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首页> 外文会议>IEEE International Conference on Bioinformatics and Biomedicine >Multiplex confounding factor correction for genomic association mapping with squared sparse linear mixed model
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Multiplex confounding factor correction for genomic association mapping with squared sparse linear mixed model

机译：平方稀疏线性混合模型的基因组关联映射的多重混杂因子校正

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Genome-wide Association Study has presented a promising way to understand the association between human genomes and complex traits. Many simple polymorphic loci have been shown to explain a significant fraction of phenotypic variability. However, challenges remain in the non-triviality of explaining complex traits associated with multifactorial genetic loci, especially considering the confounding factors caused by population structure, family structure, and cryptic relatedness. In this paper, we propose a Squared-LMM (LMM2) model, aiming to jointly correct population and genetic confounding factors. We offer two strategies of utilizing LMM2 for association mapping: 1) It serves as an extension of univariate LMM, which could effectively correct population structure, but consider each SNP in isolation. 2) It is integrated with the multivariate regression model to discover association relationship between complex traits and multifactorial genetic loci. We refer to this second model as sparse Squared-LMM (sLMM2). Further, we extend LMM2/sLMM2 by raising the power of our squared model to the LMMn/sLMMn model. We demonstrate the practical use of our model with synthetic phenotypic variants generated from genetic loci of Arabidopsis Thaliana. The experiment shows that our method achieves a more accurate and significant prediction on the association relationship between traits and loci. We also evaluate our models on collected phenotypes and genotypes with the number of candidate genes that the models could discover. The results suggest the potential and promising usage of our method in genome-wide association studies.
机译：全基因组关联研究提出了一种有前途的方式来理解人类基因组与复杂性状之间的关联。研究表明，许多简单的多态性位点可以解释很大一部分的表型变异性。然而，在解释与多因素遗传基因座相关的复杂性状的非平凡性方面仍然存在挑战，特别是考虑到由人口结构，家庭结构和隐秘的相关性引起的混杂因素。本文提出了平方LMM（LMM 2 ）模型，旨在共同校正种群和遗传混杂因素。我们提供了两种利用LMM 2 进行关联映射的策略：1）它用作单变量LMM的扩展，可以有效地校正种群结构，但要单独考虑每个SNP。 2）与多元回归模型相结合，发现复杂性状与多因素遗传基因座之间的关联关系。我们将第二个模型称为稀疏平方LMM（sLMM 2 ）。此外，通过将平方模型的功效提高到LMM n / sLMM n 2 / sLMM 2 sup>模型。我们证明了我们的模型与拟南芥拟南芥的遗传基因位点合成的表型变体的实际使用。实验表明，我们的方法对性状与基因座之间的关联关系进行了更准确，更显着的预测。我们还评估了收集的表型和基因型的模型，以及该模型可能发现的候选基因的数量。结果表明我们的方法在全基因组关联研究中的潜在和有希望的用途。

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