New methods for constructing and evaluating polygenic scores

摘要 在过去的十年里，在识别复杂疾病的遗传基础方面取得了重大进展。 以及为风险增加的个人开发多基因预测指标。多基因预测模型有 现在正在接近几种重要疾病的临床关联点。然而，由于大多数 多基因风险是由于极大量的小效应变异，很难最大限度地构建 有效的预测模型，即使使用非常大的GWAS样本。目前，最大的样本是目前 适用于欧洲血统的个人。在这些样本中开发的预测模型通常不支持 虽然可移植性有限的确切原因还不完全清楚，但仍有可能影响到其他群体。在……里面 这个项目我们将(1)衡量不同因素的具体重要性，这些因素对有限的 可跨组移植；(2)实施和评估计算多基因的新统计方法 预测者使用跨群体的联合推理，并使用功能信息作为先验；以及(3) 实施和评估新的统计方法，将遗传信息与其他类型的临床数据相结合 临床环境中用于前瞻性预测的数据。总之，我们的项目将提供一个高效的 群体内和群体间多基因预测的统计方法。

ABSTRACT In the last decade there has been major progress toward identifying the genetic bases of complex diseases and developing polygenic predictors for individuals who are at increased risk. Polygenic prediction models are now approaching the point of clinical relevance for several important diseases. However, since most of the polygenic risk is due to extremely large numbers of small-effect variants it is difficult to construct maximally efficient prediction models even using very large GWAS samples. At present, the largest samples are currently available for European ancestry individuals. Prediction models developed in these samples usually do not port well into other groups, although the precise reasons for the limited portability are not yet fully understood. In this project we will (1) measure the specific importance of different factors that contribute to the limited portability across groups; (2) implement and evaluate new statistical methods for computing polygenic predictors using joint inference across populations, and using functional information as priors; and (3) implement and evaluate new statistical methods for combining genetic information with other types of clinical data for prospective prediction in clinical settings. In summary our project will provide a framework of efficient statistical methods for polygenic prediction within and across populations.