Abstract
Several recurrent, relatively common copy number variants (CNVs) have been shown to confer significant risk
for neuropsychiatric disorders (NPD). These CNVs are under-identified in the general population and under-
characterized with respect to the broad range of possible phenotypic manifestation and penetrance. The
15q13.3 BP4-5 recurrent deletion (15q13.3DS) confers risk for early onset NPDs such as autism spectrum
disorder (ASD) and intellectual developmental disorder (IDD), What is less clear is the impact of this CNV on
NPDs with later onset, such as major depressive disorder(MDD), bipolar disorder, and schizophrenia, as well
as numerous other later-onset medical and neurological conditions such as epilepsy, and neurodegenerative
disorders. Similarly, little is known about the relationship between ASD-associated CNVs and potentially
clinically relevant dimensional neurobehavioral traits among carriers who do not meet full clinical criteria for an
early onset NPD. There is a need to characterize a wider population base to better understand the penetrance
of these CNVs, along with other potentially associated medical, psychiatric, and neurological phenotypes.
There are likely additional factors (such as underlying polygenic risk) that may impact the phenotypic
expression of these variants that already exist in the information already collected. We will identify a cohort of
200 individuals with 15q13.3DS with access to their electronic heath records(EHR). We will recruit 75
individuals for deeper quantitative phenotyping. We will identify additional clinical characteristics in addition to
confirming the currently known phenotypes associated with 15q13.3DS by leveraging available longitudinal
EHRs. We will then combine the EHR data with the quantitative phenotypic data that we have collected using
well-referenced and validated tools to create a deep dataset. This dataset will then be used to train and
systematically test a predictive algorithm of 15q13.3DS diagnosis and risk prediction for penetrance of its
various manifestations. To complete these series of studies as part of a K23 Mentored Career Development
Award Dr. Soda will work with a team of mentors with complementary skillsets, complete relevant coursework
towards a biomedical informatics certificate, and participate in related practicum experiences. This will help Dr.
Soda meet his training aims; 1. Learn to operate in, manage, harmonize complex health-related data through
biomedical informatics. 2. Learn the skills needed to conduct genomic analysis on the effect of rare as well as
common variants found in the genome and its relation to symptom penetrance. 3. Learn advanced analysis of
biomedical data with machine learning techniques. The completion of this proposal will uncover previously
unknown syndrome presentations, identify factors related to psychiatric disorder penetrance in individuals with
15q13.3DS and will lay the groundwork for Dr. Soda to achieve independence towards R01 funding to assess
the sensitivity/ specificity of such developed tools in identifying individuals with rare genetic syndromes.