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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.
Implements a hierarchical multiple testing procedure. In the context of eQTL studies, TreeQTL provides methods allowing control of the false discovery rate or family wise error rate for the discovery of eSNPs or eGenes, as well as control of the expected average proportion of false discoveries for eAssociations involving the identified eSNPs or eGenes. In the context of multi-trait association studies, TreeQTL can be used to control the error rate for the discovery of variants associated to any phenotypes and the average false discovery rate of phenotypes influenced by such variants.
GFlasso / Graph-guided Fused lasso
A program for association analysis that searches for genetic variations influencing a group of correlated traits. This approach represents the dependency structure among the quantitative traits explicitly as a network, and leverages this trait network to encode structured regularizations in a multivariate regression model over the genotypes and traits, so that the genetic markers that jointly influence subgroups of highly correlated traits can be detected with high sensitivity and specificity.
LLR / Latent Low-Rank
Colocalizes genetic risk variants through the analysis of summary statistics, specifically Z-scores. LLR is a statistical approach to prioritizing risk variants using the pleiotropy across multiple related studies. The software can efficiently handle the analysis of large-scale genomic data. Its advantages were demonstrated through simulation studies and joint analysis of 18 genome-wide association studies (GWAS) data sets. It is useful for the integrative analysis of multiple GWAS data.
GSU / Generalized Similarity U
Tests the association between complex objects. The theoretical properties of GSU was studied in a general setting and then focused on the application of the test to sequencing association studies. Based on theoretical analysis, it was proposed to use Laplacian kernel based similarity for GSU to boost power and enhance robustness. Through simulation, GSU did have advantages over existing methods in terms of power and robustness. It was further performed a whole genome sequencing (WGS) scan for Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, identifying three genes, APOE, APOC1 and TOMM40, associated with imaging phenotype.
A method for assigning statistical significance in GWAS. We propose a procedure in which all the SNPs are analyzed in a multiple generalized linear model, and we show its use for extremely high-dimensional datasets. Our method yields P-values for assessing significance of single SNPs or groups of SNPs while controlling for all other SNPs and the family wise error rate (FWER). Thus, our method tests whether or not a SNP carries any additional information about the phenotype beyond that available by all the other SNPs. This rules out spurious correlations between phenotypes and SNPs that can arise from marginal methods because the ‘spuriously correlated’ SNP merely happens to be correlated with the ‘truly causal’ SNP. In addition, the method offers a data driven approach to identifying and refining groups of SNPs that jointly contain informative signals about the phenotype.
TATES / Trait-based Association Test that uses Extended Simes procedure
A software tool for multivariate GWAS based on P-values from GWAS. For each component of a multivariate trait, TATES combines p-values obtained in standard univariate GWAS to acquire one trait-based p-value, while correcting for correlations between components. Extensive simulations, probing a wide variety of genotype-phenotype models, show that TATES's false positive rate is correct, and that TATES's statistical power to detect causal variants explaining 0.5% of the variance can be 2.5-9 times higher than the power of univariate tests based on composite scores and 1.5-2 times higher than the power of the standard MANOVA. Unlike other multivariate methods, TATES detects both genetic variants that are common to multiple phenotypes and genetic variants that are specific to a single phenotype, i.e. TATES provides a more complete view of the genetic architecture of complex traits.
GATE / Group Accumulated Test Evidence
Allows association studies between multiple traits and a unique single nucleotide polymorphism (SNP). GATE performs the principal component analysis (PCA) on all traits and calculates the p-values of the association analysis. It utilizes the Fisher-combined method to combine p-values within and between groups. The tool uses all obtained principal components (PCs) instead of some selected PCs. It is directly applicable to multiple traits association studies with covariates.
An efficient genome-wide association test for multivariate phenotypes based on the Fisher combination function
A method that relaxes the unrealistic independence assumption of the classical Fisher combination test and is computationally efficient. In order to demonstrate applications of the proposed method, we conduct statistical analysis on the database of the Study of Addiction: Genetics and Environment (SAGE). The proposed method outperforms existing methods in most settings and also has great applications in GWAS on complex diseases with multiple phenotypes such as the substance abuse disorders.
TOWmuT / Testing an Optimally Weighted combination of variants with MUltiple Traits
Tests the association between multiple traits and a weighted combination of variants (rare and/or common) in a genomic region. TOWmuT is a statistical method that perform joint analysis of multiple traits in gene-based association studies. This method is applicable to different types of traits and can include covariates. It also identifies a small number of causal variants which are associated with multiple traits with the backward elimination (BE) method.
UNITY / Unifying Non-Infinitesimal Trait analYsis
Allows users to quantify relationships between two complex traits. UNITY is a statistical method, based on a merging of genetic correlation and colocalization, that is able to assess the proportion of causal variants shared between a pair of complex traits. Users can concomitantly model a set of local parameters including the effect of a single nucleotides polymorphism (SNP) on each of the traits and global parameters such as the trait heritability.
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