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Abundant contribution of short tandem repeats to gene expression variation in humans

Nature Genetics volume 48, pages 22–29 (2016)Cite this article

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Abstract

The contribution of repetitive elements to quantitative human traits is largely unknown. Here we report a genome-wide survey of the contribution of short tandem repeats (STRs), which constitute one of the most polymorphic and abundant repeat classes, to gene expression in humans. Our survey identified 2,060 significant expression STRs (eSTRs). These eSTRs were replicable in orthogonal populations and expression assays. We used variance partitioning to disentangle the contribution of eSTRs from that of linked SNPs and indels and found that eSTRs contribute 10–15% of the cis heritability mediated by all common variants. Further functional genomic analyses showed that eSTRs are enriched in conserved regions, colocalize with regulatory elements and may modulate certain histone modifications. By analyzing known genome-wide association study (GWAS) signals and searching for new associations in 1,685 whole genomes from deeply phenotyped individuals, we found that eSTRs are enriched in various clinically relevant conditions. These results highlight the contribution of STRs to the genetic architecture of quantitative human traits.

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Figure 1: eSTR discovery and replication.
Figure 2: Variance partitioning using linear mixed models.
Figure 3: eSTR associations in the context of eSNPs.
Figure 4: Conservation and epigenetic analysis of eSTR loci.
Figure 5: Association of eSTRs with clinical phenotypes.

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Acknowledgements

We thank T. Lappalainen, A. Goren, T. Hashimoto and D. Zielinksi for useful comments and discussions. M.G. was supported by the National Defense Science and Engineering Graduate Fellowship. Y.E. holds a Career Award at the Scientific Interface from the Burroughs Wellcome Fund. This study was supported by a gift from Andria and Paul Heafy (Y.E.), National Institute of Justice (NIJ) grant 2014-DN-BX-K089 (Y.E. and T.W.), and US National Institutes of Health (NIH) grants 1U01HG007037 (H.Z.), R01MH084703 (J.K.P.), R01HG006399 (A.L.P.), HG006696 (A.J.S.), DA033660 (A.J.S.) and MH097018 (A.J.S.) and by research grant 6-FY13-92 from the March of Dimes Foundation (A.J.S.).

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Authors and Affiliations

  1. Whitehead Institute for Biomedical Research, Cambridge, Massachusetts, USA

    Melissa Gymrek, Thomas Willems, Barak Markus & Yaniv Erlich

  2. Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Melissa Gymrek

  3. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    Melissa Gymrek, Mark J Daly & Alkes L Price

  4. New York Genome Center, New York, New York, USA

    Melissa Gymrek, Thomas Willems & Yaniv Erlich

  5. Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Thomas Willems

  6. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA

    Audrey Guilmatre & Andrew J Sharp

  7. Department of Pediatric Hematology, Robert Debré Hospital, Paris, France

    Audrey Guilmatre

  8. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Haoyang Zeng

  9. Department of Genetics and Biology, Stanford University, Stanford, California, USA

    Stoyan Georgiev & Jonathan K Pritchard

  10. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA

    Mark J Daly

  11. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA

    Alkes L Price

  12. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA

    Alkes L Price

  13. Howard Hughes Medical Institute, Chevy Chase, Maryland, USA

    Jonathan K Pritchard

  14. Department of Computer Science, Fu Foundation School of Engineering, Columbia University, New York, New York, USA

    Yaniv Erlich

  15. Center for Computational Biology and Bioinformatics, Columbia University, New York, New York, USA

    Yaniv Erlich

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  1. Melissa Gymrek
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Contributions

M.G. and Y.E. conceived the study. M.G., T.W., H.Z., B.M. and Y.E. performed analyses. A.G. performed experimental work to generate high-coverage sequencing data for promoter STRs. S.G., M.J.D., A.L.P. and J.K.P. provided statistical input. A.J.S. contributed data and analyses. M.G., T.W. and Y.E. wrote the manuscript.

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Correspondence to Yaniv Erlich.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 STR genotype errors reduce power to detect eSTR associations.

(a) Power to detect associations and (b) estimated variance explained for different simulated values of variance explained by the STR. (black, observed capillary electrophoresis genotypes; blue, lobSTR genotypes).

Supplementary Figure 2 Number of STRs tested per gene.

The histogram gives the number of STRs within 100 kb of each gene that passed quality filters and were included in the eSTR analysis.

