{
 "id": "epw9f2nk46",
 "slug": "eqtl-mapping",
 "title": "eQTL mapping",
 "updated": "2026-10-15",
 "topic_path": [
  {
   "id": "life",
   "label": "Life and health",
   "api_url": "https://www.edgepedia.org/api/v1/topics/life"
  },
  {
   "id": "life.biology",
   "label": "Biological foundations",
   "api_url": "https://www.edgepedia.org/api/v1/topics/life.biology"
  },
  {
   "id": "life.biology.genetics",
   "label": "Genetics and genomic reference",
   "api_url": "https://www.edgepedia.org/api/v1/topics/life.biology.genetics"
  },
  {
   "id": "life.biology.genetics.genomics_projects_databases",
   "label": "Genomics, sequencing, and genome resources",
   "api_url": "https://www.edgepedia.org/api/v1/topics/life.biology.genetics.genomics_projects_databases"
  },
  {
   "id": "life.biology.genetics.genomics_projects_databases.functional-genomics-and-screening",
   "label": "Functional genomics and screening",
   "api_url": "https://www.edgepedia.org/api/v1/topics/life.biology.genetics.genomics_projects_databases.functional-genomics-and-screening"
  }
 ],
 "geo": [
  {
   "id": "geo.nongeo.t2001.life.biology.genetics.genomics_projects_databases",
   "label": "Non-geographic · 2001 to 2020: Genomics, sequencing, and genome resources",
   "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.life.biology.genetics.genomics_projects_databases",
   "path": [
    {
     "id": "geo.nongeo",
     "label": "Non-geographic",
     "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo"
    },
    {
     "id": "geo.nongeo.t2001",
     "label": "Non-geographic · 2001 to 2020",
     "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001"
    },
    {
     "id": "geo.nongeo.t2001.life",
     "label": "Life and health",
     "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.life"
    },
    {
     "id": "geo.nongeo.t2001.life.biology",
     "label": "Biological foundations",
     "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.life.biology"
    },
    {
     "id": "geo.nongeo.t2001.life.biology.genetics",
     "label": "Genetics and genomic reference",
     "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.life.biology.genetics"
    },
    {
     "id": "geo.nongeo.t2001.life.biology.genetics.genomics_projects_databases",
     "label": "Genomics, sequencing, and genome resources",
     "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.life.biology.genetics.genomics_projects_databases"
    }
   ]
  }
 ],
 "excerpt": "eQTL mapping is a statistical genetics method that tests inherited DNA variants for association with gene expression, producing catalogs of variant–gene pairs that regulate transcript abundance.",
 "snippet": "eQTL mapping is a statistical genetics method that tests inherited DNA variants for association with gene expression, producing catalogs of variant–gene pairs that regulate transcript abundance.",
 "node": "life.biology.genetics.genomics_projects_databases.functional-genomics-and-screening",
 "markdown": "# eQTL mapping\n\neQTL mapping is a statistical genetics method that tests inherited DNA variants for association with measured gene expression levels, producing a catalog of variant–gene pairs that regulate transcript abundance. A single study typically tests more than 20,000 genes against up to millions of SNPs, so the output is a large set of variant–gene associations assessed with P values and false discovery rate (FDR) correction.<sup>[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC10656365/)</sup> Modern resources operate at cohort scale: GTEx version 8 mapped cis-eQTLs for 23,268 genes across 49 tissues from 15,201 RNA-seq samples of 838 donors,<sup>[2](https://www.science.org/doi/10.1126/science.aaz1776)</sup> and the eQTLGen [Consortium](https://www.edgepedia.org/consortium) meta-analyzed blood expression from 31,684 individuals across 37 cohorts.