Developing the genetics-enhanced model to derive personalized reference ranges for bone density

开发遗传学增强模型以获得个性化的骨密度参考范围

• 批准号: 10170374
• 负责人: Qing Wu
• 金额: $ 20.73万
• 依托单位: UNIVERSITY OF NEVADA LAS VEGAS
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10170374
• 关键词: Accounting; Address; Age; Algorithms; Assessment tool; Big Data; Biological Markers; Blood Pressure; Bone Density; Caring; Caucasians; Centers of Research Excellence; Cessation of life; Characteristics; Cholesterol; Clinical; Clinical Treatment; Data; Data Analyses; Development; Diagnosis; Disease; Environment; Fracture; Generations; Genes; Genetic; Genetic Enhancement; Genetic Load; Genetic Research; Genomics; Genotype; Glucose; Goals; Grant; Human; Human Genome; Individual; Lead; Longevity; Measurement; Meta-Analysis; Methodology; Methods; Modeling; Nevada; Osteoporosis; Outcome; Outcomes Research; Patients; Physiological; Play; Prevention; Principal Investigator; Public Health; Race; Reference Values; Research; Research Project Grants; Risk; Role; Sample Size; Sampling; Scoring Method; Single Nucleotide Polymorphism; Specificity; Statistical Models; Testing; Update; Variant; Woman; Women' s Group; Women' s Health; World Health Organization; base; bone; clinical Diagnosis; clinical decision-making; clinical practice; clinically significant; cohort; database of Genotypes and Phenotypes; fracture risk; fragility fracture; genome analysis; genome sequencing; genome wide association study; genomic data; high risk; improved; individual variation; innovation; multidisciplinary; novel; osteoporosis with pathological fracture; personalized health care; personalized medicine; programs; sex; statistics; translational genetics

ABSTRACT: RESEARCH PROJECT 2 Clinical reference ranges are typically derived from limited samples, using simplistic statistics. These traditional reference ranges do not take into account the normal variability in genes, environment, and other characteristics. This “one-size-fits-all” approach has been found to cause misdiagnosis and, in some cases, death. Our long-term goal is to develop innovative methodologies to generate a new generation of personalized reference ranges. The reference ranges for bone mineral density (BMD) have become increasingly controversial, primarily due to the fact that a majority of patients who sustain fragility fractures are shown to have a normal BMD value, defined by the commonplace T-score method. This is mainly because the T-score method was based on the "one size fits all” paradigm, without taking into account normal variability in individual genomic makeup and other characteristics. Genetic factors contribute more than 60% of BMD variation. With human longevity on the rise, increased osteoporotic fractures are becoming a major public health problem. The objective of this application is to develop an innovative method to derive personalized BMD reference ranges for Caucasian women, the group with the highest risk of osteoporotic fracture. On the basis of preliminary data produced by the applicant, the central hypothesis of this application is that the genetics-enhanced method will be a significantly better predictor of osteoporotic fracture than the T-score method and prior model-based methods lacking a genetic component. This hypothesis will be tested by pursuing three specific aims: 1) determine the contribution of genetic factors to normal BMD variation in Caucasian women; 2) Develop a novel genetics-enhanced method for deriving personalized reference ranges; and 3) validate the genetics-enhanced method in cohort data. For Aim 1, this project will leverage existing genomic data and findings to conduct an updated meta-analysis. We will identify the best subset of single nucleotide polymorphisms (SNPs) and genetic loading scores in predicting normal BMD variation. For Aim 2, existing dbGaP data that include large samples of healthy Caucasian women will be used to develop the best- performing genetics-enhanced model, which can produce a personalized threshold of BMD for each individual. Under Aim 3, Women's Health Initiative data will be utilized to validate the genetics-enhanced method by comparing its predictive accuracy for fracture with existing methods. This innovative method will replace the traditional, one-size-fits-all approach, fundamentally shifting current research and clinical practice paradigms from one static cutoff point for everyone to a personalized threshold that accounts for individual genomic makeup and other characteristics. The proposed research will provide personalized BMD reference ranges and, as such, is expected to significantly increase the accuracy of osteoporosis diagnosis. Of increased significance, this approach can be used to generate many other types of personalized reference ranges, which will improve diagnosis and treatment of a variety of diseases. !

摘要：研究项目 2 临床参考范围通常是使用简单的统计数据从有限的样本中得出的。这些传统的 参考范围没有考虑基因、环境和其他因素的正常变异性 特征。人们发现这种“一刀切”的方法会导致误诊，并且在某些情况下， 死亡。我们的长期目标是开发创新方法来产生新一代 个性化参考范围。骨密度（BMD）的参考范围已成为 越来越有争议，主要是因为大多数发生脆性骨折的患者 显示具有正常的 BMD 值，由常见的 T 分数方法定义。这主要是因为 T 分数方法基于“一刀切”范式，没有考虑正常变异性 个体基因组组成和其他特征。遗传因素对BMD的贡献率超过60% 变化。随着人类寿命的延长，骨质疏松性骨折的增加正成为主要公众关注的问题 健康问题。该应用程序的目标是开发一种创新方法来获得个性化 白种人女性的 BMD 参考范围是骨质疏松性骨折风险最高的群体。上 根据申请人提供的初步数据，本申请的中心假设是 遗传学增强方法将是比 T 分数更好的骨质疏松性骨折预测指标 方法和现有的基于模型的方法缺乏遗传成分。这个假设将被检验 追求三个具体目标：1）确定遗传因素对正常 BMD 变异的贡献 白人女性； 2）开发一种新颖的遗传学增强方法来推导个性化参考范围； 3) 在队列数据中验证遗传学增强方法。对于目标 1，该项目将利用现有的 基因组数据和发现进行更新的荟萃分析。我们将确定单个的最佳子集 核苷酸多态性 (SNP) 和遗传负荷评分可预测正常 BMD 变化。对于目标 2， 现有的 dbGaP 数据（包括大量健康白人女性样本）将用于开发最佳- 执行遗传学增强模型，可以为每个人生成个性化的 BMD 阈值。 根据目标 3，妇女健康倡议数据将用于验证遗传学增强方法： 将其骨折预测准确性与现有方法进行比较。这种创新方法将取代 传统的、一刀切的方法，从根本上改变了当前的研究和临床实践范式 从每个人的一个静态截止点到考虑个体基因组的个性化阈值 化妆和其他特征。拟议的研究将提供个性化的 BMD 参考范围 因此，预计将显着提高骨质疏松症诊断的准确性。增加的 重要的是，这种方法可用于生成许多其他类型的个性化参考范围， 将改善多种疾病的诊断和治疗。 ！