Supplementary Figure 3 Unlinked controls follow the null.

QQ plot of association tests between random unlinked STRs and genes.

Supplementary Figure 4 Validation of eSTR analysis using high-coverage genotype calls.

(a) Comparison of STR dosage in low-coverage 1000 Genomes calls versus calls from high-coverage targeted sequencing of promoter STRs. Bubble area represents the number of calls at each data point. For reference, the bubble at (−20, −20) represents 176 calls. “0” denotes the reference allele. The transparent bubble in the center represents calls that are homozygous reference in both data sets. (b) Distribution of the sizes of errors for discordant allele calls. The majority of errors (89.4%) are off by one or two repeat units. (c) Comparison of eSTR effect sizes between the low- and high-coverage data sets. Red dots denote eSTRs with concordant effect directions.

Supplementary Figure 5 Expression values are moderately reproducible across platforms.

(a) Distribution of Spearman rank correlation coefficients between gene expression profiles of individuals measured on microarray versus RNA sequencing platforms. (b) Distribution of Spearman rank correlation coefficients between the order of individuals ranked by expression levels across transcripts measured using microarray versus RNA sequencing platforms.

Supplementary Figure 6 Variance partitioning simulations with a single causal SNP.

Plots show variance partitioning results from simulations in which each gene has a single causal eSNP. (a,b) The distributions of . Black points denote the true value of the variance explained by the causal SNP. (c,d) The distributions of . (a,c) The LMM simulations with STRs as fixed effects. (b,d) The LMM simulations with STRs as random effects. (a–d) Red dots denote the average value of the estimator. Red bars denote the median value of the estimator. The figure shows that the median values of the lead STRs are largely insensitive to the presence of a strong SNP eQTL.

Supplementary Figure 7 Variance partitioning simulations with two causal SNPs.

Plots show variance partitioning results from simulations in which each gene has two causal eSNPs. (a) The distributions of . Black points denote the true value of the variance explained by the causal SNPs. (b) The distributions of . Red dots denote the average value of the estimator. Red bars denote the median value of the estimator.

Supplementary Figure 8 STR genotype errors cause underestimation of .

The distribution of observed for each simulated value of is shown for an LMM analysis conducted using true genotypes (black) versus observed genotypes (blue). In the presence of genotyping errors, is strongly underestimated.

Supplementary Figure 9 Partitioning variance when treating the STR as a random effect.

The heat map shows the distribution of and for each gene. Gray lines give the medians of each distribution.

Supplementary Figure 10 Enrichment of eSTRs at promoters and enhancers.

For each distance bin around (a) the TSS and (b) center of H3K27ac peaks, the plot shows the percentage of STRs that were analyzed in that bin that were called as significant eSTRs. (c,d) The number of STRs in each distance bin. Black lines show the number of STRs that were included in our analysis (meaning that they showed sufficient variability and are near genes). Red lines show the number of all STRs in the genome in each bin. Black lines were smoothed by averaging sliding windows of three consecutive data points. In a and b, bins were 10 kb; in c and d, bins were 500 bp.

Supplementary Figure 11 STRs modulate epigenetic signatures.

(a) Schematic of the application of GERV to predict histone modification signatures for different STR alleles. For each eSTR (red) and control STR (gray), we measured the magnitude of the slope between the STR allele and the GERV score and then tested whether the magnitudes were significantly different between the two sets. (b) Comparison of the distribution of slope magnitudes for eSTRs (red) and controls (gray).

Supplementary Figure 12 Enrichment of eSTR genes in GWAS.

Number of eSTR genes (red dashed line) overlapping GWAS genes for each trait. Gray bars give the distribution of the number of overlapping genes from 1,000 control sets of STRs matched on the basis of expression in LCLs and cis heritability. (RA, rheumatoid arthritis; CAD, coronary artery disease; T1D, type 1 diabetes; T2D, type 2 diabetes.)

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–12, Supplementary Note and Supplementary Tables 1–9. (PDF 1826 kb)

Supplementary Data Set 1: Significant eSTRs

A table of all STR × gene associations at a gene-level FDR of 5%. (CSV 18004 kb)

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Gymrek, M., Willems, T., Guilmatre, A. et al. Abundant contribution of short tandem repeats to gene expression variation in humans. Nat Genet 48, 22–29 (2016). https://doi.org/10.1038/ng.3461

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