<sup>[3](https://www.nature.com/articles/s41588-021-00913-z)</sup>\n\n| Key fact | Value |\n|---|---|\n| Core output | Variant–gene pairs with effect size, P value, and FDR; GTEx v8 found cis-eQTLs for 23,268 genes and 4,278,636 significant variants (43% of variants with MAF ≥ 0.01)<sup>[2](https://www.science.org/doi/10.1126/science.aaz1776)</sup> |\n| Statistical model | Linear regression \\( y_{ng} = \\beta_{g} + \\beta_{gs} x_{ns} + \\varepsilon_{ngs} \\), testing \\( \\beta_{gs} = 0 \\)<sup>[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC10656365/)</sup> |\n| cis window | Variants within 1 Mb of the transcription start site; empirical support for the threshold comes from the 0.85–1.3 Mb range where cis signal falls to background<sup>[4](https://www.nature.com/articles/nature24277)</sup> |\n| Blood reference scale | eQTLGen: 31,684 samples, cis-eQTLs for 16,987 genes (88% of tested genes)<sup>[3](https://www.nature.com/articles/s41588-021-00913-z)</sup> |\n| Power requirement | 1,685 samples at power 0.80 to detect eGenes at the median effect size of 0.124<sup>[5](https://escholarship.org/content/qt9gq442bs/qt9gq442bs.pdf)</sup> |\n| GWAS connection | 43% of 5,385 GTEx GWAS loci colocalize with a cis-eQTL<sup>[2](https://www.science.org/doi/10.1126/science.aaz1776)</sup> |\n| Testing burden | More than 20,000 genes \\( \\times \\) up to millions of SNPs per study<sup>[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC10656365/)</sup> |\n\n## How it works\n\nThe standard model is a linear regression of normalized expression on genotype. For sample \\( n \\) and gene \\( g \\), \\( y_{ng} = \\beta_{g} + \\beta_{gs} x_{ns} + \\varepsilon_{ngs} \\), where \\( x_{ns} \\) encodes the genotype at variant \\( s \\) and the null hypothesis of no regulatory effect is \\( \\beta_{gs} = 0 \\).<sup>[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC10656365/)</sup> Matrix eQTL implements this with additive or ANOVA genotype effects and supports covariates such as sex, population stratification, and clinical variables.<sup>[6](https://github.com/andreyshabalin/MatrixEQTL/)</sup> RNA-seq count data may be better modeled by a negative binomial generalized linear regression than the Gaussian model suited to microarrays, at higher computational cost.<sup>[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC10656365/)</sup>\n\nThe linkage-statistic framework descends from the interval mapping of Lander and Botstein, which models genotype–phenotype additivity through a mixture model and scores candidate intervals with LOD scores.<sup>[7](https://doi.org/10.1093/genetics/121.1.185)</sup> Because applying standard QTL tests to every transcript inflates the FDR, significance is assessed empirically: GTEx uses adaptive permutations in FastQTL, fits the resulting nominal P values to a [Beta distribution](https://www.edgepedia.org/beta-distribution), and corrects with Storey's q-value method;<sup>[4](https://www.nature.com/articles/nature24277)</sup> other pipelines use Benjamini–Hochberg FDR.<sup>[8](https://link.springer.com/article/10.1186/s12859-021-04307-0)</sup> The MOM (mixture over markers) empirical Bayes model shares information across markers and transcripts to control FDR without substantial power loss.<sup>[9](https://www.columbia.edu/~my2550/papers/eqtl.final.pdf)</sup>\n\n## How it is done\n\nA pipeline runs cohort design, genotyping, expression measurement, normalization, testing, and fine-mapping in that order.