Browse
Submit Data
Databases
API
Help

Dataset Information

25 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A new gene-based association test for genome-wide association studies.

ABSTRACT: Genome-wide association studies are widely used today to discover genetic factors that modify the risk of complex diseases. Usually, these methods work in a SNP-by-SNP fashion. We present a gene-based test that can be applied in the context of genome-wide association studies. We compare both strategies, SNP-based and gene-based, in a sample of cases and controls for rheumatoid arthritis.We obtained different results using each strategy. The SNP-based test found the PTPN22 gene while the gene-based test found the PHF19-TRAF1-C5 region. That suggests that no single strategy performs better than another in all cases and that a certain underlying genetic architecture can be delineated more easily with one strategy rather than with another.

SUBMITTER: Buil A

PROVIDER: S-EPMC2795904 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

A new gene-based association test for genome-wide association studies.

Buil Alfonso A Martinez-Perez Angel A Perera-Lluna Alexandre A Rib Leonor L Caminal Pere P Soria Jose Manuel JM

BMC proceedings 20091215

Genome-wide association studies are widely used today to discover genetic factors that modify the risk of complex diseases. Usually, these methods work in a SNP-by-SNP fashion. We present a gene-based test that can be applied in the context of genome-wide association studies. We compare both strategies, SNP-based and gene-based, in a sample of cases and controls for rheumatoid arthritis.We obtained different results using each strategy. The SNP-based test found the PTPN22 gene while the gene-bas ...[more]

PMID: 20017997

Similar Datasets

A versatile gene-based test for genome-wide association studies.

Project description:We have derived a versatile gene-based test for genome-wide association studies (GWAS). Our approach, called VEGAS (versatile gene-based association study), is applicable to all GWAS designs, including family-based GWAS, meta-analyses of GWAS on the basis of summary data, and DNA-pooling-based GWAS, where existing approaches based on permutation are not possible, as well as singleton data, where they are. The test incorporates information from a full set of markers (or a defined subset) within a gene and accounts for linkage disequilibrium between markers by using simulations from the multivariate normal distribution. We show that for an association study using singletons, our approach produces results equivalent to those obtained via permutation in a fraction of the computation time. We demonstrate proof-of-principle by using the gene-based test to replicate several genes known to be associated on the basis of results from a family-based GWAS for height in 11,536 individuals and a DNA-pooling-based GWAS for melanoma in approximately 1300 cases and controls. Our method has the potential to identify novel associated genes; provide a basis for selecting SNPs for replication; and be directly used in network (pathway) approaches that require per-gene association test statistics. We have implemented the approach in both an easy-to-use web interface, which only requires the uploading of markers with their association p-values, and a separate downloadable application.

| S-EPMC2896770 | biostudies-literature

A new association test based on disease allele selection for case-control genome-wide association studies.

Project description:BACKGROUND:Current robust association tests for case-control genome-wide association study (GWAS) data are mainly based on the assumption of some specific genetic models. Due to the richness of the genetic models, this assumption may not be appropriate. Therefore, robust but powerful association approaches are desirable. RESULTS:In this paper, we propose a new approach to testing for the association between the genotype and phenotype for case-control GWAS. This method assumes a generalized genetic model and is based on the selected disease allele to obtain a p-value from the more powerful one-sided test. Through a comprehensive simulation study we assess the performance of the new test by comparing it with existing methods. Some real data applications are also used to illustrate the use of the proposed test. CONCLUSIONS:Based on the simulation results and real data application, the proposed test is powerful and robust.

| S-EPMC4059871 | biostudies-other

An efficient gene-gene interaction test for genome-wide association studies in trio families.

Project description:Several efficient gene-gene interaction tests have been developed for unrelated case-control samples in genome-wide association studies (GWAS), making it possible to test tens of billions of interaction pairs of single-nucleotide polymorphisms (SNPs) in a reasonable timeframe. However, current family-based gene-gene interaction tests are computationally expensive and are not applicable to genome-wide interaction analysis.We developed an efficient family-based gene-gene interaction test, GCORE, for trios (i.e. two parents and one affected sib). The GCORE compares interlocus correlations at two SNPs between the transmitted and non-transmitted alleles. We used simulation studies to compare the statistical properties such as type I error rates and power for the GCORE with several other family-based interaction tests under various scenarios. We applied the GCORE to a family-based GWAS for autism consisting of approximately 2000 trios. Testing a total of 22 471 383 013 interaction pairs in the GWAS can be finished in 36?h by the GCORE without large-scale computing resources, demonstrating that the test is practical for genome-wide gene-gene interaction analysis in trios.GCORE is implemented with C?++?and is available at http://gscore.sourceforge.netrchung@nhri.org.twSupplementary data are available at Bioinformatics online.

| S-EPMC5939888 | biostudies-literature

Robust Reference Powered Association Test of Genome-Wide Association Studies.

