eQTL mapping in fetal-like pancreatic progenitor cells reveals early developmental insights into diabetes risk

Disease-associated loci share properties with response eQTLs under common environmental exposures

Many of the genetic loci associated with disease are expected to have context-dependent regulatory effects that are underrepresented in the transcriptomes of healthy, steady-state adult tissues. To understand gene regulation across diverse environmental conditions and cellular contexts, we treated a broad array of human cell types with three environmental exposures in vitro. With single-cell RNA-sequencing data from 1.4 million cells across 51 individuals, we identified hundreds of response expression quantitative loci (eQTLs) that are associated with inter-individual differences in regulatory changes following treatment with nicotine, caffeine, or ethanol in diverse cell types. We also identified dynamic regulatory effects that vary across differentiation trajectories in response to exposure. In contrast to steady-state eQTLs, and similar to disease risk loci, response eQTLs are enriched in distal enhancers and are regulating genes that experienced strong selective constraint, contain complex regulatory landscapes, and display diverse biological functions. We identified response eQTLs that coincide with disease-associated loci not explained by steady-state eQTLs. Our results highlight the complexity of genetic regulatory effects and suggest that our ability to interpret disease-associated loci will benefit from the pursuit of studies of gene-by-environment interactions in diverse biological contexts.

Multiomic QTL mapping reveals phenotypic complexity of GWAS loci and prioritizes putative causal variants

Most GWAS loci are presumed to affect gene regulation; however, only ∼43% colocalize with expression quantitative trait loci (eQTLs). To address this colocalization gap, we map eQTLs, chromatin accessibility QTLs (caQTLs), and histone acetylation QTLs (haQTLs) using molecular samples from three early developmental-like tissues. Through colocalization, we annotate 10.4% (n = 540) of GWAS loci in 15 traits by QTL phenotype, temporal specificity, and complexity. We show that integration of chromatin QTLs results in a 2.3-fold higher annotation rate of GWAS loci because they capture distal GWAS loci missed by eQTLs, and that 5.4% (n = 13) of GWAS colocalizing eQTLs are early developmental specific. Finally, we utilize the iPSCORE multiomic QTLs to prioritize putative causal variants overlapping transcription factor motifs to elucidate the potential genetic underpinnings of 296 GWAS-QTL colocalizations.

Cell type and dynamic state govern genetic regulation of gene expression in heterogeneous differentiating cultures

Identifying the molecular effects of human genetic variation across cellular contexts is crucial for understanding the mechanisms underlying disease-associated loci, yet many cell types and developmental stages remain underexplored. Here, we harnessed the potential of heterogeneous differentiating cultures (HDCs), an in vitro system in which pluripotent cells asynchronously differentiate into a broad spectrum of cell types. We generated HDCs for 53 human donors and collected single-cell RNA sequencing data from over 900,000 cells. We identified expression quantitative trait loci in 29 cell types and characterized regulatory dynamics across diverse differentiation trajectories. This revealed novel regulatory variants for genes involved in key developmental and disease-related processes while replicating known effects from primary tissues and dynamic regulatory effects associated with a range of complex traits.

Multi-omics characterization of type 2 diabetes associated genetic variation

Discerning the mechanisms driving type 2 diabetes (T2D) pathophysiology from genome-wide association studies (GWAS) remains a challenge. To this end, we integrated omics information from 16 multi-tissue and multi-ancestry expression, protein, and metabolite quantitative trait loci (QTL) studies and 46 multi-ancestry GWAS for T2D-related traits with the largest, most ancestrally diverse T2D GWAS to date. Of the 1,289 T2D GWAS index variants, 716 (56%) demonstrated strong evidence of colocalization with a molecular or T2D-related trait, implicating 657 cis-effector genes, 1,691 distal-effector genes, 731 metabolites, and 43 T2D-related traits. We identified 773 of these cis- and distal-effector genes using either expression QTL data from understudied ancestry groups or inclusion of T2D index variants enriched in underrepresented populations, emphasizing the value of increasing population diversity in functional mapping. Linking these variants, genes, metabolites, and traits into a network, we elucidated mechanisms through which T2D-associated variation may impact disease risk. Finally, we showed that drugs targeting effector proteins were enriched in those approved to treat T2D, highlighting the potential of these results to prioritize drug targets for T2D. These results represent a leap in the molecular characterization of T2D-associated genetic variation and will aid in translating genetic findings into novel therapeutic strategies.

