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Functional architecture of low-frequency variants highlights strength of negative selection across coding and non-coding annotations

Nature Genetics volume 50, pages 1600–1607 (2018)Cite this article

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Abstract

Common variant heritability has been widely reported to be concentrated in variants within cell-type-specific non-coding functional annotations, but little is known about low-frequency variant functional architectures. We partitioned the heritability of both low-frequency (0.5%≤ minor allele frequency <5%) and common (minor allele frequency ≥5%) variants in 40 UK Biobank traits across a broad set of functional annotations. We determined that non-synonymous coding variants explain 17 ± 1% of low-frequency variant heritability (\(h_{{\mathrm{lf}}}^2\)) versus 2.1 ± 0.2% of common variant heritability (\(h_{\mathrm{c}}^2\)). Cell-type-specific non-coding annotations that were significantly enriched for \(h_{\mathrm{c}}^2\) of corresponding traits were similarly enriched for \(h_{{\mathrm{lf}}}^2\) 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 \(h_{{\mathrm{lf}}}^2\) versus 12 ± 2% of \(h_{\mathrm{c}}^2\) 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%).

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Fig. 1: Simulations to assess LFVE estimates.
Fig. 2: Common variant heritability \(\left( {{\boldsymbol{h}}_{\mathrm{c}}^2} \right)\) and low-frequency variant heritability \(\left( {{\boldsymbol{h}}_{{\mathrm{lf}}}^2} \right)\) estimates for 40 UK Biobank traits.
Fig. 3: Functional low-frequency and common variant architectures across 27 independent UK Biobank traits.
Fig. 4: Low-frequency and common variant architectures of CTS annotations.
Fig. 5: Low-frequency and common variant enrichments for non-synonymous variants vary with the strength of selection on the underlying genes.
Fig. 6: Forward simulations enable inferences about negative selection and rare variant architectures.

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Data availability

Baseline-LF annotations are available at https://data.broadinstitute.org/alkesgroup/LDSCORE/baselineLF.tar.gz. BOLT-LMM association statistics computed in this study are available at https://data.broadinstitute.org/alkesgroup/UKBB/UKBB_409K.

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Acknowledgements

We thank A. Gusev, C. Marquez-Luna, M. Hujoel, Y. Reshef, F. Hormozdiari, O. Weissbrod, B. Neale, A. Siepel, and S. M. Gazal for helpful discussions. This research has been conducted using the UK Biobank Resource (application number 16549). This research was funded by NIH grants U01 HG009379, R01 MH101244, R01 MH107649, R01 MH109978 and U01 HG009088. P.R.L. was supported by a Burroughs Wellcome Fund Career Award at the Scientific Interfaces and the Next Generation Fund at the Broad Institute of MIT and Harvard.

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Authors and Affiliations

  1. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Steven Gazal, Armin Schoech & Alkes L. Price

  2. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Steven Gazal, Po-Ru Loh, Hilary K. Finucane, Andrea Ganna, Armin Schoech, Shamil Sunyaev & Alkes L. Price

  3. Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Po-Ru Loh & Shamil Sunyaev

  4. Schmidt Fellows Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Hilary K. Finucane

  5. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Andrea Ganna

  6. Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

    Andrea Ganna

  7. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Armin Schoech & Alkes L. Price

  8. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Shamil Sunyaev

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Contributions

S.G. and A.L.P. designed experiments. S.G. performed experiments. S.G., P.R.L., H.K.F., A.G., and A.S. analyzed data. S.G. and A.L.P. wrote the manuscript with assistance from P.R.L., H.K.F., A.G., A.S., and S.S..

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Correspondence to Steven Gazal or Alkes L. Price.

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Gazal, S., Loh, PR., Finucane, H.K. et al. Functional architecture of low-frequency variants highlights strength of negative selection across coding and non-coding annotations. Nat Genet 50, 1600–1607 (2018). https://doi.org/10.1038/s41588-018-0231-8

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