\n\n**Genotyping and QC.** Standard SNP filtering keeps variants with call rate > 0.95, Hardy–Weinberg P > \\( 1 \\times 10^{-6} \\), and MAF > 0.01, with 10 genotype PCs and 100 expression PCs as covariates, genotype-inferred sex, and imputation to the 1000 Genomes 30X panel after Eagle v2.4.1 pre-phasing and Minimac4.<sup>[10](https://eqtlgen.github.io/eqtlgen-web-site/eQTLGen-p2-cookbook.html)</sup> PLINK-based QC removes markers with excessive missingness, Hardy–Weinberg violations, low MAF, abnormal heterozygosity, related subjects, and divergent ancestry.<sup>[8](https://link.springer.com/article/10.1186/s12859-021-04307-0)</sup> In GTEx v8, whole-genome sequencing at a median 32x depth yielded 43,066,422 QC-passed SNVs, and tissue mRNA was sequenced to a median 82.6 million reads.<sup>[2](https://www.science.org/doi/10.1126/science.aaz1776)</sup>\n\n**Expression processing.** The GTEx pipeline normalizes read counts between samples with TMM, keeps genes with ≥ 0.1 TPM (Transcripts per million; normalized gene expression measure) in ≥ 20% of samples and ≥ 6 unnormalized reads in ≥ 20% of samples, then inverse normal transforms each gene across samples.<sup>[11](https://github.com/broadinstitute/gtex-pipeline/blob/master/qtl/README.md)</sup> The eQTL Catalogue instead uses conditional quantile normalization (cqn) with gene length and GC content as covariates, followed by inverse normal transformation.<sup>[12](https://www.ebi.ac.uk/eqtl/Methods/)</sup> The eQTLQC pipeline log10-transforms TPM with a pseudocount of \\( 10^{-4} \\), adjusts batch effects with ComBat from SVA, and removes latent covariates with fsva.<sup>[8](https://link.springer.com/article/10.1186/s12859-021-04307-0)</sup>\n\n**Testing.** The number of PEER covariate factors scales with sample size: 15 for \\( N < 150 \\), 30 for \\( 150 \\leq N < 250 \\), 45 for \\( 250 \\leq N < 350 \\), and 60 for \\( N \\geq 350 \\).<sup>[11](https://github.com/broadinstitute/gtex-pipeline/blob/master/qtl/README.md)</sup> Association testing uses FastQTL, Matrix eQTL, or QTLtools with a 1 Mb cis window and permutation passes (--permute 1000 10000).<sup>[11](https://github.com/broadinstitute/gtex-pipeline/blob/master/qtl/README.md)</sup> Downstream statistical fine-mapping uses susieR to produce credible sets.<sup>[12](https://www.ebi.ac.uk/eqtl/Methods/)</sup>\n\n**Power.** Cis-QTL discovery correlates strongly with tissue sample size (Spearman's rho = 0.95), and discovery of strong eGenes (over twofold effects) saturates at about 1,500 genes in tissues with more than 200 samples.<sup>[2](https://www.science.org/doi/10.1126/science.aaz1776)</sup> Sample size matters more than sequencing depth: a 10-fold reduction in RNA-seq coverage costs only a 2.5-fold reduction in eQTL power, so 1,490 individuals at 5.9 million reads per sample outperform 570 individuals at 13.9 million reads.<sup>[5](https://escholarship.org/content/qt9gq442bs/qt9gq442bs.pdf)</sup>\n\n## Origin\n\nThe immediate precursor treated gene products as quantitative traits: Damerval and colleagues mapped QTL underlying gene product variation in 1994, in Genetics.<sup>[13](https://doi.org/10.1093/genetics/137.1.289)</sup> Jansen framed the genome-wide version as \"genetical genomics\" in 2001, in Trends in Genetics, arguing that segregation information adds value to expression profiling.<sup>[14](https://doi.org/10.1016/s0168-9525%2801%2902310-1)</sup> In 2002, Brem and colleagues reported linkage analysis of genome-wide expression patterns in a cross between a laboratory and a wild strain of <i>[Saccharomyces cerevisiae](https://www.edgepedia.org/saccharomyces-cerevisiae)</i>, in Science.