Inferring phenogentic mechanisms in psychiatric and other complex traits

描述(由申请人提供)：有几个关于常见复杂特征的病因学的信息来源(我们的主要兴趣是精神障碍)。在思考遗传和环境因素如何相互作用以影响表型时，我们需要考虑流行病学、遗传学和进化信息。流行病学数据告诉我们一些关于流行率(及其在人群内和人群之间的变化)、亲属之间的相关性以及与特定环境因素的关系的信息。进化数据提供了关于突变率、人口结构(包括分类交配、迁徙、漂移、近亲交配等)的影响，以及自然选择在塑造性状病因结构的遗传部分方面的潜在影响。遗传数据告诉我们有证据表明特定基因座存在连锁(即涉及或缺乏位于某些染色体区域的基因)或直接的基因类型关联(即所测基因或LD中的基因与其相关的致病效应或缺乏)。然而，很少有人把这些数据放在一起看。在这项应用中，我们建议进一步开发和应用我们的方法来同时考虑所有这些数据类型，以努力更好地理解真正的病因架构。我们将与各种基于模拟的方法合作，考虑哪些表型遗传模型与我们拥有的关于进化、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的现有数据相兼容(既有成功的，也有我们认为是新奇的转折的，也有不成功的)。这些数据类型中的每一种都提供了关于特定疾病的病因学模型范围可能的信息，尽管当然没有办法推断实际的真相。我们的目标是剔除与现有知识不一致的考虑模型，并比较各种研究设计和推理方法在可信的病因学模型范围内的能力。预测以前的研究应该发现这些基因将被拒绝的模型，以及与我们现有的流行病学和进化信息不一致的模型。我们将探索一组合理的模型，并进一步开发我们的一套推理分析方法，用于在一种不同的数据结构中进行联合连锁和LD分析，我们预计这将是在这种高度复杂的精神特征中进行预期基因识别的最强大的方法，我们有大量的数据，但不幸的是，从这些数据中提取的真正知识很少。 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来帮助我们更好地了解常见疾病的病因学要素，如精神分裂症、躁郁症和阿尔茨海默病。这些基于进化和流行病学推断的方法应该允许我们更有效地利用最近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，来揭开这些疾病的复杂病因结构。希望更好地了解这些疾病的真实性质将有助于制定未来的公共卫生战略。

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.