PT  - JOURNAL ARTICLE
AU  - Tianxing Ma
AU  - Haochen Li
AU  - Xuegong Zhang
TI  - Discovering single-cell eQTLs from scRNA-seq data only
AID  - 10.1101/2021.06.10.447906
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.06.10.447906
4099  - http://biorxiv.org/content/early/2021/06/10/2021.06.10.447906.short
4100  - http://biorxiv.org/content/early/2021/06/10/2021.06.10.447906.full
AB  - eQTL studies are essential for understanding genomic regulation. Effects of genetic variations on gene regulation are cell-type-specific and cellular-context-related, so studying eQTLs at a single-cell level is crucial. The ideal solution is to use both mutation and expression data from the same cells. However, current technology of such paired data in single cells is still immature. We present a new method, eQTLsingle, to discover eQTLs only with single cell RNA-seq (scRNA-seq) data, without genomic data. It detects mutations from scRNA-seq data and models gene expression of different genotypes with the zero-inflated negative binomial (ZINB) model to find associations between genotypes and phenotypes at single-cell level. On a glioblastoma and gliomasphere scRNA-seq dataset, eQTLsingle discovered hundreds of cell-type-specific tumor-related eQTLs, most of which cannot be found in bulk eQTL studies. Detailed analyses on examples of the discovered eQTLs revealed important underlying regulatory mechanisms. eQTLsingle is a unique powerful tool for utilizing the huge scRNA-seq resources for single-cell eQTL studies, and it is available for free academic use at https://github.com/horsedayday/eQTLsingle.Competing Interest StatementThe authors have declared no competing interest.eQTLexpression quantitative trait locusscRNA-seqsingle-cell RNA sequencingZINBzero-inflated negative binomialSNVsingle nucleotide variantLRTlikelihood ratio testGBMglioblastoma multiformeCSCcancer stem cellsPCAprincipal component analysisLGGlow-grade gliomasTFtranscription factorChIP-seqchromatin immunoprecipitation sequencingPWMposition weight matrixTSStranscription start sitePMFprobability mass functionNBnegative binomialMLEmaximum likelihood estimation