Genomic Prediction of Human Disease

人类疾病的基因组预测

• 批准号: 10090715
• 负责人: Fabio Morgante
• 金额: $ 26.84万
• 依托单位: CLEMSON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-10 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10090715
• 关键词: Accounting; African American; Agriculture; Architecture; Bayesian Method; Big Data; Blood Pressure; Cardiovascular Diseases; Cause of Death; Centers of Research Excellence; Clinical; Complex; Computing Methodologies; Data; Data Set; Disease; Ethics; European; Exposure to; Funding; Genes; Genetic; Genetic Drift; Genetic Heterogeneity; Genomics; Genotype; Goals; Heritability; Heterogeneity; Human; Human Genetics; Individual; Knowledge; Maps; Medical; Methodology; Methods; Modeling; Modernization; Phenotype; Play; Population; Population Heterogeneity; Reporting; Research; Risk Factors; Role; Sample Size; Sampling; Source; Statistical Methods; Statistical Models; Structure; Training; Trans-Omics for Precision Medicine; Variant; biobank; disorder risk; epigenomics; genetic analysis; genetic architecture; genetic variant; genome wide association study; genome-wide; human data; human disease; improved; insight; interest; large datasets; novel; personalized medicine; precision medicine; predictive modeling; risk prediction; sex; trait

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.

项目摘要 拟议的研究的长期目标是研究研究的遗传机制 假设会影响常见疾病。复杂性状的遗传分析已在很大程度上进行了 在同一祖先的人群中，主要是欧洲血统。除了道德上 值得怀疑的是，这对于疾病风险预测是​​有问题的，因为它已经表明了预测准确性 与训练样本和目标样品之间的遗传差异相称的下降。一 该观察结果的假设是，不同的人群可能暴露于不同的情况下（例如， 环境条件），在存在的情况下导致人群之间的不同影响大小 基因型逐个相互作用。此外，与上下文相关的效果会影响预测准确性 同一祖先的群体（例如不同的性别）之间的基本上。因此，预测模型 计算基因相互作用的性能比疾病风险的标准预测模型更好 在人类中。尽管此类模型在农业和模型物种中提供了提高的准确性，但此主题 尚未在人类中进行调查。该建议将通过调查的重要性来填补这一空白 逐个基因相互作用与多疗法样本中血压特征的遗传结构的相互作用，以及 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），也是复杂性状的出色模型（它们是中等遗传的特征， 仅通用变体仅解释了总遗传力的一半，而GWAS命中仅解释了少数 总变化的百分比）。拟议的研究将利用公共可用的大型数据集， 包括（但不限于）英国生物库，以及那些是精密医学跨词的一部分 （最高的）财团。在特定目标1中，重点将是估计解释的差异的比例 通过并映射多务样本中的基因相互作用，使用已经存在的结合 线性混合模型和贝叶斯方法。在特定目标2中，重点将放在增加预测上 通过将基因相互作用纳入单疗法和多务实样品的准确性 预测模型。而现有的线性混合模型和用于农业数据的贝叶斯方法 将应用，还将开发一种更适合人类数据的新预测方法。简而言之，主 想法是针对可用上下文明确对基因相互作用进行建模，同时也考虑 其他未知的效应来源异质性之间的异质性。该建议将为您提供新颖的见解 血压特征的遗传结构将提高多种干燥样品的预测准确性 以及可以应用于任何感兴趣特征的新颖分析策略/方法。