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Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets

Nature Genetics volume 48, pages 481–487 (2016)Cite this article

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Abstract

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with human complex traits. However, the genes or functional DNA elements through which these variants exert their effects on the traits are often unknown. We propose a method (called SMR) that integrates summary-level data from GWAS with data from expression quantitative trait locus (eQTL) studies to identify genes whose expression levels are associated with a complex trait because of pleiotropy. We apply the method to five human complex traits using GWAS data on up to 339,224 individuals and eQTL data on 5,311 individuals, and we prioritize 126 genes (for example, TRAF1 and ANKRD55 for rheumatoid arthritis and SNX19 and NMRAL1 for schizophrenia), of which 25 genes are new candidates; 77 genes are not the nearest annotated gene to the top associated GWAS SNP. These genes provide important leads to design future functional studies to understand the mechanism whereby DNA variation leads to complex trait variation.

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Figure 1: Association between gene expression and phenotype through genotypes.
Figure 2: Manhattan plots of SMR tests for association between gene expression and complex traits.
Figure 3: Prioritizing genes at a GWAS locus using SMR analysis.
Figure 4: Prioritizing genes at the TRAF1-C5 locus for rheumatoid arthritis.
Figure 5: The SNX19 locus for schizophrenia.

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Acknowledgements

We thank J. McGrath for helpful comments. This research was supported by the Australian Research Council (DP130102666), the Australian National Health and Medical Research Council (grants 1078037, 1048853 and 1046880) and the Sylvia and Charles Viertel Charitable Foundation. This study makes use of data from the database of Genotypes and Phenotypes (dbGaP) available under accession phs000090.v3.p1 (see the Supplementary Note for the full set of acknowledgments for these data).

Author information

Authors and Affiliations

  1. Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia

    Zhihong Zhu, Futao Zhang, Andrew Bakshi, Matthew R Robinson, Joseph E Powell, Naomi R Wray, Peter M Visscher & Jian Yang

  2. State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou, China

    Han Hu

  3. Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia

    Joseph E Powell

  4. Molecular Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

    Grant W Montgomery

  5. Faculty of Veterinary and Agricultural Science, University of Melbourne, Parkville, Victoria, Australia

    Michael E Goddard

  6. Biosciences Research Division, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia

    Michael E Goddard

  7. University of Queensland Diamantina Institute, Translation Research Institute, Brisbane, Queensland, Australia

    Peter M Visscher & Jian Yang

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  1. Zhihong Zhu
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Contributions

J.Y. conceived and designed the study. J.Y. and Z.Z. derived the theories. Z.Z. performed simulations and statistical analyses. F.Z., Z.Z. and J.Y. developed the software tool. H.H., A.B., M.R.R., J.E.P., G.W.M., M.E.G., N.R.W. and P.M.V. contributed by providing statistical support and/or advice on interpretation of results. J.E.P., G.W.M. and P.M.V. provided the Brisbane Systems Genetics Study data. J.Y. and Z.Z. wrote the manuscript with the participation of all authors.

Corresponding author

Correspondence to Jian Yang.

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Integrated supplementary information

Supplementary Figure 1 QQ plots of P values from the SMR tests under a range of simulation scenarios.

The simulation strategy is described in the Supplementary Note. Shown are the results from 10,000 simulations. MR one sample, Mendelian randomization analysis in one sample; SMR one sample, summary data–based Mendelian randomization analysis in one sample; SMR two samples, the effect sizes of a SNP on gene expression (βzx) and a SNP on phenotype (bzy) are estimated from two independent samples.

Supplementary Figure 2 Prioritizing genes at the NMRAL1 locus for schizophrenia.

(a) Top, P values from the GWAS for schizophrenia1 (brown dots) and P values from SMR tests (diamonds) using Westra eQTL data2. Bottom, P values from the Westra and Ramasamy eQTL studies3 for NMRALI1. Shown in a are all the SNPs available in the GWAS and eQTL data. (b,c) Effect sizes of the SNPs (used for the HEIDI test) from the GWAS against those from the Westra and Ramasamy eQTL studies. The orange dashed lines represent the estimate of bxy at the top cis-eQTL (rather than the regression line). Error bars are the standard errors of SNP effects.

