New technique maps genetic variants driving neurodegenerative disease risk
Disease development is often shaped by genetics, with how much or how little a gene is expressed influencing disease risk. While advances in technology and sequencing methods has led to a greater understanding of gene structure, location and function, conventional genetic studies often fail to connect genetic variants to actual changes in gene expression, particularly in rare or unique brain cell types. A new tool aims to change that.
A team of researchers has developed a new method that substantially improves the ability to map the genetic variants that drive disease, particularly neurodegenerative diseases. Instead of analyzing genetic effects by grouping cells into specific categories and determining genetic effects for each type individually, the team modeled the effects shared among seven different brain cell types.
The new approach, published in Nature Communications, outperforms existing methodologies, identifying approximately 75% more genes of interest. The researchers also found new genes linked to the risk of Alzheimer’s disease and amyotrophic lateral sclerosis (ALS) and therapeutic targets, some of which have already-existing promising treatments.
“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,” said the senior author of the study. “It has the potential to create a new paradigm for understanding brain related disease.”
The research team’s work focuses on understanding how genes influence disease risk. They explained that, for example, genes like APOE can increase the risk of developing Alzheimer’s disease between three-fold and nine-fold. Neuroinflammation may also play a role in the development of neurodegenerative diseases, something that the author explained specialized cells in the brain like microglia — the brain’s immune cells — may contribute to.
Scientists often use genome-wide association studies to identify regions of the genome associated with particular diseases. However, genome-wide association studies often rely on bulk tissue samples where different cell types are mixed together. It’s like a smoothie where all the ingredients are blended, making it difficult to distinguish one fruit, or cell type, from another and to detect individual flavors, or “signals,” from rare cell types, said a co-author on the study. More recent studies might instead utilize single-cell data, which allows scientists to investigate cell types individually, but sample sizes are typically small, especially for rare cell types like microglia.
“To better understand the risk of these genes in brain-related cell types, we need a better strategy to analyze the data,” the author said. “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.”
Here, the team developed a new method, dubbed BASIC for Bulk And Single cell eQTL Integration across Cell states, which integrates both bulk tissue samples and single-cell data. The researchers looked across cell types to identify combinations of genes that, when expressed, produced similar effects across multiple cell types as well as effects that were unique to certain cell types.
“Nature is extremely intelligent,” the author said. “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 of 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.”
Compared to conventional methods, the researchers identified roughly 75% more genes that may play a role in disease risk using BASIC. The improvement is equivalent to bolstering sample size by nearly 77%. When they applied this method to analyze 12 different brain-related diseases, like Alzheimer’s disease, ALS and addiction, the team was able to more accurately identify genetic targets linked to disease by over 53% compared to single-cell data alone and by 111% over bulk tissue analysis. They also identified new genes linked to neurodegenerative disease, including ALS and Alzheimer’s disease, that have been overlooked by conventional approaches.
The researchers then used this information to identify drug compounds that could reverse gene expression associated with disease, such as alfacalcidol — a synthetic version of vitamin D — for schizophrenia and cabergoline — which is typically prescribed to treat high levels of the hormone prolactin — for Alzheimer’s disease. These are existing medications that have already been approved by the Food and Drug Administration as safe and effective for treating other diseases and could potentially be repurposed. However, more research is needed to fully understand the implications of their findings, the team said.
