Browse
Submit Data
Databases
API
Help

Dataset Information

7 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Rapid scoring of genes in microbial pan-genome-wide association studies with Scoary.

ABSTRACT: Genome-wide association studies (GWAS) have become indispensable in human medicine and genomics, but very few have been carried out on bacteria. Here we introduce Scoary, an ultra-fast, easy-to-use, and widely applicable software tool that scores the components of the pan-genome for associations to observed phenotypic traits while accounting for population stratification, with minimal assumptions about evolutionary processes. We call our approach pan-GWAS to distinguish it from traditional, single nucleotide polymorphism (SNP)-based GWAS. Scoary is implemented in Python and is available under an open source GPLv3 license at https://github.com/AdmiralenOla/Scoary .

SUBMITTER: Brynildsrud O

PROVIDER: S-EPMC5124306 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Rapid scoring of genes in microbial pan-genome-wide association studies with Scoary.

Brynildsrud Ola O Bohlin Jon J Scheffer Lonneke L Eldholm Vegard V

Genome biology 20161125 1

Genome-wide association studies (GWAS) have become indispensable in human medicine and genomics, but very few have been carried out on bacteria. Here we introduce Scoary, an ultra-fast, easy-to-use, and widely applicable software tool that scores the components of the pan-genome for associations to observed phenotypic traits while accounting for population stratification, with minimal assumptions about evolutionary processes. We call our approach pan-GWAS to distinguish it from traditional, sing ...[more]

PMID: 27887642

Similar Datasets

RAPID detection of gene-gene interactions in genome-wide association studies.

Project description:In complex disorders, independently evolving locus pairs might interact to confer disease susceptibility, with only a modest effect at each locus. With genome-wide association studies on large cohorts, testing all pairs for interaction confers a heavy computational burden, and a loss of power due to large Bonferroni-like corrections. Correspondingly, limiting the tests to pairs that show marginal effect at either locus, also has reduced power. Here, we describe an algorithm that discovers interacting locus pairs without explicitly testing all pairs, or requiring a marginal effect at each locus. The central idea is a mathematical transformation that maps 'statistical correlation between locus pairs' to 'distance between two points in a Euclidean space'. This enables the use of geometric properties to identify proximal points (correlated locus pairs), without testing each pair explicitly. For large datasets (? 10(6) SNPs), this reduces the number of tests from 10(12) to 10(6), significantly reducing the computational burden, without loss of power. The speed of the test allows for correction using permutation-based tests. The algorithm is encoded in a tool called RAPID (RApid Pair IDentification) for identifying paired interactions in case-control GWAS.We validated RAPID with extensive tests on simulated and real datasets. On simulated models of interaction, RAPID easily identified pairs with small marginal effects. On the benchmark disease, datasets from The Wellcome Trust Case Control Consortium, RAPID ran in about 1 CPU-hour per dataset, and identified many significant interactions. In many cases, the interacting loci were known to be important for the disease, but were not individually associated in the genome-wide scan.http://bix.ucsd.edu/projects/rapid.

| S-EPMC3493125 | biostudies-literature

Genome-Wide Association and Transcriptome-Wide Association Studies Identify Novel Susceptibility Genes Contributing to Colorectal Cancer.

Project description:BackgroundColorectal cancer (CRC) is among the most common cancers diagnosed worldwide. Although genome-wide association studies have effectively identified the genetic basis of CRC, there is still unexplained variability in genetic risk. Transcriptome-wide association studies (TWAS) integrate summary statistics from CRC genome-wide association studies (GWAS) with gene expression data to prioritize these GWAS findings and uncover additional gene-trait correlations.MethodsFirst, we carried out a post-GWAS analysis using summary statistics from a large-scale GWAS of CRC (n = 4,562 cases, n = 382,756 controls). Second, combined with the expression weight sets from GTEx (v7), susceptibility genes were identified with the FUSION software. Colocalization, conditional and fine-mapping analyses, phenome-wide association study (pheWAS), and Mendelian randomization were employed to further characterize the observed correlations.ResultsIn the post-GWAS analyses, we first identified new genome-wide significant associations: three genomic risk loci were identified at 8q24.21 (rs6983267, P = 6.98 × 10-12), 15q13.3 (rs58658771, P = 1.40 × 10-10), and 18q21.1 (rs6507874, P = 1.91 × 10-14). In addition, the TWAS also identified four loci statistically significantly associated with CRC risk, largely explained by expression regulation, including six candidate genes (DUSP10, POU5F1B, C11orf53, COLCA1, COLCA2, and GREM1-AS1). We further discovered evidence that low expression of COLCA2 is correlated with CRC risk with Mendelian randomization.ConclusionsWe discovered novel CRC risk loci and candidate functional genes by merging gene expression and GWAS summary data, offering new insight into the molecular processes underlying CRC development. This makes it easier to prioritize potential genes for follow-up functional research in CRC.

