eQTL studies: from bulk tissues to single cells

Zhang, Jingfei; Zhao, Hongyu

doi:10.1016/j.jgg.2023.05.003

Cited by 12 publications

(7 citation statements)

References 85 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Crescendo could theoretically correct counts for single-cell ATAC-seq data [63][64][65] and genomic data. Integrating genomic counts may also be useful for quantitative trait loci (QTL) analyses [66][67][68] if they are confounded by technical noise. Due to the visual benefits of correcting gene counts to be more even across datasets (or tissue slices), we envision that investigators will utilize Crescendo to aid in gene visualization and hypothesis generation in scRNA-seq or spatial transcriptomics datasets.…”

Section: Discussionmentioning

confidence: 99%

Integrating spatial transcriptomics count data with Crescendo improves visualization and detection of spatial gene patterns

Millard,

Chen,

Palshikar

et al. 2024

Preprint

View full text Add to dashboard Cite

Spatial transcriptomics allows for the analysis of a cell's gene expression in the context of its physical location. With spatial transcriptomics data, investigators often want to find genes of interest whose spatial patterns are biologically relevant in multiple samples. However, due to confounding factors in spatial data that produce noise across samples, datasets, and technologies, it is challenging to visualize genes and their spatial patterns across samples. We present Crescendo, an integration algorithm that performs correction directly on gene expression counts to reduce variation from technical confounders. We first apply Crescendo to a 3-sample spatial transcriptomics mouse brain dataset to show how Crescendo enables accurate visualization of gene expression across these spatial transcriptomic samples. We then demonstrate Crescendo's scalability by integrating a 16-sample immuno-oncology dataset of 7 million cells. Finally, we show that Crescendo can perform cross-technology integration by merging a colorectal cancer (CRC) scRNA-seq dataset with two CRC spatial transcriptomics samples. By transferring information between technologies, Crescendo can impute poorly expressed genes to improve detection of gene-gene colocalization, such as ligand-receptor interactions.

show abstract

Section: Discussionmentioning

confidence: 99%

Integrating spatial transcriptomics count data with Crescendo improves visualization and detection of spatial gene patterns

Millard,

Chen,

Palshikar

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…These results suggested that an individual's SNP could influence the effect of risk factors on the development of cancers. Mechanically, SNPs in different regions can impact gene expression 35,36 . www.nature.com/scientificreports/ Nonsynonymous coding SNPs in the coding sequence region directly modify the amino acid composition of the protein encoded by the gene 37 .…”

Section: Discussionmentioning

confidence: 99%

The effect of metabolism-related lifestyle and clinical risk factors on digestive system cancers in East Asian populations: a two-sample Mendelian randomization analysis

Cai,

Li,

Liang

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Metabolic factors play a critical role in the development of digestive system cancers (DSCs), and East Asia has the highest incidence of malignant tumors in the digestive system. We performed a two-sample Mendelian randomization analysis to explore the associations between 19 metabolism-related lifestyle and clinical risk factors and DSCs, including esophageal, gastric, colorectal, hepatocellular, biliary tract, and pancreatic cancer. The causal association was explored for all combinations of each risk factor and each DSC. We gathered information on the instrumental variables (IVs) from various sources and retrieved outcome information from Biobank Japan (BBJ). The data were all from studies of east Asian populations. Finally, 17,572 DSCs cases and 195,745 controls were included. Our analysis found that genetically predicted alcohol drinking was a strong indicator of gastric cancer (odds ratio (OR) = 0.95; 95% confidence interval (CI): 0.93–0.98) and hepatocellular carcinoma (OR = 1.11; 95% CI: 1.05–1.18), whereas coffee consumption had a potential protective effect on hepatocellular carcinoma (OR = 0.69; 95% CI: 0.53–0.90). Triglyceride was potentially associated with a decreased risk of biliary tract cancer (OR = 0.53; 95% CI: 0.34–0.81), and uric acid was associated with pancreatic cancer risk (OR = 0.59; 95% CI: 0.37–0.96). Metabolic syndrome (MetS) was associated with esophageal and gastric cancer. Additionally, there was no evidence for a causal association between other risk factors, including body mass index, waist circumference, waist-to-hip ratio, educational levels, lipoprotein cholesterol, total cholesterol, glycine, creatinine, gout, and Graves’ disease, and DSCs. The leave-one-out analysis revealed that the single nucleotide polymorphism (SNP) rs671 from the ALDH2 gene has a disproportionately high contribution to the causal association between alcohol drinking and gastric cancer and hepatocellular carcinoma, as well as the association between coffee consumption and hepatocellular carcinoma. The present study revealed multiple metabolism-related lifestyle and clinical risk factors and a valuable SNP rs671 for DSCs, highlighting the significance of metabolic factors in both the prevention and treatment of DSCs.

show abstract

“…Although the above methods can be utilized for ct-eQTLs mapping, they are not yet perfect in terms of effectiveness, applicability, and scalability. On the one hand, both bulk RNA-seq and scRNA-seq based ct-eQTLs studies are currently conducted independently, without finding effective methods to overcome the heterogeneity between the two types of data and integrate them into a unified model framework [14]. On the other hand, existing ct-eQTLs methods requires individual-level genotype and transcriptomic data, thus cannot fully make use of widely available summary statistics[9, 10, 11, 15, 16].…”

Section: Introductionmentioning

confidence: 99%

A unified framework for cell-type-specific eQTLs prioritization by integrating bulk and scRNA-seq data

Yu,

Hu,

Wan

et al. 2024

Preprint

View full text Add to dashboard Cite

Genome-wide association studies (GWASs) have identified numerous genetic variants associated with complex traits, yet the biological interpretation remains challenging, especially for variants in non-coding regions. Expression quantitative trait loci (eQTLs) studies have linked these variations to gene expression, aiding in identifying genes involved in disease mechanisms. Traditional eQTL analyses using bulk RNA sequencing (bulk RNA-seq) provide tissue-level insights but suffer from signal loss and distortion due to unaddressed cellular heterogeneity. Recently, single-cell RNA sequencing (scRNA-seq) has provided higher resolution enabling cell-type-specific eQTL (ct-eQTL) analyses. However, these studies are limited by their smaller sample sizes and technical constraints. In this paper, we present a novel statistical framework, IBSEP, which integrates bulk RNA-seq and scRNA-seq data for enhanced cteQTLs prioritization. Our method employs a Bayesian hierarchical model to combine summary statistics from both data types, overcoming the limitations while leveraging the advantages associated with each technique. Through extensive simulations and real-data analyses, including peripheral blood mononuclear cells and brain cortex datasets, IBSEP demonstrated superior performance in identifying ct-eQTLs compared to existing methods. Our approach unveils new transcriptional regulatory mechanisms specific to cell types, offering deeper insights into the genetic basis of complex diseases at a cellular resolution.

show abstract

eQTL studies: from bulk tissues to single cells

Cited by 12 publications

References 85 publications

Integrating spatial transcriptomics count data with Crescendo improves visualization and detection of spatial gene patterns

Integrating spatial transcriptomics count data with Crescendo improves visualization and detection of spatial gene patterns

The effect of metabolism-related lifestyle and clinical risk factors on digestive system cancers in East Asian populations: a two-sample Mendelian randomization analysis

A unified framework for cell-type-specific eQTLs prioritization by integrating bulk and scRNA-seq data

Contact Info

Product

Resources

About