Statistical Analysis Methods for Genetic and Epigenetic Data

尽管在过去15年里发表了数以千计的全基因组关联研究(GWAS)，但该领域仍然存在重大问题。许多发现未能在暗示假阳性的独立研究中重复。另一方面，对于任何给定的性状，采用迄今发现的所有标记通常只能解释该性状的估计遗传力的一小部分，这意味着许多假阴性。我的研究计划的一部分是专注于解决基因研究中缺乏力量的问题。我计划通过两种方式来做到这一点：1.通过专注于更有可能对某个性状产生影响的标记来增加检测实际效果的能力；2.通过一次检查多个标记而不是单独检查每个标记，减少多重测试负担并增加总效应大小。到目前为止，GWAS的大多数统计测试都忽略了外部生物学知识，试图避免偏见。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，许多研究人员制定了分数，根据变异产生生物效应的可能性对其进行排名。然而，通常情况下，只有在GWAS之后才会使用这些方法来决定研究人员应该关注哪些热门词汇。我建议早点介绍这一信息，并用它来帮助我们分析基因数据。我将开发一些方法，将注意力主要集中在那些更有可能先验产生影响的标记上。在单独的工作中，我还将研究更好的方法来组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。这样做将增加生物相关性，并通过减少多次测试负担和增加估计的效果大小来提高功率。“遗传缺失”的另一个原因是，并不是所有的遗传效应都是通过DNA实现的。DNA甲基化是可遗传的，但不会改变潜在的遗传密码。为了成功进行甲基化分析，我们必须仔细地将数据正常化，以消除非生物变异性。在这个节目中，我将检查现有的方法，并开发改进的方法。我还将研究表型特征与一种特殊形式的甲基化的关联问题，这种甲基化在人类大脑中大量存在，并引起神经学研究人员的极大兴趣。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某一特征的关联。在我的项目中，我将开发一种这样的方法，其动机是一项比较自杀受害者和对照组的大脑的研究，这些人死于意外或自然原因。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合并到关联模型中，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.