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.

项目概要 了解性状的遗传和环境结构一直是该研究的中心目标之一 过去五十年来使用双胞胎和家庭的行为遗传学方法表明： 大多数特征，包括精神疾病，都是高度遗传的，最近出现了估计方法。 无关个体 (h2SNP) 中单核苷酸多态性 (SNP) 的遗传力 (h2) 常见变异对遗传变异的重要性反过来证明了潜在的复杂性状。 人们继续认识到常见变异对大量性状遗传力负有责任 对大型全基因组数据集的投资，这使得能够可靠地发现数千个 SNP 然而，在海量的数据中，存在着一些与复杂特征相关的基本问题。 性状的遗传和环境结构仍然没有答案，新的方法论方法 需要利用越来越大的全基因组数据集来回答这些问题。 在此更新应用程序中，我们在之前的方法论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，首先，h2SNP 对精神病学的估计。 疾病仍然低于双胞胎和家庭研究中 h2 的估计值，其中有多少“仍然缺失”。 遗传性是由于罕见的风险变异造成的吗？使用我们在上一阶段资助期间开发的方法，我们 将提供迄今为止对精神分裂症的罕见风险变异与常见风险变异的重要性的最佳估计， 其次，双相情感障碍和重度抑郁症之间似乎存在大量重叠。 精神分裂症和双相情感障碍等精神疾病的等位基因是否与罕见的风险等位基因重叠？ 相同程度，还是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 第三，大型全基因组数据集的可用性正在快速增长。 能否利用这些数据来回答有关重要性的基本问题。 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？ 使用大型数据集中存在的亲属之间测量的遗传数据来进行方法学方法 帮助以新的方式回答旧问题，绕过早期的限制。最后，了解因素至关重要。 这可能会导致估计偏差并导致错误的结论。我们展示了选型交配和基因之间的关系。 环境相互作用使 h2SNP 的现有估计存在偏差，我们建议开发模型和 减轻这些偏见的软件工具，我们预计会有一些工具可以为我们打开新的前景。 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结构将有助于指导未来的分析和研究。 投资决策。