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eQTL

Summary Table

NAME CITATION YEAR
FastQTL Ongen H, Buil A, Brown AA, Dermitzakis ET, ...&, Delaneau O. (2016) Fast and efficient QTL mapper for thousands of molecular phenotypes Bioinformatics, 32 (10) 1479-1485. doi:10.1093/bioinformatics/btv722. PMID 26708335 2016
MatrixEQTL Shabalin AA. (2012) Matrix eQTL: ultra fast eQTL analysis via large matrix operations Bioinformatics, 28 (10) 1353-1358. doi:10.1093/bioinformatics/bts163. PMID 22492648 2012
RASQUAL Kumasaka N, Knights AJ, Gaffney DJ. (2016) Fine-mapping cellular QTLs with RASQUAL and ATAC-seq Nat. Genet., 48 (2) 206-213. doi:10.1038/ng.3467. PMID 26656845 2016
SAIGE-QTL Zhou, W., Cuomo, A., Xue, A., Kanai, M., Chau, G., Krishna, C., ... & Neale, B. M. (2024). Efficient and accurate mixed model association tool for single-cell eQTL analysis. medRxiv, 2024-05. NA
TReCASE Zhabotynsky V, Inoue K, Magnuson T, Mauro Calabrese J, ...&, Sun W. (2019) A statistical method for joint estimation of cis-eQTLs and parent-of-origin effects under family trio design Biometrics, 75 (3) 864-874. doi:10.1111/biom.13026. PMID 30666629 2019
tensorQTL Taylor-Weiner A, Aguet F, Haradhvala NJ, Gosai S, ...&, Getz G. (2019) Scaling computational genomics to millions of individuals with GPUs Genome Biol., 20 (1) 228. doi:10.1186/s13059-019-1836-7. PMID 31675989 2019

FastQTL

  • NAME : FastQTL
  • SHORT NAME : FastQTL
  • FULL NAME : FastQTL
  • DESCRIPTION : In order to discover quantitative trait loci (QTLs), multi-dimensional genomic datasets combining DNA-seq and ChiP-/RNA-seq require methods that rapidly correlate tens of thousands of molecular phenotypes with millions of genetic variants while appropriately controlling for multiple testing. FastQTL 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.
  • URL : https://github.com/francois-a/fastqtl
  • TITLE : Fast and efficient QTL mapper for thousands of molecular phenotypes
  • DOI : 10.1093/bioinformatics/btv722
  • ABSTRACT : MOTIVATION: In order to discover quantitative trait loci, multi-dimensional genomic datasets combining DNA-seq and ChiP-/RNA-seq require methods that rapidly correlate tens of thousands of molecular phenotypes with millions of genetic variants while appropriately controlling for multiple testing. RESULTS: We have developed FastQTL, a method that 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. The Geuvadis & GTEx pilot datasets can be now easily analyzed an order of magnitude faster than previous approaches. AVAILABILITY AND IMPLEMENTATION: Source code, binaries and comprehensive documentation of FastQTL are freely available to download at http://fastqtl.sourceforge.net/ CONTACT: emmanouil.dermitzakis@unige.ch or olivier.delaneau@unige.ch SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
  • CITATION : Ongen H, Buil A, Brown AA, Dermitzakis ET, ...&, Delaneau O. (2016) Fast and efficient QTL mapper for thousands of molecular phenotypes Bioinformatics, 32 (10) 1479-1485. doi:10.1093/bioinformatics/btv722. PMID 26708335
  • JOURNAL_INFO : Bioinformatics ; Bioinformatics ; 2016 ; 32 ; 10 ; 1479-1485
  • PUBMED_LINK : 26708335

