studies streamline analysis for rapid discovery and interpretation of genetic associations.

scholarly journals haploR: an R package for querying web-based annotation tools

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 97 ◽  
Author(s):  
Ilya Y. Zhbannikov ◽  
Konstantin Arbeev ◽  
Svetlana Ukraintseva ◽  
Anatoliy I. Yashin
Keyword(s):  
Genome Annotation ◽  
Association Studies ◽  
R Package ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Data Frame ◽  
Web Based ◽  
Genome Wide

We developed haploR, an R package for querying web based genome annotation tools HaploReg and RegulomeDB. haploR gathers information in a data frame which is suitable for downstream bioinformatic analyses. This will facilitate post-genome wide association studies streamline analysis for rapid discovery and interpretation of genetic associations.

scholarly journals Functional annotation of risk loci identified through genome-wide association studies for prostate cancer

The Prostate ◽  
2010 ◽  
Vol 71 (9) ◽  
pp. 955-963 ◽  
Author(s):  
Yizhen Lu ◽  
Zheng Zhang ◽  
Hongjie Yu ◽  
S. Lily Zheng ◽  
William B. Isaacs ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Functional Annotation ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals Identification of Novel Genome-Wide Associations for Oral Inflammatory Traits

2020 ◽  
Author(s):  
Yanjiao Jin ◽  
Jie Yang ◽  
Shuyue Zhang ◽  
Jin Li ◽  
Songlin Wang
Keyword(s):  
Candidate Genes ◽  
Immune Regulation ◽  
Functional Annotation ◽  
Inflammatory Diseases ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Protein Protein Interaction ◽  
Genome Wide ◽  
Causal Variants

Abstract Background: Oral diseases impact the majority of the world’s population. The following traits are common in oral inflammatory diseases: mouth ulcers, painful gums, bleeding gums, loose teeth, and toothache. Despite the prevalence of genome-wide association studies, the associations between these traits and common genomic variants, and whether pleiotropic loci are shared by some of these traits remain poorly understood. Methods: In this work, we conducted multi-trait joint analyses based on the summary statistics of genome-wide association studies of these five oral inflammatory traits from the UK Biobank, each of which is comprised of over 10,000 cases and over 300,000 controls. We estimated the genetic correlations between the five traits. We conducted fine-mapping and functional annotation based on multi-omics data to better understand the biological functions of the potential causal variants at each locus. To identify the pathways in which the candidate genes were mainly involved, we applied gene-set enrichment analysis, and further performed protein-protein interaction (PPI) analyses.Results: We identified 39 association signals that surpassed genome-wide significance, including three that were shared between two or more oral inflammatory traits, consistent with a strong correlation. Among these genome-wide significant loci, two were novel for both painful gums and toothache. We performed fine-mapping and identified causal variants at each novel locus. Further functional annotation based on multi-omics data suggested IL10 and IL12A/TRIM59 as potential candidate genes at the novel pleiotropic loci, respectively. Subsequent analyses of pathway enrichment and protein-protein interaction networks suggested the involvement of candidate genes at genome-wide significant loci in immune regulation.Conclusions: Our results highlighted the importance of immune regulation in the pathogenesis of oral inflammatory diseases. Some common immune-related pleiotropic loci or genetic variants are shared by multiple oral inflammatory traits. These findings will be beneficial for risk prediction, prevention, and therapy of oral inflammatory diseases.

scholarly journals Insights into the genetic basis of retinal detachment

2019 ◽  
Vol 29 (4) ◽  
pp. 689-702 ◽  
Author(s):  
Thibaud S Boutin ◽  
David G Charteris ◽  
Aman Chandra ◽  
Susan Campbell ◽  
Caroline Hayward ◽  
...  
Keyword(s):  
Retinal Detachment ◽  
Association Studies ◽  
Genetic Correlations ◽  
Self Report ◽  
Cataract Operation ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Genetic Associations ◽  
Data Set ◽  
Genome Wide

Abstract Retinal detachment (RD) is a serious and common condition, but genetic studies to date have been hampered by the small size of the assembled cohorts. In the UK Biobank data set, where RD was ascertained by self-report or hospital records, genetic correlations between RD and high myopia or cataract operation were, respectively, 0.46 (SE = 0.08) and 0.44 (SE = 0.07). These correlations are consistent with known epidemiological associations. Through meta-analysis of genome-wide association studies using UK Biobank RD cases (N = 3 977) and two cohorts, each comprising ~1 000 clinically ascertained rhegmatogenous RD patients, we uncovered 11 genome-wide significant association signals. These are near or within ZC3H11B, BMP3, COL22A1, DLG5, PLCE1, EFEMP2, TYR, FAT3, TRIM29, COL2A1 and LOXL1. Replication in the 23andMe data set, where RD is self-reported by participants, firmly establishes six RD risk loci: FAT3, COL22A1, TYR, BMP3, ZC3H11B and PLCE1. Based on the genetic associations with eye traits described to date, the first two specifically impact risk of a RD, whereas the last four point to shared aetiologies with macular condition, myopia and glaucoma. Fine-mapping prioritized the lead common missense variant (TYR S192Y) as causal variant at the TYR locus and a small set of credible causal variants at the FAT3 locus. The larger study size presented here, enabled by resources linked to health records or self-report, provides novel insights into RD aetiology and underlying pathological pathways.

scholarly journals Phenotype-specific differences in polygenicity and effect size distribution across functional annotation categories revealed by AI-MiXeR

2020 ◽  
Vol 36 (18) ◽  
pp. 4749-4756 ◽  
Author(s):  
Alexey A Shadrin ◽  
Oleksandr Frei ◽  
Olav B Smeland ◽  
Francesco Bettella ◽  
Kevin S O'Connell ◽  
...  
Keyword(s):  
Functional Annotation ◽  
Association Studies ◽  
Effect Sizes ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Protein Coding ◽  
Genome Wide ◽  
Whole Exome ◽  
Causal Variants ◽  
Complex Human Traits

Abstract Motivation Determining the relative contributions of functional genetic categories is fundamental to understanding the genetic etiology of complex human traits and diseases. Here, we present Annotation Informed-MiXeR, a likelihood-based method for estimating the number of variants influencing a phenotype and their effect sizes across different functional annotation categories of the genome using summary statistics from genome-wide association studies. Results Extensive simulations demonstrate that the model is valid for a broad range of genetic architectures. The model suggests that complex human phenotypes substantially differ in the number of causal variants, their localization in the genome and their effect sizes. Specifically, the exons of protein-coding genes harbor more than 90% of variants influencing type 2 diabetes and inflammatory bowel disease, making them good candidates for whole-exome studies. In contrast, <10% of the causal variants for schizophrenia, bipolar disorder and attention-deficit/hyperactivity disorder are located in protein-coding exons, indicating a more substantial role of regulatory mechanisms in the pathogenesis of these disorders. Availability and implementation The software is available at: https://github.com/precimed/mixer. Supplementary information Supplementary data are available at Bioinformatics online.

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.

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