Abstract
Large-scale sequencing has enabled unparalleled opportunities to investigate the role of rare coding variation in human phenotypic variability. Here, we present a pan-ancestry analysis of sequencing data from three large biobanks, including the All of Us research program. Using mixed-effects models, we performed gene-based rare variant testing for 601 diseases across 748,879 individuals, including 155,236 with ancestry dissimilar to European. We identified 363 significant associations, which highlighted core genes for the human disease phenome and identified potential novel associations, including UBR3 for cardiometabolic disease and YLPM1 for psychiatric disease. Pan-ancestry burden testing represented an inclusive and useful approach for discovery in diverse datasets, although we also highlight the importance of ancestry-specific sensitivity analyses in this setting. Finally, we found that effect sizes for rare protein-disrupting variants were concordant between samples similar to European ancestry and other genetic ancestries (βDeming = 0.7–1.0). Our results have implications for multi-ancestry and cross-biobank approaches in sequencing association studies for human disease.
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Data availability
Results from our gene-based association analyses are available for browsing and download through our online portal (https://hugeamp.org:8000/research.html?pageid=600_traits_app_home). Bulk download of summary statistics is possible via the Cardiovascular Disease Knowledge Portal (https://cvd.hugeamp.org/downloads.html). Access to individual-level UKB data, both phenotypic and genetic, is available to bona fide researchers through application on the UKB website (https://www.ukbiobank.ac.uk). The final release of the exome sequencing dataset of UKB is available only through the DNAnexus Research Analysis Platform (https://www.ukbiobank.ac.uk/enable-your-research/research-analysis-platform). Additional information about registration for access to the data is available at http://www.ukbiobank.ac.uk/register-apply/. Use of UKB data was performed under application number 17488. Access to individual phenotypic and genetic data from AoU is currently available to bona fide researchers within the USA through the AoU Researcher Workbench, a cloud-based computing platform (https://www.researchallofus.org/register/). A publicly available data browser is provided by the research program (https://databrowser.researchallofus.org/). Access to individual-level data for participants from the MGB is currently not publicly available. Other datasets used in this manuscript include: the dbNSFP database v.4.2a and v.4.3a (https://sites.google.com/site/jpopgen/dbNSFP); gnomAD exomes v.2.1 (https://gnomad.broadinstitute.org/downloads); the OMIM database (omim.org) accessed on 25 August 2022; Ensembl release 105 (https://www.ensembl.org/info/data/index.html); and the ClinVar database (https://www.ncbi.nlm.nih.gov/clinvar/) accessed in December 2022.
Code availability
QC of individual-level data was performed using Hail v.0.2 (https://hail.is) as well as PLINK v.2.0.a (https://www.cog-genomics.org/plink/2.0/). Variant annotation was performed using VEP v.105 (https://github.com/Ensembl/ensembl-vep). Main rare variant association analyses were performed using an adaptation of the R package GENESIS v.2.18 (https://rdrr.io/bioc/GENESIS/man/GENESIS-package.html), which has previously been made available by us through the GitHub repository https://github.com/seanjosephjurgens/UKBB_200KWES_CVD / v.1.2 (https://doi.org/10.5281/zenodo.11638262). Meta-analyses were performed using custom code available in the same repository, and using METAL (2017-12-21 release). Analyses that were run in R, were run within R v.4 (https://www.r-project.org).
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Acknowledgements
We gratefully thank all participants of UKB, AoU and MGB Biobank, as this study would not have been possible without their contributions. We also thank the NIH’s AoU Research Program, the UKB resource (under application number 17488) and the MGB team, for making available the participant data examined in this study. P.T.E. was supported by funding from the NIH (1RO1HL092577, 1R01HL157635), by a grant from the American Heart Association (18SFRN34110082, 961045) and from the European Union (MAESTRIA 965286). This work was also supported by an American Heart Association Strategically Focused Research Networks (SFRN) postdoctoral fellowship (18SFRN34110082) to L.-C.W. This work was supported by the John S. LaDue Memorial Fellowship for Cardiovascular Research, a Sarnoff Scholar award from the Sarnoff Cardiovascular Research Foundation and by a NIH grant (K08HL159346) to J.P.P. This work was further supported by a grant from the NIH (1K08HL153937) and a grant from the American Heart Association (862032) to K.G.A. This work was supported by a Sigrid Jusélius Fellowship to J.T.R. This work was also supported by an Amsterdam UMC doctoral fellowship and the Junior Clinical Scientist Fellowship (03-007-2022-0035) from the Dutch Heart Foundation, to S.J.J. This work was supported by the BioData Ecosystem fellowship to S.H.C. This work was also supported by a grant from the NIH (R01DK125490) to J.F.
