A method to improve capture of causal genetics and by extension, cross-population portability when constructing polygenic scores

一种在构建多基因评分时改善因果遗传学捕获以及扩展的跨群体可移植性的方法

• 批准号: 10679656
• 负责人: Renee Fonseca
• 金额: $ 4.77万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-11-01 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10679656
• 关键词: Admixture; African ancestry; Age; Agonist; Alzheimer' s Disease; Asthma; Atlases; Biological; Body mass index; Cardiovascular Diseases; Clinical; Clinical Trials; Colorado; Complex; Data; Data Analyses; Data Set; Databases; Disease; Drug Prescriptions; Environment; European; European ancestry; Exhibits; Future; Genes; Genetic; Genetic Medicine; Genetic Recombination; Historical Demography; Individual; Inequity; Insulin-Dependent Diabetes Mellitus; LDL Cholesterol Lipoproteins; Major Depressive Disorder; Malignant neoplasm of prostate; Medical; Methods; Modeling; Outcome; Participant; Patient Self-Report; Pattern; Performance; Phenotype; Population; Preventive Medicine; Preventive healthcare; Recording of previous events; Rest; Risk; Risk Estimate; Risk Factors; Scanning; Scoring Method; Serum; Signal Transduction; Single Nucleotide Polymorphism; Smoking Status; Socioeconomic Status; Structure; Systematic Bias; Testosterone; Work; biobank; clinical application; clinical care; disorder risk; diverse data; experience; gene environment interaction; genetic architecture; genome wide association study; improved; interest; malignant breast neoplasm; member; neglect; novel; personalized medicine; phenome; portability; premature; sex; sexual dimorphism; simulation; study population; tool; trait

PROJECT SUMMARY/ABSTRACT Polygenic scores (PGS) can predict disease risk in a population or an individual using genetic data and are poised to improve clinical care by making personalized preventive medicine a reality. Unfortunately, current methods of PGS are less accurate predictors across populations of non-European ancestry as a consequence of Eurocentric biases in genome-wide association studies (GWAS). PGS prediction accuracy can also vary within just a single population due to differences in the environment experienced by individuals. These biases in prediction both within and between populations severely limit the applicability of PGS. Unlike other forms of medical inequity which benefit one population while harming or neglecting another (e.g., prescription drugs such as some long-acting β2-agonist asthma treatments which exacerbate illness in African-ancestry populations), PGS perform ubiquitously better across European populations, leading clinical applications to systematically benefit individuals of European descent and neglecting the rest of the world. Despite this systematic bias, clinical trials of PGS are underway with applications in breast and prostate cancers, type I diabetes, and cardiovascular disease. Interest in PGS is only growing despite its limitations, so I propose to develop methods that can aid in mitigating some of the harm caused by premature applications of PGS. In Aim 1 I will build and apply PGS(C) a method of PGS which can account for the effects of a binarized context (e.g., sex) on a trait by incorporating gene by context interactions (GxC) into my PGS model. I will apply this model to improving prediction of sexually dimorphic traits such as major depression and Alzheimer’s disease witin multiple diverse datasets. This will yield a more portable PGS better able to predict disease risk in varied populations, incorporating biological variability, and gene by environment (GxE) interactions into prediction. In Aim 2 I will extend this method to incorporate continuous contexts (e.g., ancestry, environment, age, etc.) into prediction. Additionally, I will compare my novel PGS(C) method to existing state-of-the-art PGS methods to itdentify when each method mostly accurately predicts a trait while minimizing loss in portability. This work is a concerted effort to improve PGS portability, a crucial step in constructing a score that can bridge existing gaps in genetic medicine negatively impacting diverse and underrepresented study populations.

项目摘要/摘要 多基因评分（PGS）可以使用遗传数据预测人群或个人的疾病风险，并且 准备通过使个性化预防医学成为现实来改善临床护理。不幸的是，目前 因此，PG的方法是非欧洲血统人群中较少准确的预测指标 全基因组关联研究（GWAS）中以欧洲为中心的偏见。 PGS预测准确性也可能有所不同 由于个人经历的环境差异，仅在一个人群中。这些偏见 在人群内部和人群之间的预测中都严重限制了PG的适用性。与其他形式不同 医疗不平等受益于一个人群，同时损害或忽视另一种人口（例如，处方药 例如一些长效的β2激动剂哮喘治疗，使非洲疗法加剧疾病 人群），PGS在欧洲人群中的无处不在，使临床应用导致 系统地使欧洲血统的个人受益，并忽略了世界其他地区。尽管如此 系统偏见，PG的临床试验正在进行中，并在乳腺癌和前列腺癌中应用，I型 糖尿病和心血管疾病。对PGS的兴趣仅在于将目的地的限制增长，因此我建议 开发可以帮助减轻PG过早应用造成的一些危害的方法。目标 1我将构建并应用PGS（c）PGS的方法，该方法可以解释二进制环境的影响（例如， 性别）通过将基因通过上下文相互作用（GXC）纳入我的PGS模型，在性状上。我将应用此模型 为了提高对二态性状的预测，例如严重抑郁症和阿尔茨海默氏病智慧 多种数据集。这将产生更便携的PGS，能够更好地预测各种各样的疾病风险 人群，通过环境（GXE）相互作用将生物变异性和基因纳入预测。 目标2我将扩展这种方法，以将连续的环境（例如祖先，环境，年龄等）纳入 预言。此外，我将将我的新型PGS（C）方法与现有的最新PGS方法进行比较 当每种方法主要准确地预测性状时，同时最大程度地减少可移植性损失时，ITDENDIFY。这项工作是 共同努力改善PGS便携性，这是构建可以弥合现有差距的分数的关键步骤 遗传医学对潜水员和代表性不足的研究人群产生负面影响。