LDAK-KVIK执行快速和强大的混合模型关联分析定量和二元表型，这一成果由丹麦奥尔胡斯大学Doug Speed团队经过不懈努力而取得。2025年8月11日，国际知名学术期刊《自然—遗传学》发表了这一成果。

在这里，研究人员提出LDAK-KVIK，一个MMAA工具，用于分析定量和二元表型。LDAK-KVIK计算效率高，需要的时间小于10CPU小时数和5Gb内存，可以分析35万人的全基因组数据。通过模拟表型，该团队发现LDAK-KVIK可以为同质性和异质性数据集提供校准良好的测试统计。当应用于真实表型时，LDAK-KVIK在所有考虑的工具中具有最高的功效。例如，在40个定量UK Biobank表型（平均样样量为349000）中，LDAK-KVIK发现的独立的全基因组显著位点比经典线性回归多16%，而BOLT-LMM和REGENIE分别发现了15%和11%。LDAK-KVIK还可以进行基于基因的测试；在40个定量的英国生物银行表型中，LDAK-KVIK比现有的主要工具多发现18%的重要基因。最后，LDAK-KVIK产生最先进的多基因评分。

据介绍，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。但是，现有的MMAA工具通常具有较长的运行时间和较高的内存需求。

附：英文原文

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

Author: Hof, Jasper P., Speed, Doug

Issue&Volume: 2025-08-11

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 10CPU hours and 5Gb 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.

DOI: 10.1038/s41588-025-02286-z

Source: https://www.nature.com/articles/s41588-025-02286-z