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A suite of tools for unbiased allele-specific read mapping and discovery of molecular quantitative trait loci (QTLs). WASP uses a simple approach to overcome mapping bias that can be readily incorporated into any read mapping pipeline. First, reads are mapped normally using a mapping tool selected by the user; mapped reads that overlap single nucleotide polymorphisms (SNPs) are then identified. For each read that overlaps a SNP, its genotype is swapped with that of the other allele and it is re-mapped. If a re-mapped read fails to map to exactly the same location, it is discarded. Using simulated reads, RNA-seq reads and chromatin immunoprecipitation sequencing (ChIP-seq) reads, we demonstrate that WASP has a low error rate and is far more powerful than existing QTL-mapping approaches.

SCAN / SNP and CNV Annotation

A large-scale database of genetics and genomics data associated to a web-interface and a set of methods and algorithms that can be used for mining the data in it. The database contains two categories of single nucleotide polymorphism (SNP) annotations: 1) Physical-based annotation where SNPs are categorized according to their position relative to genes (intronic, inter-genic, etc.) and according to linkage disequilibrium (LD) patterns (an inter-genic SNP can be annotated to a gene if it is in LD with variation in the gene); 2) Functional annotation where SNPs are classified according to their effects on expression levels, i.e. whether they are expression quantitative trait loci (eQTLs) for that gene.


Detects single-nucleotide polymorphisms (SNPs) that regulates expression of genes. GOAL is based on a Bayesian approach which infers cis regulatory polymorphisms underlying gene expression variability. It uses current estimates of expression-regulators in order to estimate expression regulators assuming equal priors for each SNP and then trains the regulatory-model. The tool is able to capture a substantial proportion of putative causal regulatory genetic determinants underlying transcriptomic variance.


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.

SPIRE / Software for Polymorphism Identification Regulating Expression

A pipeline for expression Quantitative Trait Loci (eQTL) processing. SPIRE integrates different univariate and multivariate approaches for eQTL analysis, paying particular attention to the scalability of the procedure in order to support cis- as well as trans-mapping, thus allowing the identification of hotspots in Next Generation Sequencing (NGS) data. Authors demonstrated how SPIRE can handle big association study datasets, reproducing published results and improving the identification of trans-eQTLs. SPIRE can be used to analyse novel data concerning the genotypes of two different Caenorhabditis elegans strains (N2 and Hawaii) and related miRNA expression data, obtained using RNA-Seq.

KG-SCCA / Knowledge-Guided Sparse Canonical Correlation Analysis

Aims for improving learning results by incorporating valuable prior knowledge. KG-SCCA is a knowledge-guided sparse canonical correlation analysis (SCCA) algorithm able to model two types of prior knowledge: one as a group structure and the other as a network structure. It produces improved cross validation performances as well as biologically meaningful results when compared with widely used SCCA implementation in the PMA software package.


Implements two classes of statistical models for detecting simultaneously multiple associations between gene expression and genomic polymorphisms in a population. eQTLseq uses paired DNA-seq and RNA-seq assays as input :(i) the first class involves Poisson, Binomial and Negative Binomial models, which explicitly model digital gene expression as a function of genetic variation and (ii) the second class involves a Normal/Gaussian model, which relies on appropriate transformations of gene expression data.

GMAC / Genomic Mediation analysis with Adaptive Confounding adjustment

Performs genomic mediation analysis with adaptive confounding adjustment. GMAC is tailored for but not limited to genomic mediation analysis, restricting to scenarios with the presence of cis-association and random expression quantitative trait loci (eQTL). The software was applied to each of the 44 tissue types of Genotype-Tissue Expression (GTEx) data in order to study the trans-regulatory mechanism in human tissues.

xQTL Serve

Performs quantitative trait locus (xQTL) analyses on a multi-omic dataset that consists of RNA sequence (RNA-seq), DNA methylation, and histone acetylation (H3K9Ac ChIP-seq) data. xQTL Serve is a resource for assessing the impact of genetic variation on multiple types of molecular traits derived from the human brain cortex. It presents a list of single nucleotide polymorphisms (SNPs) associated with cortical gene expression, DNA methylation, and/or histone modification levels that reflects the impact of genetic variation on the transcriptome and epigenome of aging brains.

aFC / allelic Fold Change

Measures the cis-regulatory effect size. aFC is applicable to expression quantitative trait locus (eQTLs) discovered by standard eQTL calling methods. It captures the mechanistic regulation of haplotype expression in cis. This tool provides uniform estimates from both allelic expression and cis-eQTL data, and replication of cis-eQTLs using orthologous allele-specific expression (ASE) data from the same samples can complement classical replication with an independent sample.

CONFETI / Confounding Factor Estimation Through Independent component analysis

Identifies broad impact expression quantitative trait loci (eQTL). CONFETI is an analysis framework which provides a method for finding broad impact eQTL while leveraging the advantages of confounding factor analysis for eQTL discovery. The software uses Independent Component Analysis (ICA) to avoid over-correcting genetic effects in eQTL mixed model confounding factor analysis. His performance was evaluated using simulated genome-wide data.


Predicts massively parallel reporter assays (MPRA) reporter expression level from sequence, and predicts which sequence variants will lead to significant allele-specific expression. EnsembleExpr is a computational framework that achieves performance superior to any single component by integrating complementary features of different sources and properties. It provides useful yet complementary information, leading to a successful ensemble with performance surpassing any of the single ones.


