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The impact of short tandem repeat variation on gene expression

Nature Genetics volume 51, pages 1652–1659 (2019)Cite this article

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Abstract

Short tandem repeats (STRs) have been implicated in a variety of complex traits in humans. However, genome-wide studies of the effects of STRs on gene expression thus far have had limited power to detect associations and provide insights into putative mechanisms. Here, we leverage whole-genome sequencing and expression data for 17 tissues from the Genotype–Tissue Expression Project to identify more than 28,000 STRs for which repeat number is associated with expression of nearby genes (eSTRs). We use fine-mapping to quantify the probability that each eSTR is causal and characterize the top 1,400 fine-mapped eSTRs. We identify hundreds of eSTRs linked with published genome-wide association study signals and implicate specific eSTRs in complex traits, including height, schizophrenia, inflammatory bowel disease and intelligence. Overall, our results support the hypothesis that eSTRs contribute to a range of human phenotypes, and our data should serve as a valuable resource for future studies of complex traits.

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Fig. 1: Multitissue identification of eSTRs.
Fig. 2: Characterization of FM-eSTRs.
Fig. 3: FM-eSTRs colocalize with GWAS signals.
Fig. 4: Summary of FM-eSTRs classes and potential regulatory mechanisms.

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Data availability

All eSTR summary statistics are available for download on WebSTR http://webstr.ucsd.edu/downloads.

Code availability

Code for performing analyses and generating figures is available at http://github.com/gymreklab/gtex-estrs-paper.

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Acknowledgements

Research reported in this publication was supported in part by the Office of The Director, National Institutes of Health under Award Number DP5OD024577 (M.G.). We thank V. Bafna, E. Mendenhall, J. Gleeson and Y. Liu for helpful comments. See the Supplementary Note for additional acknowledgements.

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Author notes

  1. Stephanie Feupe Fotsing

    Present address: La Jolla Institute of Immunology, La Jolla, CA, USA

Authors and Affiliations

  1. Biomedical Informatics and Systems Biology, University of California San Diego, La Jolla, CA, USA

    Stephanie Feupe Fotsing

  2. Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA

    Stephanie Feupe Fotsing

  3. Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA

    Jonathan Margoliash, Shubham Saini & Melissa Gymrek

  4. Department of Medicine, University of California San Diego, La Jolla, CA, USA

    Jonathan Margoliash, Richard Yanicky, Sharona Shleizer-Burko, Alon Goren & Melissa Gymrek

  5. Department of Bioengineering, University of California San Diego, La Jolla, CA, USA

    Catherine Wang

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Contributions

S.F.F. performed all eSTR and SNP mapping, helped to perform downstream analyses and helped to draft the manuscript. J.M. performed multitissue analysis using mashR and helped to revise the manuscript. C.W. optimized and performed the reporter assay. S.S. participated in the design of the STR imputation analysis. S.S.-B. lead, designed and analyzed data from the reporter assay. R.Y. implemented the WebSTR web application. A.G. conceived and planned analyses and validation experiments of regulatory effects of eSTRs and wrote the manuscript. M.G. conceived the study, designed and performed analyses and wrote the manuscript. All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Melissa Gymrek.

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Extended data

Extended Data Fig. 1 Relationship between sample size and number of eSTRs detected.

The x-axis shows the number of samples per tissue. The y-axis shows the number of eSTRs (gene-level FDR<10%) detected in each tissue. Each dot represents a single tissue, using the same colors as shown in Fig. 1 in the main text (see box on the right). Notably, although whole blood and skeletal muscle had the highest number of samples, we identified fewer eSTRs in those tissues than in others with lower sample sizes. This is concordant with previous results for SNPs in the GTEx cohort and may reflect higher cell-type heterogeneity in these tissue samples.

Extended Data Fig. 2 Enrichment of genomic annotations as a function of CAVIAR threshold.

The x-axis represents CAVIAR thresholds in terms of the percentile (percentage of all 28,375 eSTRs excluded by those thresholds). The y-axis represents the odds ratio for enrichment in eSTRs above each percentile threshold in each of these categories: a. 5’UTRs (purple); b. 3’UTRs (blue); c. promoters (orange; TSS +/- 3kb); d. Coding regions (red) and e. Introns (green). The y-axis center values denote the log2 odds ratios comparing eSTRs passing each threshold to all STRs. Error bars represent +/−1 s.e.

Extended Data Fig. 3 Example multi-allelic FM-eSTRs.

For each plot, the x-axis represents the mean number of repeats in each individual and the y-axis represents normalized expression in the tissue for which the eSTR was most significant. Boxplots summarize the distribution of expression values for each genotype. Horizontal lines show median values, boxes span from the 25th percentile (Q1) to the 75th percentile (Q3). Whiskers extend to Q1-1.5*IQR (bottom) and Q3+1.5*IQR (top), where IQR gives the interquartile range (Q3-Q1). The red line shows the mean expression for each x-axis value.

Extended Data Fig. 4 Sharing of eSTRs across tissues.

The x-axis represents the number of tissues that share a given eSTR (absolute value of mashR Z-score >4). The y-axis represents the number of eSTRs shared across a given number of tissues.

Extended Data Fig. 5 Localization of all STRs around putative regulatory regions.

