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: 10159130
• 负责人: Matthew Charles Keller
• 金额: $ 60.64万
• 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/10159130
• 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 了解性状的遗传和环境结构一直是遗传学的中心目标之一。 行为遗传学在过去的50年里。利用双胞胎和家庭的传统方法表明， 大多数特征，包括精神疾病，都是高度遗传的。最近，估计 来自无关个体中单核苷酸多态性（SNP）（h2SNP）遗传度（h2） 证明了常见变异对复杂性状遗传变异的重要性。反过来， 认识到共同的变异是造成实质性性状遗传力的原因， 投资于大型全基因组数据集，这使得可靠地发现数千个SNP 与复杂的特征有关。然而，在这些海量的数据中， 性状的遗传和环境结构仍然没有答案，新的方法学方法， 需要利用越来越大的全基因组数据集来回答这些问题。 在这个更新应用程序中，我们在前面的方法学工作的基础上回答了四个高级问题 关于复杂性状的遗传和环境结构。第一，对精神病患者的h2SNP的估计 疾病仍然低于双胞胎和家庭研究的h2估计。这些“仍下落不明” 遗传性是由于罕见的风险变异？使用我们赠款的前一阶段开发的方法，我们 将提供迄今为止对精神分裂症罕见与常见风险变异重要性的最佳估计， 躁郁症和重度抑郁症第二，共同风险与共同风险之间似乎存在实质性重叠， 精神疾病如精神分裂症和双相情感障碍的等位基因。罕见的风险等位基因是否与 是相同的程度，还是倾向于特定的疾病？我们将使用我们以前开发的扩展 方法来帮助回答这个问题。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增长。 前所未有的速度。这些数据是否可以用来回答关于以下重要性的基本问题： 基因和环境，传统上是双胞胎和家庭设计的领域？我们建议发展 使用存在于大型数据集中的亲属之间测量的遗传数据的方法学方法， 帮助以绕过早期限制的新方式回答旧问题。最后，了解因素至关重要 这可能会使估计产生偏差并导致错误的结论。我们展示了选择性交配和基因- 环境相互作用使现有的h2SNP估计值存在偏差，我们建议开发模型， 软件工具来减轻这些偏见。到项目结束时，我们预计将有工具开辟新的前景， 行为遗传学研究，允许更清楚地了解遗传和环境 精神疾病和其他复杂特征的结构。这样做将有助于指导未来的分析和 投资决策。