Improving prediction of schizophrenia by integrating genomic and epigenomic data

• Grant number: 23/16709-0
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate (Direct)
• Start date: June 01, 2024
• End date: May 31, 2025
• Field of knowledge: Health Sciences - Medicine - Psychiatry
• Principal Investigator: Marcos Leite Santoro
• Grantee: Rafaella Ormond Sampaio
• Supervisor: Janitza Montalvo-Ortiz
• Host Institution: Escola Paulista de Medicina (EPM). Universidade Federal de São Paulo (UNIFESP). Campus São Paulo. São Paulo , SP, Brazil
• Institution abroad: Yale School of Medicine (YSM), United States
• Associated to the scholarship: 22/16317-2 - Application of polygenic risk score methods in admixed population, BP.DD

Abstract

The psychiatric disorders (PD) are among the 10 main causes of years of life lost due to disability. Schizophrenia (SCZ) is one of the most heritable PDs, with a heritability of ~0.8. The main aspects of SCZ can be divided into dimensions of positive symptoms (psychosis), negative symptoms and cognitive deficits. The large-scale genome-wide association study (GWAS) is a type of exploratory approach that investigates thousands of variants simultaneously. In recent years, the expansion of consortia in psychiatric genetics has allowed the creation of a reliable polygenic score (PGS) for schizophrenia, based on results of GWAS. Application of the PGS approach, which is based on GWAS results, currently explains about 8,5% of schizophrenia. As PDs are multifactorial diseases, longitudinal approaches are essential to understand how genetic risk, which is static, interacts with dynamic and modifiable variables. Our research group has been working with PGS, mostly in the admixed Brazilian population, over the last 7 years. Montalvo-Ortiz's group is very recognized in the psychiatric genetics field, focusing on Latin American cohorts and with a strong background on epigenomic. Investigating epigenetic changes can help understand how these factors converge or add to the prediction risk for SCZ. DNA CpG methylation has a role in regulating processes that can lead to the development of PDs and recently, the expansion of Epigenetic Wide Association Studies (EWAS) allowed the development of a Methylation Profile Score (MPS). The aim of this study is to improve prediction using genomic and epigenomic data, combining MPS and PGS for SCZ. This study will involve the participation of 251 individuals, 125 patients with SCZ from a brazilian cohort. The PGS analysis will be performed using PRS-CS software and the MPS analysis using the "RaMWAS" package in R. We will evaluate the prediction to PDs using both approaches separately and integrated. As the main finding, we expect that both approaches will be mildly predictive for PDs and that the combined analysis will provide a relative improvement.