CORE C: Abstract The Genetics, Genomics, and Informatics Core (GGIC) focuses on the application of genome- level analyses in neuroscientific investigation, both at the sequence (genetic), gene expression and epigenetic (genomics) levels. The explosion of next-generation sequencing (NGS)-based methods has made advanced computational expertise and infrastructure needed for all sequencing-based applications. The proposed Core aims at providing support for basic and advanced genetics and genomics experiments in both patient cohorts for translational studies and experimental models for basic research. Modern genetic and genomic approaches rely on sequencing technology and require substantial bioinformatics expertise and access to solid computational resources. This Core leverages a proven history of expertise, as well as support and collaboration with other investigators in the Gandal and Geschwind groups with regards to computational and informatics resources funded by NIH and private foundations. Based on this proven track record, the Core will provide IDDRC investigators with the necessary expertise and infrastructure to perform high-throughput, genome-wide genetic and genomic studies. State-of- the-art analytical methods will be used to analyze NGS, gene expression, chromatin accessibility (e.g., ATAC-seq), methylation, and single cell/nucleus scRNA-seq data, and the resulting datasets will be posted onto a database accessible to IDDRC investigators, facilitating data sharing and collaborative analyses. Over the past 15 years, UCLA computational biologists and statisticians have lead the field of integrative data analysis (Geschwind and Konopka, 2009) and network-based methods (Oldham et al., 2008; Parikshak et al., 2013; Zhang and Horvath, 2005). Further, over the past 5 years, IDDRC investigators have published pioneering work interrogating the functional genomic landscape of human brain development, including the first comprehensive atlas of single-cell gene expression in the mid-gestation human brain (Polioudakis et al., 2019), high-resolution mapping of non-coding regulatory elements driving human neurogenesis with ATAC-seq (de La Torre Ubieta et al, 2018), and large-scale expression and splicing quantitative trait loci (QTL) profiling in fetal brain (Walker et al., 2019). Expertise in the development and implementation of these methods (including single-cell analysis, network methods, and integrative approaches) will be made directly available to IDDRC investigators.
