Improving Polygenic Prediction using Next-Generation Data Sets

使用下一代数据集改进多基因预测

基本信息

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
项目类别：
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

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

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项目摘要

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核心目标。许多人类相关性的许多人类特征的可用遗传力估计表明，30-80％的表型变异是由于遗传差异的基本变异。基于基因型预测表型的能力是我们对复杂性状遗传学的理解的最终检验。自20世纪初期复杂性状遗传学的曙光以来，进步受到良好型人群中遗传数据的可用性的限制。现在，由于技术的非凡进展，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，并且功能数据已获得。全基因组测序数据的未来爆炸也被广泛预期。这将重点从数据获取转移到数据解释以及计算和统计方法的开发，以预测基因型和功能信息的表型。我们建议开发用于预测基因型表型的新方法，并将这些方法应用于新收集的有关直接医学兴趣的人类复杂性状的数据，包括定量和疾病特征。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，关于遗传诊断的潜力在识别患有特定复杂疾病风险较高的个体的潜力的持续辩论。如果成功，遗传诊断可能会告知患者早期治疗干预的选择。但是，遗传学在评估复杂疾病风险中的实际实用性已有未被证明，并被广泛争论。我们将严格检验基于基因型的表型预测实用性的假设。在特定目标1中，我们将开发和测试新的统计方法，以预测微阵列基因分型数据的表型。我们将研究几种模型选择和收缩策略。我们将评估独立估计单个标记的贡献或同时拟合所有标记的贡献是更有效的。在特定目标2中，我们将改善各种血统人群的多基因预测。重要的是，医疗进展不仅限于欧洲人口。我们的方法将在人群中产生预测，考虑到等位基因频率的人口差异，等位基因变异的速率和连锁不平衡的模式。在特定目标3中，我们将开发和测试用于预测测序数据表型的统计方法。测序数据提供了一系列不同的统计挑战，因为它们包含低频和稀有的等位基因变体，并且通常无法估计单个稀有变体的影响。在特定目标4中，我们将功能数据纳入表型预测的方法中。我们将研究功能数据的合并是否可以改善遗传数据的表型预测。