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Examination of a novel expression-based gene-SNP annotation strategy to identify tissue-specific contributions to heritability in multiple traits

Abstract

Complex traits show clear patterns of tissue-specific expression influenced by single nucleotide polymorphisms (SNPs), yet current strategies aggregate SNP effects to genes by employing simple physical proximity-based windows. Here, we examined whether incorporating SNPs with effects on tissue-specific cis-expression would improve our ability to detect trait-relevant tissues across 31 complex traits using stratified linkage disequilibrium score regression (S-LDSC). We found that a physical proximity annotation produced more significant tissue enrichments and larger S-LDSC regression coefficients, as compared to an expression-based annotation. Furthermore, we showed that our expression-based annotation did not outperform an annotation strategy in which an equal number of randomly chosen SNPs were annotated to genes within the same genomic window, suggesting extensive redundancy among SNP effect estimates due to linkage disequilibrium. That said, current sample sizes limit estimation of cis-genetic SNP effects; therefore, we recommend reexamination of the expression-based annotation when larger tissue-specific expression datasets become available. To examine the influence of sample size, we used a large whole blood eQTL reference panel (N = 31,684) applying a similar expression-based annotation strategy. We found that significant cis-expression QTLs in whole blood did not outperform the physical proximity annotation when estimating tissue-specific SNP heritability enrichment for either high- or low-density lipoprotein phenotypes but performed similarly for inflammatory bowel disease. Finally, we report new and updated tissue enrichment estimates across 31 complex traits, such as significant heritability enrichment of the frontal cortex for cognitive performance, educational attainment, and intelligence, providing further evidence of this structure’s importance in higher cognitive function.

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Fig. 1: Gene-SNP annotation comparison flowchart.
Fig. 2: Flowchart of LDSC coefficient permutations.
Fig. 3: Negative log10 p values for LDSC regression coefficients for specifically expressed gene set within-brain analyses utilizing a physical proximity (triangle) and expression-based (circle) annotation across 13 tissues categorized into four distinct subgroups for eight complex traits.
Fig. 4: Distribution of 1000 permuted S-LDSC coefficients when using a random annotation procedure for schizophrenia in the frontal cortex within-brain analyses.

Data availability

GWAS summary statistics (Supplementary Table 2), FUSION expression weights (http://gusevlab.org/projects/fusion/), and S-LDSC specifically expressed genes (https://alkesgroup.broadinstitute.org/LDSCORE/) are available online. All data generated herein are reported in the Supplementary tables. ICD10 United Kingdom Biobank GWAS summary statistics are available upon request.

Code availability

All code is available upon request.

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Acknowledgements

We thank the participants and researchers of the original studies whose summary statistics and expression weights were utilized in this work. We also thank the authors of FUSION for generating the expression weights, as well as the Finucane lab for the specifically expressed gene sets. This work utilized the Summit supercomputer, which is supported by the National Science Foundation (awards ACI-1532235 and ACI-1532236), the University of Colorado Boulder, and Colorado State University. The Summit supercomputer is a joint effort of the University of Colorado Boulder and Colorado State University.

Funding

This work was supported by the National Institutes of Health (grant numbers T32 DA017637 to John K. Hewitt, R01 AG046938-06 to Chandra A. Reynolds, R01 DA044283-01 to Scott I. Vrieze, and R01 MH100141-06 to Matthew C. Keller) and the University of Colorado Boulder Institute for Behavioral Genetics.

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TJM and LME contributed to study conception, design, manuscript preparation, and approval of the final version of the manuscript. TJM conducted analyses and curated datasets.

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Correspondence to Travis J. Mize or Luke M. Evans.

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Mize, T.J., Evans, L.M. Examination of a novel expression-based gene-SNP annotation strategy to identify tissue-specific contributions to heritability in multiple traits. Eur J Hum Genet (2022). https://doi.org/10.1038/s41431-022-01244-1

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