MatrixEQTL

  • NAME : MatrixEQTL
  • SHORT NAME : Matrix eQTL
  • FULL NAME : Matrix eQTL
  • DESCRIPTION : Matrix eQTL is designed for fast eQTL analysis on large datasets. Matrix eQTL can test for association between genotype and gene expression using linear regression with either additive or ANOVA genotype effects. The models can include covariates to account for factors as population stratification, gender, and clinical variables. It also supports models with heteroscedastic and/or correlated errors, false discovery rate estimation and separate treatment of local (cis) and distant (trans) eQTLs.
  • URL : http://www.bios.unc.edu/research/genomic_software/Matrix_eQTL/
  • TITLE : Matrix eQTL: ultra fast eQTL analysis via large matrix operations
  • DOI : 10.1093/bioinformatics/bts163
  • ABSTRACT : MOTIVATION: Expression quantitative trait loci (eQTL) analysis links variations in gene expression levels to genotypes. For modern datasets, eQTL analysis is a computationally intensive task as it involves testing for association of billions of transcript-SNP (single-nucleotide polymorphism) pair. The heavy computational burden makes eQTL analysis less popular and sometimes forces analysts to restrict their attention to just a small subset of transcript-SNP pairs. As more transcripts and SNPs get interrogated over a growing number of samples, the demand for faster tools for eQTL analysis grows stronger. RESULTS: We have developed a new software for computationally efficient eQTL analysis called Matrix eQTL. In tests on large datasets, it was 2-3 orders of magnitude faster than existing popular tools for QTL/eQTL analysis, while finding the same eQTLs. The fast performance is achieved by special preprocessing and expressing the most computationally intensive part of the algorithm in terms of large matrix operations. Matrix eQTL supports additive linear and ANOVA models with covariates, including models with correlated and heteroskedastic errors. The issue of multiple testing is addressed by calculating false discovery rate; this can be done separately for cis- and trans-eQTLs.
  • CITATION : Shabalin AA. (2012) Matrix eQTL: ultra fast eQTL analysis via large matrix operations Bioinformatics, 28 (10) 1353-1358. doi:10.1093/bioinformatics/bts163. PMID 22492648
  • JOURNAL_INFO : Bioinformatics (Oxford, England) ; Bioinformatics ; 2012 ; 28 ; 10 ; 1353-1358
  • PUBMED_LINK : 22492648

RASQUAL

  • NAME : RASQUAL
  • SHORT NAME : RASQUAL
  • FULL NAME : Robust Allele Specific QUAntitation and quality controL
  • DESCRIPTION : RASQUAL (Robust Allele Specific QUAntification and quality controL) maps QTLs for sequenced based cellular traits by combining population and allele-specific signals.
  • URL : https://github.com/natsuhiko/rasqual
  • TITLE : Fine-mapping cellular QTLs with RASQUAL and ATAC-seq
  • DOI : 10.1038/ng.3467
  • ABSTRACT : When cellular traits are measured using high-throughput DNA sequencing, quantitative trait loci (QTLs) manifest as fragment count differences between individuals and allelic differences within individuals. We present RASQUAL (Robust Allele-Specific Quantitation and Quality Control), a new statistical approach for association mapping that models genetic effects and accounts for biases in sequencing data using a single, probabilistic framework. RASQUAL substantially improves fine-mapping accuracy and sensitivity relative to existing methods in RNA-seq, DNase-seq and ChIP-seq data. We illustrate how RASQUAL can be used to maximize association detection by generating the first map of chromatin accessibility QTLs (caQTLs) in a European population using ATAC-seq. Despite a modest sample size, we identified 2,707 independent caQTLs (at a false discovery rate of 10%) and demonstrated how RASQUAL and ATAC-seq can provide powerful information for fine-mapping gene-regulatory variants and for linking distal regulatory elements with gene promoters. Our results highlight how combining between-individual and allele-specific genetic signals improves the functional interpretation of noncoding variation.
  • CITATION : Kumasaka N, Knights AJ, Gaffney DJ. (2016) Fine-mapping cellular QTLs with RASQUAL and ATAC-seq Nat. Genet., 48 (2) 206-213. doi:10.1038/ng.3467. PMID 26656845
  • JOURNAL_INFO : Nature genetics ; Nat. Genet. ; 2016 ; 48 ; 2 ; 206-213
  • PUBMED_LINK : 26656845

SAIGE-QTL

  • NAME : SAIGE-QTL
  • SHORT NAME : SAIGE-QTL
  • FULL NAME : SAIGE-QTL
  • DESCRIPTION : SAIGE-QTL is a robust and scalable tool that can directly map eQTLs using single-cell profiles without needing aggregation at the pseudobulk level.
  • URL : https://github.com/weizhou0/qtl
  • KEYWORDS : single -cell eQTL, rare variant, set-based test, trans-eQTL, SPA
  • CITATION : Zhou, W., Cuomo, A., Xue, A., Kanai, M., Chau, G., Krishna, C., ... & Neale, B. M. (2024). Efficient and accurate mixed model association tool for single-cell eQTL analysis. medRxiv, 2024-05.

