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: 8862508
• 负责人: SHAMIL SUNYAEV
• 金额: $ 49.16万
• 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/8862508
• 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; Future; Gene Expression; Gene Frequency; Genes; Genetic; Genetic Markers; Genetic Risk; Genetic Variation; Genetic study; Genotype; Goals; Health; 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; Untranslated RNA; 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; rare variant; simulation; targeted sequencing; trait; transcription factor

DESCRIPTION (provided by applicant): 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 disequilibriu. 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.

描述（由申请人提供）：了解基因型和表型之间的关系是遗传学的中心目标。对许多与医学相关的人类特征的现有遗传力估计表明，30-80%的表型变异是由于潜在的遗传变异。基于基因型预测表型的能力是对我们对复杂性状遗传学理解的最终考验。自20世纪初复杂性状遗传学出现以来，进展一直受到良好表型群体遗传数据可用性的限制。现在，由于技术的非凡进步，微阵列基因分型数据集、外显子组测序数据集和靶向测序数据集可用于大型临床表型人群，并且功能数据正在变得可用。全基因组测序数据的未来爆炸式增长也被广泛预期。这将重点从数据获取转移到数据解释和计算和统计方法的发展，以预测基因型和功能信息的表型。我们建议开发从基因型预测表型的新方法，并将这些方法应用于新收集的直接医学兴趣的人类复杂性状的数据，包括数量和疾病性状。我们在表型预测方面的工作将为复杂性状的等位基因结构提供信息，并将为未来的遗传研究提供指导。从实际的角度来看，遗传诊断在识别生命早期特定复杂疾病高风险个体方面的潜力正在进行辩论。如果成功，基因诊断可以为早期治疗干预的患者选择提供信息。然而，遗传学在评估复杂疾病风险方面的实际应用还有待进一步研究