Improving Polygenic Prediction using Next-Generation Data Sets

描述(申请人提供)：了解基因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性状的现有遗传力估计表明，30-80%的表型变异是由潜在的遗传变异引起的。根据基因类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问世以来，在表型良好的群体中遗传数据的可用性一直限制了进展。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已可用于大量临床表型人群，功能数据也正在变得可用。未来全基因组测序数据的爆炸性增长也被广泛预期。这将重点从数据获取转移到数据解释和开发计算和统计方法，以根据基因类型和功能信息预测表型。我们建议开发从基因类型预测表型的新方法，并将这些方法应用于新收集的具有直接医学意义的人类复杂性状的数据，包括数量性状和疾病性状。我们在表型预测方面的工作将对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。从实践的角度来看，关于基因诊断在确定早期患有特定复杂疾病的风险增加的个人方面的潜力正在进行着辩论。如果成功，基因诊断可能会为选择患者进行早期治疗干预提供信息。然而，遗传学在评估复杂疾病风险方面的实用价值 没有得到证实，也引起了广泛的争论。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中预测表型。我们将研究几种型号选择和收缩策略。我们将评估单独估计单个标记的贡献还是同时拟合所有标记更有效。在具体目标2中，我们将改进不同血统人群的多基因预测。重要的是，医学进步不限于欧洲人口。我们的方法将在人类群体中产生预测，考虑到等位基因频率、等位基因变异率和连锁不平衡模式的群体差异。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截然不同的统计挑战，因为它们包含低频率和罕见的等位基因变异，而且通常无法估计个别罕见变异的影响。在具体目标4中，我们将把功能数据融入到表型预测方法中。我们将调查结合功能数据是否可以改善从遗传数据进行的表型预测。

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.