A new UCLA-led study has identified several new risk genes for Alzheimer’s disease and a rare, related brain disorder called progressive supranuclear palsy (PSP) using a combination of new testing methods enabling mass screening of genetic variants in a single experiment.
The study, published today in the journal Sciencealso presents a new, revised model showing how common genetic variants, while individually having a very low impact on disease, can collectively increase disease risk by disrupting specific transcription programs across the genome.
Typically, researchers have relied on genome-wide association studies (GWAS) in which they study the genomes of a large group of people to identify genetic variants that increase disease risk. This is done by testing for markers along the chromosome, or loci, associated with a disease. Each locus has on average dozens – and sometimes hundreds or thousands – of genetic markers in common that are co-inherited and therefore associated with disease, making it difficult to identify functional variants that cause disease.
Identifying causative variants and the genes they impact is a major challenge in modern genetics and biomedicine. This study provides an effective roadmap to address this issue.
For this study, the authors conducted one of the first known uses of high-throughput assays to study neurodegenerative diseases. The authors performed massively parallel reporter assays (MPRA) to simultaneously test 5,706 genetic variants in 25 loci associated with Alzheimer’s disease and nine loci associated with PSP, a neurological disease much rarer than Alzheimer’s disease. but with similar pathology.
From this test, the authors with high confidence were able to identify 320 functional genetic variants. To validate the results, they ran a clustered CRISPR screen on 42 of these high-confidence variants in multiple cell types.
“We have combined several advances that allow us to conduct high-throughput biology, in which instead of doing one experiment at a time, we do thousands of experiments in parallel in a sort of shared format. This allows us to approach this challenge of how to move thousands of disease-associated genetic variants to identifying which ones are functional and which genes they affect,” said Dr Dan Geschwind, the study’s corresponding author and Gordon Professor Emeritus. and Virginia MacDonald of Human Genetics, Neurology and Psychiatry at UCLA.
Their data provided evidence implicating several new risk genes for Alzheimer’s disease, including C4A, PVRL2 and APOC1, and other new risk genes for PSP (PLEKHM1 and KANSL1). The authors were also able to validate several previously identified risk loci. The next steps would be to study how the newly identified risk genes interact in model cells and systems, Geschwind said.
The study provides proof-of-principle that high-throughput testing can provide a “very effective” roadmap for further research, Geschwind said, but he stressed that these approaches need to be judiciously paired with more targeted experiments, as they were in this study.
“This success doesn’t mean we can abandon the kind of detailed, painstaking experimentation that studies individual genes in model systems,” he said. “It just provides a key step between GWAS and understanding disease mechanisms.”
Yonatan Cooper, the study’s lead author, said the combination of approaches taken by the researchers gave them greater confidence in their findings, while underscoring the inherent challenge of interpreting human genetic variation.
“We believe that the integration of multiple methodologies will be essential for future work annotating disease-related variation in research and clinical settings,” said Cooper, who is an MD candidate under the David Geffen School of UCLA Medical Scientist Training Program. Medicine.
The authors were also able to show in PSP at least one mechanism in which multiple disease-associated loci acted additively to disrupt a core set of transcription factors, which essentially turn genes on and off, known to work together in types of specific cells. . Geschwind said this indicates that common genetic variation located across the genome affects specific regulatory networks in specific cell types. This discovery, he said, identifies potential new drug targets and suggests that rather than targeting a gene, targeting a gene network could be an effective approach.
“We’re entering a new stage of therapies – it’s starting to be plausible to think about targeting networks,” Geschwind said.
University of California – Los Angeles Health Sciences