Estimating the genetic and environmental architecture of psychiatric disorders

项目总结 了解性状的遗传和环境结构一直是 行为遗传学在过去的五十年里。使用双胞胎和家庭的传统方法表明 大多数特征，包括精神障碍，都是高度可遗传的。最近，估计 无关个体(H2SNP)的单核苷酸多态(SNPs)的遗传度(H2)有 证明了常见变异对复杂性状背后的遗传变异的重要性。反过来, 人们意识到常见的变异是导致大量性状遗传的原因，这一认识促使人们继续 对大型全基因组数据集的投资，从而可靠地发现了数千个SNP 与复杂的特征相关的。然而，在这股数据洪流中，关于 遗传和环境特征的架构仍然没有答案，新的方法论方法 利用越来越大的全基因组数据集来回答这些问题。 在这个续订应用程序中，我们在前面的方法论工作的基础上回答四个高级问题 关于复杂性状的遗传和环境结构。首先，对精神疾病患者的h2SNP估计 疾病仍然低于双胞胎和家庭研究中对H2的估计。其中有多少“仍然下落不明” 遗传性是由罕见的风险变异引起的吗？使用我们在上一次赠款期间开发的方法，我们 将提供到目前为止对罕见和常见的精神分裂症风险变异的重要性的最佳估计， 双相情感障碍和严重的抑郁症。其次，共同风险之间似乎有很大的重叠。 精神障碍的等位基因，如精神分裂症和双相情感障碍。罕见的风险等位基因是否与 同样的程度，或者它们往往是特定于疾病的？我们将使用我们之前开发的扩展 帮助回答这个问题的方法。第三，大型全基因组数据集的可用性正在以 前所未有的速度。能否利用这些数据来回答有关以下问题的基本问题 基因和环境，传统上属于双胞胎和家庭设计的领域？我们提出了发展建议 使用存在于大型数据集中的亲属中测量的基因数据的方法学方法 帮助以新的方式回答老问题，绕过以前的限制。最后，了解各种因素是至关重要的。 这可能会对估计产生偏差，并导致错误的结论。我们展示了各种交配和基因如何- 环境相互作用对现有的h2SNP估计有偏差，我们建议开发模型和 缓解这些偏见的软件工具。到项目结束时，我们预计将拥有打开新前景的工具 行为遗传学研究，使我们能够更清楚地了解遗传和环境 精神障碍的架构和其他复杂的特征。这样做将有助于指导未来的分析和 投资决策。

PROJECT SUMMARY Understanding the genetic and environmental architecture of traits has been one of the central goals of behavioral genetics over the last fifty years. Traditional approaches using twins and families have shown that most traits, including psychiatric disorders, are highly heritable. More recently, methods that estimate heritability (h2) from single nucleotide polymorphisms (SNPs) in unrelated individuals (h2SNP) have demonstrated the importance of common variants to the genetic variation underlying complex traits. In turn, the realization that common variants are responsible for substantial trait heritability has motivated continued investment in large whole-genome datasets, which have allowed the discovery of thousands of SNPs reliably associated with complex traits. In the midst of this deluge of data, however, fundamental questions about the genetic and environmental architecture of traits remain unanswered, and new methodological approaches that leverage increasingly large whole-genome datasets are needed to answer them. In this Renewal application, we build on our previous methodological work to answer four high-level questions about the genetic and environmental architecture of complex traits. First, estimates of h2SNP for psychiatric disorders remain lower than estimates of h2 from twin and family studies. How much of this “still missing” heritability is due to rare risk variants? Using methods developed during the previous period of our grant, we will provide the best estimates to date of the importance of rare versus common risk variants of schizophrenia, bipolar disorder, and major depression. Second, there appears to be substantial overlap between common risk alleles for psychiatric disorders such as schizophrenia and bipolar disorder. Do rare risk alleles overlap to the same degree, or do they tend to be disorder-specific? We will use extensions of our previously developed methods to help answer this question. Third, the availability of large whole-genome datasets is growing at an unprecedented rate. Can this data be leveraged to answer fundamental questions about the importance of genes and the environment, traditionally the domain of twin and family designs? We propose the development of methodological approaches that use measured genetic data among relatives that exist in large datasets to help answer old questions in new ways that bypass earlier limitations. Finally, it is crucial to understand factors that can bias estimates and lead to incorrect conclusions. We show how assortative mating and gene-by- environment interactions bias existing estimates of h2SNP, and we propose the development of models and software tools that mitigate these biases. By project's end, we anticipate having tools that open up new vistas to behavioral genetics research, allowing for a clearer understanding of the genetic and environmental architecture of psychiatric disorders and other complex traits. Doing so will help guide future analytic and investment decisions.