Elucidating the full phenotype of the 15q13.3 deletion syndrome - 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.
