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Functionally informed fine-mapping and polygenic localization of complex trait heritability

Nature Genetics volume 52, pages 1355–1363 (2020)Cite this article

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Abstract

Fine-mapping aims to identify causal variants impacting complex traits. We propose PolyFun, a computationally scalable framework to improve fine-mapping accuracy by leveraging functional annotations across the entire genome—not just genome-wide-significant loci—to specify prior probabilities for fine-mapping methods such as SuSiE or FINEMAP. In simulations, PolyFun + SuSiE and PolyFun + FINEMAP were well calibrated and identified >20% more variants with a posterior causal probability >0.95 than identified in their nonfunctionally informed counterparts. In analyses of 49 UK Biobank traits (average n = 318,000), PolyFun + SuSiE identified 3,025 fine-mapped variant–trait pairs with posterior causal probability >0.95, a >32% improvement versus SuSiE. We used posterior mean per-SNP heritabilities from PolyFun + SuSiE to perform polygenic localization, constructing minimal sets of common SNPs causally explaining 50% of common SNP heritability; these sets ranged in size from 28 (hair color) to 3,400 (height) to 2 million (number of children). In conclusion, PolyFun prioritizes variants for functional follow-up and provides insights into complex trait architectures.

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Fig. 1: Calibration, power and computational cost of fine-mapping methods in main simulations.
Fig. 2: Summary of fine-mapping results for UK Biobank traits.
Fig. 3: Examples of the advantages of functionally informed fine-mapping for UK Biobank traits.
Fig. 4: Functional enrichment of SuSiE fine-mapped common SNPs for UK Biobank traits.
Fig. 5: Polygenic localization results for UK Biobank traits.

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Data availability

PolyFun fine-mapping results generated in the present study are available for public download at http://data.broadinstitute.org/alkesgroup/polyfun_results. Summary LD information generated in the present study is available for public download at https://data.broadinstitute.org/alkesgroup/UKBB_LD. Baseline-LF v2.2.UKB annotations and LD scores for UK Biobank SNPs are available at https://data.broadinstitute.org/alkesgroup/LDSCORE/baselineLF_v2.2.UKB.tar.gz. Access to the UK Biobank resource is available via application (http://www.ukbiobank.ac.uk).

Code availability

PolyFun and PolyLoc software is available at https://github.com/omerwe/polyfun. SuSiE software is available at https://github.com/stephenslab/susieR. FINEMAP software is available at http://www.christianbenner.com/#.

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Acknowledgements

We thank B. Pasaniuc, G. Kichaev, M. Stephens, G. Wang, M. Kanai, B. M. Schilder and T. Raj for helpful discussions. This research was conducted using the UK Biobank Resource under application no. 16549 and was funded by National Institutes of Health grants (nos. U01 HG009379, R37 MH107649, R01 MH101244 and R01 HG006399) and the Academy of Finland grants (nos. 288509 and 312076). H.K.F. is supported by E. and W. Schmidt. Computational analyses were performed on the O2 High-Performance Compute Cluster at Harvard Medical School.

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Authors and Affiliations

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

    Omer Weissbrod, Farhad Hormozdiari, Steven Gazal, Armin P. Schoech, Bryce van de Geijn, Yakir Reshef & Alkes L. Price

  2. Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland

    Christian Benner & Matti Pirinen

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

    Ran Cui, Jacob Ulirsch, Luke O’Connor, Hilary K. Finucane & Alkes L. Price

  4. Program in Biological and Biomedical Sciences, Harvard Medical School, Cambridge, MA, USA

    Jacob Ulirsch

  5. The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Carla Márquez-Luna

  6. Department of Public Health, University of Helsinki, Helsinki, Finland

    Matti Pirinen

  7. Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland

    Matti Pirinen

  8. Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

    Hilary K. Finucane

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  1. Omer Weissbrod
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Contributions

O.W. and A.L.P. designed the study. O.W. and S.G. analyzed the data. C.B. extended the FINEMAP software. O.W. and A.L.P. wrote the manuscript with assistance from F.H., C.B., R.C., J.U., S.G., A.P.S., B.v.d.G., Y.R., C.M.L., L.O., M.P. and H.K.F.