| S-EPMC9270168 | biostudies-literature

Automated quality control for genome wide association studies.

Project description:This paper provides details on the necessary steps to assess and control data in genome wide association studies (GWAS) using genotype information on a large number of genetic markers for large number of individuals. Due to varied study designs and genotyping platforms between multiple sites/projects as well as potential genotyping errors, it is important to ensure high quality data. Scripts and directions are provided to facilitate others in this process.

| S-EPMC5007749 | biostudies-other

Selecting causal genes from genome-wide association studies via functionally coherent subnetworks.

Project description:Genome-wide association (GWA) studies have linked thousands of loci to human diseases, but the causal genes and variants at these loci generally remain unknown. Although investigators typically focus on genes closest to the associated polymorphisms, the causal gene is often more distal. Reliance on published work to prioritize candidates is biased toward well-characterized genes. We describe a 'prix fixe' strategy and software that uses genome-scale shared-function networks to identify sets of mutually functionally related genes spanning multiple GWA loci. Using associations from ?100 GWA studies covering ten cancer types, our approach outperformed the common alternative strategy in ranking known cancer genes. As more GWA loci are discovered, the strategy will have increased power to elucidate the causes of human disease.

| S-EPMC4480866 | biostudies-literature

Genome-wide association studies of adolescent idiopathic scoliosis suggest candidate susceptibility genes.

Project description:Adolescent idiopathic scoliosis (AIS) is an unexplained and common spinal deformity seen in otherwise healthy children. Its pathophysiology is poorly understood despite intensive investigation. Although genetic underpinnings are clear, replicated susceptibility loci that could provide insight into etiology have not been forthcoming. To address these issues, we performed genome-wide association studies (GWAS) of ?327 000 single nucleotide polymorphisms (SNPs) in 419 AIS families. We found strongest evidence of association with chromosome 3p26.3 SNPs in the proximity of the CHL1 gene (P < 8 × 10(-8) for rs1400180). We genotyped additional chromosome 3p26.3 SNPs and tested replication in two follow-up case-control cohorts, obtaining strongest results when all three cohorts were combined (rs10510181 odds ratio = 1.49, 95% confidence interval = 1.29-1.73, P = 2.58 × 10(-8)), but these were not confirmed in a separate GWAS. CHL1 is of interest, as it encodes an axon guidance protein related to Robo3. Mutations in the Robo3 protein cause horizontal gaze palsy with progressive scoliosis (HGPPS), a rare disease marked by severe scoliosis. Other top associations in our GWAS were with SNPs in the DSCAM gene encoding an axon guidance protein in the same structural class with Chl1 and Robo3. We additionally found AIS associations with loci in CNTNAP2, supporting a previous study linking this gene with AIS. Cntnap2 is also of functional interest, as it interacts directly with L1 and Robo class proteins and participates in axon pathfinding. Our results suggest the relevance of axon guidance pathways in AIS susceptibility, although these findings require further study, particularly given the apparent genetic heterogeneity in this disease.

| S-EPMC3049353 | biostudies-literature

Functional analysis of candidate genes from genome-wide association studies of hearing.

