Hof Jasper P, Speed Doug
Radboud University Medical Center, IQ Health Science Department, Nijmegen, the Netherlands.
Aarhus University, Center for Quantitative Genetics and Genomics, Aarhus, Denmark.
Nat Genet. 2025 Aug 11. doi: 10.1038/s41588-025-02286-z.
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.
混合模型关联分析（MMAA）是进行全基因组关联研究的首选工具。然而，现有的MMAA工具通常运行时间长且内存要求高。在此，我们介绍LDAK-KVIK，一种用于分析定量和二元表型的MMAA工具。LDAK-KVIK计算效率高，分析350,000个个体的全基因组数据所需的CPU时间不到10小时，内存不到5GB。使用模拟表型，我们表明LDAK-KVIK对于同质和异质数据集都能产生校准良好的检验统计量。当应用于实际表型时，LDAK-KVIK在所有考虑的工具中具有最高的功效。例如，在40个英国生物银行的定量表型（平均样本量349,000）中，LDAK-KVIK发现的独立全基因组显著位点比经典线性回归多16%，而BOLT-LMM和REGENIE分别多发现15%和11%。LDAK-KVIK还可用于进行基于基因的检验；在40个英国生物银行的定量表型中，LDAK-KVIK发现的显著基因比领先的现有工具多18%。最后，LDAK-KVIK产生了最先进的多基因评分。
