Improving Polygenic Prediction using Next-Generation Data Sets

使用下一代数据集改进多基因预测

• 批准号: 8632422
• 负责人: SHAMIL SUNYAEV
• 金额: $ 54.33万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-06-15 至 2018-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8632422
• 关键词: Accounting; Architecture; Binding; Chromatin; Code; Complex; Complex Genetic Trait; Computer software; Computing Methodologies; DNA; Data; Data Set; Development; Diagnostic; Disease; European; Explosion; Frequencies; Functional RNA; Future; Gene Expression; Gene Frequency; Genes; Genetic; Genetic Markers; Genetic Risk; Genetic Variation; Genotype; Goals; Height; Heritability; Human; Individual; Life; Linkage Disequilibrium; Lipids; Medical; Methods; Microarray Analysis; Modeling; Myocardial Infarction; Patient Selection; Patients; Pattern; Performance; Phenotype; Population; Population Genetics; Population Heterogeneity; Proteins; Publications; Risk; Sampling; Statistical Methods; Statistical Models; Study models; Testing; Therapeutic Intervention; Training; Variant; Work; base; case control; cell type; data acquisition; disorder risk; exome; exome sequencing; genetic variant; genome sequencing; genome wide association study; human disease; improved; interest; next generation; public health relevance; rare variant; simulation; trait; transcription factor

Understanding the relationship between genotype and phenotype is the central goal of genetics. Available heritability estimates for many human traits of medical relevance suggest that 30-80% of phenotypic variation is due to underlying genetic variation. The ability to predict phenotypes based on genotypes is the ultimate test of our understanding of complex trait genetics. Since the dawn of complex trait genetics in the early 20th century, progress has been limited by the availability of genetic data in well-phenotyped populations. Now, due to the extraordinary progress in technology, microarray genotyping datasets, exome sequencing datasets and targeted sequencing datasets are available for large clinically phenotyped populations, and functional data is becoming available. A future explosion of whole-genome sequencing data is also widely anticipated. This shifts the focus from data acquisition to data interpretation and development of computational and statistical methods for predicting phenotypes from genotypes and functional information. We propose to develop new methods for predicting phenotypes from genotypes and apply these methods to newly collected data on human complex traits of direct medical interest, including both quantitative and disease traits. Our work on phenotype prediction will be informative about the allelic architecture of complex traits and will provide guidance for future genetic studies. From a practical perspective, there is an ongoing debate on the potential of genetic diagnostics in identification of individuals at elevated risk for specific complex diseases early in life. If successful, genetic diagnostics may inform selection of patients for early therapeutic intervention. However, the practical utility of genetics in evaluating risk of complex diseases has not been proven and is widely debated. We will rigorously test the hypothesis of the utility of genotype-based phenotypic predictions. In Specific Aim 1 we will develop and test new statistical methods for predicting phenotypes from microarray genotyping data. We will investigate several model selection and shrinkage strategies. We will evaluate whether it is more efficient to estimate contributions of individual markers independently or to fit all markers simultaneously. In Specific Aim 2 we will improve polygenic prediction in populations of diverse ancestry. It is important that medical progress not be limited to European populations. Our methods will generate predictions across human populations, accounting for population differences in allele frequencies, rates of allelic variation and patterns of linkage disequilibrium. In Specific Aim 3 we will develop and test statistical methods for predicting phenotypes from sequencing data. Sequencing data provide a distinct set of statistical challenges because they contain low-frequency and rare allelic variants, and often the effects of individual rare variants cannot be estimated. In Specific Aim 4 we will incorporate functional data into methods for phenotype prediction. We will investigate whether incorporation of functional data can improve phenotype predictions from genetic data.

了解基因型和表型之间的关系是遗传学的核心目标。可用的 许多人类相关性的许多人类特征的遗传力估计表明，30-80％的表型变化 是由于基本的遗传变异。根据基因型预测表型的能力是最终测试 我们对复杂性状遗传学的理解。自20世纪初期复杂性状遗传学的曙光以来 世纪，进展受到良好型人群中遗传数据的可用性的限制。现在，到期 对于技术的非凡进展，微阵列基因分型数据集，外显子组测序数据集和 有针对性的测序数据集可用于大型临床表型人群，功能数据为 可用。全基因组测序数据的未来爆炸也被广泛预期。这发生了变化 从数据获取到计算和统计方法的数据解释和开发的重点 用于预测基因型和功能信息的表型。我们建议开发新方法 预测基因型的表型，并将这些方法应用于新收集的人类复合物的数据 直接医疗利益的特征，包括定量和疾病特征。我们在表型预测方面的工作 关于复杂特征的等位基因结构的信息，将为未来的遗传提供指导 研究。从实际的角度来看，关于遗传诊断潜力的持续辩论 鉴定患有特定复杂疾病较早风险的个体。如果成功，遗传 诊断可能会为患者的选择提供早期治疗干预的选择。但是，实际实用性 评估复杂疾病风险的遗传因素尚未得到证明，并且被广泛争论。我们会严格 测试基于基因型的表型预测实用性的假设。 在特定目标1中，我们将开发和测试用于预测微阵列表型的新统计方法 基因分型数据。我们将研究几种模型选择和收缩策略。我们将评估 是独立估计单个标记的贡献还是适合所有标记的贡献更有效 同时地。在特定目标2中，我们将改善各种血统人群的多基因预测。它 重要的是，医疗进展不仅限于欧洲人口。我们的方法将生成 人口之间的预测，考虑等位基因频率的人口差异，率 连锁不平衡的等位基因变化和模式。在特定目标3中，我们将开发和测试统计 从测序数据预测表型的方法。测序数据提供了一组不同的统计 挑战是因为它们包含低频和罕见的等位基因变体，并且通常会产生稀有的影响 变体无法估计。在特定目标4中，我们将功能数据纳入 表型预测。我们将调查功能数据的合并是否可以改善表型 遗传数据的预测。