Estimating the genetic and environmental architecture of psychiatric disorders

估计精神疾病的遗传和环境结构

• 批准号: 9900864
• 负责人: Matthew Charles Keller
• 金额: $ 63.95万
• 依托单位: UNIVERSITY OF COLORADO
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-02-04 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9900864
• 关键词: Accounting; Address; Affect; Alleles; Architecture; Behavioral; Behavioral Genetics; Bipolar Disorder; Bypass; Code; Complex; Data; Data Set; Development; Disease; Environment; Environmental Risk Factor; Family; Family Study; Funding; Future; Gene Frequency; Genes; Genetic; Genetic Research; Genetic Variation; Genetic study; Genome; Genomics; Goals; Grant; Heritability; Individual; Influentials; Investments; Lead; Major Depressive Disorder; Manic; Measures; Mental disorders; Methodology; Methods; Minor; Modeling; Partner in relationship; Sampling; Schizophrenia; Siblings; Single Nucleotide Polymorphism; Software Tools; Source; Trans-Omics for Precision Medicine; Twin Multiple Birth; Twin Studies; Variant; Work; base; biobank; case control; causal variant; design; gene environment interaction; genetic approach; genetic architecture; genetic pedigree; genetic variant; genome-wide; insight; large datasets; model development; novel; novel strategies; psychiatric genomics; psychogenetics; rare variant; risk variant; tool; trait; whole genome

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.

项目摘要 了解特征的遗传和环境建筑已成为 在过去的五十年中的行为遗传学。使用双胞胎和家庭的传统方法表明 包括精神疾病在内的大多数特征都是高度遗传的。最近，估计的方法 来自单个核苷酸多态性（SNP）的遗传力（H2）（H2SNP）具有 证明了共同变异对复杂性状的遗传变异的重要性。反过来， 意识到，普通变体负责实质性特征的遗传能力一直融合 投资大型全基因组数据集，这些数据集允许发现成千上万的SNP。 与复杂的特征相关。但是，在这一数据泛滥的过程中，有关该数据的基本问题 特征的遗传和环境建筑仍然没有得到答复，以及新的方法论方法 需要越来越大的全基因组数据集来回答它们。 在此续订应用程序中，我们基于以前的方法学工作来回答四个高级问题 关于复杂性状的遗传和环境建筑。首先，精神病的H2SNP估计 疾病仍低于双胞胎和家庭研究中H2的估计。这些“仍然缺少”多少 遗传力是由于罕见的风险变体引起的吗？使用在我们赠款的上一个期间开发的方法，我们 将提供迄今为止精神分裂症的稀有风险变异与常见风险变异的重要性的最佳估计， 躁郁症和严重抑郁症。其次，共同风险之间似乎存在很大的重叠 精神疾病的等位基因，例如精神分裂症和躁郁症。做罕见的风险等位基因与 相同的程度，还是它们倾向于特定于疾病？我们将使用以前开发的扩展 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增长 空前的率。可以利用此数据来回答有关重要性的基本问题 基因与环境，传统上是双胞胎和家庭设计的领域？我们提出发展 在大型数据集中存在测量遗传数据的方法论方法 帮助以绕过较早限制的新方式回答旧问题。最后，了解因素至关重要 这可能会偏向估计并导致不正确的结论。我们展示了分类交配和基因如何 环境相互作用偏向H2SNP的现有估计，我们提出了模型的发展和 减轻这些偏见的软件工具。到项目的结尾，我们预计拥有可以为 行为遗传学研究，可以更清楚地了解遗传和环境 精神疾病和其他复杂特征的建筑。这样做将有助于指导未来的分析和 投资决策。