Association mapping software tools | Genome-wide association study data analysis
In the recent years, in order to dissect complex quantitative traits and identify candidate genes affecting such traits, the association mapping approach has been widely used. This strategy relies on detecting linkage disequilibrium (LD) between genetic markers and genes controlling the phenotype of interest by exploiting the recombination events accumulating over many generations and thus increasing the accuracy of the associations detected.
Reduces computational time for analyzing large genome-wide association studies (GWASs) data sets. EMMAX intends to prevent the overdispersion of test statistics using a statistical model that explicitly takes into account of sample structure, rather than correcting the overdispersed test statistics resulting from a lack of considering genetic relatedness in the statistical model.
Implements general linear model and mixed linear model approaches for controlling population and family structure. For result interpretation, the program allows for linkage disequilibrium statistics to be calculated and visualized graphically. Database browsing and data importation is facilitated by integrated middleware. Other features include analyzing insertions/deletions, calculating diversity statistics, integration of phenotypic and genotypic data, imputing missing data and calculating principal components.
Implements advanced statistical methods including the compressed mixed linear model (CMLM) and CMLM-based genomic prediction and selection. The GAPIT package can handle large datasets in excess of 10 000 individuals and 1 million single-nucleotide polymorphisms with minimal computational time. It also provides users access to tables and graphs for interpreting results.
A free, open-source whole genome association analysis toolset, designed to perform a range of basic, large-scale analyses in a computationally efficient manner. The focus of PLINK is purely on analysis of genotype/phenotype data, so there is no support for steps prior to this (e.g. study design and planning, generating genotype or CNV calls from raw data). Through integration with gPLINK and Haploview, there is some support for the subsequent visualization, annotation and storage of results.
Assists users with simultaneous inference in semi-parametric models. multcomp is a package that provides a general implementation of a framework that extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalized linear models, linear mixed effects models, the Cox model,or robust linear models, for instance.
An R library for genome-wide association (GWA) analysis. GenABEL implements effective storage and handling of GWA data, fast procedures for genetic data quality control, testing of association of single nucleotide polymorphisms with binary or quantitative traits, visualization of results and also provides easy interfaces to standard statistical and graphical procedures implemented in base R and special R libraries for genetic analysis.
Aggregates association strength of individual markers into pre-specified biological pathways. VEGAS2 is a a versatile pathway-based approach for genome-wide association studies (GWAS) data that accounts for gene size and linkage disequilibrium between markers using simulations from the multivariate normal distribution. First, it calculates the gene-based test statistics for all genes using the VEGAS (VErsatile Gene-based Association Study) approach which accounts for the linkage disequilibrium (LD) between the single nucleotide polymorphisms (SNPs) within a gene through simulation. Second, for each of a set of pre-specified gene-sets, the relevant gene-based results are carried forward to compute a pathway-based test.