Detecting genetic variants with low effect sizes using a moderate sample size is difficult, hindering downstream efforts to learn pathology and estimating heritability. In this work, by utilizing informative weights learned from training genetically predicted gene expression models, we formed an alternative approach to estimate the polygenic term in a linear mixed model (LMM). Our LMM estimates the genetic background by incorporating their relevance to gene expression. Our protocol, expression-directed linear mixed model (edLMM), enables the discovery of subtle signals of low-effect variants using moderate sample size. By applying edLMM to cohorts of around 5,000 individuals with either binary (WTCCC) or quantitative (NFBC1966) traits, we demonstrated its power gain at the low-effect end of the genetic etiology spectrum. In aggregate, the additional low-effect variants detected by edLMM substantially improved estimation of missing heritability. edLMM moves precision medicine forward by accurately detecting the contribution of low-effect genetic variants to human diseases.

中文翻译：

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发现低效遗传变异的表达导向线性混合模型 (edLMM)。

使用中等样本量检测低效应量的遗传变异很困难，这阻碍了下游学习病理学和估计遗传力的努力。在这项工作中，通过利用从训练遗传预测基因表达模型中学到的信息权重，我们形成了一种替代方法来估计线性混合模型（LMM）中的多基因项。我们的 LMM 通过结合遗传背景与基因表达的相关性来估计遗传背景。我们的协议，表达导向的线性混合模型（edLMM），能够使用适度的样本量发现低效应变异的微妙信号。通过将 edLMM 应用到大约 5,000 名具有二元 (WTCCC) 或数量 (NFBC1966) 特征的个体队列中，我们证明了它在遗传病因谱低效端的力量增益。总的来说，edLMM 检测到的额外低效变异大大改善了对缺失遗传力的估计。 edLMM 通过准确检测低效遗传变异对人类疾病的影响，推动精准医学向前发展。