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 临床参考范围通常使用简单的统计数据得出有限的样本。这些传统 参考范围未考虑基因，环境和其他基因的正常变异性 特征。发现这种“千篇一律”的方法会导致诊断，在某些情况下， 死亡。我们的长期目标是开发创新的方法来产生新一代 个性化参考范围。骨矿物质密度（BMD）的参考范围已成为 越来越有争议的主要是由于大多数维持脆弱性骨折的患者是 证明具有正常的BMD值，该值由常见的T评分方法定义。这主要是因为 T得分方法基于“一个尺寸适合所有”范式，而无需考虑正常的可变性 单个基因组构成和其他特征。遗传因素贡献超过60％的BMD 变化。随着人类寿命的增长，骨质疏松性骨折的增加正在成为主要的公众 健康问题。该应用的目的是开发一种创新方法来得出个性化 BMD参考范围是白种妇女的范围，该组的骨质疏松性骨折风险最高。在 应用程序生成的初步数据的基础，该应用程序的中心假设是 遗传学增强方法将比T评分更好地预测骨质疏松性骨折的预测指标 方法和先前的基于模型的方法缺乏遗传成分。该假设将通过 追求三个具体目标：1）确定遗传因素对正常BMD变化的贡献 高加索妇女； 2）开发一种新型的遗传增强方法，用于得出个性化的参考范围； 3）验证队列数据中的遗传增强方法。对于AIM 1，该项目将利用现有 基因组数据和发现进行更新的荟萃分析。我们将确定单身的最佳子集 核苷酸多态性（SNP）和遗传负荷得分在预测正常的BMD变化中。对于目标2， 现有的DBGAP数据包括大量健康白种妇女样本，将用于开发最好的 执行遗传学增强模型，可以为每个人产生个性化的BMD阈值。 在AIM 3下，将利用妇女的健康计划数据来验证遗传学增强方法 将其对断裂的预测准确性与现有方法进行比较。这种创新的方法将取代 传统的，千篇一律的方法，从根本上改变了当前的研究和临床实践范例 从一个静态截止点到每个人的个性化阈值 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 意义上，这种方法可用于生成许多其他类型的个性化参考范围， 将改善各种疾病的诊断和治疗。 呢