Integrated mendelian randomization analyses highlight AFF3 as a novel eQTL-mediated susceptibility gene in renal cancer and its potential mechanisms

BACKGROUNDS

A growing number of expression quantitative trait loci (eQTLs) have been found to be linked with tumorigenesis. In this article, we employed integrated Mendelian randomization (MR) analyses to identify novel susceptibility genes in renal cancer (RC) and reveal their potential mechanisms.

METHODS

Two-sample MR analyses were performed to infer causal relationships between eQTLs, metabolites, and RC risks through the "TwoSampleMR" R package. Sensitivity analyses, such as heterogeneity, pleiotropy, and leave-one-out analysis, were used to assess the stability of our outcomes. Summary-data-based MR (SMR) analyses were used to verify the causal relationships among cis-eQTLs and RC risks via the SMR 1.3.1 software.

RESULTS

Our results provided the first evidence for AFF3 eQTL elevating RC risks, suggesting its oncogenic roles (IVW method; odds ratio (OR) = 1.0005; 95% confidence interval (CI) = 1.0001-1.0010; P = 0.0285; heterogeneity = 0.9588; pleiotropy = 0.8397). Further SMR analysis validated the causal relationships among AFF3 cis-eQTLs and RC risks (P < 0.05). Moreover, the TCGA-KIRC, the ICGC-RC, and the GSE159115 datasets verified that the AFF3 gene was more highly expressed in RC tumors than normal control via scRNA-sequencing and bulk RNA-sequencing (P < 0.05). Gene set enrichment analysis (GSEA) analysis identified six potential biological pathways of AFF3 involved in RC. As for the potential mechanism of AFF3 in RC, we concluded in this article that AFF3 eQTL could negatively modulate the levels of the X-11,315 metabolite (IVW method; OR = 0.9127; 95% CI = 0.8530-0.9765; P = 0.0081; heterogeneity = 0.4150; pleiotropy = 0.8852), exhibiting preventive effects against RC risks (IVW method; OR = 0.9987; 95% CI = 0.9975-0.9999; P = 0.0380; heterogeneity = 0.5362; pleiotropy = 0.9808).

CONCLUSIONS

We concluded that AFF3 could serve as a novel eQTL-mediated susceptibility gene in RC and reveal its potential mechanism of elevating RC risks via negatively regulating the X-11,315 metabolite levels.

Multi-omic QTL mapping in early developmental tissues reveals phenotypic and temporal complexity of regulatory variants underlying GWAS loci

Most GWAS loci are presumed to affect gene regulation, however, only ∼43% colocalize with expression quantitative trait loci (eQTLs). To address this colocalization gap, we identify eQTLs, chromatin accessibility QTLs (caQTLs), and histone acetylation QTLs (haQTLs) using molecular samples from three early developmental (EDev) tissues. Through colocalization, we annotate 586 GWAS loci for 17 traits by QTL complexity, QTL phenotype, and QTL temporal specificity. We show that GWAS loci are highly enriched for colocalization with complex QTL modules that affect multiple elements (genes and/or peaks). We also demonstrate that caQTLs and haQTLs capture regulatory variations not associated with eQTLs and explain ∼49% of the functionally annotated GWAS loci. Additionally, we show that EDev-unique QTLs are strongly depleted for colocalizing with GWAS loci. By conducting one of the largest multi-omic QTL studies to date, we demonstrate that many GWAS loci exhibit phenotypic complexity and therefore, are missed by traditional eQTL analyses.

Complex regulatory networks influence pluripotent cell state transitions in human iPSCs

Stem cells exist in vitro in a spectrum of interconvertible pluripotent states. Analyzing hundreds of hiPSCs derived from different individuals, we show the proportions of these pluripotent states vary considerably across lines. We discover 13 gene network modules (GNMs) and 13 regulatory network modules (RNMs), which are highly correlated with each other suggesting that the coordinated co-accessibility of regulatory elements in the RNMs likely underlie the coordinated expression of genes in the GNMs. Epigenetic analyses reveal that regulatory networks underlying self-renewal and pluripotency are more complex than previously realized. Genetic analyses identify thousands of regulatory variants that overlapped predicted transcription factor binding sites and are associated with chromatin accessibility in the hiPSCs. We show that the master regulator of pluripotency, the NANOG-OCT4 Complex, and its associated network are significantly enriched for regulatory variants with large effects, suggesting that they play a role in the varying cellular proportions of pluripotency states between hiPSCs. Our work bins tens of thousands of regulatory elements in hiPSCs into discrete regulatory networks, shows that pluripotency and self-renewal processes have a surprising level of regulatory complexity, and suggests that genetic factors may contribute to cell state transitions in human iPSC lines.