Statistical Analysis Methods for Genetic and Epigenetic Data

尽管在过去的15年中发表了数千项全基因组关联研究（GWAS），但该领域仍然存在重大问题。 许多发现未能在独立研究中复制，这表明假阳性。 另一方面，采用迄今为止为任何给定性状发现的所有标记通常只能解释该性状估计遗传力的一小部分，这表明许多假阴性。 我的研究计划的一部分集中在解决遗传研究中缺乏力量的问题上。 我打算用两种方法来做到这一点：1。通过关注更可能显示对性状的影响的标记来增加检测真实的影响的能力，以及2.通过一次检查多个标志物而不是单独检查每个标志物，减少多重测试负担并增加总效应量。 到目前为止，GWAS中的大多数统计检验忽略了外部生物学知识，以避免偏倚。然而，多年的遗传学和基因组学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，许多研究人员已经开发出了根据变异产生生物效应的可能性来对变异进行排名的评分。 然而，通常情况下，这些只是在GWAS之后使用，以决定研究人员应该关注哪些热门作品。 我建议更早地介绍这些信息，并使用它来帮助我们分析基因型数据。 我将开发的方法，将集中注意力主要在标记，更有可能先天有影响。 在单独的工作中，我也将研究更好的方法来结合联合收割机数据从多个标记，使协会在基因或基因组区域的水平可以评估。 这样做将增加生物学相关性，并通过减少多重检验负担和增加估计效应量来提高功效。 “缺失遗传性”的其他原因是，并非所有的遗传效应都只来自DNA。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，我们必须仔细地将数据标准化以去除非生物学变异性。在这个程序中，我将检查现有的方法，这是这样做，并制定改进的方法。 我还将研究表型性状与一种特殊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性状的关联。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.