Estimating the frequencies and population specificities of risk alleles

估计风险等位基因的频率和群体特异性

• 批准号: 8611972
• 负责人: Matthew Charles Keller
• 金额: $ 37.76万
• 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 2017-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8611972
• 关键词: Accounting; African; Alleles; Architecture; Bipolar Disorder; Data; Data Set; Development; Disease; Ethnic Origin; European; Extended Family; Family; Family Study; Frequencies; Future; Gene Frequency; Genes; Genetic; Genetic Variation; Genomics; Goals; Haplotypes; Health; Heritability; Human; Human Genetics; Individual; Investigation; Investments; Joints; Linkage Disequilibrium; Major Depressive Disorder; Mental Health; Mental disorders; Methodology; Methods; Modeling; Molecular; National Institute of Mental Health; Pathogenesis; Pattern; Phase; Phenotype; Population; Probability; Relative (related person); Research Personnel; Risk; Sampling; Schizophrenia; Simulate; Single Nucleotide Polymorphism; Specificity; Translating; Twin Multiple Birth; Uncertainty; Variant; Work; base; case control; design; family genetics; genetic variant; genome sequencing; genome wide association study; genome-wide; insight; method development; public health relevance; rare variant; risk variant; tool; trait

DESCRIPTION (provided by applicant): Understanding the genetic architecture of traits-the frequencies, numbers, and effects of genetic variants that cause interpersonal differences-has been one of the central goals of statistical, molecular and evolutionary genetics over the last fify years. Twin/family studies have showed that most traits, including mental disorders, are highly heritable, recent genome-wide association studies (GWAS) have discovered thousands of single nucleotide polymorphisms (SNPs) reliably associated with these traits, and forthcoming whole-genome sequence data will allow a much more thorough investigation into genetic variants that underlie trait heritability. In the midst of this deluge of data, however, fundamenta questions about the genetic architecture of traits remain unanswered or are poorly characterized. Although twin/family studies have detailed the heritability of hundreds of traits, the degree to which this heritability is due to additive effects of genetic variants remains unclea. Although GWAS has demonstrated that a huge number of genetic variants must be responsible for trait heritability, the relative importance of common (shared by people worldwide) versus rare (specific to populations or extended families) genetic variants remains unclear. Finally, it is unclear whether genetic variants that predict traits in one ethnicity or population typically predit those same traits in other ethnicities or populations. As the field turns to whole-genome sequencing in the years ahead, it is crucial, now more than ever, to have a better understanding of these fundamental questions about the genetic architecture of traits. Doing so should help guide future analytic and investment decisions. We propose the development of methodologies that will help investigators greatly reduce the uncertainty surrounding the genetic architecture of traits using existing SNP data and, as it becomes available, sequence data. First, we demonstrate a method that allows the full additive genetic variation of a trait to be accurately estimated using simulated SNP data, and describe several advances that we will work on in order to make this method feasible to use on real SNP data. Second, we describe how sequence data can be used to accurately estimate the importance of common versus rare genetic variants, and propose the development of a method that will allow this approach to be used on existing SNP data. Third, we show a method that allows investigators to understand the degree to which SNPs that predict a trait in one ethnicity also predict that trait in another ethnicity, and we propose developing two extensions of this that (a) clarify why such differences occur and (b) make this approach applicable to understanding the specificity of SNP associations between subpopulations. Finally, we will apply these methods to the three largest case-control SNP datasets on Major Depressive Disorder, Bipolar Disorder, and Schizophrenia. By project's end, we anticipate having tools that allow for a much clearer understanding of the genetic architecture of these and other heritable phenotypes.

描述(由申请人提供)：了解特征的遗传结构--导致人际差异的遗传变异的频率、数量和影响--一直是过去五年来统计、分子和进化遗传学的中心目标之一。双胞胎/家系研究表明，包括精神障碍在内的大多数性状都是高度可遗传的，最近的全基因组关联研究(Gwas)发现了数千个与这些性状可靠相关的单核苷酸多态(SNPs)，即将到来的全基因组序列数据将使人们能够更深入地研究构成性状遗传性的遗传变异。然而，在这海量的数据中，关于性状的遗传结构的基本问题仍然没有得到回答，或者描述得很差。尽管双胞胎/家族研究已经详细说明了数百个性状的遗传力，但这种遗传力在多大程度上是由遗传变异的加性效应造成的，目前还不清楚。尽管Gwas已经证明了大量的遗传变异必须对性状遗传性负责，但常见的(世界各地的人都有)和罕见的(特定于人群或大家庭)遗传变异的相对重要性仍然不清楚。最后，目前尚不清楚在一个种族或群体中预测特征的基因变异是否通常会先于其他种族或群体中的相同特征。随着这一领域在未来几年转向全基因组测序，现在比以往任何时候都更重要的是，更好地理解这些关于性状遗传结构的基本问题。这样做应该有助于指导未来的分析和投资决策。我们建议开发方法，帮助研究人员极大地减少围绕基因结构的不确定性 利用现有的SNP数据和可利用的序列数据进行特征分析。首先，我们演示了一种方法，该方法允许使用模拟的SNP数据准确地估计一个性状的全加性遗传变异，并描述了我们将致力于的几个进展，以便使该方法适用于真实的SNP数据。其次，我们描述了如何使用序列数据来准确估计常见和罕见基因变异的重要性，并提出了一种方法的开发，该方法将允许该方法用于现有的SNP数据。第三，我们展示了一种方法，允许研究人员了解预测一个种族中的一个性状的SNP在多大程度上也预测另一个种族中的该性状，我们建议对此进行两个扩展：(A)澄清为什么会发生这种差异；(B)使这种方法适用于理解亚群体之间SNP关联的特异性。最后，我们将把这些方法应用于关于抑郁症、双相情感障碍和精神分裂症的三个最大的病例对照SNP数据集。到项目结束时，我们预计会有工具，允许更清楚地了解这些和其他可遗传表型的遗传结构。