Project description:Genome-wide association studies (GWASs) have identified abundant genetic susceptibility loci, GWAS of small sample size are far less from meeting the previous expectations due to low statistical power and false positive results. Effective statistical methods are required to further improve the analyses of massive GWAS data. Here we presented a new statistic (Robust Reference Powered Association Test) to use large public database (gnomad) as reference to reduce concern of potential population stratification. To evaluate the performance of this statistic for various situations, we simulated multiple sets of sample size and frequencies to compute statistical power. Furthermore, we applied our method to several real datasets (psoriasis genome-wide association datasets and schizophrenia genome-wide association dataset) to evaluate the performance. Careful analyses indicated that our newly developed statistic outperformed several previously developed GWAS applications. Importantly, this statistic is more robust than naive merging method in the presence of small control-reference differentiation, therefore likely to detect more association signals.

| S-EPMC6465778 | biostudies-literature

Gene-based meta-analysis of genome-wide association studies implicates new loci involved in obesity.

Project description:To date, genome-wide association studies (GWASs) have identified >100 loci with single variants associated with body mass index (BMI). This approach may miss loci with high allelic heterogeneity; therefore, the aim of the present study was to use gene-based meta-analysis to identify regions with high allelic heterogeneity to discover additional obesity susceptibility loci. We included GWAS data from 123 865 individuals of European descent from 46 cohorts in Stage 1 and Metabochip data from additional 103 046 individuals from 43 cohorts in Stage 2, all within the Genetic Investigation of ANthropometric Traits (GIANT) consortium. Each cohort was tested for association between ?2.4 million (Stage 1) or ?200 000 (Stage 2) imputed or genotyped single variants and BMI, and summary statistics were subsequently meta-analyzed in 17 941 genes. We used the 'VErsatile Gene-based Association Study' (VEGAS) approach to assign variants to genes and to calculate gene-based P-values based on simulations. The VEGAS method was applied to each cohort separately before a gene-based meta-analysis was performed. In Stage 1, two known (FTO and TMEM18) and six novel (PEX2, MTFR2, SSFA2, IARS2, CEP295 and TXNDC12) loci were associated with BMI (P < 2.8 × 10(-6) for 17 941 gene tests). We confirmed all loci, and six of them were gene-wide significant in Stage 2 alone. We provide biological support for the loci by pathway, expression and methylation analyses. Our results indicate that gene-based meta-analysis of GWAS provides a useful strategy to find loci of interest that were not identified in standard single-marker analyses due to high allelic heterogeneity.

| S-EPMC4643645 | biostudies-literature

HYST: a hybrid set-based test for genome-wide association studies, with application to protein-protein interaction-based association analysis.

Project description:The extended Simes' test (known as GATES) and scaled chi-square test were proposed to combine a set of dependent genome-wide association signals at multiple single-nucleotide polymorphisms (SNPs) for assessing the overall significance of association at the gene or pathway levels. The two tests use different strategies to combine association p values and can outperform each other when the number of and linkage disequilibrium between SNPs vary. In this paper, we introduce a hybrid set-based test (HYST) combining the two tests for genome-wide association studies (GWASs). We describe how HYST can be used to evaluate statistical significance for association at the protein-protein interaction (PPI) level in order to increase power for detecting disease-susceptibility genes of moderate effect size. Computer simulations demonstrated that HYST had a reasonable type 1 error rate and was generally more powerful than its parents and other alternative tests to detect a PPI pair where both genes are associated with the disease of interest. We applied the method to three complex disease GWAS data sets in the public domain; the method detected a number of highly connected significant PPI pairs involving multiple confirmed disease-susceptibility genes not found in the SNP- and gene-based association analyses. These results indicate that HYST can be effectively used to examine a collection of predefined SNP sets based on prior biological knowledge for revealing additional disease-predisposing genes of modest effects in GWASs.

| S-EPMC3511992 | biostudies-literature

Gene and pathway-based second-wave analysis of genome-wide association studies.

