Improving Polygenic Prediction using Next-Generation Data Sets

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。许多人类医学相关性状的遗传力估计表明，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理解复杂性状遗传学的最终考验。自世纪初复杂性状遗传学出现以来，由于表型良好的群体中遗传数据的可用性，进展受到限制。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，功能数据也变得可用。全基因组测序数据的未来爆炸也被广泛预期。这将重点从数据采集转移到数据解释和开发用于从基因型和功能信息预测表型的计算和统计方法。我们建议开发新的方法，从基因型预测表型，并将这些方法应用于新收集的数据，人类复杂的性状的直接医学利益，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，并为未来的遗传研究提供指导。从实践的角度来看，有一个正在进行的辩论的潜力，遗传诊断在识别个人在生命早期的特定复杂疾病的高风险。如果成功的话，基因诊断可以为选择早期治疗干预的患者提供信息。然而，遗传学在评估复杂疾病风险方面的实际效用， 尚未被证实，并被广泛讨论。我们将严格检验基于基因型的表型预测的实用性的假设。在具体目标1中，我们将开发和测试新的统计方法，用于从微阵列基因分型数据预测表型。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是否更有效。在具体目标2中，我们将改进不同祖先群体中的多基因预测。重要的是，医学进步不仅限于欧洲人口。我们的方法将产生跨人群的预测，解释等位基因频率，等位基因变异率和连锁不平衡模式的人群差异。在具体目标3中，我们将开发和测试从测序数据预测表型的统计方法。测序数据提供了一组独特的统计挑战，因为它们包含低频率和罕见的等位基因变异，并且通常无法估计单个罕见变异的影响。在具体目标4中，我们将把功能数据纳入表型预测方法。我们将研究是否纳入功能数据可以提高遗传数据的表型预测。

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.