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Improving the trans-ancestry portability of polygenic risk scores by prioritizing variants in predicted cell-type-specific regulatory elements

Nature Genetics volume 52, pages 1346–1354 (2020)Cite this article

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Abstract

Poor trans-ancestry portability of polygenic risk scores is a consequence of Eurocentric genetic studies and limited knowledge of shared causal variants. Leveraging regulatory annotations may improve portability by prioritizing functional over tagging variants. We constructed a resource of 707 cell-type-specific IMPACT regulatory annotations by aggregating 5,345 epigenetic datasets to predict binding patterns of 142 transcription factors across 245 cell types. We then partitioned the common SNP heritability of 111 genome-wide association study summary statistics of European (average n ≈ 189,000) and East Asian (average n ≈ 157,000) origin. IMPACT annotations captured consistent SNP heritability between populations, suggesting prioritization of shared functional variants. Variant prioritization using IMPACT resulted in increased trans-ancestry portability of polygenic risk scores from Europeans to East Asians across all 21 phenotypes analyzed (49.9% mean relative increase in R2). Our study identifies a crucial role for functional annotations such as IMPACT to improve the trans-ancestry portability of genetic data.

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Fig. 1: Study design to identify regulatory annotations that prioritize regulatory variants in a multi-ancestry setting.
Fig. 2: IMPACT annotates relevant cell-type-specific regulatory elements.
Fig. 3: Trans-ancestry concordance of regulatory elements defined by IMPACT.
Fig. 4: Mechanism by which IMPACT prioritization of shared regulatory variants might improve trans-ancestry PRS performance.
Fig. 5: Identifying shared regulatory variants with IMPACT annotations to improve the trans-ancestry portability of PRS.

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Data availability

Data are available at: IMPACT Github repository: https://github.com/immunogenomics/IMPACT; IMPACT 707 annotations: https://github.com/immunogenomics/IMPACT/tree/master/IMPACT707. Data were obtained from the following resources: HOMER: http://homer.ucsd.edu/homer/motif/; S-LDSC: https://github.com/bulik/ldsc; 1000 Genomes: http://www.internationalgenome.org/; cell-type-specifically expressed gene set annotations and LD scores: https://data.broadinstitute.org/alkesgroup/LDSCORE/LDSC_SEG_ldscores/; cell-type-specific histone modification ChIP–seq datasets: https://data.broadinstitute.org/alkesgroup/LDSCORE/; Plink: https://www.cog-genomics.org/plink2; Riken website: http://jenger.riken.jp/en/; Price Lab GWAS summary statistics: https://data.broadinstitute.org/alkesgroup/sumstats_formatted/; Neale Lab GWAS summary statistics: http://www.nealelab.is/uk-biobank; GWAS catalog: https://www.ebi.ac.uk/gwas/; Deep Learning: https://data.broadinstitute.org/alkesgroup/LDSCORE/DeepLearning/.

Code availability

We have provided code to recreate our analyses at https://github.com/immunogenomics/IMPACT/tree/master/IMPACT707/AnalysisCode.

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Acknowledgements

This work is supported in part by funding from the National Institutes of Health (grant nos. NHGRI T32 HG002295, UH2AR067677, 1U01HG009088, U01 HG009379 and 1R01AR063759).

Author information

Author notes

  1. These authors contributed equally: Tiffany Amariuta, Kazuyoshi Ishigaki.

Authors and Affiliations

  1. Center for Data Sciences, Harvard Medical School, Boston, MA, USA

    Tiffany Amariuta, Kazuyoshi Ishigaki & Soumya Raychaudhuri

  2. Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Tiffany Amariuta, Kazuyoshi Ishigaki & Soumya Raychaudhuri

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

    Tiffany Amariuta, Kazuyoshi Ishigaki, Alkes L. Price & Soumya Raychaudhuri

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

    Tiffany Amariuta & Soumya Raychaudhuri

  5. Graduate School of Arts and Sciences, Harvard University, Cambridge, MA, USA

    Tiffany Amariuta

  6. Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan

    Kazuyoshi Ishigaki, Masaru Koido & Chikashi Terao

  7. Laboratory for Developmental Genetics, RIKEN Center for Integrative Medical Sciences (IMS), Kanagawa, Japan

    Hiroki Sugishita

  8. Medical Sciences Innovation Hub Program, RIKEN, Kanagawa, Japan

    Tazro Ohta & Eiryo Kawakami

  9. Database Center for Life Science, Joint Support-Center for Data Science Research, Research Organization of Information and Systems, Shizuoka, Japan

    Tazro Ohta

  10. Division of Molecular Pathology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Masaru Koido & Yoshinori Murakami

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

    Kushal K. Dey & Alkes L. Price

  12. Laboratory of Genome Technology, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Koichi Matsuda

  13. Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan

    Koichi Matsuda

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

    Alkes L. Price

  15. Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan

    Eiryo Kawakami

  16. Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan

    Chikashi Terao

  17. Department of Applied Genetics, The School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan

    Chikashi Terao

  18. Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK

    Soumya Raychaudhuri

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  1. Tiffany Amariuta
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Contributions

T.A., K.I. and S.R. conceived and designed the study. T.A., K.I., A.L.P. and S.R. conducted statistical genetic analysis. T.A. and S.R. conducted functional genomic data analysis. H.S., T.O. and E.K. performed TF ChIP–seq data collection and analysis. K.K.D., M.K. and A.L.P. performed deep learning analysis. K.I., K.M., Y.M. and C.T. managed and analyzed BBJ data. T.A., K.I. and S.R. wrote the initial draft of the manuscript. All co-authors contributed to the final manuscript.