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Authors and Affiliations
Contributions
S.J.J. and P.T.E. conceived and designed the study. S.J.J., X.W., S.H.C., L.-C.W., S. Koyama and J.P.P. performed data curation and data processing. S.J.J. and X.W. performed the main statistical and bioinformatic analyses, with S.H.C. providing important bioinformatic support. M.C., R.W., C.R., K.J.B., S. Kany, A.L.E., L.F.J.M.W. and J.T.R. contributed critically to the analysis plan. P.N., K.G.A., C.R.B., S.A.L., K.L.L., and P.T.E. supervised the study. T.N., P.S. and D.J. created the online web portal on the Human Disease Knowledge Portal. J.F. and N.P.B. supervised the creation of the online web portal. S.J.J., X.W. and P.T.E. wrote the manuscript. All authors critically revised and approved the manuscript.
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Competing interests
P.T.E. has received sponsored research support from Bayer AG, Bristol Myers Squibb and Pfizer and Novo Nordisk. S.A.L. is an employee of Novartis as of July 2022. S.A.L. previously received sponsored research support from Bristol Myers Squibb, Pfizer, Boehringer Ingelheim, Fitbit, Medtronic, Premier and IBM, and has consulted for Bristol Myers Squibb, Pfizer, Blackstone Life Sciences and Invitae. P.N. reports research grants from Allelica, Amgen, Apple, Boston Scientific, Genentech/Roche and Novartis, personal fees from Allelica, Apple, AstraZeneca, Blackstone Life Sciences, Creative Education Concepts, CRISPR Therapeutics, Eli Lilly & Co, Esperion Therapeutics, Foresite Labs, Genentech/Roche, GV, HeartFlow, Magnet Biomedicine, Merck, Novartis, TenSixteen Bio and Tourmaline Bio, equity in Bolt, Candela, Mercury, MyOme, Parameter Health, Preciseli and TenSixteen Bio, and spousal employment at Vertex Pharmaceuticals, all unrelated to the present work. B.M.P. serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. S. Kany is supported by the Walter Benjamin Fellowship from the Deutsche Forschungsgemeinschaft (521832260). The remaining authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Prevalence of rare likely pathogenic and pathogenic variants in cardiomyopathy genes across UKB and MGB datasets.
a,b, Bar graphs reflect the percentage of biobank participants found to carry pathogenic or likely pathogenic variants for dilated cardiomyopathy (a) or hypertrophic cardiomyopathy (b) with 95% binomial confidence intervals, where light blue represent Massachusetts Biobank (MGB) and dark green represents UK Biobank (UKB). The absolute number of carriers identified in a given grouping is added above each bar. The total number of participants was n = 51,815 in MGB and n = 454,162 in UKB. Pathogenic or likely pathogenic variants reported in ClinVar and submitted by clinical testing labs from 2015 onwards were included, as well as high-confidence LOF variants affecting canonical transcripts for select genes (where truncation is considered pathogenic for the disease); for TTN, only variants affecting the cardiac exons were included. Variants were filtered to MAF < 0.1%. The combined prevalences (all genes combined) are shown on the far right of the panels. Overall, rare disease-causing variants for both disorders were more frequent in MGB vs. UKB (non-overlapping 95% binomial confidence intervals).
