Disease variants identified by genome-wide association studies (GWAS) tend to overlap with expression quantitative trait loci (eQTLs), but it remains unclear whether this overlap is driven by gene expression levels mediating genetic effects on disease. Here, we introduce a new method, mediated expression score regression (MESC), to estimate disease heritability mediated by the cis genetic component of gene expression levels.
We applied MESC to GWAS summary statistics for 42 traits (average N?=?323,000) and cis-eQTL summary statistics for 48 tissues from the Genotype-Tissue Expression (GTEx) consortium. Averaging across traits, only 11? ?2% of heritability was mediated by assayed gene expression levels. Expression-mediated heritability was enriched in genes with evidence of selective constraint and genes with disease-appropriate annotations. Our results demonstrate that assayed bulk tissue eQTLs, although disease relevant, cannot explain the majority of disease heritability.
Components of heritability in a UK Biobank cohort
We will analyze heritability of several polygenic traits. We will use existing methods and methods under development for partitioning heritability by functional annotation (e.g. cell-type-specific enhancer regions, gene pathways, etc.) to learn about underlying trait biology. We will also examine how SNP heritability varies across LD and MAF categories. Finally, we will evaluate missing heritability using new methods to estimate heritability explained by haplotypes, narrow-sense heritability (using PSMC) and epistatic components of heritability (using Hadamard products). We plan to study a wide range of health-related phenotypes, including diseases and quantitative traits like height and BMI. The data in the UK Biobank?s cohort will allow us to partition heritability at higher resolution and to evaluate missing heritability. These will inform both our understanding of trait biology and the design of future genetic studies. Both of these outcomes will benefit attempts to find actionable drug targets for human disease. Moreover, the methods we develop for partitioning heritability will be published and made open-source for use by the broader research community. We will work with annotations from Finucane et al. 2015 Nat Gen as well as gene sets and new annotations from the ENCODE and Roadmap Epigenomics Consortia and others. We will apply LD score regression [Finucane et al. 2015 Nat Genet], BOLT-REML [Loh et al. 2015 Nat Genet], and a new method under development to assess heritability enrichment of these annotations, as well as enrichment/depletion by LD and MAF, within/across traits and populations. We will also apply new methods to estimate heritability explained by haplotypes, total narrow-sense heritability and epistatic components of heritability. We will analyze the full cohort.
|Lead investigator:||Dr Alkes Price|
|Lead institution:||Harvard School of Public Health|
3 related Returns
|Return ID||App ID||Description||Archive Date|
|2522||16549||Functional architecture of low-frequency variants highlights strength of negative selection across coding and non-coding annotations||22 Oct 2020|
|3149||16549||Functionally informed fine-mapping and polygenic localization of complex trait heritability||26 Feb 2021|
|726||16549||Linkage disequilibrium - dependent architecture of human complex traits shows action of negative selection||17 Oct 2017|
|3108||Quantifying genetic effects on disease mediated by assayed gene expression levels||Yao et al||2014||Nature Genetics (2020)|