Genetic effects on promoter usage are highly context-specific and contribute to complex traits
Abstract
Genetic variants regulating RNA splicing and transcript usage have been implicated in both common and rare diseases. Although transcript usage quantitative trait loci (tuQTLs) have been mapped across multiple cell types and contexts, it is challenging to distinguish between the main molecular mechanisms controlling transcript usage: promoter choice, splicing and 3ʹ end choice. Here, we analysed RNA-seq data from human macrophages exposed to three inflammatory and one metabolic stimulus. In addition to conventional gene-level and transcript-level analyses, we also directly quantified promoter usage, splicing and 3ʹ end usage. We found that promoters, splicing and 3ʹ ends were predominantly controlled by independent genetic variants enriched in distinct genomic features. Promoter usage QTLs were also 50% more likely to be context-specific than other tuQTLs and constituted 25% of the transcript-level colocalisations with complex traits. Thus, promoter usage might be an underappreciated molecular mechanism mediating complex trait associations in a context-specific manner.
Data availability
RNA-seq data from the acLDL stimulation study is available from ENA (PRJEB20734) and EGA (EGAS00001000876). RNA-seq data from the IFNɣ + Salmonella study is available from ENA (PRJEB18997) and EGA (EGAS00001002236). The imputed genotype data for HipSci cell lines is available from ENA (PRJEB11749) and EGA (EGAD00010000773). Processed data and QTL summary statistics are available from Zenodo: https://zenodo.org/communities/macrophage-tuqtls/.
-
Genetic effects on promoter usage are highly context-specific and contribute to complex traitsEuropean Nucleotide Archive, PRJEB20734.
-
Genetic effects on promoter usage are highly context-specific and contribute to complex traitsEuropean Genome-phenome Archive, EGAS00001000876.
-
Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune responseEuropean Nucleotide Archive, PRJEB18997.
-
Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune responseEuropean Genome-phenome Archive, EGAS00001002236.
-
Common genetic variation drives molecular heterogeneity in human iPSCsEuropean Nucleotide Archive, PRJEB11749.
-
Common genetic variation drives molecular heterogeneity in human iPSCsEuropean Genome-phenome Archive, EGAD00010000773.
Article and author information
Author details
Funding
Wellcome (WT09805)
- Kaur Alasoo
- Julia Rodrigues
- Daniel J Gaffney
British Heart Foundation (RG/13/13/30194)
- John Danesh
- Daniel F Freitag
- Dirk S Paul
Estonian Research Council (MOBJD67)
- Kaur Alasoo
Wellcome (WT099754/Z/12/Z)
- Kaur Alasoo
Estonian Research Council (IUT34-4)
- Kaur Alasoo
Wellcome (WT098503)
- Daniel J Gaffney
British Heart Foundation Cambridge Centre of Excellence (RE/13/6/30180)
- John Danesh
- Daniel F Freitag
- Dirk S Paul
Medical Research Council (MR/L003120/1)
- John Danesh
- Daniel F Freitag
- Dirk S Paul
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Stephen Parker, University of Michigan, United States
Ethics
Human subjects: Human induced pluripotent stem cells (iPSCs) lines from 123 healthy donors (72 female and 51 male) (Supplementary file 1) were obtained from the HipSci project (Kilpinen et al., 2017). Of these lines, 57 were initially grown in feeder-dependent medium and 66 were grown in feeder-free E8 medium. The cell lines were screened for mycoplasma by the HipSci project (Kilpinen et al., 2017). All samples for the HipSci project (Kilpinen et al., 2017) were collected from consented research volunteers recruited from the NIHR Cambridge BioResource (http://www.cambridgebioresource.org.uk). Samples were initially collected under ethics for iPSC derivation (REC Ref: 09/H0304/77, V2 04/01/2013), which require managed data access for all genetically identifying data. Later samples were collected under a revised consent (REC Ref: 09/H0304/77, V3 15/03/2013) under which all data, except from the Y chromosome from males, can be made openly available. The ethics approval was obtained from East of England - Cambridge East Research Ethics Committee. The iPSC lines used in this study are commercially available via the European Collection of Authenticated Cell Cultures. No new primary human samples were collected for this study.
Version history
- Received: September 5, 2018
- Accepted: January 8, 2019
- Accepted Manuscript published: January 8, 2019 (version 1)
- Version of Record published: January 28, 2019 (version 2)
Copyright
© 2019, Alasoo et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 3,625
- views
-
- 431
- downloads
-
- 50
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Computational and Systems Biology
- Developmental Biology
Organisms utilize gene regulatory networks (GRN) to make fate decisions, but the regulatory mechanisms of transcription factors (TF) in GRNs are exceedingly intricate. A longstanding question in this field is how these tangled interactions synergistically contribute to decision-making procedures. To comprehensively understand the role of regulatory logic in cell fate decisions, we constructed a logic-incorporated GRN model and examined its behavior under two distinct driving forces (noise-driven and signal-driven). Under the noise-driven mode, we distilled the relationship among fate bias, regulatory logic, and noise profile. Under the signal-driven mode, we bridged regulatory logic and progression-accuracy trade-off, and uncovered distinctive trajectories of reprogramming influenced by logic motifs. In differentiation, we characterized a special logic-dependent priming stage by the solution landscape. Finally, we applied our findings to decipher three biological instances: hematopoiesis, embryogenesis, and trans-differentiation. Orthogonal to the classical analysis of expression profile, we harnessed noise patterns to construct the GRN corresponding to fate transition. Our work presents a generalizable framework for top-down fate-decision studies and a practical approach to the taxonomy of cell fate decisions.
-
- Computational and Systems Biology
- Genetics and Genomics
We propose a new framework for human genetic association studies: at each locus, a deep learning model (in this study, Sei) is used to calculate the functional genomic activity score for two haplotypes per individual. This score, defined as the Haplotype Function Score (HFS), replaces the original genotype in association studies. Applying the HFS framework to 14 complex traits in the UK Biobank, we identified 3619 independent HFS–trait associations with a significance of p < 5 × 10−8. Fine-mapping revealed 2699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with single-nucleotide polymorphism (SNP)-based analysis. HFS-based enrichment analysis uncovered 727 pathway–trait associations and 153 tissue–trait associations with strong biological interpretability, including ‘circadian pathway-chronotype’ and ‘arachidonic acid-intelligence’. Lastly, we applied least absolute shrinkage and selection operator (LASSO) regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1–39.8% in cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding the genetic basis of human complex traits.