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

摘要：研究课题2

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. !