eQTL

Toolkit for molecular QTL (molQTL) mapping using linear mixed models that handle complex relatedness, aimed at high-throughput omics phenotypes with strong performance for discovery, fine mapping, and trait–molQTL colocalization versus common linear-mapper pipelines.

molQTL, xQTL, LMM, relatedness, colocalization, fine mapping

Molecular quantitative trait loci (molQTL) mapping is one of the most popular approaches to systematically characterize functional impacts of genomic variants, leading to advanced understanding of the regulatory mechanisms underpinning complex traits and diseases. However, when applied to high-throughput molecular phenotypes, the existing molQTL mapping tools often implement simple linear models, overlooking complex inter-individual relatedness, leading to false positives and insufficient statistical power. Here, we introduce OmiGA, an ultra-efficient omics genetic analysis toolkit, for molQTL mapping based on linear mixed model in populations with complex relatedness. Both computational simulations and real data analyses demonstrate that OmiGA outperforms the existing popular tools regarding molQTL discovery power, fine mapping of causal variants, colocalization of molQTL and trait associations, and computational efficiency. In summary, we recommend OmiGA for molQTL mapping in populations with complex relatedness, for example, those in the Farm animal Genotype-Tissue Expression project and family-based molQTL studies in humans.

Large stable releases (notably v6p and v8) distributing expression matrices, covariates, and variant calls; default inputs for TWAS, colocalization, and enrichment after GWAS.

Cell-type-specific eQTL analysis using computational deconvolution of bulk GTEx RNA-seq data across multiple tissues.

Analysis of genetic effects on gene expression using 7,051 RNA-seq samples from 449 donors across 44 tissues. Identified thousands of cis- and trans-eQTLs.

First comprehensive description of the GTEx project design, including tissue collection protocols, RNA-seq and genotyping methods, and the initial data release strategy.

Pilot analysis of RNA-seq data from ~175 donors across 43 tissues, demonstrating tissue-specific and shared cis-eQTL architecture.

Multitissue RNA-seq and genotyping in ~175 donors across 43 tissues; defined tissue-specific and shared cis-eQTL architecture and established GTEx as the reference expression resource.

v8 flagship paper: comprehensive atlas of genetic regulatory effects across 49 human tissues from 838 donors. Includes cis-eQTL, trans-eQTL, sQTL, and expression correlation networks.