Large scale “-omics” data sets have increasing impact across the drug development lifecycle from target discovery through precision medicine by enabling biological insights at the molecular, cellular, and patient level. In this presentation, I will describe several case studies where big molecular data sets are influenced by patient ancestry, demographics, clinical characteristics, etc. Understanding and accounting for diversity in -omics analysis is essential to generate accurate and generalizable insights.
Learning Objectives:
Inform participants of the impact that ancestral, demographic, and other subject characteristics have on -omics data setc.
Empasize the need to record, document, take into account, and explore these influences on -omics data.
Provide examples of how to account for subject diversity in a way that enables more robust, accurate, and generalizable insights.