Core B: Statistical and Computational Analysis Core

PROJECT SUMMARY The Statistical and Computational Analysis (SCA) Core will be a source of innovative data analytics and high-quality data management for the new Prospective Genetic Risk Evaluation and Assessment (PROGRESS) in Autism Center at Columbia University, focused on identifying and understanding genomic risk factors, caregiver experiences and decision-making, and early-stage neurodevelopmental patterns in children at identified genetic risk for autism. The SCA Core brings together experienced biostatisticians, computational biologists, bioinformaticians, and genomics experts to deploy rigorous and reproducible data science methods in pursuit of the PROGRESS Center’s aims. Members of the SCA Core have extensive expertise in leading and executing analysis for complex research projects in autism and developmental psychopathology and have deep collaborations with several co-investigators across the Center Projects. The SCA will pursue the following four aims: Aim 1: To perform computational analysis of genomic data to identify new risk genes and develop composite genomic risk scores that predict autism risk based on genetic variants across the entire allele frequency spectrum (Project 1 analyses). Aim 2: To conduct statistical analysis of survey and behavioral outcomes in the PROGRESS cohort to model parental outcomes and effects of genetic risk on children’s longitudinal neurobehavioral development (Project 2 and 3 analyses). Aim 3: To integrate genetic, caregiver, and infant neurobehavioral development data across projects to obtain a complete picture of children’s autism risk signatures (cross-Project analyses). Aim 4: To maximize data quality and availability in all PROGRESS Center research, including through a central data management platform that ensures rigor and reproducibility. By integrating a robust data management and analytic pipeline with population-based, longitudinal data collection, the SCA Core will allow the PROGRESS Center to reveal the implications of early life genetic information, integrating genomic risk with neurodevelopmental trajectories and family impact.