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

摘要：研究项目2 临床参考范围通常是从有限的样本中得出，使用简单的统计方法。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可变性 特点。这种“一刀切”的方法被发现会导致误诊，在某些情况下， 死亡。我们的长期目标是开发创新的方法，以产生新一代 个性化的参考范围。骨密度(BMD)的参考范围已成为 越来越有争议，主要是因为大多数脆性骨折患者 显示有一个正常的骨密度值，由常见的T-Score方法定义。这主要是因为 T-Score方法是基于“一刀切”的范例，没有考虑到 个体基因组构成等特征。遗传因素贡献了超过60%的BMD 变种。随着人类寿命的延长，骨质疏松性骨折的增加正在成为一种主要的公共现象 健康问题。这个应用程序的目标是开发一种创新的方法来派生个性化 骨密度参考范围为高加索女性，这是骨质疏松性骨折风险最高的群体。论 根据申请人提供的初步数据，本申请的中心假设是 遗传学增强方法对骨质疏松性骨折的预测明显好于T评分 方法和现有的缺乏遗传成分的基于模型的方法。这一假设将通过以下方式检验 追求三个具体目标：1)确定遗传因素对正常骨密度变异的贡献 高加索妇女；2)开发一种新的遗传学增强的方法来得出个性化的参考范围； 3)在队列数据中验证遗传增强方法。对于目标1，该项目将利用现有的 基因组数据和发现，以进行更新的荟萃分析。我们将确定Single的最佳子集 核苷酸多态(SNPs)和遗传负荷评分在预测正常骨密度变异中的作用对于目标2， 现有的DBGaP数据包括大量健康的高加索女性样本，将被用于开发最好的- 进行遗传学增强模型，可以为每个个体产生个性化的BMD阈值。 在目标3下，妇女健康倡议数据将被用来验证遗传增强方法，方法是 将其对裂缝的预测精度与现有方法进行了比较。这种创新的方法将取代 一刀切的传统方法，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分界点到个人基因组的个性化门槛 化妆等特点。拟议的研究将提供个性化的BMD参考范围 因此，预计将显著提高骨质疏松症诊断的准确性。增加的 此方法可用于生成许多其他类型的个性化参考范围，其中 将提高各种疾病的诊断和治疗水平。 好了！

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. !