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、遗传学和进化方面的信息。流行病学数据告诉我们一些关于患病率（及其在人群内部和人群之间的变化）、亲属之间的相关性以及与特定环境因素的关系。进化数据告诉我们突变率、人口结构的影响（包括选型交配、迁移、漂变、近亲繁殖等），以及自然选择在塑造性状病因学结构的遗传部分方面的潜在影响。遗传数据告诉我们特定位点的关联证据（即，位于某些染色体区域的基因的参与或缺乏），或直接的基因型关联（即，所测基因或与它们相关的LD基因的病因效应或缺乏）。然而，很少把这些数据放在一起看。在这个应用中，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，以更好地理解真正的病因结构。我们将使用各种基于模拟的方法来考虑哪些表型模型与我们现有的关于进化的数据兼容，特定特征的流行病学，以及过去对这些特征的基因发现的尝试（既有成功的，也有不成功的，我们认为这是一个新颖的转折）。这些数据类型中的每一种都告诉我们，对于某一特定疾病，病原学模型的合理范围是什么，尽管当然没有办法推断出实际的真相。我们的目标是从考虑模型中剔除与现有知识不一致的模型，并比较各种研究设计和推理方法在合理的病因模型集范围内的能力。预测先前的研究应该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。我们将探索一组合理的模型，并进一步发展我们的一组推理分析方法，用于联合链接和LD分析的异构数据结构，我们预计这将是对这种高度复杂的精神特征进行前瞻性基因鉴定的最强大的方法，我们有大量的数据，但不幸的是，从中提取的真正知识很少。

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.