Aims to support the Arabidopsis and plant genetics community to utilities eQTL (expression quantitative trait loci) data in their research. AraQTL stores and combines all published Arabidopsis eQTL data and allows for investigations over different experiments. This tool can be explored and compared 5 genome-wide EQTL-sets. AraQTL data was obtained from multiple recombinant inbred line (RIL) populations, Ler X Cvi, Bay X Sha, Cvi x Col, Bur x Col and Tsu x Kas and collected from several different stages and plant parts, like seeds, seedlings, whole rosettes or specific leaves.


Enables testing for interactions between context and sets of variants, and accounts for polygenic effects. iSet is a module that provides control for population structure, either using principal components that are included as fixed covariates, or using an additional random effect term. It can also be used to estimate the total phenotypic variance explained by variant sets and the relative proportions captured by persistent, rescaling-GxC and heterogeneity-GxC effects. iSet is a part of the Limix package.


forum (1)
Implements a popular cis-QTL mapping strategy in a user- and cluster-friendly tool. FastQTL also proposes an efficient permutation procedure to control for multiple testing. The outcome of permutations is modeled using beta distributions trained from a few permutations and from which adjusted p-values can be estimated at any level of significance with little computational cost. FastQTL also provides a modular base onto which new functionalities are being implemented, such as fine mapping of causal variants, conditional analysis to discover multiple independent QTLs per phenotype and interaction analysis to discover sex or disease specific QTLs.


A powerful and computationally optimized package that implements a joint hierarchical Bayesian model where all genes and SNPs are modeled concurrently to detect eQTLs. Model parameters are estimated using a Markov chain Monte Carlo algorithm. The free and widely used openMP parallel library speeds up computation. iBMQ incorporates genotypic and gene expression data into a single model while coping with the high dimensionality of eQTL data (large number of genes), borrowing strength from all gene expression data for the mapping procedures and controlling the number of false positives to a desirable level.

Lasso / Least absolute shrinkage and selection operator

A quantitative trait loci (QTL) mapping method guided by prior knowledge for identifying candidate genes. Lasso includes three steps: i) identifying QTLs from QTL mapping; ii) generating and selecting gene modules; iii) prior knowledge guided QTL mapping. Lasso also can find the robust gene modules significantly associated with traits and SNP markers simultaneously. In addition, Lasso is a method applicable for different experimental design and a variety of species.


A sparse regression method that can detect both group-wise and individual associations between SNPs and expression traits. geQTL can also correct the effects of potential confounders. Our method employs computationally efficient technique, thus it is able to fulfill large scale studies. Moreover, our method can automatically infer the proper number of group-wise associations. We perform extensive experiments on both simulated datasets and yeast datasets to demonstrate the effectiveness and efficiency of the proposed method. The results show that geQTL can effectively detect both individual and group-wise signals and outperforms the state-of-the-arts by a large margin.

MT-eQTL / multi-tissue eQTL analysis

A hierarchical Bayesian model for multi-tissue expression Quantitative Trait Locus (eQTL) analysis. MT-eQTL explicitly captures patterns of variation in the presence or absence of eQTL, as well as the heterogeneity of effect sizes across tissues. It is applied to the new, 9-tissue data set from the GTEx initiative. The analysis results provide useful directions for follow-up biological research, and it is likely that the proposed approach could have a significant contribution to the eQTL analysis of the emerging multi-tissue data.

HEFT / Hidden Expression Factor analysis

A combined multivariate regression and factor analysis method that identifies individual and pleiotropic effects of eQTL in the presence of unmeasured covariates/hidden factors. HEFT is a likelihood approach that learns the structure of hidden factors from multivariate gene expression data and makes use of a ridge estimator for simultaneous factor learning and detection of eQTL. HEFT requires no pre-estimation of hidden factor effects, it provides p-values, and is fast enough to complete an eQTL analysis of thousands of expression variables when analyzing hundreds of thousands of SNPs on a standard desktop in < 24hours.

TRECASE_MLE / Total REad Count and Allele-Specific Expression in RNA-Seq Data with Maximum-Likelihood Estimation

Provides a method for expression quantitative trait loci (eQTL) mapping. TRECASE_MLE is composed of five mains stages: it (i) evaluates candidates for association and highlights the single nucleotide polymorphism (SNP) with the minimum p-value for each gene, (ii), determines the significance of each minimum-p SNP (iii) detects eQTLs into minimum-p SNPs, (iv) separates cis- and trans-acting regulations into them (v) and then estimates their effect sizes at detected eQTLs.


A method for fast and efficient eQTL mapping in homozygous inbred populations with binary allele calls. FastMap exploits the discrete nature and structure of the measured single nucleotide polymorphisms (SNPs). In particular, SNPs are organized into a Hamming distance-based tree that minimizes the number of arithmetic operations required to calculate the association of a SNP by making use of the association of its parent SNP in the tree. FastMap's tree can be used to perform both single marker mapping and haplotype association mapping over an m-SNP window.


A tool for fast eQTL meta-analysis of arbitrary sample size and arbitrary number of datasets. Further, this tool accommodates versatile modeling, eg. non-parametric model and mixed effect models. In addition, Meta-eQTL readily handles calculation of chrX eQTLs. Meta-eQTL is a set of command line utilities written in R, with some computationally intensive parts written in C. The software runs on Linux platforms and is designed to intelligently adapt to high performance computing (HPC) cluster.