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: 8773616
• 负责人: Matthew Charles Keller
• 金额: $ 42.21万
• 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 2015-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8773616
• 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; Genetic study; 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; 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; 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）：了解性状的遗传结构-频率，数量和导致人际差异的遗传变异的影响-在过去的五十年中一直是统计，分子和进化遗传学的中心目标之一。双胞胎/家庭研究表明，大多数性状，包括精神障碍，是高度遗传的，最近的全基因组关联研究（GWAS）已经发现了数千个单核苷酸多态性（SNP）与这些性状可靠地相关，即将到来的全基因组序列数据将允许更彻底的调查遗传变异的基础性状遗传。然而，在大量的数据中，关于性状遗传结构的基本问题仍然没有得到解答，或者没有得到很好的描述。虽然双胞胎/家庭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于遗传变异的加性效应造成的仍然不清楚。虽然GWAS已经证明了大量的遗传变异必须对性状遗传力负责，但常见（全世界人民共享）与罕见（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，目前还不清楚预测一个种族或人群中特征的遗传变异是否通常预测其他种族或人群中的相同特征。随着该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。我们建议开发的方法，这将有助于研究人员大大减少周围的遗传结构的不确定性， 性状使用现有的SNP数据，并在它变得可用时，序列数据。首先，我们展示了一种方法，允许使用模拟SNP数据准确估计性状的完全加性遗传变异，并描述了我们将努力的几个进展，以使这种方法可行地用于真实的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与罕见遗传变异的重要性，并提出了一种方法，将允许这种方法用于现有的SNP数据的发展。第三，我们展示了一种方法，该方法允许研究人员了解在一个种族中预测一种性状的SNP在多大程度上也预测另一个种族中的该性状，并且我们提出开发两种扩展，即（a）澄清为什么会发生这种差异和（B）使这种方法适用于了解亚群之间SNP关联的特异性。最后，我们将这些方法应用于三个最大的病例对照SNP数据集，分别是抑郁症、双相情感障碍和精神分裂症。到项目结束时，我们预计将有工具可以更清楚地了解这些和其他遗传表型的遗传结构。