Statistical Analysis Methods for Genetic and Epigenetic Data

尽管在过去的15年中发表了成千上万的全基因组关联研究（GWAS），但该领域仍存在重大问题。许多研究结果在独立研究中未能得到重复，显示出假阳性。另一方面，将迄今为止发现的所有标记用于任何给定特征，通常只能解释该特征估计遗传能力的一小部分，这意味着许多假阴性。我的研究项目的一部分集中在解决基因研究中缺乏权力的问题。我计划用两种方式来做到这一点：通过专注于更有可能显示对特征的影响的标记，增加检测实际影响的能力。通过一次检查多个标记物而不是单独检查每个标记物，减少多次测试负担并增加总效应大小。迄今为止，大多数GWAS的统计检验忽略了外部生物学知识，试图避免偏倚。然而，多年的遗传和基因组工作意味着我们现在对许多遗传标记的功能有了非常可靠的注释。近年来，许多研究人员开发了分数，根据变异产生生物效应的可能性对其进行排名。然而，这些通常只在GWAS之后使用，以决定研究人员应该关注哪些顶部命中。我建议更早地介绍这些信息，并利用它来帮助我们分析基因型数据。我将开发一些方法，将注意力主要集中在更有可能先验地产生影响的标记上。在单独的工作中，我还将研究更好的方法来结合来自多个标记的数据，以便可以评估基因或基因组区域水平上的关联。这样做将增加生物学相关性，并通过减少多重测试负担和增加估计的效应大小来提高功效。“缺失遗传性”的另一个原因是，并非所有的遗传效应都只通过DNA产生。DNA甲基化是可遗传的，但不会改变潜在的遗传密码。为了使甲基化分析成功，我们必须仔细地对数据进行标准化，以消除非生物变异性。在这个节目中，我将检查现有的方法，这是完成和开发改进的方法。我还将研究表型特征与一种特定形式的甲基化的关联问题，这种甲基化在人脑中大量存在，神经学研究人员对此非常感兴趣。虽然新的方法允许我们间接测量这种类型的DNA甲基化，但没有可靠的统计方法来测试与特征的关联。在我的项目中，我将开发这样一种方法，其动机是一项比较自杀受害者和死于意外或自然原因的对照组大脑的研究。由于已知个体的甲基化模式随年龄而变化，我还将研究如何将年龄和其他非遗传协变量纳入关联模型，以便发现真正的表观遗传效应。

Despite the thousands of genome-wide association studies (GWAS) that have been published in the past 15 years there are still major issues in the field. Many findings have failed to replicate in independent studies suggesting false positives. On the other hand, taking all of the markers found so far for any given trait typically only explains a small fraction of the estimated heritability of that trait suggesting many false negatives. Part of my research program is focused on addressing this lack of power in genetic studies. I plan to do this in two ways: 1. increase power to detect real effects by focusing on markers that are more likely to show an effect on a trait and 2. reduce the multiple testing burden and increase the total effect size by examining multiple markers at once rather than each one individually. To date most statistical testing in GWAS ignores external biological knowledge in an attempt to avoid bias. Years of genetic and genomic work, however, means that we now have very reliable annotations on the function of many genetic markers. In recent years a number of researchers have developed scores that rank variants in terms of their likelihood to have a biological effect. Usually, however, these are only used after a GWAS to decide on which top hits the researchers should focus. I propose to introduce this information earlier and use it to help inform our analysis of the genotype data. I will develop methods that will focus attention primarily on markers that are more likely apriori to have effects. In separate work I will also examine better methods to combine data from multiple markers so that association at the level of a gene or genomic region can be assessed. Doing this will increase biological relevance and also improve power by reducing the multiple testing burden and increasing the estimated effect sizes. Other reasons for the “missing heritability” is that not all heritable effects come through DNA alone. DNA methylation is heritable but does not change the underlying genetic code. For methylation analysis to be successful we must carefully normalize the data to remove non-biological variability. In this program I will examine existing ways that this is done and develop improved methods. I will also examine the question of association of phenotypic traits with a particular form of methylation which is abundant in the human brain and of great interest to neurological researchers. Although new methodology allows us to measure this type of DNA methylation indirectly, there is no reliable statistical method to test for association with a trait. In my program I will develop such a method motivated by a study comparing the brains of suicide victims and controls who died from accidental or natural causes. Since an individual's methylation pattern is known to vary with age I will also examine how to incorporate age and other non-genetic covariates into the association models so that true epigenetic effects can be found.