Methods and Insights from Single-Cell Expression Quantitative Trait Loci

Kang, Je-Won; Raveane, Alessandro; Nathan, Aparna; Soranzo, Nicole; Raychaudhuri, Soumya

doi:10.1146/annurev-genom-101422-100437

Cited by 20 publications

(7 citation statements)

References 176 publications

(251 reference statements)

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“…73 Characterizing human phenotypes based on cause−effect relationships between the exposome and genomic variability is a vital step that will benefit substantially from the integration of genomics and exposome research fields. The application of newer omics technologies to large biosample collections is providing unprecedented insights into human health, for example single-cell sequencing of blood samples in population-scale biobanks; 76,77 these data sets can be advantageous to the exposome field through the immune-profiling of blood cells as an indicator of an individual's past exposure to infectious agents. Qualitative proxies of exposures (for example, dietary self-reporting, smoking, and mental health questionnaires in the UK BioBank 38 ) exist in such cohorts which are beginning to be leveraged to identify GxE associations for single-exposure interactions e.g.…”

Section: ■ Opportunities To Integrate Exposome Research and Populatio...mentioning

confidence: 99%

Adopting Mechanistic Molecular Biology Approaches in Exposome Research for Causal Understanding

Foreman,

Warth,

Hessel

et al. 2024

Environ. Sci. Technol.

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Through investigating the combined impact of the environmental exposures experienced by an individual throughout their lifetime, exposome research provides opportunities to understand and mitigate negative health outcomes. While current exposome research is driven by epidemiological studies that identify associations between exposures and effects, new frameworks integrating more substantial population-level metadata, including electronic health and administrative records, will shed further light on characterizing environmental exposure risks. Molecular biology offers methods and concepts to study the biological and health impacts of exposomes in experimental and computational systems. Of particular importance is the growing use of omics readouts in epidemiological and clinical studies. This paper calls for the adoption of mechanistic molecular biology approaches in exposome research as an essential step in understanding the genotype and exposure interactions underlying human phenotypes. A series of recommendations are presented to make the necessary and appropriate steps to move from exposure association to causation, with a huge potential to inform precision medicine and population health. This includes establishing hypothesis-driven laboratory testing within the exposome field, supported by appropriate methods to read across from model systems research to human.

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Section: ■ Opportunities To Integrate Exposome Research and Populatio...mentioning

confidence: 99%

Adopting Mechanistic Molecular Biology Approaches in Exposome Research for Causal Understanding

Foreman,

Warth,

Hessel

et al. 2024

Environ. Sci. Technol.

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“…Genome-wide studies have identified thousands of loci that influence gene expression; these loci are known as expression quantitative trait loci or eQTLs 1,2 . eQTLs serve as an important bridge between DNA sequence variation and organismal phenotypes and provide a mechanism by which noncoding variants can underlie complex traits [3][4][5] .…”

Section: Introductionmentioning

confidence: 99%

Single-cell eQTL mapping in yeast reveals a tradeoff between growth and reproduction

Boocock,

Alexander,

Tapia

et al. 2023

Preprint

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Expression quantitative trait loci (eQTLs) provide a key bridge between noncoding DNA sequence variants and organismal traits. The effects of eQTLs can differ among tissues, cell types, and cellular states, but these differences are obscured by gene expression measurements in bulk populations. We developed a one-pot approach to map eQTLs inSaccharomyces cerevisiaeby single-cell RNA sequencing (scRNA-seq) and applied it to over 100,000 single cells from three crosses. We used scRNA-seq data to genotype each cell, measure gene expression, and classify the cells by cell-cycle stage. We mapped thousands of local and distant eQTLs and identified interactions between eQTL effects and cell-cycle stages. We took advantage of single-cell expression information to identify hundreds of genes with allele-specific effects on expression noise. We used cell-cycle stage classification to map 20 loci that influence cell-cycle progression. One of these loci influenced the expression of genes involved in the mating response. We showed that the effects of this locus arise from a common variant (W82R) in the geneGPA1, which encodes a signaling protein that negatively regulates the mating pathway. The 82R allele increases mating efficiency at the cost of slower cell-cycle progression and is associated with a higher rate of outcrossing in nature. Our results provide a more granular picture of the effects of genetic variants on gene expression and downstream traits.

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“…The method trains prediction models of gene expression, using genetic variants as features, to capture gene expression's cell-type or temporal specificity. Cellular state, a transient phenotype for a given cell type, can create heterogeneity within the cell type, which can limit the detection of context-dependent eQTLs 9,14,15,16 . Therefore, we also train additional prediction models after adjusting for cell state.…”

Section: Introductionmentioning

confidence: 99%

Mapping the landscape of lineage-specific dynamic regulation of gene expression using single-cell transcriptomics and application to genetics of complex disease

Abe,

Lin,

Zhou

et al. 2023

Preprint

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Single-cell transcriptome data can provide insights into how genetic variation influences biological processes involved in human biology and disease. However, the identification of gene-level associations in distinct cell types faces several challenges, including the limited reference resource from population scale studies, data sparsity in single-cell RNA sequencing, and the complex cell-state pattern of expression within individual cell types. Here we develop genetic models of cell type specific and cell state adjusted gene expression in dopaminergic neurons in the process of specializing from induced pluripotent stem cells. The resulting framework quantifies the dynamics of the genetic regulation of gene expression and estimates its cell type specificity. As an application, we show that the approach detects known and new genes associated with schizophrenia and enables insights into context-dependent disease mechanisms. We provide a genomic resource from a phenome-wide application of our models to more than 1500 phenotypes from the UK Biobank. Using longitudinal genetically determined expression, we implement a predictive causality framework, evaluating the prediction of future values of a target gene expression using prior values of a putative regulatory gene. Collectively, this work demonstrates the insights that can be gained into the molecular underpinnings of diseases by quantifying the genetic control of gene expression at single-cell resolution.

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Methods and Insights from Single-Cell Expression Quantitative Trait Loci

Cited by 20 publications

References 176 publications

Adopting Mechanistic Molecular Biology Approaches in Exposome Research for Causal Understanding

Adopting Mechanistic Molecular Biology Approaches in Exposome Research for Causal Understanding

Single-cell eQTL mapping in yeast reveals a tradeoff between growth and reproduction

Mapping the landscape of lineage-specific dynamic regulation of gene expression using single-cell transcriptomics and application to genetics of complex disease

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