Extended Data Fig. 2 Evidence of age-related somaticism and phenotype associations for potentially somatic gene variants.
a, Volcano plot with results from linear regression models predicting age from rare variant carrier status (in a meta-analysis of UKB, AoU and MGB), with the -log10(PCauchy) on the y-axis and the estimated effect size per year for the most significant mask on the x-axis (βage).The same pipeline was used as for our primary analysis. The horizontal dotted line shows a suggestive significance cutoff of P < 1 × 10−6, while the vertical lines highlight βage = −0.1 and βage = 0.1, respectively. Significant genes (P < 1 × 10−6) are annotated with their gene names; all genes that were significantly associated with any outcome in our primary analysis (Supplementary Table 16) and with age are also annotated with their gene names. Gene masks reaching P < 1 × 10−6 and βage > 0.1 can be considered suggestively affected by age and therefore raise suspicion that they are affected by age-related somatic variants. Indeed, many of these genes are known clonal hematopoiesis of indeterminate potential (CHIP) genes (Supplementary Note). b, Heatmap with Phecodes on the x-axis and genes on the y-axis. The heatmap shows results for genes that reached significance in our PheWAS for leukemic/hematological outcomes and/or genes associated with age. These genes are plotted against a range of representative phenotypes suggestively associated at P < 1 × 10−5 with any of the genes. The color in each cell represents the odds ratio (OR) for the most significant mask (lowest nominal P-value) with red indicating increased disease risk (OR > 1) and blue indicating decreased disease risk (OR < 1). Significance levels are shown in each cell using circles and boxes, with a small dot representing nominal PCauchy < 0.05, a larger dot representing PCauchy < 0.001, a black box representing PCauchy = 1 × 10−5 and a black box with smaller white box representing QCauchy < 0.01 in our primary analysis. ORs were estimated using inverse-variance weighted meta-analysis of two-sided Firth’s logistic regression results. The reported P-values are Cauchy P-values that represent the omnibus P-value of all masks for a gene-phecode pair after combining them using the Cauchy distribution (unadjusted for multiple testing). Q-values represent the Benjamini-Hochberg FDR adjustments of these P-values. P-values for mask-phecode pairs (prior to the Cauchy combination) were derived from a Z score-based meta-analysis of score tests from logistic mixed-effects models with saddle-point-approximation. All statistical tests and P-values are two-sided. LOF, loss-of-function; OR, odds ratio.
Extended Data Fig. 3 No apparent discovery benefit in rare variant burden testing from ancestral diversity at current case numbers in AoU.
a,b, Grouped barcharts with the number of significant signals identified from rare variant burden testing on the y-axis, comparing results from two different sub-samples of datasets. Blue bars represent results for LOF variants only (MAFpopulation-max < 0.1%), while dark red bars show results for the Cauchy combination of 6 masks and light red bars represent a Cauchy combination of 9 masks (including 3 low-frequency variant masks at MAFpopulation-max < 1%). Plot in a shows results for the subset of AoU consisting of individuals genetically similar to European ancestry (n = 106,057 samples with complete EHR linkage; EUR) on the left side of each comparison, while the right side shows results for an ancestrally diverse sub-sample of AoU of equal size (n = 106,057 samples with complete EHR linkage; Mixed). Results are restricted to 584 phecodes that were testable in both subsamples. Plot in b shows those same sub-samples of AoU in a meta-analysis with UKB, restricting to 530 phecodes that were testable across AoU subsamples and in the UKB dataset. c, Violin plot showing prevalence ratios for all 601 phecodes in AoU, where the prevalence ratios represent the ratio between prevalence among EUR samples and within individuals genetically dissimilar to European ancestry (non-EUR). Prevalence ratios are presented on the log2-scale, where one unit difference represents a doubling/halving of the relative prevalence. The black line represents prevalence ratio of 0 (no difference), while the dotted lines represent prevalence ratios of 1 and −1. Select phenotypes enriched on either side are annotated. Many phecodes are relatively enriched in EUR as compared to non-EUR, which might contribute to the slightly diminished discovery yield within the ancestrally diverse subsample of AoU as compared to the EUR subsample. The Cauchy P-values represent the omnibus P-value of all relevant masks for a gene-phecode pair after combining them using the Cauchy distribution (unadjusted for multiple testing). P-values for mask-phecode pairs (prior to the Cauchy combination) in AoU were derived from a saddle-point-approximation score tests from logistic mixed-effects models, while meta-analysis P-values were derived from Z score-based meta-analysis of such score tests. All statistical tests and P-values are two-sided.
Extended Data Fig. 4 Broad Human Disease Knowledge Portal showing phenome-wide results for YLPM1.
