Phenotypic signatures in clinical data enable systematic identification of patients for genetic testing

Abstract

Around five percent of the population is affected by a rare disease, most often due to genetic variation. A genetic test is the quickest path to a diagnosis, yet most suffer through years of diagnostic odyssey before getting a test, if they receive one at all. Identifying patients that are likely to have a genetic disease and therefore need genetic testing is paramount to improving diagnosis and treatment. While there are thousands of previously described genetic diseases with specific phenotypic presentations, a common feature among them is the presence of multiple rare phenotypes which often span organ systems. Here, we hypothesize that these patients can be identified from longitudinal clinical data in the electronic health record (EHR). We used diagnostic information from the EHRs of 2,286 patients that received a chromosomal microarray and 9,144 matched controls to train and test a prediction model. We identified high prediction accuracy (AUROC = 0.97, AUPR = 0.92) in a held-out test sample and in 172,265 hospital patients where cases were defined broadly as interacting with a genetics provider (AUROC = 0.9, AUPR = 0.63). High probabilities (median = 0.97) were associated with 46 patients carrying a known pathogenic copy number variant (CNV) among a subset of 6,445 genotyped patients. Our model identified many more patients needing a genetic test while increasing the proportion having a putative genetic disease compared to the current nonsytematic approach. Taken together, we demonstrate that phenotypic patterns representative of a genetic disease can be captured from EHR data and provide an opportunity to systematize decision making on genetic testing to speed up diagnosis, improve care, and reduce costs.

A cross-disorder dosage sensitivity map of the human genome

“…1. BioVU Biobank at Vanderbilt University (BioVU): We obtained CNV calls for 66,360 BioVU participants who had been genotyped from peripheral blood samples on the Illumina MegaEx microarray platform as previously described (Morley et al, 2021). After excluding all related individuals, individuals with known chromosomal abnormalities (i.e., aneuploidies, abnormal karyotypes, or CNVs >50Mb), or individuals with incident blood cancers, we retained 46,967 samples.…”

Leveraging electronic health records to inform genetic counseling practice surrounding psychiatric disorders

“…Leveraging EHR data could be of great use for future directions of GC research to aid in elucidating patterns of referral, prevalence of phenotypes within subpopulations, and has the potential to reach well beyond psychiatric disorders in attempt to address patient needs. Methodology utilizing EHR data with predictive modeling is becoming increasingly recognized as a powerful tool to enhance patient care with potential to optimize screening protocols for patients needing referral for cancer genetic counseling, predicting pediatric patients at risk of genetic disease, and even aid in interventions as clinical decision support tools for patients at risk for self‐ harm (Morley et al., 2021; Sin et al., 2018; Walsh et al., 2018). Ultimately, we hope these findings could be used to inform policy changes allowing for increased efficacy in training and genetic counseling surrounding psychiatric disorders.…”

Future of Perioperative Precision Medicine: Integration of Molecular Science, Dynamic Health Care Informatics, and Implementation of Predictive Pathways in Real Time

“…24 Such integrative studies, deriving phenotypes from the EHR and matching up with genomic testing, are increasing and gaining traction as an efficient means to identify selected patients, reduce costs, speed up diagnosis, and improve care. 25 Futurists and advocates of PM opine that the best way to assimilate the vastness of the field into clinical utility is by open-science and open-resource tools. The Center for Precision Medicine at Vanderbilt University Medical Center has developed "phecodes" by integrating EHR-based phenotyping from International Classification of Diseases (ICD) codes and integrating it with genotypes.…”

“…24 Such integrative studies, deriving phenotypes from the EHR and matching up with genomic testing, are increasing and gaining traction as an efficient means to identify selected patients, reduce costs, speed up diagnosis, and improve care. 25…”