Project description:The underlying causes of age-related hearing loss (ARHL) are not well understood, but it is clear from heritability estimates that genetics plays a role in addition to environmental factors. Genome-wide association studies (GWAS) in human populations can point to candidate genes that may be involved in ARHL, but follow-up analysis is needed to assess the role of these genes in the disease process. Some genetic variants may contribute a small amount to a disease, while other variants may have a large effect size, but the genetic architecture of ARHL is not yet well-defined. In this study, we asked if a set of 17 candidate genes highlighted by early GWAS reports of ARHL have detectable effects on hearing by knocking down expression levels of each gene in the mouse and analysing auditory function. We found two of the genes have an impact on hearing. Mutation of Dclk1 led to late-onset progressive increase in ABR thresholds and the A430005L14Rik (C1orf174) mutants showed worse recovery from noise-induced damage than controls. We did not detect any abnormal responses in the remaining 15 mutant lines either in thresholds or from our battery of suprathreshold ABR tests, and we discuss the possible reasons for this.

| S-EPMC6996162 | biostudies-literature

A meta-analysis of genome-wide association studies identifies multiple longevity genes.

Project description:Human longevity is heritable, but genome-wide association (GWA) studies have had limited success. Here, we perform two meta-analyses of GWA studies of a rigorous longevity phenotype definition including 11,262/3484 cases surviving at or beyond the age corresponding to the 90th/99th survival percentile, respectively, and 25,483 controls whose age at death or at last contact was at or below the age corresponding to the 60th survival percentile. Consistent with previous reports, rs429358 (apolipoprotein E (ApoE) ε4) is associated with lower odds of surviving to the 90th and 99th percentile age, while rs7412 (ApoE ε2) shows the opposite. Moreover, rs7676745, located near GPR78, associates with lower odds of surviving to the 90th percentile age. Gene-level association analysis reveals a role for tissue-specific expression of multiple genes in longevity. Finally, genetic correlation of the longevity GWA results with that of several disease-related phenotypes points to a shared genetic architecture between health and longevity.

| S-EPMC6694136 | biostudies-literature

Genome-wide association studies in mice.

Project description:Genome-wide association studies (GWASs) have transformed the field of human genetics and have led to the discovery of hundreds of genes that are implicated in human disease. The technological advances that drove this revolution are now poised to transform genetic studies in model organisms, including mice. However, the design of GWASs in mouse strains is fundamentally different from the design of human GWASs, creating new challenges and opportunities. This Review gives an overview of the novel study designs for mouse GWASs, which dramatically improve both the statistical power and resolution compared to classical gene-mapping approaches.

| S-EPMC3625632 | biostudies-literature

Genome-wide association studies with metabolomics.

Project description:Genome-wide association studies (GWAS) analyze the genetic component of a phenotype or the etiology of a disease. Despite the success of many GWAS, little progress has been made in uncovering the underlying mechanisms for many diseases. The use of metabolomics as a readout of molecular phenotypes has enabled the discovery of previously undetected associations between diseases and signaling and metabolic pathways. In addition, combining GWAS and metabolomic information allows the simultaneous analysis of the genetic and environmental impacts on homeostasis. Most success has been seen in metabolic diseases such as diabetes, obesity and dyslipidemia. Recently, associations between loci such as FADS1, ELOVL2 or SLC16A9 and lipid concentrations have been explained by GWAS with metabolomics. Combining GWAS with metabolomics (mGWAS) provides the robust and quantitative information required for the development of specific diagnostics and targeted drugs. This review discusses the limitations of GWAS and presents examples of how metabolomics can overcome these limitations with the focus on metabolic diseases.

| S-EPMC3446262 | biostudies-literature

Genome-wide association studies: a primer.

Project description:There have been nearly 400 genome-wide association studies (GWAS) published since 2005. The GWAS approach has been exceptionally successful in identifying common genetic variants that predispose to a variety of complex human diseases and biochemical and anthropometric traits. Although this approach is relatively new, there are many excellent reviews of different aspects of the GWAS method. Here, we provide a primer, an annotated overview of the GWAS method with particular reference to psychiatric genetics. We dissect the GWAS methodology into its components and provide a brief description with citations and links to reviews that cover the topic in detail.

| S-EPMC4181332 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data