Collaborative multi-site project to speed the identification and management of rare genetic immune diseases

Summary
The subject of this proposal is a new, collaborative approach to improve the diagnosis of primary
immunodeficiency diseases (PIDs). These patients have individually rare, monogenic disorders leading to
severe infections, autoimmunity, and inflammation. The prevalence of PIDs is ~1:10,000 and approximately
half have antibody deficiencies as their main immunological phenotype. Most doctors are unaware of these
diseases and many patients go years without a diagnosis, costing the system tens of thousands of dollars per
patient yearly and unnecessarily increasing morbidity and mortality. There is a tremendous, untapped
opportunity to advance the diagnosis of patients with PIDs.
We propose to utilize new machine-learning approaches to algorithmically identify patients with PIDs
from their electronic health records (EHR). To accomplish our goals, we have built a coalition of computational
genomics groups at UCLA, UCSF, and Vanderbilt (Computational team), and clinical immunology groups at
the five University of California medical centers (Los Angeles, San Francisco, Irvine, San Diego, and Davis)
(Immunology team). We propose to: Identify patients with rare immune diseases by phenotype risk
scoring (Aim 1). We will speed the identification of patients with rare immune diseases by surveilling the
EHR using a phenotype risk scoring approach, building upon recently published work in Science. We will
apply this approach to the UCLA, UCSF, and Vanderbilt clinical data repositories to identify potential cases.
We will improve risk scoring by considering gender, age, and race/ethnicity. We will classify patients by
whether they have an infection phenotype or immune dysregulation phenotype. Subsequently, we will expand
to the larger, UC Health-wide Data Warehouse (UCHWDW), entailing 15+ million patients across all UC
medical centers. We will then Identify the genetic immune diseases for these newly found subjects
(Aim 2). We will follow the state-of-the-art approach employed by the UCLA and Vanderbilt Undiagnosed
Disease Network (UDN) sites. We will start by sequencing all the known antibody deficiency patients across the
Immunology team sites while collaboratively pre-reviewing identified cases from Aim 1 on monthly video-calls.
For selected subjects, we will perform whole genome and RNA sequencing. Clinical and research laboratory
testing will bring closure to the diagnostic odyssey for these subjects.
The overall impact of this work accelerates the diagnosis and cure of PIDs. This project will also serve as a
demonstration of how immunology sites can work together sharing electronic medical records and genomic
data to advance care.

Grant Number: 5R01AI153827-05
NIH Institute/Center: NIH
Principal Investigator: MANISH BUTTE