Framingham Heart Study, 73 Mt. Wayte, Framingham, MA 01702, USA.
Population Sciences Branch, National Heart, Lung, and Blood Institute, 31 Center Drive, Bethesda, DC 20892, USA.
Genetics. 2024 Oct 7;228(2). doi: 10.1093/genetics/iyae143.
The linear mixed model (LMM) has become a standard in genetic association studies to account for population stratification and relatedness in the samples to reduce false positives. Much recent progresses in LMM focused on approximate computations. Exact methods remained computationally demanding and without theoretical assurance. The computation is particularly challenging for multiomics studies where tens of thousands of phenotypes are tested for association with millions of genetic markers. We present IDUL and IDUL† that use iterative dispersion updates to fit LMMs, where IDUL† is a modified version of IDUL that guarantees likelihood increase between updates. Practically, IDUL and IDUL† produced identical results, both are markedly more efficient than the state-of-the-art Newton-Raphson method, and in particular, both are highly efficient for additional phenotypes, making them ideal to study genetic determinants of multiomics phenotypes. Theoretically, the LMM likelihood is asymptotically unimodal, and therefore the gradient ascent algorithm IDUL† is asymptotically exact. A software package implementing IDUL and IDUL† for genetic association studies is freely available at https://github.com/haplotype/IDUL.
线性混合模型 (LMM) 已成为遗传关联研究中的标准方法，用于解释样本中的群体分层和相关性，以减少假阳性。最近，LMM 的许多进展都集中在近似计算上。精确方法仍然需要大量的计算资源，并且没有理论保证。对于多组学研究，计算特别具有挑战性，因为要测试数以万计的表型与数百万个遗传标记的关联。我们提出了 IDUL 和 IDUL†，它们使用迭代分散更新来拟合 LMM，其中 IDUL†是 IDUL 的修改版本，保证了更新之间的似然增加。实际上，IDUL 和 IDUL†产生了相同的结果，两者都明显比最先进的牛顿-拉普森方法效率更高，特别是对于额外的表型，它们的效率非常高，这使得它们成为研究多组学表型的遗传决定因素的理想方法。从理论上讲，LMM 的似然是渐近单峰的，因此梯度上升算法 IDUL†是渐近精确的。用于遗传关联研究的实现 IDUL 和 IDUL†的软件包可在 https://github.com/haplotype/IDUL 上免费获得。
