BACKGROUND The current understanding of the genetic basis of complex human diseases is that they are caused and affected by many common and rare genetic variants. A considerable number of the disease-associated variants have been identified by Genome Wide Association Studies, however, they can explain only a small proportion of heritability. One of the possible reasons for the missing heritability is that many undiscovered disease-causing variants are weakly associated with the disease. This can pose serious challenges to many statistical methods, which seems to be only capable of identifying disease-associated variants with relatively stronger coefficients. RESULTS In order to help identify weaker variants, we propose a novel statistical method, Constrained Sparse multi-locus Linear Mixed Model (CS-LMM) that aims to uncover genetic variants of weaker associations by incorporating known associations as a prior knowledge in the model. Moreover, CS-LMM accounts for polygenic effects as well as corrects for complex relatednesses. Our simulation experiments show that CS-LMM outperforms other competing existing methods in various settings when the combinations of MAFs and coefficients reflect different scenarios in complex human diseases. CONCLUSIONS We also apply our method to the GWAS data of alcoholism and Alzheimer's disease and exploratively discover several SNPs. Many of these discoveries are supported through literature survey. Furthermore, our association results strengthen the belief in genetic links between alcoholism and Alzheimer's disease.

中文翻译：

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在已知协会的指导下发现较弱的遗传协会。

背景技术当前对复杂人类疾病的遗传基础的理解是，它们是由许多常见和罕见的遗传变异引起和影响的。基因组广泛关联研究已经鉴定出许多与疾病相关的变异，但是，它们只能解释一小部分的遗传力。遗传力缺失的可能原因之一是许多未发现的致病变异与疾病的相关性很弱。这可能给许多统计方法带来严峻挑战，这些统计方法似乎只能识别具有相对较强系数的疾病相关变体。结果为了帮助识别较弱的变体，我们提出了一种新颖的统计方法，约束稀疏多位点线性混合模型（CS-LMM），旨在通过将已知关联作为模型中的先验知识来发现较弱关联的遗传变异。此外，CS-LMM解释了多基因效应，并纠正了复杂的相关性。我们的仿真实验表明，当MAF和系数的组合反映复杂人类疾病的不同情况时，CS-LMM在各种情况下均优于其他竞争方法。结论我们还将我们的方法应用于酒精中毒和阿尔茨海默氏病的GWAS数据，并探索性地发现了几种SNP。这些发现中有许多都得到文献调查的支持。此外，我们的关联结果加强了人们对酒精中毒与阿尔茨海默氏病之间遗传联系的信念。