Ryan Christ

Topic: Moving from GWAS to Casual Genes and Variants

Project Summary: The size and ethnic diversity of emerging sequencing datasets are growing rapidly. Combining these data with emerging single cell omic datasets and AI models for predicting gene activity (eg: expression) offers an unprecedented opportunity to uncover the causal genes and cell types that drive human traits and disease. However, in emerging sequencing datasets, the strong, often perfect, linkage among associated ultra-rare variants can yield an unwieldy list of candidate causal variants. This problem is exacerbated by the presence of multiple causal variants (allelic heterogeneity) and migration events, both of which are more common in ethnically diverse datasets. This fine mapping enigma motivates our current research. Using novel statistical methods, we aim to develop an automated yet interpretable approach that does not seek to isolate causal variants, but rather to directly identify target genes and pathways from phenotypic and single cell xQTL data across different cohorts.