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.

项目概要/摘要 多基因评分 (PGS) 可以使用遗传数据预测人群或个体的疾病风险， 不幸的是，目前正准备通过使个性化预防医学成为现实来改善临床护理。 因此，PGS 方法对于非欧洲血统人群的预测不太准确 全基因组关联研究 (GWAS) 中的欧洲中心偏差的影响也可能有所不同。 由于个体所经历的环境差异，这些偏见只存在于单一人群中。 与其他形式的 PGS 不同，群体内部和群体之间的预测严重限制了 PGS 的适用性。 医疗不平等使一个群体受益，同时伤害或忽视另一个群体（例如处方药 例如一些长效 β2 激动剂哮喘治疗药物会加重非洲血统的疾病 人群），PGS 在欧洲人群中普遍表现得更好，导致临床应用 尽管如此，个人还是系统地受益于欧洲血统而忽视了世界其他地区。 由于存在系统偏差，PGS 的临床试验正在进行中，用于乳腺癌和前列腺癌 I 型 尽管存在局限性，但对 PGS 的兴趣仍在不断增长，因此我建议 开发有助于减轻过早应用 PGS In Aim 造成的一些危害的方法。 1 我将构建并应用 PGS(C) 一种 PGS 方法，它可以解释二值化上下文的影响（例如， 通过将基因与背景相互作用 (GxC) 合并到我的 PGS 模型中，我将应用此模型。 改善对性别二态特征的预测，例如重度抑郁症和阿尔茨海默病 这将产生更便携的 PGS，能够更好地预测各种疾病风险。 群体，将生物变异性和基因与环境（GxE）相互作用纳入预测。 目标 2 我将扩展此方法，将连续的上下文（例如，祖先、环境、年龄等）纳入到 此外，我会将我的新颖 PGS(C) 方法与现有最先进的 PGS 方法进行比较。 它确定每种方法何时最准确地预测特征最小化，同时损失可移植性。 共同努力提高 PGS 可移植性，这是构建可以弥补现有差距的分数的关键一步 遗传医学对多样化和代表性不足的研究人群产生负面影响。