Friday, November 22, 2024
11/22/2024
PROJECT SUMMARY Mental health conditions negatively impact the quality of life of millions of individuals every year. Genomics research has challenged long-held etiological boundaries among psychiatric, developmental, and neurological diagnoses, collectively known as developmental brain disorders (DBD). Clinically distinct DBD, including autism and schizophrenia (SCZ), share etiologies across a continuum of rare through common genomic variants that confer varying impacts on brain function. Employing a genomics-first approach linked to electronic health record (EHR) data, we will study the full continuum of DBD-related genomic variants and their combined effects on phenotypic expression. A novel feature is our inclusion of a largely unexplored class of DBD-related copy number variants (CNVs) of intermediate effect size (e.g., 15q11.2 BP1-2 deletions) that fall between the two extremes of rare and common variation. We will leverage existing data from DiscovEHR, Geisinger’s large-scale genomics initiative of >260,000 participants with exome sequence, SNP genotype, and longitudinal EHR data. We will investigate how the interplay across the full continuum of genomic variants contributes to clinical DBD and medical comorbidities through the following aims: 1) Evaluate the prevalence of DBD genomic variants of large and intermediate effect size in a healthcare system-based population. Exome sequence data from DiscovEHR participants will be analyzed to assess the prevalence of DBD variants of large and intermediate effect size in genomic regions and genes known to be strong contributors to DBD risk. A subset of variant- positive individuals will be phenotyped in Aims 2 and 3. 2) Conduct retrospective e-phenotyping, using existing structured and unstructured EHR data, to investigate clinical variability and penetrance of DBD variants. Building on Geisinger’s innovative EHR-based data extraction and PheWAS methodologies, we will develop tiered, replicable strategies for highly accurate capture of DBD and medical phenotypes. These phenotypes will be validated through systematic chart review of 1200 individuals with DBD variants. 3) Perform prospective direct phenotyping using in-person and online assessments to compare quantitative traits in individuals with DBD variants of large and intermediate effect size. Given the known limitations of using EHR data to capture fine-grained cognitive and behavioral phenotypes, we will augment Aim 2 with in-person assessments for variants of large and intermediate effect size (n=1250 total) and additional online surveys for intermediate CNVs and controls (n=1000 each). 4) Evaluate the impact of PGS on clinical risk or resilience for DBD in the presence of a DBD variant of large or intermediate effect size. We will model the added impact of PGS on risk or resilience for DBD in individuals with variants of large and intermediate effect sizes. These investigations may ultimately lead to individual-level DBD risk predictions, similar to genomic algorithms in place for cancer and cardiovascular disease. Identification of individuals at highest risk for SCZ and other DBD will drive earlier detection and even prevention, consistent with the goals of NIMH’s 2020 strategic plan.
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