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: 9181336
• 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 2018-07-04
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9181336
• 关键词: 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; Research Personnel; Risk; Sampling; Schizophrenia; Single Nucleotide Polymorphism; Specificity; Translating; Twin Multiple Birth; Uncertainty; Variant; Work; base; case control; design; genetic variant; genome sequencing; genome wide association study; genome-wide; insight; method development; public health relevance; rare variant; risk variant; tool; trait; whole genome

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 - 遗传变异的频率，数量和影响，这些遗传变异是造成人际差异的遗传结构，这是统计，分子和进化遗传学的核心目标之一。双胞胎/家庭研究表明，包括精神障碍在内的大多数特征是高度可靠的，全基因组全基因组的关联研究（GWAS），发现成千上万的单核苷酸多态性（SNP）可靠地可靠地与这些特征相关，并且即将出现的全基因组序列数据将使对基因差异的概念构成属于遗传性特征概念性。然而，在这一数据泛滥的情况下，基金会对性状的遗传结构的疑问仍未得到解答或表征不佳。尽管双胞胎/家庭研究详细介绍了数百个特征的遗传力，但这种遗传性是由于遗传变异的加性效应所致。尽管GWAS证明，大量遗传变异必须负责特质遗传力，但共同（全球人民共享）与稀有（特定于人群或大家庭的特定）遗传变异的相对重要性仍然不清楚。最后，目前尚不清楚是否可以预测一个种族或人口中的性状的遗传变异通常是其他种族或人口中相同的特征。随着领域在未来几年中转向全基因组测序，现在比以往任何时候都更重要的是，对这些关于特征遗传结构的基本问题有更好的了解。这样做应该有助于指导未来的分析和投资决策。我们提出了方法的发展，这些方法学将有助于研究人员大大降低围绕遗传结构的不确定性 使用现有SNP数据的特征，并在其上获得序列数据。首先，我们演示了一种方法，该方法允许使用模拟SNP数据准确地估算性状的完整添加遗传变异，并描述我们将在实现真实SNP数据上可行的几个进步。其次，我们描述了如何使用序列数据准确估计常见遗传变异的重要性，并提出了一种方法的开发，该方法将允许这种方法用于现有SNP数据。第三，我们展示了一种方法，该方法允许研究人员了解一个预测一个种族特征的SNP的程度，也预测了另一个种族中的特征，我们建议开发两个扩展，以阐明（a）澄清这种差异的原因，以及（b）使该方法适用于了解子群体之间SNP关联的特异性。最后，我们将将这些方法应用于主要抑郁症，躁郁症和精神分裂症的三个最大病例对照SNP数据集。到了项目的末尾，我们预计拥有工具，可以更清楚地了解这些和其他可遗传的表型的遗传结构。