TReCASE

  • NAME : TReCASE
  • SHORT NAME : TReCASE (asSeq)
  • FULL NAME : Total Read Count and Allele-Specific Expression
  • DESCRIPTION : A Statistical Framework for eQTL Mapping Using RNA-seq Data.
  • URL : http://www.bios.unc.edu/~wsun/software.htm
  • TITLE : A statistical method for joint estimation of cis-eQTLs and parent-of-origin effects under family trio design
  • DOI : 10.1111/biom.13026
  • ABSTRACT : RNA sequencing allows one to study allelic imbalance of gene expression, which may be due to genetic factors or genomic imprinting (i.e., higher expression of maternal or paternal allele). It is desirable to model both genetic and parent-of-origin effects simultaneously to avoid confounding and to improve the power to detect either effect. In studies of genetically tractable model organisms, separation of genetic and parent-of-origin effects can be achieved by studying reciprocal cross of two inbred strains. In contrast, this task is much more challenging in outbred populations such as humans. To address this challenge, we propose a new framework to combine experimental strategies and novel statistical methods. Specifically, we propose to study genetic and imprinting effects in family trios with RNA-seq data from the children and genotype data from both parents and children, and quantify genetic effects by cis-eQTLs. Towards this end, we have extended our method that studies the eQTLs of RNA-seq data (Sun, Biometrics 2012, 68(1): 1-11) to model both cis-eQTL and parent-of-origin effects, and evaluated its performance using extensive simulations. Since sample size may be limited in family trios, we have developed a data analysis pipeline that borrows information from external data of unrelated individuals for cis-eQTL mapping. We have also collected RNA-seq data from the children of 30 family trios, applied our method to analyze this dataset, and identified some previously reported imprinted genes as well as some new candidates of imprinted genes.
  • COPYRIGHT : http://onlinelibrary.wiley.com/termsAndConditions#vor
  • CITATION : Zhabotynsky V, Inoue K, Magnuson T, Mauro Calabrese J, ...&, Sun W. (2019) A statistical method for joint estimation of cis-eQTLs and parent-of-origin effects under family trio design Biometrics, 75 (3) 864-874. doi:10.1111/biom.13026. PMID 30666629
  • JOURNAL_INFO : Biometrics ; Biometrics ; 2019 ; 75 ; 3 ; 864-874
  • PUBMED_LINK : 30666629

tensorQTL

  • NAME : tensorQTL
  • SHORT NAME : tensorQTL
  • FULL NAME : tensorQTL
  • DESCRIPTION : tensorQTL is a GPU-enabled QTL mapper, achieving ~200-300 fold faster cis- and trans-QTL mapping compared to CPU-based implementations.
  • URL : https://github.com/broadinstitute/tensorqtl
  • TITLE : Scaling computational genomics to millions of individuals with GPUs
  • DOI : 10.1186/s13059-019-1836-7
  • ABSTRACT : Current genomics methods are designed to handle tens to thousands of samples but will need to scale to millions to match the pace of data and hypothesis generation in biomedical science. Here, we show that high efficiency at low cost can be achieved by leveraging general-purpose libraries for computing using graphics processing units (GPUs), such as PyTorch and TensorFlow. We demonstrate > 200-fold decreases in runtime and ~ 5-10-fold reductions in cost relative to CPUs. We anticipate that the accessibility of these libraries will lead to a widespread adoption of GPUs in computational genomics.
  • COPYRIGHT : http://creativecommons.org/licenses/by/4.0/
  • CITATION : Taylor-Weiner A, Aguet F, Haradhvala NJ, Gosai S, ...&, Getz G. (2019) Scaling computational genomics to millions of individuals with GPUs Genome Biol., 20 (1) 228. doi:10.1186/s13059-019-1836-7. PMID 31675989
  • JOURNAL_INFO : Genome biology ; Genome Biol. ; 2019 ; 20 ; 1 ; 228
  • PUBMED_LINK : 31675989