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Identification of common genetic variants controlling transcript isoform variation in human whole blood

Nature Genetics volume 47, pages 345–352 (2015)Cite this article

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Abstract

An understanding of the genetic variation underlying transcript splicing is essential to dissect the molecular mechanisms of common disease. The available evidence from splicing quantitative trait locus (sQTL) studies has been limited to small samples. We performed genome-wide screening to identify SNPs that might control mRNA splicing in whole blood collected from 5,257 Framingham Heart Study participants. We identified 572,333 cis sQTLs involving 2,650 unique genes. Many sQTL-associated genes (40%) undergo alternative splicing. Using the National Human Genome Research Institute (NHGRI) genome-wide association study (GWAS) catalog, we determined that 528 unique sQTLs were significantly enriched for 8,845 SNPs associated with traits in previous GWAS. In particular, we found 395 (4.5%) GWAS SNPs with evidence of cis sQTLs but not gene-level cis expression quantitative trait loci (eQTLs), suggesting that sQTL analysis could provide additional insights into the functional mechanism underlying GWAS results. Our findings provide an informative sQTL resource for further characterizing the potential functional roles of SNPs that control transcript isoforms relevant to common diseases.

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Figure 1: Overview of sQTL analysis.
Figure 2: An example of the PTGS1 gene with its associated cis sQTL located in the 3′ acceptor splice site.
Figure 3: Cis sQTLs are highly enriched in binding sites for RNA-binding proteins.
Figure 4: Functional annotation of genes and exons with cis-sQTL associations.

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Acknowledgements

This research was conducted in part using data and resources from the FHS of the National Heart, Lung, and Blood Institute (NHLBI) of the US National Institutes of Health (NIH) and the Boston University School of Medicine. The analyses reflect intellectual input and resource development from the FHS investigators participating in the SNP Health Association Resource (SHARe) project and in the Systems Approach to Biomarker Research in Cardiovascular Disease (SABRe) project.

We thank J. Zhu and Y. Yang at the DNA Sequencing and Genomics Core of the NHLBI for detailed review of our manuscript and helpful suggestions. We also thank J. Dupuis at the Boston University School of Public Health for her statistical suggestions.

This study used the high-performance computational capabilities of the Biowulf Linux cluster at the US NIH (http://biowulf.nih.gov/).

The FHS is funded by US NIH contract N01-HC-25195; this work was also supported by the NHLBI, Division of Intramural Research.

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Author notes

  1. Xiaoling Zhang and Roby Joehanes: These authors contributed equally to this work.

Authors and Affiliations

  1. Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, Maryland, USA

    Xiaoling Zhang, Roby Joehanes, Brian H Chen, Tianxiao Huan, Andrew D Johnson, Daniel Levy & Christopher J O'Donnell

  2. National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, USA

    Xiaoling Zhang, Roby Joehanes, Brian H Chen, Tianxiao Huan, Andrew D Johnson, Daniel Levy & Christopher J O'Donnell

  3. Mathematical and Statistical Computing Laboratory, Center for Information Technology, US National Institutes of Health, Bethesda, Maryland, USA

    Roby Joehanes, Saixia Ying & Peter J Munson

  4. Division of Cardiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA

    Christopher J O'Donnell

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Contributions

X.Z. designed the study, developed the method, performed the analyses and wrote the manuscript. C.J.O'D. conceived and coordinated the project and wrote the manuscript. B.H.C. provided key input and revised the manuscript. R.J., S.Y. and P.J.M. provided the normalized expression Exon array data. T.H., A.D.J., P.J.M. and D.L. reviewed the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Christopher J O'Donnell.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Schematic for coverage of Affymetrix exon array probe sets across the entire length of the transcript.

Black regions represent exons, whereas gray regions represent introns. The short dashes underneath the exon regions indicate individual probes of 25 nt in length representing the probe set. The highlighted red box indicates an alternative splicing event (exon 2 is spliced out in mRNA transcript isoform 1), which can be detected by analyzing the exon-level probe sets one by one in this genomic locus. The Affymetrix GeneChip Human Exon 1.0 ST array allows for exon-level expression profiling on a single chip and can interrogate over 280,000 core exons in the human genome.

Supplementary Figure 2 The number of exons and SNPs versus the gene length.

Histograms of (a) the number of core probe sets/exons per gene and (b) the number of SNPs with minor allele frequency (MAF) > 0.01 within 50 kb of each gene. Correlation plots of (c) the number of probe sets/exons to the number of SNPs located within 50 kb of each gene, (d) the length of genes to the number of probe sets/exons and (e) the number of SNPs located within 50 kb of each gene.

Supplementary Figure 3 For the 2,650 genes only found in exon-level analysis, shown is the distribution of fold differences in expression levels between the 2 homozygous genotypes.

Supplementary Figure 4 Examples of the different types of transcript isoform events observed.

Supplementary Figure 5 Examples of two GWAS SNPs that are in close LD (r2 > 0.8) with both the peak signal of a cis eQTL and the peak signal of a cis sQTL (the index SNP is represented as a purple diamond).

Supplementary Figure 6 The correlation plot of the –log(P values) of cis-sQTL results with and without adjusting for the cell counts of seven major blood cell types: total white blood cell count (WBC), total platelet count (PLT), and subfractions (percentages) of neutrophils, lymphocytes, monocytes, eosinophils and basophils.

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Zhang, X., Joehanes, R., Chen, B. et al. Identification of common genetic variants controlling transcript isoform variation in human whole blood. Nat Genet 47, 345–352 (2015). https://doi.org/10.1038/ng.3220

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