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Scalable generalized linear mixed model for region-based association tests in large biobanks and cohorts

Nature Genetics volume 52, pages 634–639 (2020)Cite this article

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Abstract

With very large sample sizes, biobanks provide an exciting opportunity to identify genetic components of complex traits. To analyze rare variants, region-based multiple-variant aggregate tests are commonly used to increase power for association tests. However, because of the substantial computational cost, existing region-based tests cannot analyze hundreds of thousands of samples while accounting for confounders such as population stratification and sample relatedness. Here we propose a scalable generalized mixed-model region-based association test, SAIGE-GENE, that is applicable to exome-wide and genome-wide region-based analysis for hundreds of thousands of samples and can account for unbalanced case–control ratios for binary traits. Through extensive simulation studies and analysis of the HUNT study with 69,716 Norwegian samples and the UK Biobank data with 408,910 White British samples, we show that SAIGE-GENE can efficiently analyze large-sample data (N > 400,000) with type I error rates well controlled.

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Fig. 1: Estimated and projected computational cost by sample size (N) for gene-based tests of 15,342 genes, each containing 50 rare variants.
Fig. 2: Quantile–quantile plots of exome-wide gene-based association results.

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Data availability

The summary statistics and quantile–quantile plots for 53 quantitative phenotypes and 10 binary phenotypes in the UK Biobank by SAIGE-GENE are available for public download at https://www.leelabsg.org/resources.

Code availability

SAIGE-GENE is implemented as an open-source R package available at https://github.com/weizhouUMICH/SAIGE/.

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Acknowledgements

This research has been conducted using the UK Biobank Resource under application number 45227. The Nord-Trøndelag Health Study (the HUNT study) is a collaboration between the HUNT Research Centre (Faculty of Medicine, Norwegian University of Science and Technology (NTNU)), the Nord-Trøndelag County Council, the Central Norway Health Authority and the Norwegian Institute of Public Health. The K.G. Jebsen Center for Genetic Epidemiology is financed by Stiftelsen Kristian Gerhard Jebsen, the Faculty of Medicine and Health Sciences at the Norwegian University of Science and Technology (NTNU) and the Central Norway Regional Health Authority. S.L. and W.B. were supported by National Institutes of Health grant R01HG008773. W.Z. was supported by the National Human Genome Research Institute of the National Institutes of Health under award number T32HG010464.

Author information

Author notes

  1. These authors contributed equally: Wei Zhou, Zhangchen Zhao.

Authors and Affiliations

  1. Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA

    Wei Zhou, Zhangchen Zhao, Lars G. Fritsche, Jonathon LeFaive, Sarah A. Gagliano Taliun, Wenjian Bi, Goncalo R. Abecasis & Seunggeun Lee

  2. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

    Wei Zhou, Mark J. Daly & Benjamin M. Neale

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

    Wei Zhou, Mark J. Daly & Benjamin M. Neale

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

    Wei Zhou, Mark J. Daly & Benjamin M. Neale

  5. Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA

    Zhangchen Zhao, Lars G. Fritsche, Jonathon LeFaive, Sarah A. Gagliano Taliun, Wenjian Bi, Goncalo R. Abecasis & Seunggeun Lee

  6. Division of Cardiology, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA

    Jonas B. Nielsen & Cristen J. Willer

  7. K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Trondheim, Norway

    Maiken E. Gabrielsen & Kristian Hveem

  8. Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki, Finland

    Mark J. Daly

  9. HUNT Research Centre, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Levanger, Norway

    Kristian Hveem

  10. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA

    Cristen J. Willer

  11. Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI, USA

    Cristen J. Willer

  12. Graduate School of Data Science, Seoul National University, Seoul, Republic of Korea

    Seunggeun Lee

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  1. Wei Zhou
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Contributions

W.Z., Z.Z. and S.L. designed experiments. W.Z., Z.Z. and S.L. performed experiments. W.Z. implemented the software with input from W.B. and J.L. J.B.N., L.G.F. and S.A.G.T. constructed phenotypes for UK Biobank data. M.E.G. and K.H. provided data for the HUNT study. W.Z., Z.Z., C.J.W., S.L. and G.R.A. analyzed UK Biobank data. B.M.N. and M.J.D. provided helpful advice. W.Z., Z.Z. and S.L. wrote the manuscript with input from S.A.G.T. and M.E.G.

Corresponding authors

Correspondence to Wei Zhou or Seunggeun Lee.

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Competing interests

G.R.A. is an employee of Regeneron Pharmaceuticals. He owns stock and stock options for Regeneron Pharmaceuticals. B.M.N. is a member of the Deep Genomics Scientific Advisory Board, has received travel expenses from Illumina and also serves as a consultant for Avanir and Trigeminal solutions.

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Extended data

Extended Data Fig. 1 Workflow of SAIGE-GENE.

SAIGE-GENE consists of two steps: (1) Fitting the null generalized linear mixed model (GLMM) to estimate variance components and other model parameters; (2) Testing for association between each genetic variant set, such as a gene or a region, and the phenotype.

