马尔可夫逻辑网络在遗传数据分析中的应用
Markov Logic Networks in the Analysis of Genetic Data
摘要：
复杂的、非加性的遗传相互作用是常见的，并且在确定表型时可能是关键的。然而，全基因组关联研究（GWAS）和连锁数据的类似统计研究在寻找基因型-表型关联时假设基因相互作用的加性模型。这些统计方法将多个基因对表型的复合效应视为每个单独基因的部分影响的总和，并且通常会错过遗传效应的实质性部分。这些方法不使用任何关于潜在基因型-表型机制的生物学知识。人工智能领域的建模方法将确定性知识纳入模型以进行统计分析，可以应用于将先验知识纳入遗传分析。我们选择使用最通用的此类方法--马尔可夫逻辑网络（MLN），作为将确定性知识与统计分析相结合的框架。使用简单的逻辑回归型MLN，我们已经能够复制传统统计方法的结果。此外，我们表明，即使是简单的模型，我们能够超越找到独立的标记与表型使用联合推理，避免了独立的假设。该方法适用于酵母孢子形成，由非线性基因相互作用的表型的遗传数据。除了检测所有先前鉴定的与孢子形成相关的基因座之外，我们的方法还能够鉴定具有小影响的四个基因座。由于它们对孢子形成的影响很小，这四个基因座没有被检测到的方法，不占标记之间的依赖性，由于基因相互作用。我们展示了如何使用更复杂的模型检测基因相互作用，这可以作为一个通用的框架，将系统生物学与遗传学。
英文摘要：
Complex, non-additive genetic interactions are common and can be critical in determining phenotypes. Genome-wide association studies (GWAS) and similar statistical studies of linkage data, however, assume additive models of gene interactions in looking for genotype-phenotype associations. These statistical methods view the compound effects of multiple genes on a phenotype as a sum of partial influences of each individual gene and can often miss a substantial part of the heritable effect. Such methods do not use any biological knowledge about underlying genotype-phenotype mechanisms. Modeling approaches from the AI field that incorporate deterministic knowledge into models to perform statistical analysis can be applied to include prior knowledge in genetic analysis. We chose to use the most general such approach, Markov Logic Networks (MLNs), as a framework for combining deterministic knowledge with statistical analysis. Using simple, logistic regression-type MLNs we have been able to replicate the results of traditional statistical methods. Moreover, we show that even with simple models we are able to go beyond finding independent markers linked to a phenotype by using joint inference that avoids an independence assumption. The method is applied to genetic data on yeast sporulation, a phenotype governed by non-linear gene interactions. In addition to detecting all of the previously identified loci associated with sporulation, our method is able to identify four loci with small effects. Since their effect on sporulation is small, these four loci were not detected with methods that do not account for dependence between markers due to gene interactions. We show how gene interactions can be detected using more complex models, which can be used as a general framework for incorporating systems biology with genetics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