Left and right plots show localization around transcription start sites and DNaseI HS clusters, respectively. The y-axis denotes the fraction of STRs of each type in each bin. For promoters, the x-axis is divided into 100bp bins. For DNaseI HS sites, the x-axis is divided into 50bp bins. In each plot, values were smoothed by taking a sliding average of each four consecutive bins. Only STR-gene pairs included in our analysis are considered. Each plot compares localization of the two possible sequences of a given repeat unit on the coding strand. Top plots compare repeat units of the form CnG vs. their reverse complement on the opposite strand, middle plots compare AC vs. GT repeats, and bottom plots compare A vs. T repeats. The strand of each STR was determined based on the coding strand of each target gene.

Extended Data Fig. 6 Relative probability of eSTRs around TSSs and DNaseI HS sites for a range of CAVIAR scores.

Plots are shown for FM-eSTRs defined using multiple CAVIAR thresholds (0, corresponding to all eSTRs, 0.3, as used in the main text, or 0.5). a., c., and e. show the relative probability of an STR to be an FM-eSTR around TSSs. The black lines represent the probability of an STR in each bin to be an FM-eSTR. Values were scaled relative to the genome-wide average. b., d., and f. show the relative probability of an STR to be an FM-eSTR around DNaseI HS clusters. Values were smoothed by taking a sliding average of each four consecutive bins.

Extended Data Fig. 7 Nucleosome occupancy and DNaseI hypersensitivity show distinct patterns around eSTRs.

a-c. Nucleosome density around STRs with different repeat unit lengths. Nucleosome density in GM12878 in 5bp windows is averaged across all STRs analyzed (dashed) and FM-eSTRs (solid) relative to the center of the STR. b. DNaseI HS density around STRs with different repeat unit lengths. The number of DNaseI HS reads in GM12878 (gray), fat (red), tibial nerve (yellow), and skin (cyan) is averaged across all STRs in each category. Solid lines show FM-eSTRs. Dashed lines show all STRs. Left=homopolymers, middle=dinucleotides, right=tetranucleotides. Other repeat unit lengths were excluded since they have low numbers of FM-eSTRs (see Fig. 4a). Dashed vertical lines in (d) show the STR position +/- 147bp.

Extended Data Fig. 8 Strand-biased characteristics of FM-eSTRs.

Top panel: the y-axis shows the number of FM-eSTRs with each repeat unit on the template strand. Bottom panel: the y-axis shows the percentage of FM-eSTRs with each repeat unit on the template strand that have positive effect sizes. Gray bars denote A-rich repeat units (A/AC/AAC/AAAC) and red bars denote T-rich repeat units (T/GT/GTT/GTTT). Single asterisks denote repeat units nominally enriched or depleted (two-sided binomial p<0.05). Double asterisks denote repeat units significantly enriched after controlling for multiple hypothesis testing (Bonferroni adjusted p<0.05). Asterisks above brackets show significant differences between repeat unit pairs. Asterisks on x-axis labels denote departure from the 50% positive effect sizes expected by chance. Error bars give 95% confidence intervals.

Extended Data Fig. 9 Example GWAS signals co-localized with FM-eSTRs.

Left: For each plot, the x-axis represents the mean number of repeats in each individual and the y-axis represents normalized expression in the tissue with the most significant eSTR signal at each locus. Boxplots summarize the distribution of expression values for each genotype. Box plots are as defined in Fig. 1c. The red line shows the mean expression for each x-axis value. Right: Top panels give genes in each region. The target gene for the eQTL associations is shown in black. Middle panels give the -log10 p-values of association of the effect-size between each SNP (black points) and the expression of the target gene. The FM-eSTR is denoted by a red star. Bottom panels give the -log10 p-values of association between each SNP and the trait based on published GWAS summary statistics. P-values are two-sided and are based on t-statistics computed for effect sizes (β) (see Methods). Dashed gray horizontal lines give the genome-wide significance threshold of 5E-8.

Extended Data Fig. 10 Example GWAS signal for schizophrenia potentially driven by an eSTR for MED19 .

a. eSTR association for MED19. The x-axis shows STR genotypes at an AC repeat (chr11:57523883) as the mean number of repeats in each individual and the y-axis shows normalized MED19 expression in subcutaneous adipose. Each point represents a single individual. Red lines show the mean expression for each x-axis value. Boxplots are as defined in Fig. 1c. b. Summary statistics for MED19 expression and schizophrenia. The top panel shows genes in the region around MED19. The middle panel shows the -log10 p-values of association between each variant and MED19 expression in subcutaneous adipose tissue in the GTEx cohort. The FM-eSTR is denoted by a red star. The bottom panel shows the -log10 p-values of association for each variant with schizophrenia reported by the Psychiatric Genomics Consortium. The dashed gray horizontal line shows genome-wide significance threshold of 5E-8. c. Detailed view of the MED19 locus. A UCSC genome browser screenshot is shown for the region in the gray box in (b). The FM-eSTR is shown in red. The bottom track shows transcription factor (TF) and chromatin regulator binding sites profiled by ENCODE. The bottom panel shows long-range interactions reported by Mifsud, et al. using Capture Hi-C on GM12878. Interactions shown in black include MED19. Interactions to loci outside of the window depicted are not shown.

Supplementary information

Supplementary Information

Supplementary Note and Figs. 1–14

Reporting Summary

Supplementary Table 1

Supplementary Tables 1–7

Supplementary Data 1

All unique eSTRs identified across 17 tissues

Supplementary Data 2

Complete eSTR summary statistics

Supplementary Data 3

FM-eSTRs within 1 Mb of published hits from the NHGRI GWAS Catalog

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Fotsing, S.F., Margoliash, J., Wang, C. et al. The impact of short tandem repeat variation on gene expression. Nat Genet 51, 1652–1659 (2019). https://doi.org/10.1038/s41588-019-0521-9

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