Output from a search for the gene YLPM1 on the Broad Human Disease Knowledge Portal, which showcases the results from our primary meta-analysis of UKB, AoU and MGB. The top of the figure shows a dot plot with each dot representing a different phecode tested for association with YLPM1, where the y-axis shows the -log10(Cauchy P-value) and the x-axis represents different phenotypes grouped by broad phecode category. The arrows represent directionality, with an upwards arrow indicating that rare variants in YLPM1 are associated with increased risk of the given phecode, and downwards arrows representing decreased risk; directionality is based in the ‘Best Mask’ which is the mask that yielded the lowest nominal P-value in burden testing. The dotted line represents the significance level used for phenome-wide testing of a single gene on the portal (α = 5 × 10−5). Phenotypes reaching this level of significance are highlighted in black text. The bottom of the figure shows the associated results table as presented on the portal, including details on the most strongly associated phecodes, the Cauchy P-values for burden testing of rare variant masks, the Cauchy P-values for burden testing of rare and low-frequency masks, and the beta coefficient of the ‘Best Mask’ (ie the mask that reached the lowest nominal P-value in burden testing). Results can be queried through the following link: https://hugeamp.org:8000/research.html?ancestry=mixed&cohort=UKB_450k_AoU_250k_MGB_53k_META_overlapcorrected&file=600Traits.csv&gene=YLPM1&pageid=600_traits_app. Betas, which represent log(odds ratios), were estimated using inverse-variance weighted meta-analysis of two-sided Firth’s logistic regression results. The reported P-values are Cauchy P-values that represent the omnibus P-value of all masks for a gene-phecode pair after combining them using the Cauchy distribution (unadjusted for multiple testing). P-values for mask-phecode pairs (prior to the Cauchy combination) were derived from Z-score-based meta-analysis of score tests from logistic mixed-effects models. All statistical tests and P-values are two-sided. LOF, loss-of-function; OR, odds ratio.
Extended Data Fig. 5 Broad Human Disease Knowledge Portal showing phenome-wide results for UBR3.
Output from a search for the gene UBR3 on the Broad Human Disease Knowledge Portal, which showcases the results from our primary meta-analysis of UKB, AoU and MGB. The top of the figure shows a dot plot with each dot representing a different phecode, where the y-axis shows the -log10(Cauchy P-value) and the x-axis represents different phenotypes grouped by broad phecode category. The arrows represent directionality, with an upwards arrow indicating that rare variants in UBR3 are associated with increased risk of the given phecode, and downwards arrows representing decreased risk; directionality is based in the ‘Best Mask’ which is the mask that yielded the lowest nominal P-value in burden testing. The dotted line represents the significance level used for phenome-wide testing of a single gene on the portal (α = 5 × 10−5). Phenotypes reaching this level of significance are highlighted in black text. The bottom of the figure shows the associated results table as presented on the portal, including details on the most strongly associated phecodes, the Cauchy P-values for burden testing of rare variant masks, the Cauchy P-values for burden testing of rare and low-frequency masks, and the beta coefficient of the ‘Best Mask’ (ie the mask that reached the lowest nominal P-value in burden testing). Results can be queried through the following link: https://hugeamp.org:8000/research.html?ancestry=mixed&cohort=UKB_450k_AoU_250k_MGB_53k_META_overlapcorrected&file=600Traits.csv&gene=UBR3&pageid=600_traits_app. Betas, which represent log(odds ratios), were estimated using inverse-variance weighted meta-analysis of two-sided Firth’s logistic regression results. The reported P-values are Cauchy P-values that represent the omnibus P-value of all masks for a gene-phecode pair after combining them using the Cauchy distribution (unadjusted for multiple testing). P-values for mask-phecode pairs (prior to the Cauchy combination) were derived from Z-score-based meta-analysis of score tests from logistic mixed-effects models. All statistical tests and P-values are two-sided. LOF, loss-of-function; OR, odds ratio.
Extended Data Fig. 6 Broad Human Disease Knowledge Portal showing exome-wide results for the phecode ‘Cardiomyopathy’.
