Genomic Prediction of Human Disease

项目总结 这项拟议研究的长期目标是调查未被充分研究的遗传机制 假想会影响常见疾病。对复杂性状的遗传分析在很大程度上已经完成 在同一血统的个体中，主要是欧洲血统的。除了在道德上 值得怀疑的是，这对于疾病风险预测是有问题的，因为已经证明预测的准确性 随着训练样本和目标样本之间的遗传差异的增加而成比例地下降。一 该观察的假设是不同的人群可能暴露在不同的环境中(例如， 环境条件)，这导致在存在以下情况下不同人群的影响大小 不同背景下的基因相互作用。此外，上下文相关效应可能会影响预测精度 基本上在同一祖先的群体(例如，不同性别)之间。因此，预测模型 考虑基因与环境的相互作用可能比标准的疾病风险预测模型表现得更好 在人类身上。虽然这样的模型提高了农业和模式物种的准确性，但这一主题 尚未在人类身上进行研究。这项提案将通过调查以下方面的重要性来填补这一空白 在多个祖先样本中，血压特征的遗传结构与基因间的相互作用 将它们纳入统计模型，以提高表型预测的准确性。血压 特征是非常重要的医学特征(例如，它们是全球主要死亡原因的危险因素， 心血管疾病)，也是复杂性状的优秀模型(它们是适度可遗传的性状， 普通的变异只能解释不到总遗传力的一半，而GWAS的成功只能解释少数几个 占总变异的百分比)。这项拟议的研究将利用公开可用的大型数据集， 包括(但不限于)英国生物库和那些属于精密医学Transans-Omics的机构 (TOPMed)财团。在具体目标1中，重点将放在估计所解释的差异比例上 通过和映射多祖先样本中的基因-上下文相互作用，使用已有的 线性混合模型和贝叶斯方法。在具体目标2中，重点将放在增加预测上 在单一祖先和多祖先样本中通过将基因与上下文的相互作用结合到 预测模型。而现有的用于农业数据的线性混合模型和贝叶斯方法 将被应用，还将开发一种更适合人类数据的新预测方法。简而言之，主要的 这个想法是为可用的上下文显式地对基因-上下文相互作用进行建模，同时也考虑到 其他未知的效应来源在祖先之间的异质性。这项提议将为 血压性状的遗传结构将提高多血统样本的预测精度 以及可以应用于任何感兴趣的特征的新的分析策略/方法。

PROJECT SUMMARY The long-term goal of the proposed research is to investigate understudied genetic mechanisms that are hypothesized to influence common diseases. Genetic analyses of complex traits have been largely performed within populations of individuals of the same ancestry, mainly of European descent. Besides being ethically questionable, this is problematic for disease risk prediction as it has been shown that prediction accuracy declines proportionally to increasing genetic divergence between training samples and target samples. One hypothesis for this observation is that different populations are likely exposed to different contexts (e.g., environmental conditions), which results in different effect sizes across populations in the presence of genotype-by-context interactions. In addition, context-dependent effects can influence prediction accuracy substantially even between groups (e.g., different sexes) of the same ancestry. Thus, prediction models that account for gene-by-context interactions could perform better than standard prediction models for disease risk in humans. While such models have provided increased accuracy in agricultural and model species, this topic has not yet been investigated in humans. This proposal will fill this gap by investigating the importance of gene-by-context interactions to the genetic architecture of blood pressure traits in multi-ancestry samples, and their incorporation into statistical models to increase the accuracy of phenotypic prediction. Blood pressure traits are very important medical traits (e.g., they a risk factor for the leading cause of death worldwide, cardiovascular disease) and are also excellent models of complex traits (they are moderately heritable traits, common variants alone explain only less than half of the total heritability, and GWAS hits explain only a few percent of the total variation). The proposed research will make use of publicly available large datasets, including (but not limited to) the UK Biobank and those being part of the Trans-Omics for Precision Medicine (TOPMed) consortium. In Specific Aim 1, the focus will be on estimating the proportion of variance explained by and map gene-by-context interactions in multi-ancestry samples using a combination of already existing linear mixed models and Bayesian methods. In Specific Aim 2, the focus will be on increasing prediction accuracy in both single-ancestry and multi-ancestry samples by incorporating gene-by-context interactions into prediction models. While existing linear mixed models and Bayesian methods developed for agricultural data will be applied, a new prediction method better suited to human data will also be developed. Briefly, the main idea is to model gene-by-context interactions explicitly for the available contexts, while also accounting for other unknown sources of effect heterogeneity among ancestries. This proposal will provide novel insights into the genetic architecture of blood pressure traits that will improve prediction accuracy in multi-ancestry samples as well as a novel analysis strategy/methodology that can be applied to any trait of interest.