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Correspondence to Soumya Raychaudhuri.

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Extended data

Extended Data Fig. 1 Data collection.

a) TF ChIP-seq collection from NCBI: (left) cell type and TF diversity where ‘Cell Deriv’ indicates number of unique parental cell types, for example GM12878 and GM10847 are both B cell lines, (right) diversity of tissue types. b) (left) Epigenomic and sequence features to be used in IMPACT models, (right) diversity of histone modification ChIP-seq in features. c) Diversity of European (EUR) and East Asian (EAS) GWAS summary statistics across phenotypic categories.

Extended Data Fig. 2 IMPACT annotation-trait associations.

Significant cell type-phenotype associations across 707 IMPACT regulatory annotations and 111 complex traits and diseases at τ* 5% FDR, color indicates -log10 FDR 5% adjusted P value of τ*. Zooms shows particular cell type categories enriched for polygenic trait associations.

Extended Data Fig. 3 Proportion of heritability in the top 5% of SNPs.

a) Common SNP heritability captured by the top 5% of SNPs according to the lead cell type association for each EUR GWAS. Lead association determined by largest τ* estimate that is significantly positive. b) Similar for each EAS GWAS. Gray bars indicate the standard error of the heritability estimate. Color represents the category of the complex trait or disease.

Extended Data Fig. 4 τ* comparison of IMPACT annotations versus cell-type-specific histone marks.

Comparison of two different functional annotations, IMPACT and cell-type-specific histone marks, to capture polygenic heritability assessed by quantifying τ* per-SNP heritability value. Circled are five representative traits used throughout the study: asthma, RA, PrCa, MCV, and height.

Extended Data Fig. 5 Common per-SNP heritability (τ*) estimate for sets of independent IMPACT cell type annotations across 29 traits.

Dotted line is the identity line, y=x. τ* values with their standard errors are colored green if significantly positive in EUR and not EAS, red if significantly positive in EAS but not in EUR, green if significantly positive in both EUR and EAS, and gray if not significantly positive in either population.

Extended Data Fig. 6 Population concordance of heterozygosity (2pq) among variants prioritized by IMPACT compared to standard P+T.

a) Heterozygosity of variants from genome-wide EUR and EAS PrCa summary statistics in the top 5% of the lead IMPACT annotation for EUR PrCa. b) Heterozygosity of variants from genome-wide EUR and EAS PrCa summary statistics using standard P+T. c) Heterozygosity of variants from genome-wide EUR and EAS PrCa summary statistics in the bottom 95% of the lead IMPACT annotation for PrCa; mutually exclusive with SNPs in A). d) Meta-analysis of heterozygosity correlations between populations across 21 traits shared between EUR and EAS cohorts over 17 GWAS P value thresholds (with reference to the EUR GWAS).

Extended Data Fig. 7 Population divergence, measured by Fst, among variants prioritized by IMPACT compared to standard P+T.

Larger values indicate a reduction in heterozygosity. Meta-analysis of Fst between EUR and EAS populations across 21 traits shared between EUR and EAS cohorts over 17 GWAS P value thresholds (with reference to the EUR GWAS).

Extended Data Fig. 8 EUR PRS model evaluated on EAS individuals from BBJ.

For each trait, we evaluate the predictive value of standard PRS models (top 100% of IMPACT SNPs) and functionally informed PRS models (using a subset of SNPs prioritized by IMPACT). The top 100% of SNPs according to IMPACT represents the PRS model with no functional annotation information. Intervals represent the 95% CI around the R2 estimate. For quantitative traits, R2 represents the proportion of variance captured by the linear PRS model. For case–control traits, R2 represents the liability scale R2 from the logistic regression PRS model.

Extended Data Fig. 9 Trans-ethnic and within-population PRS models evaluated on the same 5,000 BBJ individuals.

a) Phenotypic variance (R2) in 5,000 BBJ individuals explained by IMPACT-informed PRS-EUR (light pink) and standard PRS-EUR (light blue). b) Phenotypic variance (R2) in 5,000 BBJ individuals explained by IMPACT-informed PRS-EAS (light pink) and standard PRS-EAS (light blue). Error bars indicate 95% CI calculated via 1,000 bootstraps.

Extended Data Fig. 10 PRS accuracy is robust to loci of large effect.

We recomputed confidence intervals around the R2 estimates (panels A and B) and around the relative improvements in R2 estimates of IMPACT PRS over standard P+T PRS (panels C and D) via block jackknife across the genome, using 200 adjacent equally-sized bins and iteratively removing variants within each bin and computing the R2. a) Trans-ethnic analysis of EUR PRS to BBJ individuals. b) Within-population analysis of EAS PRS to BBJ individuals. Error bars indicate 95% confidence interval (CI) around the R2 estimates. c) Trans-ethnic analysis of EUR PRS to BBJ individuals, relative improvement in R2 estimates defined as (IMPACT R2 - standard P+T R2)/standard P+T R2. d) Within-population analysis of EAS PRS to BBJ individuals, relative improvement in R2 estimates defined as (IMPACT R2 - standard P+T R2)/standard P+T R2.

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Amariuta, T., Ishigaki, K., Sugishita, H. et al. Improving the trans-ancestry portability of polygenic risk scores by prioritizing variants in predicted cell-type-specific regulatory elements. Nat Genet 52, 1346–1354 (2020). https://doi.org/10.1038/s41588-020-00740-8

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