Output from a search for the phecode Cardiomyopathy on the Broad Human Disease Knowledge Portal, which showcases the results from our primary meta-analysis of UKB, AoU and MGB. A Manhattan plot is shown in the top left, with each dot representing a different gene tested for association with Cardiomyopathy, where the y-axis shows the -log10(Cauchy P-value) and the x-axis represents genomic coordinates. In this figure, results are restricted to ‘rare variant’ masks only (MAF < 0.1%). The dotted line represents the significance threshold used for a single phenotype on the portal (α = 1 × 10−6). A quantile-quantile plot in the top right shows the observed genome-wide test statistics on the y-axis, against the expected test statistics under the null hypothesis on the x-axis; the red line represents the x = y line. The bottom of the figure shows the associated results table as presented on the portal, including details on the most strongly associated genes, the Cauchy P-values for burden testing of rare variant masks, the Cauchy P-values for burden testing of rare and low-frequency masks, the P-value and beta coefficient for the ‘Best Mask’ (that is the mask with the lowest nominal P-value in burden testing), and information on case/control numbers. The table here is restricted to 9 genes with at least suggestive evidence (P < 3 × 10−6). Results can be queried through the following link: https://hugeamp.org:8000/research.html?ancestry=mixed&cohort=UKB_450k_AoU_250k_MGB_53k_META_overlapcorrected&file=600Traits.csv&pageid=600_traits_app&phenotype=phecode_425.0. Betas, which represent log(odds ratios), were estimated using inverse-variance weighted meta-analysis of two-sided Firth’s logistic regression results. The reported P-values for ‘rare’ and ‘low-freq’ represent omnibus P-values of all relevant masks for a gene-phecode pair after combining them using the Cauchy distribution (unadjusted for multiple testing). P-values for mask-phecode pairs (prior to the Cauchy combination) were derived from Z-score-based meta-analysis of score tests from logistic mixed-effects models with saddle-point-approximation. All statistical tests and P-values are two-sided. LOF, loss-of-function; OR, odds ratio.
Extended Data Fig. 7 Broad Human Disease Knowledge Portal showing exome-wide results for the phecode ‘Diabetes Mellitus’.
Output from a search for the phecode Diabetes Mellitus on the Broad Human Disease Knowledge Portal, which showcases the results from our primary meta-analysis of UKB, AoU and MGB. A Manhattan plot is shown in the top left, with each dot representing a different gene tested for association with Diabetes Mellitus, where the y-axis shows the -log10(Cauchy P-value) and the x-axis represents genomic coordinates. In this figure, results include both ‘rare variant’ and ‘low-frequency’ masks (MAF < 1%). The dotted line represents the significance threshold used for a single phenotype on the portal (α = 1 × 10−6). A quantile-quantile plot in the top right shows the observed genome-wide test statistics on the y-axis, against the expected test statistics under the null hypothesis on the x-axis; the red line represents the x = y line. The bottom of the figure shows the associated results table as presented on the portal, including details on the most strongly associated genes, the Cauchy P-values for burden testing of rare variant masks, the Cauchy P-values for burden testing of rare and low-frequency masks, the P-value and beta coefficient for the ‘Best Mask’ (that is the mask with the lowest nominal P-value in burden testing), and information on case/control numbers. The table here is restricted to 10 genes with at least suggestive evidence (P < 3 × 10−6). Results can be queried through the following link: https://hugeamp.org:8000/research.html?ancestry=mixed&cohort=UKB_450k_AoU_250k_MGB_53k_META_overlapcorrected&file=600Traits.csv&pageid=600_traits_app&phenotype=phecode_250.0. Betas, which represent log(odds ratios), were estimated using inverse-variance-weighted meta-analysis of two-sided Firth’s logistic regression results. The reported P-values for ‘rare’ and ‘low-freq’ represent omnibus P-values of all relevant masks for a gene-phecode pair after combining them using the Cauchy distribution (unadjusted for multiple testing). P-values for mask-phecode pairs - prior to the Cauchy combination - were derived from Z-score-based meta-analysis of score tests from logistic mixed-effects models with saddle-point-approximation. All statistical tests and P-values are two-sided. LOF, loss-of-function; OR, odds ratio.
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Jurgens, S.J., Wang, X., Choi, S.H. et al. Rare coding variant analysis for human diseases across biobanks and ancestries. Nat Genet (2024). https://doi.org/10.1038/s41588-024-01894-5
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DOI: https://doi.org/10.1038/s41588-024-01894-5
