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LDAK-KVIK performs fast and powerful mixed-model association analysis of quantitative and binary phenotypes

Nature Genetics volume 57, pages 2116–2123 (2025)Cite this article

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Abstract

Mixed-model association analysis (MMAA) is the preferred tool for performing genome-wide association studies. However, existing MMAA tools often have long runtimes and high memory requirements. Here we present LDAK-KVIK, an MMAA tool for analysis of quantitative and binary phenotypes. LDAK-KVIK is computationally efficient, requiring less than 10 CPU hours and 5 Gb memory to analyze genome-wide data for 350,000 individuals. Using simulated phenotypes, we show that LDAK-KVIK produces well-calibrated test statistics for both homogeneous and heterogeneous datasets. When applied to real phenotypes, LDAK-KVIK has the highest power among all tools considered. For example, across 40 quantitative UK Biobank phenotypes (average sample size 349,000), LDAK-KVIK finds 16% more independent, genome-wide significant loci than classical linear regression, whereas BOLT-LMM and REGENIE find 15% and 11% more, respectively. LDAK-KVIK can also be used to perform gene-based tests; across the 40 quantitative UK Biobank phenotypes, LDAK-KVIK finds 18% more significant genes than the leading existing tool. Last, LDAK-KVIK produces state-of-the-art polygenic scores.

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Fig. 1: Type 1 errors and power of MMAA tools for quantitative phenotypes.
Fig. 2: Exploiting the dependency of per-SNP heritability on MAF.
Fig. 3: Performance of MMAA tools when analyzing 40 quantitative UK Biobank phenotypes.
Fig. 4: Accuracy of step 1 PGS and power of LDAK-KVIK-GBAT.

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Data availability

Our study used data from UK Biobank, which we applied for and downloaded from www.ukbiobank.ac.uk. The UK Biobank has ethics approval from the North West Multi-centre Research Ethics Committee. The summary statistics and PGS SNP weightings from our enlarged analysis of 62 quantitative phenotypes can be downloaded from www.ldak-kvik.com/summaries.

Code availability

LDAK-KVIK is part of the software package LDAK. The latest version of LDAK can be downloaded from www.dougspeed.com and the version used in this work is available via Zenodo at https://doi.org/10.5281/zenodo.15747229 (ref. 46). Full instructions for running LDAK-KVIK are provided at www.ldak-kvik.com.

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Acknowledgements

This research was conducted using the UK Biobank Resource under application number 21432. The computing for this project was performed on the GenomeDK cluster (Aarhus University). We thank A. Halager and D. Søndergaard (Aarhus University) for programming suggestions; D. Balding (University of Melbourne) for helpful comments on the manuscript; J. Bajzik, A. Depope and M. Robinson (Institute of Science and Technology Austria) for assistance with the UK Biobank Research Analysis Platform; H. Heydari (University of Toronto) for advice on heritability estimation; and S. Jin (Karolinska Institute) for testing LDAK-KVIK. D.S. is supported by the Aarhus University Research Foundation (AUFF), by the Independent Research Fund Denmark (project no. 7025-00094B) and by a European Research Council Consolidator Grant (ID 101088901, acronym ClassifyDiseases).

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Authors and Affiliations

  1. Radboud University Medical Center, IQ Health Science Department, Nijmegen, the Netherlands

    Jasper P. Hof

  2. Aarhus University, Center for Quantitative Genetics and Genomics, Aarhus, Denmark

    Jasper P. Hof & Doug Speed

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  1. Jasper P. Hof
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  2. Doug Speed
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J.H. and D.S. jointly developed the software, performed the analysis and wrote the manuscript.

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Correspondence to Doug Speed.

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Nature Genetics thanks Longda Jiang, Po-Ru Loh and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Hof, J.P., Speed, D. LDAK-KVIK performs fast and powerful mixed-model association analysis of quantitative and binary phenotypes. Nat Genet 57, 2116–2123 (2025). https://doi.org/10.1038/s41588-025-02286-z

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