<sup>[15](https://doi.org/10.1126/science.1069516)</sup> In that cross, over 1,500 genes were differentially expressed between the parents, expression levels of 570 genes were linked to one or more loci, and eight trans-acting loci each affected 7 to 94 genes of related function.<sup>[15](https://doi.org/10.1126/science.1069516)</sup> In 2003, Schadt and colleagues surveyed the genetics of gene expression in maize, mouse, and man by treating expression values as quantitative traits, in Nature.<sup>[16](https://doi.org/10.1038/nature01434)</sup> Early studies were small: 113 yeast segregants, 111 selfed progeny of inbred mice, and 32 recombinant inbred mouse strains.<sup>[17](https://bmcgenomics.biomedcentral.com/articles/10.1186/1471-2164-7-125)</sup> Later work confirmed causal yeast hotspot genes including MKT1, HAP1, IRA2, GPA1, and the mating-type locus.<sup>[18](https://elifesciences.org/articles/35471)</sup>\n\n## Variants\n\nCis-eQTLs are variants near the gene; trans-eQTLs are far away or on another chromosome. Operationally, cis-eQTLs are SNPs within about one million base pairs of the candidate gene, and trans-eQTLs lie on different chromosomes or far away on the same chromosome.<sup>[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC10656365/)</sup> The 1 Mb threshold is empirically supported: evidence of cis-regulation falls below background between 0.85 and 1.3 Mb from the TSS.<sup>[4](https://www.nature.com/articles/nature24277)</sup>\n\nPower is asymmetric. With a few hundred samples, the typical eQTL study size, there is limited power for the genome-wide search that trans-eQTLs require, and trans effects tend to be smaller than cis effects.<sup>[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC10656365/)</sup> GTEx identified 673 trans-eQTLs at 10% genome-wide FDR across 16 tissues using Matrix eQTL with an additive model testing variants and genes on different chromosomes.<sup>[4](https://www.nature.com/articles/nature24277)</sup> Trans effects often cluster: hundreds or thousands of genes are trans-regulated by a small number of genomic regions called regulatory hotspots.<sup>[19](https://pmc.ncbi.nlm.nih.gov/articles/PMC2600931/)</sup> Allele-specific expression provides an operational discriminator: in the yeast BY × RM cross, a follow-up study estimated that 1,270 of 5,727 genes (22%) represent true self-linkages from polymorphisms in the encoding genes, and used TaqMan qPCR assays of allele-specific expression in diploid hybrids to separate cis-acting from trans-acting local variation.<sup>[20](https://journals.plos.org/plosgenetics/article/file?id=10.1371%2Fjournal.pgen.0010025&type=printable)</sup>\n\nThe same regression framework defines the molecular QTL family. GTEx mapped splicing QTLs (sQTLs) from LeafCutter intron excision ratios, finding 14,424 genes with a cis-sQTL at 5% FDR in at least one tissue.<sup>[2](https://www.science.org/doi/10.1126/science.aaz1776)</sup> Single-cell eQTL mapping is a recent variant: the OneK1K cohort profiled 1.27 million peripheral blood mononuclear cells from 982 donors, mapping eQTLs in 14 immune cell types and identifying 26,597 independent cis-eQTLs.<sup>[21](https://www.science.org/doi/10.1126/science.abf3041)</sup> Method variants include SURGE, a matrix-factorization latent-factor approach that discovers context-specific eQTLs without pre-specifying contexts,<sup>[22](https://link.springer.com/article/10.1186/s13059-023-03152-z)</sup> FastGxC, which reduces context-specific eQTL computation from years to minutes,<sup>[23](https://www.cell.com/cell-genomics/fulltext/S2666-979X%2826%2900112-6)</sup> and eQTLsingle, which discovers eQTLs from scRNA-seq without genomic data.