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In this paper, we investigated how heritability of low-frequency variants (0.5%= minor allele frequency <5%) is distributed across the genome, and compared it to the distribution of the heritability of common variants We determined that non-synonymous coding variants explain 17 1% of low-frequency variant heritability (hl2f) versus 2.1 0.2% of common variant heritability (hc2). Cell-type-specific non-coding annotations that were significantly enriched for hc2 of corresponding traits were similarly enriched for hl2f for most traits, but more enriched for brain-related annotations and traits. For example, H3K4me3 marks in brain dorsolateral prefrontal cortex explain 57 12% of hl2f versus 12 2% of hc2 for neuroticism. Forward simulations confirmed that low-frequency variant enrichment depends on the mean selection coefficient of causal variants in the annotation, and can be used to predict effect size variance of causal rare variants (minor allele frequency <0.5%).
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.