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. 2017 Sep 14;18(1):170.
doi: 10.1186/s13059-017-1286-z.

Natural genetic variation of the cardiac transcriptome in non-diseased donors and patients with dilated cardiomyopathy

Matthias Heinig  1   2 , Michiel E Adriaens  3   4 , Sebastian Schafer  5   6 , Hanneke W M van Deutekom  3 , Elisabeth M Lodder  3 , James S Ware  7   8   9 , Valentin Schneider  10 , Leanne E Felkin  7   8 , Esther E Creemers  3 , Benjamin Meder  11   12 , Hugo A Katus  11   12 , Frank Rühle  13 , Monika Stoll  13   14 , François Cambien  15   16 , Eric Villard  15   16 , Philippe Charron  16   17 , Andras Varro  18 , Nanette H Bishopric  19   20 , Alfred L George Jr  21   22 , Cristobal Dos Remedios  23 , Aida Moreno-Moral  24 , Francesco Pesce  7   8 , Anja Bauerfeind  10 , Franz Rüschendorf  10 , Carola Rintisch  10 , Enrico Petretto  24 , Paul J Barton  7   8 , Stuart A Cook  5   6   7   8   9 , Yigal M Pinto  3 , Connie R Bezzina  25 , Norbert Hubner  26   27   28   29
Affiliations

Natural genetic variation of the cardiac transcriptome in non-diseased donors and patients with dilated cardiomyopathy

Matthias Heinig et al. Genome Biol. .

Abstract

Background: Genetic variation is an important determinant of RNA transcription and splicing, which in turn contributes to variation in human traits, including cardiovascular diseases.

Results: Here we report the first in-depth survey of heart transcriptome variation using RNA-sequencing in 97 patients with dilated cardiomyopathy and 108 non-diseased controls. We reveal extensive differences of gene expression and splicing between dilated cardiomyopathy patients and controls, affecting known as well as novel dilated cardiomyopathy genes. Moreover, we show a widespread effect of genetic variation on the regulation of transcription, isoform usage, and allele-specific expression. Systematic annotation of genome-wide association SNPs identifies 60 functional candidate genes for heart phenotypes, representing 20% of all published heart genome-wide association loci. Focusing on the dilated cardiomyopathy phenotype we found that eQTL variants are also enriched for dilated cardiomyopathy genome-wide association signals in two independent cohorts.

Conclusions: RNA transcription, splicing, and allele-specific expression are each important determinants of the dilated cardiomyopathy phenotype and are controlled by genetic factors. Our results represent a powerful resource for the field of cardiovascular genetics.

Keywords: Dilated cardiomyopathy; Gene expression; Genetics; Heart; eQTL.

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Conflict of interest statement

Ethics approval and consent to participate

All studies were carried out according to institutional guidelines, and with appropriate informed consent from participants or next of kin (in case of donor heart samples). Institutional ethics committees of the clinical centers where the cardiac samples were collected reviewed and approved all protocols. The ethical review boards of University of Szeged (Ethical Review Board of the University of Szeged Medical Center; Szeged, Hungary), Vanderbilt University (Institutional Review Board of Vanderbilt University School of Medicine; Nashville, USA, IRB#100664), University of Miami (Institutional Review Board of the University of Miami School of Medicine; Miami, USA, protocol # 20010028), and the University of Sydney (Human Research Ethics Committee (HREC), Project Title: The Sydney Human Heart Tissue Bank (SHB), project number 2012/2814; Sydney, Australia) approved procurement and handling of the human donor cardiac material. DCM tissue studies complied with UK Human Tissue Act guidelines and were carried out with approval from the Royal Brompton and Harefield local ethical review committee and the National Research Ethics Service Committee South Central, Hampshire B (reference 09/H0504/104). Investigations conformed to the principles outlined in the Helsinki Declaration of the World Medical Association. All data were analyzed anonymously.

Competing interests

The authors declare that they have no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
DCM-associated expression of TBX20 targets. Differential expression of human orthologs of TBX20 targets in the mouse heart is shown as a heatmap of gene expression values standardized to mean zero and standard deviation one
Fig. 2
Fig. 2
DCM- and control-specific eQTL. Boxplots show examples of eQTL where the genotype only affects expression levels in a DCM patients or b controls. Expression levels are shown as log transformed normalized read counts. The x-axis indicates the genotype of the SNP
Fig. 3
Fig. 3
Functional annotation of QTL variants. Enrichment of sQTL (a) and (b) eQTL in functional categories is shown as estimated odds ratios and 95% confidence intervals of the multiple logistic regression model on the x-axis for each annotation category on the y-axis. Odds ratios greater than 1 indicate an enrichment of QTL variants in the given functional elements, while odds ratios less than 1 indicate a depletion. Significant odds ratios are shown as filled circles (P < 0.05)
Fig. 4
Fig. 4
Enrichment for significant eQTLs, miRNA interference, and significant differential splicing in genes with allele-specific expression. Odds ratios with 95% confidence intervals for enrichment are given. a All genes with allele-specific expression in at least one individual. Significant enrichment for significant eQTLs, differential splicing, and presence of miRNA binding sites was observed. b All genes with differential allele-specific expression between DCM and non-diseased controls with alternative/reference allele frequency difference >0.10. Significant enrichment for differential splicing and presence of miRNA binding sites was observed, with suggestive enrichment for significant eQTLs
Fig. 5
Fig. 5
Enrichment of QTL and ASE variants for DCM GWAs. Cumulative density function (CDF) plots for DCM GWA P values for LD blocks that have sQTL (red) and eQTL (yellow) compared to the background set of all tested LD blocks using GWA data from a German DCM population (a) and a European DCM population (b). Similarly, CDF plots of DCM GWA P values for LD blocks with ASE variants (red) are compared to the background set of all LD blocks with coding SNPs tested for ASE (grey) for a German DCM population (c) and a European DCM population (d)
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