Supplementary Figure 3 Genes with multiple tagging probes that pass the SMR and HEIDI tests—the ANKRD55 locus for rheumatoid arthritis.

(a) Top, dots in brown represent P values for SNPs from the latest GWAS for rheumatoid arthritis4, diamonds represent P values for probes from the SMR analysis and triangles represent probes without a cis-eQTL at PeQTL < 5.0 × 10−8. Probes that passed the HEIDI test (PHEIDI ≥ 0.05) are highlighted in red. Bottom, P values from eQTL studies for the two probes that both tag ANKRD55. Shown in a are all SNPs available in the GWAS and eQTL summary data (rather than those in common). (b,c) Effect sizes of the SNPs (used in the HEIDI test) from the GWAS against those from the eQTL study for the two probes. The orange dashed lines represent the estimate of bxy at the top cis-eQTL. Error bars are the standard errors of SNP effects. (d) Plot of expression levels of the two probes against each other in the data (328 unrelated individuals) from the Brisbane Systems Genetics Study5.

Supplementary Figure 4 Power of detecting heterogeneity in bxy as a function of LD between two causal variants in simulations.

We simulated data under the alternative hypothesis that there are two distinct causal variants, one affecting gene expression and one affecting the trait. The method for simulation is described in the Supplementary Note.

Supplementary Figure 5 Distribution of LD between the top associated GWAS SNPs and the simulated causal eQTLs.

The results are from an SMR analysis of real GWAS data for five complex traits (Supplementary Table 6) and simulated eQTL data. The eQTL data were simulated on the basis of real genotypes, mimicking the Westra study but with the causal variants of cis-eQTLs randomly placed across the genome (see the Supplementary Note for details). There were a number of simulated probes that passed the SMR and HEIDI tests (Supplementary Table 14). For these probes, the overlaps between the GWAS and eQTL signals are due to the strong LD between the GWAS causal variants and the causal eQTLs. Unfortunately, the GWAS causal variants are unknown. Shown are LD r2 values for the top associated GWAS SNPs and the simulated causal eQTLs in these probe regions, which are likely to be underestimations of LD between the causal variants. For regions where there were multiple independent GWAS signals as indicated by a GCTA-COJO analysis6, we selected the top GWAS SNP, conditioning the secondary GWAS signal (a secondary GWAS signal was defined as the top associated GWAS SNP in the region when conditioning on the GWAS SNP used in the SMR test).

Supplementary Figure 6 Estimating SNP effects from z statistics.

The reported GWAS effect sizes are equivalent to the SNP effects reported in the GWAS summary data. The estimated GWAS effect sizes are equivalent to the SNP effects estimated from z statistics using the sample size of the whole study rather than the per-SNP sample size reported in the summary data (Supplementary Note). This is just a demonstration of the difference between the reported estimate of effect size and the effect size estimated from z statistics without taking the per-SNP sample size into account. In our SMR analyses for height, we used the reported estimate of effect size.

Supplementary Figure 7 An example of pleiotropic signal being diluted by multiple associated SNPs in GWAS.

(a) Effect sizes of the SNPs (used in the HEIDI test) from the latest GWAS for height7 against those from the Westra eQTL study2 at the SPI1 locus. We detected a secondary signal in the GWAS data. (b) Effect sizes conditioning on the secondary signal. The orange dashed lines represent the estimate of bxy at the top cis-eQTL. Error bars are the standard errors of SNP effects.

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Supplementary Figures 1–7 and Supplementary Note. (PDF 4402 kb)

Supplementary Tables 1–16

Supplementary Tables 1–16. (XLSX 161 kb)

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Zhu, Z., Zhang, F., Hu, H. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet 48, 481–487 (2016). https://doi.org/10.1038/ng.3538

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