<sup>[24](https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2023.1182579/full)</sup>\n\n## Applications\n\nA prominent application is interpreting GWAS loci. Of 5,385 significant GWAS loci across 87 complex traits in GTEx, 43% colocalized with a cis-eQTL and 23% with a cis-sQTL using ENLOC, with PrediXcan used to associate traits with genetically regulated expression.<sup>[2](https://www.science.org/doi/10.1126/science.aaz1776)</sup> PrediXcan trains expression prediction models with Elastic Net regularization to estimate genetically regulated gene expression (GReX).<sup>[25](https://pmc.ncbi.nlm.nih.gov/articles/PMC10846672/)</sup> In whole blood, 48% of previously published significant GWAS SNPs were significant eQTLs in a 5,257-person microarray study, which also found 59 trans-eQTL clusters each targeting 6 to 229 genes.<sup>[26](https://link.springer.com/article/10.1186/s13059-016-1142-6)</sup> In eQTLGen, expression of 13% of genes correlated with polygenic scores for 1,263 phenotypes.<sup>[3](https://www.nature.com/articles/s41588-021-00913-z)</sup> In OneK1K, 19% of cis-eQTLs shared a causal locus with a GWAS risk association, and [Mendelian randomization](https://www.edgepedia.org/mendelian-randomization) uncovered 305 loci contributing to autoimmune disease through cell-type-specific expression changes.<sup>[21](https://www.science.org/doi/10.1126/science.abf3041)</sup> The overlap is partial and structured: eQTLs explain only a small fraction of GWAS signals, and cis-eQTL hits cluster strongly near transcription start sites whereas GWAS hits do not.<sup>[27](https://www.nature.com/articles/s41588-023-01529-1)</sup>\n\nPublic resources serve these uses directly. The GTEx Portal offers a Locus Browser, an eQTL [Dashboard](https://www.edgepedia.org/dashboard) for batch queries by gene and tissue, and an eQTL [Calculator](https://www.edgepedia.org/calculator) for testing user-supplied eQTLs.<sup>[28](https://gtexportal.org/home/)</sup> The eQTL Catalogue uniformly recomputed QTLs from 21 studies to remove differences in variants tested, allele codings, and quantified features, and its 2023 update added X chromosome QTLs and demonstrated colocalization between [UK Biobank](https://www.edgepedia.org/uk-biobank) vitamin D levels and its molecular QTLs.<sup>[29](https://journals.plos.org/plosgenetics/article?id=10.1371%2Fjournal.pgen.1010932)</sup>\n\n## Limitations and alternatives\n\n**Batch effects and latent covariates.** Unmodeled technical variation produces false associations; pipelines adjust with ComBat, surrogate variables (SVA/fsva), PEER factors, and genotype and expression PCs.<sup>[8](https://link.springer.com/article/10.1186/s12859-021-04307-0)</sup> **Cell-type composition** is a specific confounder for trans-eQTLs: in eQTLGen, distal trans-eQTLs detected for 37% of 10,317 trait-associated variants showed lower replication rates, partly due to low replication power and confounding by cell type composition.<sup>[3](https://www.nature.com/articles/s41588-021-00913-z)</sup> **Population structure** is corrected with mixed model methods for genetic analysis.<sup>[30](https://pmc.ncbi.nlm.nih.gov/articles/PMC8871770/)</sup> **Multiple testing** is heaviest for trans scans, which may explain a sizable proportion of expression variation but carry a substantial burden.<sup>[25](https://pmc.ncbi.nlm.nih.gov/articles/PMC10846672/)</sup>\n\nBulk RNA sequencing averages expression across cell types and cell states, which single-cell assays overcome by capturing transcriptional states of individual cells.