Inferring phenogentic mechanisms in psychiatric and other complex traits

描述（由申请人提供）：关于常见复杂特征的病因学，有几个可用的信息来源（其中我们的主要兴趣是精神疾病）。在考虑遗传和环境因素如何相互作用影响表型时，我们需要考虑流行病学、遗传和进化信息。流行病学数据告诉我们关于患病率的一些情况（以及它在人群内和人群之间的变化），它在亲属之间的相关性，以及与特定环境因素的关系。进化数据告诉我们突变的速率，人口结构的影响（包括强迫交配，迁移，漂移，近亲繁殖等），以及自然选择在塑造性状的病因结构的遗传部分中的潜在影响。遗传数据告诉我们特定基因座的连锁（即位于某些染色体区域的基因的参与或缺乏）或直接基因型关联（即测量基因或LD中的基因与它们的病因学影响或缺乏）的证据。然而，这些数据很少被一并看待。在这个应用程序中，我们建议进一步开发和应用我们的方法，同时考虑所有这些数据类型，以更好地了解真正的病因学架构。我们将使用各种基于模拟的方法来考虑哪些表型遗传模型与我们现有的关于进化的数据兼容，给定性状的流行病学，以及过去为这些性状寻找基因的尝试（都是成功的，而且，我们认为这是一个新的转折，不成功的）。这些数据类型中的每一种都告诉我们，对于给定的疾病，什么范围的病因学模型是合理的，尽管当然没有办法推断出实际的真相。我们的目标是消除考虑模型，这是不符合现有的知识，并比较各种研究设计和推理方法的能力范围内的合理的病因模型。那些预测先前的研究应该已经发现这些基因的模型将被拒绝，与我们现有的流行病学和进化信息不一致的模型也将被拒绝。我们将探索一套合理的模型，并进一步发展我们的一套推理分析方法，用于联合连锁和LD分析的异质数据结构，我们预计这将是最强大的前瞻性基因识别在这个高度复杂的精神病特征，我们有大量的数据，但不幸的是很少真实的知识从中提取。 公共卫生相关性：这项研究的总体目标是开发和应用新的方法，以帮助我们更好地了解常见疾病，如精神分裂症，双相情感障碍和阿尔茨海默病的病因学要素。这些方法，基于进化和流行病学的推断，应该使我们能够更有效地利用最近的生物技术革命的成就，通过更强大的统计分析和实验设计的优化来解开这些疾病的复杂病因结构。人们希望，更好地了解这些疾病的真正性质将是有益的发展前景的公共卫生战略。

DESCRIPTION (provided by applicant): There are several sources of information available about the etiology of common complex traits (of which our primary interest is in psychiatric disorders). We have epidemiological, genetic, and evolutionary information to consider in thinking about how genetic and environmental factors interact to influence phenotypes. Epidemiological data tells us something about the prevalence (and its variation within and between populations), its correlation among relatives, and relationships to specific environmental factors. Evolutionary data informs about the rates of mutation, the effects of demographic structure (including assortative mating, migration, drift, inbreeding and the like), and potential effects of natural selection in molding the genetic portion of the etiological architecture of a trait. Genetic data tells us of evidence of linkage (i.e. involvement - or lack thereof - of genes located in certain chromosomal regions), or direct genotypic associations (i.e. etiological effects - or lack thereof - of measured genes, or genes in LD with them) of specific loci. Rarely are these data looked at jointly, however. In this application, we propose to further develop and apply our methods for simultaneously considering all of these data types in an effort to better understand the true etiological architecture. We will work with various simulation-based approaches to consider what phenogenetic models are compatible with the existing data we have about evolution, epidemiology of a given trait, and past attempts at gene finding for those traits (both successful, and, in what we believe is a novel twist, unsuccessful ones as well). Each of these data types informs about what range of etiological models would be plausible for a given disease, although of course there is no way to infer actual truth. Our goal is to eliminate from consideration models, which are inconsistent with existing knowledge, and to compare the power of various study designs and inferential methods under the range of plausible set of etiological models. Models which would have predicted that previous studies should have found the genes would be rejected, as would models inconsistent with our existing epidemiological and evolutionary information. We will explore the set of plausible models and further develop our set of inferential analysis methods for joint linkage and LD analysis in the sort of heterogeneous data structures that we expect will be the most powerful for prospective gene identification in this highly complex psychiatric traits, for which we have massive amounts of data, but unfortunately little real knowledge extracted from them. PUBLIC HEALTH RELEVANCE: The overall aim of this study is to develop and apply new methods to help us better understand elements of the etiology of common diseases such as schizophrenia, bipolar, and Alzheimer disease. These methods, based both on evolutionary and epidemiological inference should allow us to more efficiently exploit the accomplishments of the recent biotechnological revolution for unraveling the complex etiological architecture of these diseases, both through more powerful statistical analysis and optimization of experimental design. It is hoped that better understanding of the true nature of these disorders will be useful for development of prospective public health strategies.