Extended Data Fig. 2 Plots of the variance ratio of the score statistics by MAC for rare variants with and without the full GRM for sample relatedness (left) and with the full GRM and a sparse GRM for closely related samples (right).

a, Genotypes were simulated for 500 families and 5,000 independent individuals based on the pedigree structure shown in Supplementary Fig. 1 and the null model was fitted for the simulated quantitative trait with h2 = 0.2. The sparse GRM was constructed using a coefficient of relatedness cutoff 0.2. b, 20,000 samples with White British ancestry were randomly selected from the UK Biobank and the null model was fitted for the automated read pulse rate. The sparse GRM was constructed using a coefficient of relatedness cutoff 0.125. c, 20,000 samples were randomly selected form the HUNT study and the null model was fitted for HDL. The sparse GRM was constructed using a coefficient of relatedness cutoff 0.125.

Extended Data Fig. 3 Scatter plots of association P-values from SAIGE-GENE versus SMMAT and EmmaX-SKAT for the Burden, SKAT, and SKAT-O tests based on simulation data on the -log10 scale.

1,000,000 genes were tested with 1,000 families, each having 10 members, as shown in the Supplementary Fig. 1. The Pearson’s correlation coefficients r2 > 0.99 for -log10(P-values) between SAIGE and SMMAT and between SAIGE and EmmaX-SKAT. a, h2 = 0.2. b, h2 = 0.4.

Extended Data Fig. 4 Scatter plots of association P-values from SAIGE-GENE versus SMMAT and EmmaX-SKAT for the Burden, SKAT, and SKAT-O tests based on real data analysis on the -log10 scale.

a,b, 12,000 genes were tested for automated read pulse rate on 20,000 randomly selected white British samples in the HRC-imputed UK Biobank (a) and for HDL on 20,000 randomly selected samples in HUNT (b). Missense and stop-gain variants with MAF ≤ 1% were included. The Pearson’s correlation coefficients r2 > 0.99 for -log10(P-values) between SAIGE and SMMAT and between SAIGE and EmmaX-SKAT.

Extended Data Fig. 5 Scatter plots of association P-values on the -log10 scale from SAIGE-GENE with two sample relatedness cutoffs for the sparse GRM, 0.125 and 0.2. 15,338 genes were tested for automated read pulse rate in white British samples in the HRC-imputed UK Biobank (N = 385,365).

N, sample size. Missense and stop-gain variants with MAF ≤ 1% were included. a, Burden. b, SKAT. c, SKAT-O.

Extended Data Fig. 6 Quantile-quantile plots of association P-values for 10 million variant sets from the simulation study for phenotypes with various case-control ratios (N = 100,000).

a, Case:Control = 1:9. b, Case:Control = 1:19. c, Case:Control = 1:99. N, sample size.

Extended Data Fig. 7 Empirical computation time.

a,b, Step 1 for fitting a null mixed model (a) and Step 2 for association tests (b), respectively, by sample sizes (N) for gene-based tests for 15,342 genes, each containing 50 rare variants. Benchmarking was performed on randomly sub-sampled UK Biobank data with 408,144 White British participants for waist-to-hip ratio. The reported run time was median of five runs with samples randomly selected from the full sample set using different sampling seeds. The reported computation time for EmmaX-SKAT and SMMAT was projected when N > 20,000. As the number of tested markers varies by sample sizes, the computation time was projected for 50 markers per gene for plotting. Numerical data are provided in Supplementary Table 1.

Extended Data Fig. 8 Log-log plot of the estimated run time as a function of number of markers per gene.

Benchmarking was performed on randomly sub-sampled 400,000 UK Biobank data with 408,144 white British participants for waist-to-hip ratio on 15,342 genes. The plotted run time was median of five runs with samples randomly selected from the full sample set using different sampling seeds. The computation time for other different number of markers per gene was projected based on the benchmarked time.

Extended Data Fig. 9 Log-log plots of the estimated run time and memory usage as a function of sample size (N) for genome-wide tests for 286,000 chunks.

a, Run time. b, Memory usage. Each chunk contains 50 variants on average, given that there are 14.3 million markers in the HRC-imputed UK Biobank with MAF ≤ 1% and imputation info score ≥ 0.8. Numerical data are provided in Supplementary Table 1. Benchmarking was performed on randomly sub-sampled UK Biobank data with 408,144 white British participants for waist-to-hip ratio. The plotted run time and memory were medians of five runs with samples randomly selected from the full sample set using different sampling seeds.

Extended Data Fig. 10 Log-log plots of the estimated run time for as a function of sample size (N) for SAIGE-GENE with and without using the robust adjustment.

a, Exome-wide gene-based tests for 15,871 genes. b, Genome-wide tests for 286,000 chunks. Each gene or chunk contains 50 variants on average. Benchmarking was performed on randomly sub-sampled UK Biobank data with 402,163 white British participants tested for glaucoma (PheCode: 365, 4,462 cases and 397,701 controls). The case-control ratio remained the same in subsampled data sets. The reported run time was median of five runs with samples randomly selected from the full sample set using different sampling seeds. As the number of tested markers varies by sample sizes, the computation time was projected for 50 markers per gene for plotting. Numerical data are provided in Supplementary Table 2.

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Zhou, W., Zhao, Z., Nielsen, J.B. et al. Scalable generalized linear mixed model for region-based association tests in large biobanks and cohorts. Nat Genet 52, 634–639 (2020). https://doi.org/10.1038/s41588-020-0621-6

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