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Correspondence to Omer Weissbrod or Alkes L. Price.

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Extended data

Extended Data Fig. 1 Assessing the individual impact of step 1 of PolyFun (estimating functional enrichment) via perturbation analysis, by randomly shuffling different proportions of annotation coefficient estimates.

For each evaluated value of the proportion of shuffled annotation coefficient estimates, we report the number of experiments having each obtained FDR level >0 (left panel) and the number of experiments having each obtained power level >0 (right panel), out of 1000 experiments. FDR and power are reported with respect to identifying PIP ≥ 0.95 SNPs. Experiments with FDR = 0 (resp. power=0) are not reported in the left panel (resp. right panel) to improve clarity. Numerical reports are provided in Supplementary Table 6.

Extended Data Fig. 2 Assessing the individual impact of step 2 of PolyFun (estimating per-SNP heritabilities on odd/even chromosomes) via perturbation analysis, by using both odd and even chromosomes to estimate functional enrichment.

The figure is similar to Extended Data Figure 1 but applies a different perturbation (using both odd and even chromosomes to estimate functional enrichment). Numerical reports are provided in Supplementary Table 6.

Extended Data Fig. 3 Assessing the individual impact of step 3 of PolyFun (partitioning all SNPs into 20 bins of similar per-SNP heritability) via perturbation analysis, by varying the number of per-SNP heritability bins.

The figure is similar to Extended Data Figure 1 but applies a different perturbation (changing the number of per-SNP heritability bins). Numerical reports are provided in Supplementary Table 6.

Extended Data Fig. 4 Assessing the individual impact of step 4 of PolyFun (re-estimating per-SNP heritabilities within each bin excluding the target chromosome) via perturbation analysis, by not excluding the target chromosome from the re-estimation procedure.

The figure is similar to Extended Data Figure 1 but applies a different perturbation (disables the exclusion of the target chromosome, either when using the default sample size N = 320 K or when using a smaller sample size of N = 10 K). Numerical reports are provided in Supplementary Table 6.

Extended Data Fig. 5 Assessing the individual impact of step 5 of PolyFun (specifying prior causal probabilities in proportion of the re-estimated per-SNP heritabilities) via perturbation analysis, by randomly permuting estimated prior causal probabilities.

The figure is similar to Extended Data Figure 1 but applies a different perturbation (randomly permuting estimated prior causal probabilities). Numerical reports are provided in Supplementary Table 6.

Extended Data Fig. 6 Visualization of fine-mapping results for UK Biobank traits.

We display an ideogram of all 2,225 PIP > 0.95 fine-mapped SNPs identified by PolyFun + SuSiE across 49 UK Biobank traits. Traits are color-coded into groups (see legend and Supplementary Table 8). White circles indicate SNPs that are pleiotropic for ≥2 genetically uncorrelated traits, with circles to the right of a white circle denoting the genetically uncorrelated traits (max of 5 colored circles due to space limitations). Numerical results are reported in Supplementary Table 10.

Extended Data Fig. 7 Functional enrichment of PolyFun + SuSiE fine-mapped common SNPs for UK Biobank traits.

The figure is analogous to Fig. 4 but uses PIPs computed by PolyFun + SuSiE instead of SuSiE. Numerical results are reported in Supplementary Table 26.

Extended Data Fig. 8 Functional enrichment of SuSiE fine-mapped MAF > 0.001 SNPs for UK Biobank traits.

The figure is analogous to Fig. 4 but uses MAF > 0.001 SNPs instead of common (MAF > 0.05) SNPs. Numerical results are reported in Supplementary Table 27.

Extended Data Fig. 9 Functional enrichment of SuSiE fine-mapped low-frequency and rare SNPs for UK Biobank traits.

The figure is analogous to Fig. 4 but uses only low-frequency and rare SNPs (0.05>MAF > 0.001) instead of common (MAF > 0.05) SNPs. Numerical results are reported in Supplementary Table 28.

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Weissbrod, O., Hormozdiari, F., Benner, C. et al. Functionally informed fine-mapping and polygenic localization of complex trait heritability. Nat Genet 52, 1355–1363 (2020). https://doi.org/10.1038/s41588-020-00735-5

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