1 - 31 of 31 results

GWAR

Implements robust methods for analysis and meta-analysis of genome-wide association studies (GWAS) within the statistical package Stata. GWAR proposes the Cochran-Armitage trend test under a recessive, additive and dominant model of inheritance as well as robust methods based on the MERT statistic, the MAX statistic and the MIN2. All the aforementioned approaches were employed in a fixed or a random effects meta-analysis setting for summary data with weights equal to the reciprocal of the combined cases and controls.

ETMA / Epistasis Test in Meta-Analysis

A Markov chain Monte Carlo-based method using genotype summary data to obtain consistent estimates of epistasis effects in meta-analysis. We defined a series of conditions to generate simulation data and tested the power and type I error rates in ETMA, individual data analysis and conventional meta-regression-based method. ETMA not only successfully facilitated consistency of evidence but also yielded acceptable type I error and higher power than conventional meta-regression.

CPBayes

Measures the evidence of aggregate-level pleiotropic association and estimates an optimal subset of traits associated with the risk locus. CPBayes uses a unified Bayesian statistical framework based on a spike and slab prior. It performs a fully Bayesian analysis by employing the Markov chain Monte Carlo (MCMC) technique Gibbs sampling. The tool analyzes pleiotropy using summary-level data across a wide range of studies for two or more phenotypes - separate genome-wide association studies (GWAS) with or without shared subjects, cohort study for multiple traits.

GACT / Genome build and Allele definition Conversion Tool

A comprehensive tool with both powerful command-line and user-friendly web interface versions to predict, and convert both genome builds and allele definitions between multiple GWAS (or deep sequencing) genotype data, which is required for all imputations and genome-wide meta-analyses. GACT will facilitate and ease a broad use of the GWAS data from the dbGaP and other publicly available genotype repositories for large-scale secondary analyses and multi-laboratory collaborations in the genetic association studies of human diseases.

METACARPA / META-analysis Accounting for Relatedness using arbitrary Precision Arithmetic

Serves for meta-analysing genetic association studies. METACARPA expands a method allowing users to meta-analyse p-values. It employs overlapping or related samples when details of the overlap or relatedness are inexistent. This tool is able to scale with large numbers of variants. It provides function that allows users to avoid to calculate any single-point analysis and to concentrate on the calculation of the matrix.

rqt

Offers gene-level genome-wide association study (GWAS) meta-analysis. The rqt package can be easily included into bioinformatics pipeline or used as stand-alone. The workflow of gene-level meta-analysis consists of the following steps: (i) reducing the number of predictors, thereby alleviating correlation problem in variants; (ii) then the regression model is fitted on the reduced dataset to obtain corresponding regression coefficient; (iii) these coefficients are then to be pooled into a total index representing a total gene-level effect size and corresponding statistics is calculated. P- and q- values are then calculated using this statistics from asymptotic approximation or permutation procedure; (iv) the final step is combining gene-level p-values calculated from each study with Fisher’s combined probability method.

MetaGAP / Meta-GWAS Accuracy and Power

Infers the statistical power to detect associated single nucleotide polymorphisms (SNPs) and the predictive accuracy of the poly-genic scores (PGS) in a meta-analysis of genome-wide association studies (GWAS) results from genetically and phenotypically heterogeneous studies. The MetaGAP calculator assumes the use of a fixed-effects meta-analysis method. The MetaGAP calculator helps researchers to gauge how sensitive their results will be to heterogeneity in genetic effects across studies