<sup>[31](https://www.annualreviews.org/content/journals/10.1146/annurev-genom-101422-100437)</sup> TWAS, the integration of eQTL mapping with GWAS, is prone to spurious prioritization when using expression data from non-trait-related tissues, owing to substantial cross-cell-type variation in expression levels and eQTL strengths, and linkage disequilibrium contamination complicates downstream inference; it nonetheless prioritizes candidate causal genes more accurately than simple baselines.<sup>[25](https://pmc.ncbi.nlm.nih.gov/articles/PMC10846672/)</sup>\n\n## References\n\n1. [eQTL studies: from bulk tissues to single cells](https://pmc.ncbi.nlm.nih.gov/articles/PMC10656365/)\n2. [The GTEx Consortium atlas of genetic regulatory effects across human tissues (Science 2020)](https://www.science.org/doi/10.1126/science.aaz1776)\n3. [Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression (eQTLGen, Nature Genetics 2021)](https://www.nature.com/articles/s41588-021-00913-z)\n4. [Genetic effects on gene expression across human tissues (GTEx Consortium, Nature 2017)](https://www.nature.com/articles/nature24277)\n5. [Powerful eQTL mapping through low-coverage RNA sequencing](https://escholarship.org/content/qt9gq442bs/qt9gq442bs.pdf)\n6. [Matrix eQTL: Ultra fast eQTL analysis via large matrix operations (package repository)](https://github.com/andreyshabalin/MatrixEQTL/)\n7. [E S Lander, D Botstein (1989). Mapping mendelian factors underlying quantitative traits using RFLP linkage maps.. Genetics.](https://doi.org/10.1093/genetics/121.1.185)\n8. [A pipeline for RNA-seq based eQTL analysis with automated quality control procedures (eQTLQC, BMC Bioinformatics 2021)](https://link.springer.com/article/10.1186/s12859-021-04307-0)\n9. [Statistical Methods for Expression Quantitative Trait Loci (eQTL) Mapping (Kendziorski et al., Biometrics 2006)](https://www.columbia.edu/~my2550/papers/eqtl.final.pdf)\n10. [Cookbook for eQTLGen phase II analyses](https://eqtlgen.github.io/eqtlgen-web-site/eQTLGen-p2-cookbook.html)\n11. [GTEx eQTL discovery pipeline (broadinstitute/gtex-pipeline qtl README)](https://github.com/broadinstitute/gtex-pipeline/blob/master/qtl/README.md)\n12. [Methods, eQTL Catalogue, EMBL-EBI](https://www.ebi.ac.uk/eqtl/Methods/)\n13. [C Damerval and colleagues (1994). Quantitative trait loci underlying gene product variation: a novel perspective for analyzing regulation of genome expression.. Genetics.](https://doi.org/10.1093/genetics/137.1.289)\n14. [Genetical genomics: the added value from segregation (Trends in Genetics, 2001)](https://doi.org/10.1016/s0168-9525%2801%2902310-1)\n15. [Rachel B. Brem and colleagues (2002). Genetic Dissection of Transcriptional Regulation in Budding Yeast. Science.](https://doi.org/10.1126/science.1069516)\n16. [Eric E. Schadt and colleagues (2003). Genetics of gene expression surveyed in maize, mouse and man. Nature.](https://doi.org/10.1038/nature01434)\n17. [Causal inference of regulator-target pairs by gene mapping of expression phenotypes (BMC Genomics 2006)](https://bmcgenomics.biomedcentral.com/articles/10.1186/1471-2164-7-125)\n18. [Genetics of trans-regulatory variation in gene expression (eLife 2018)](https://elifesciences.org/articles/35471)\n19. [Accurate Discovery of eQTLs Under Confounding (ICE eQTL)](https://pmc.ncbi.nlm.nih.gov/articles/PMC2600931/)\n20. [Local Regulatory Variation in Saccharomyces cerevisiae (Ronald et al., PLOS Genetics 2005)](https://journals.plos.org/plosgenetics/article/file?id=10.1371%2Fjournal.pgen.0010025&type=printable)\n21. [Single-cell eQTL mapping identifies cell type–specific genetic control of autoimmune disease (OneK1K, Science 