Technique Maps Genetic Variants that Drive Brain Diseases

Gene expression influences disease risk, but conventional genetic studies struggle to connect variants to changes in rare brain cell types. A Penn State College of Medicine team recently developed BASIC, or Bulk And Single cell eQTL Integration across Cell states, which models shared genetic effects across seven brain cell types by combining bulk tissue and single-cell data. Published in Nature Communications, this approach identifies roughly 75% more disease-related genes than prior methods, equivalent to increasing sample size by nearly 77%, and uncovers new targets for Alzheimer’s disease and ALS with links to existing treatments.

Senior author Bibo Jiang explained, “There’s a lot of emphasis on data generation, but relatively modest efforts devoted to better analyzing the data. There’s a lot more information that could be extracted from existing data sets and our work seeks to better digest this information.” Genome-wide association studies often use mixed cell types, likened by co-author Dajiang Liu to a smoothie that hides signals from rare cells like microglia, which may drive neuroinflammation alongside genes such as APOE that raise Alzheimer’s risk three- to nine-fold.

According to Liu, “To better understand the risk of these genes in brain-related cell types, we need a better strategy to analyze the data. We can’t change the proportion of cell types—what’s rare is rare—but rare cell types share many of the same genetic effects as the more common cell types. If we can better determine what is shared and what is distinct between brain cell types, we can better understand how rare cell types like microglia potentially influence disease risk.” BASIC reveals both shared and unique effects, outperforming single-cell analysis by over 53% and bulk tissue by 111% across 12 brain diseases including addiction.

Liu added, “Nature is extremely intelligent. Genes that are critical for human survival often have shared effects across different cell types. If it’s critical for survival, you want more than one cell type to express that gene. With this combination, we can much more powerfully analyze which genes are associated with disease risk and how the genes are regulated in brain cells.”