Project description:Despite the great success of genome-wide association studies (GWAS) in identification of the common genetic variants associated with complex diseases, the current GWAS have focused on single-SNP analysis. However, single-SNP analysis often identifies only a few of the most significant SNPs that account for a small proportion of the genetic variants and offers only a limited understanding of complex diseases. To overcome these limitations, we propose gene and pathway-based association analysis as a new paradigm for GWAS. As a proof of concept, we performed a comprehensive gene and pathway-based association analysis of 13 published GWAS. Our results showed that the proposed new paradigm for GWAS not only identified the genes that include significant SNPs found by single-SNP analysis, but also detected new genes in which each single SNP conferred a small disease risk; however, their joint actions were implicated in the development of diseases. The results also showed that the new paradigm for GWAS was able to identify biologically meaningful pathways associated with the diseases, which were confirmed by a gene-set-rich analysis using gene expression data.

| S-EPMC2987176 | biostudies-literature

Pathway-based analysis using reduced gene subsets in genome-wide association studies.

Project description:BACKGROUND: Single Nucleotide Polymorphism (SNP) analysis only captures a small proportion of associated genetic variants in Genome-Wide Association Studies (GWAS) partly due to small marginal effects. Pathway level analysis incorporating prior biological information offers another way to analyze GWAS's of complex diseases, and promises to reveal the mechanisms leading to complex diseases. Biologically defined pathways are typically comprised of numerous genes. If only a subset of genes in the pathways is associated with disease then a joint analysis including all individual genes would result in a loss of power. To address this issue, we propose a pathway-based method that allows us to test for joint effects by using a pre-selected gene subset. In the proposed approach, each gene is considered as the basic unit, which reduces the number of genetic variants considered and hence reduces the degrees of freedom in the joint analysis. The proposed approach also can be used to investigate the joint effect of several genes in a candidate gene study. RESULTS: We applied this new method to a published GWAS of psoriasis and identified 6 biologically plausible pathways, after adjustment for multiple testing. The pathways identified in our analysis overlap with those reported in previous studies. Further, using simulations across a range of gene numbers and effect sizes, we demonstrate that the proposed approach enjoys higher power than several other approaches to detect associated pathways. CONCLUSIONS: The proposed method could increase the power to discover susceptibility pathways and to identify associated genes using GWAS. In our analysis of genome-wide psoriasis data, we have identified a number of relevant pathways for psoriasis.

| S-EPMC3033801 | biostudies-literature

Gene-Based Testing of Interactions Using XGBoost in Genome-Wide Association Studies.

Project description:Among the myriad of statistical methods that identify gene-gene interactions in the realm of qualitative genome-wide association studies, gene-based interactions are not only powerful statistically, but also they are interpretable biologically. However, they have limited statistical detection by making assumptions on the association between traits and single nucleotide polymorphisms. Thus, a gene-based method (GGInt-XGBoost) originated from XGBoost is proposed in this article. Assuming that log odds ratio of disease traits satisfies the additive relationship if the pair of genes had no interactions, the difference in error between the XGBoost model with and without additive constraint could indicate gene-gene interaction; we then used a permutation-based statistical test to assess this difference and to provide a statistical p-value to represent the significance of the interaction. Experimental results on both simulation and real data showed that our approach had superior performance than previous experiments to detect gene-gene interactions.

| S-EPMC8716787 | biostudies-literature

Testing gene-gene interactions in genome wide association studies.

Project description:Detection of gene-gene interaction has become increasingly popular over the past decade in genome wide association studies (GWAS). Besides traditional logistic regression analysis for detecting interactions between two markers, new methods have been developed in recent years such as comparing linkage disequilibrium (LD) in case and control groups. All these methods form the building blocks of most screening strategies for disease susceptibility loci in GWAS. In this paper, we are interested in comparing the competing methods and providing practical guidelines for selecting appropriate testing methods for interaction in GWAS. We first review a series of existing statistical methods to detect interactions, and then examine different definitions of interactions to gain insight into the theoretical relationship between the existing testing methods. Lastly, we perform extensive simulations to compare powers of various methods to detect either interaction between two markers at two unlinked loci or the overall association allowing for both interaction and main effects. This investigation reveals informative characteristics of various methods that are helpful to GWAS investigators.

| S-EPMC4487553 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

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