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Predicting causal variants affecting expression by using whole-genome sequencing and RNA-seq from multiple human tissues

Nature Genetics volume 49, pages 1747–1751 (2017)Cite this article

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Abstract

Genetic association mapping produces statistical links between phenotypes and genomic regions, but identifying causal variants remains difficult. Whole-genome sequencing (WGS) can help by providing complete knowledge of all genetic variants, but it is financially prohibitive for well-powered GWAS studies. We performed mapping of expression quantitative trait loci (eQTLs) with WGS and RNA-seq, and found that lead eQTL variants called with WGS were more likely to be causal. Through simulations, we derived properties of causal variants and used them to develop a method for identifying likely causal SNPs. We estimated that 25–70% of causal variants were located in open-chromatin regions, depending on the tissue and experiment. Finally, we identified a set of high-confidence causal variants and showed that these were more enriched in GWAS associations than other eQTLs. Of those, we found 65 associations with GWAS traits and provide examples in which genes implicated by expression are functionally validated as being relevant for complex traits.

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Figure 1: eQTL discovery with different genotyping technologies.
Figure 2: Relative enrichment in eQTLs discovered with different genotyping technologies in functional regions.
Figure 3: Distribution of the CaVEMaN estimated causal probabilities for LEVs.
Figure 4: Proportion of LEVs in DHS regions, plotted against causal probability.
Figure 5: Proportion of functional variants in regions identified by single ChIP–seq experiments.
Figure 6: HCCVs statistically associated with GWAS traits.

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Acknowledgements

We thank N. Lykoskoufis for assistance with the enrichment analysis. T.S. is supported as an NIHR Senior Research Fellow. This project was supported by a Helse Sør-Øst grant (2011060) to A.B. and an MRC Project Grant (L01999X/1) to K.S., and by grants from the NIH-NIMH (NIH-R01MH101814-GTEx), an IMI-Joint Undertaking of the European Commission (UE7-DIRECT-115317-1), the European Commission (UE7-EUROBATS-259749), the European Research Council (UE7-POPRNASEQ-260927), the Louis Jeantet Foundation, the Swiss National Science Foundation (31003A-149984 and 31003A-170096), and SystemsX (2012/201-SysGenetix) to E.T.D. The TwinsUK study was funded by the Wellcome Trust; European Community's Seventh Framework Programme (FP7/2007-2013) and the Medical Research Council. The study also received support from the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre, based at Guy's and St Thomas' NHS Foundation Trust, in partnership with King's College London. SNP genotyping was performed by The Wellcome Trust Sanger Institute and National Eye Institute via NIH-CIDR. This study used data generated by the UK10K Consortium. Funding for UK10K was provided by the Wellcome Trust under award WT091310. A full list of the investigators who contributed to the generation of the UK10K data is available at http://www.UK10K.org/. This research was supported by grants from the European Research Council. Computation was performed at the Vital-IT Center (http://www.vital-it.ch/) for high-performance computing of the SIB Swiss Institute of Bioinformatics.

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Authors and Affiliations

  1. Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland

    Andrew Anand Brown, Ana Viñuela, Olivier Delaneau & Emmanouil T Dermitzakis

  2. Institute of Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, Switzerland

    Andrew Anand Brown, Ana Viñuela, Olivier Delaneau & Emmanouil T Dermitzakis

  3. Swiss Institute of Bioinformatics, Geneva, Switzerland

    Andrew Anand Brown, Ana Viñuela, Olivier Delaneau & Emmanouil T Dermitzakis

  4. NORMENT, KG Jebsen Centre for Psychosis Research, Oslo University Hospital, Oslo, Norway

    Andrew Anand Brown

  5. Department of Twin Research and Genetic Epidemiology, King's College London, London, UK

    Tim D Spector & Kerrin S Small

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Contributions

A.A.B. and E.T.D. designed the study. A.A.B. ran the analyses. A.A.B., A.V., and E.T.D. interpreted the results. A.A.B., A.V., and E.T.D. wrote the manuscript. O.D. provided methodological suggestions. K.S.S. and T.D.S. contributed data.

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Correspondence to Andrew Anand Brown or Emmanouil T Dermitzakis.

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Integrated supplementary information

Supplementary Figure 1 Rank of statistical association for the causal variant in simulations.

Based on five simulations per tissue, the x-axis shows the rank of the causal variant, and the y-axis the proportion of times this outcome occurred. We notice that, as the whole blood experiment was smaller than the other experiments, sample size does not seem to affect the distribution. The causal variant is the most associated variant in 45% of cases, and among the ten most significantly associated variants 89% of times. The boxes show the 25th and 75th percentiles, and the whiskers end at the furthest value from the edge of the box that is not more than 1.5 times the inter-quartile range. Any values outside these whiskers are outliers that are plotted directly.

Supplementary Figure 2 Minor allele frequencies of LEVs called with the two technologies.

The LEVs called using sequence have a lower minor allele frequency than those called using arrays (0.26 vs. 0.27). The box edges show the 25th and 75th percentiles, and the whiskers end at the maximum and minimum values.

Supplementary Figure 3 Impute-derived INFO scores for the two genotyping arrays of the sequence LEVs.

The dots in pink reflect LEVs that were filtered from the array data due to poor imputation quality.

Supplementary Figure 4 Relationship between CaVEMaN score and causal probability in simulations.

The CaVEMaN score is calibrated using the simulations to estimate the probability that the lead eQTL variant is causal. The estimated calibration functions are consistent across tissues, with the exception of blood, which is slightly less conservative than the other tissues, probably due to the smaller sample size.

Supplementary Figure 5 Validation of CaVEMaN probabilities with the Geuvadis dataset.

Five simulated datasets were produced based on the genotype data and eQTLs mapped in Geuvadis. CaVEMaN was run on all of these datasets, and we plot the median CaVEMaN causal probabilities for LEVs, binned into 10 groups, against the true proportions of LEVs in these bins that were causal on the trait. We also show on this plot the equivalent analysis performed using dap-g and a further simulation where the assumption of only one genetic signal in the region is violated by simulating a secondary eQTL.

Supplementary Figure 6 Comparison between CaVEMaN and CAVIAR estimates of causal probabilities.

The comparison was only performed for genes with only one eQTL to minimize differences due to multiple eQTL mapping strategies. Spearman correlation between the two estimates was 0.856.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–6, Supplementary Tables 1 and 2 and Supplementary Note

Life Sciences Reporting Summary

Supplementary Data Set 1

A full list of all eQTLs discovered in the five experiments, together with P value for association and causal probability score

Supplementary Data Set 2

A list of high confidence causal variants which are also significantly associated with a GWAS trait, together with an estimate produced by coloc of the probability of a shared genetic signal

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Brown, A., Viñuela, A., Delaneau, O. et al. Predicting causal variants affecting expression by using whole-genome sequencing and RNA-seq from multiple human tissues. Nat Genet 49, 1747–1751 (2017). https://doi.org/10.1038/ng.3979

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