2022)](https://www.science.org/doi/10.1126/science.abf3041)\n22. [SURGE: uncovering context-specific genetic-regulation of gene expression from single-cell RNA sequencing using latent-factor models (Genome Biology 2023)](https://link.springer.com/article/10.1186/s13059-023-03152-z)\n23. [FastGxC: Fast and powerful context-specific eQTL mapping in bulk and single-cell data (Cell Genomics 2026)](https://www.cell.com/cell-genomics/fulltext/S2666-979X%2826%2900112-6)\n24. [Expression quantitative trait locus studies in the era of single-cell omics (Frontiers in Genetics 2023)](https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2023.1182579/full)\n25. [Transcriptome-Wide Association Studies (TWAS): methodologies, applications, and challenges](https://pmc.ncbi.nlm.nih.gov/articles/PMC10846672/)\n26. [Integrated genome-wide analysis of expression quantitative trait loci aids interpretation of genomic association studies (Genome Biology 2016)](https://link.springer.com/article/10.1186/s13059-016-1142-6)\n27. [Systematic differences in discovery of genetic effects on gene expression and complex traits (Nature Genetics 2023)](https://www.nature.com/articles/s41588-023-01529-1)\n28. [GTEx Portal](https://gtexportal.org/home/)\n29. [eQTL Catalogue 2023: New datasets, X chromosome QTLs, and improved detection and visualisation of transcript-level QTLs (PLOS Genetics)](https://journals.plos.org/plosgenetics/article?id=10.1371%2Fjournal.pgen.1010932)\n30. [Towards the Genetic Architecture of Complex Gene Expression Traits: Challenges and Prospects for eQTL Mapping in Humans](https://pmc.ncbi.nlm.nih.gov/articles/PMC8871770/)\n31. [Methods and Insights from Single-Cell Expression Quantitative Trait Loci (Annual Review of Genomics and Human Genetics)](https://www.annualreviews.org/content/journals/10.1146/annurev-genom-101422-100437)\n\n---\n*Topic: Encyclopedia › Life and health › Biological foundations › Genetics and genomic reference › Genomics, sequencing, and genome resources › Functional genomics and screening*\n\n*Initially written Sep 29, 2026 · Edited: Oct 15, 2026*\n\n*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*\n\nLicense: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license\n",
 "same_as": [
  "https://www.columbia.edu/~my2550/papers/eqtl.final.pdf"
 ],
 "url": "https://www.edgepedia.org/eqtl-mapping",
 "markdown_url": "https://www.edgepedia.org/eqtl-mapping.md",
 "license": {
  "name": "Edgepedia Community License 1.0",
  "url": "https://www.edgepedia.org/edgepedia/license",
  "summary": "Free with credit, commercial use included. AI training is open to everyone. For other uses, organizations over USD 100M in revenue or 100M monthly users license separately.",
  "spdx": "LicenseRef-Edgepedia-Community-1.0"
 },
 "credit": "\"eQTL mapping\", Edgepedia (EdgeChat), https://www.edgepedia.org/eqtl-mapping. Edgepedia Community License 1.0.",
 "credit_md": "\"[eQTL mapping](https://www.edgepedia.org/eqtl-mapping)\", Edgepedia (EdgeChat), [https://www.edgepedia.org/eqtl-mapping](https://www.edgepedia.org/eqtl-mapping). [Edgepedia Community License 1.0](https://www.edgepedia.org/edgepedia/license).",
 "credit_html": "\"<a href=\"https://www.edgepedia.org/eqtl-mapping\">eQTL mapping</a>\", Edgepedia (EdgeChat), <a href=\"https://www.edgepedia.org/eqtl-mapping\">https://www.edgepedia.org/eqtl-mapping</a>. <a href=\"https://www.edgepedia.org/edgepedia/license\">Edgepedia Community License 1.0</a>.",
 "speakable": "eQTL mapping is a statistical genetics method that tests inherited DNA variants for association with gene expression, producing catalogs of variant–gene pairs that regulate transcript abundance."
}