Functional regulatory variants implicate distinct transcriptional networks in dementia

Predicting the function of noncoding variation is a major challenge in modern genetics. In this study, we used massively parallel reporter assays to screen 5706 variants identified from genome-wide association studies for both Alzheimer’s disease (AD) and progressive supranuclear palsy (PSP), identifying 320 functional regulatory variants (frVars) across 27 loci, including the complex 17q21.31 region. We identified and validated multiple risk loci using CRISPR interference or excision, including complement 4 (C4A) and APOC1 in AD and PLEKHM1 and KANSL1 in PSP. Functional variants disrupt transcription factor binding sites converging on enhancers with cell type–specific activity in PSP and AD, implicating a neuronal SP1-driven regulatory network in PSP pathogenesis. These analyses suggest that noncoding genetic risk is driven by common genetic variants through their aggregate activity on specific transcriptional programs. Description Zooming in on dementia genes Neurodegenerative disorders are a major problem worldwide, and their incidence is continuing to increase. These are usually complex polygenic traits, and although many gene loci have been identified in association with these disorders, the functional roles of these loci are unclear and most of them fall within noncoding regions of the genome. Cooper et al. used massively parallel reporter assays to screen noncoding variants reported in genome-wide association studies of two neurodegenerative disorders, followed by functional validation in neurons and microglia. With this combination of methods, the authors identified regulatory variants in several different genes that play functional roles in disease pathogenesis as well as interactions between these genes. —YN High-throughput functional analysis reveals cell-type-specific regulatory networks that mediate genetic risk for dementia. INTRODUCTION The widespread adoption of genome-wide association studies (GWASs) has revolutionized the detection of genetic loci associated with complex traits. However, identification of the causal variants and mechanisms underlying genotype–phenotype associations poses an enormous challenge because most common susceptibility loci reside in noncoding genomic regions and are composed of many correlated polymorphisms owing to linkage disequilibrium (LD). Massively parallel reporter assays (MPRAs) permit the high-throughput functional characterization of noncoding genetic variation, yet they have not been systematically applied to neurodegenerative disease. In this study, we used MPRA coupled with CRISPR-based validation to identify likely causal genetic variants underlying two neurodegenerative conditions that are neuropathologically linked by intracellular tau protein aggregation—Alzheimer’s disease (AD) and progressive supranuclear palsy (PSP). RATIONALE Neurodegenerative dementias such as AD and PSP are a major cause of morbidity and mortality worldwide. There are no disease-modifying therapeutics for either disorder, motivating major efforts to characterize disease pathogenesis. Identifying causal genetic factors and downstream associated risk genes are fundamental initial steps in developing a mechanistic understanding that would enable therapeutic development. RESULTS We tested the transcriptional regulatory activity of 5706 noncoding single-nucleotide variants, representing 25 genome-wide significant loci associated with AD and nine loci associated with PSP, by MPRA, using the neuroepithelial-like human embryonic kidney 293T (HEK293T) cell line. We identified 320 different functional regulatory variants (frVars) that affect gene expression within 27 of these loci. AD frVars were enriched within microglial enhancers, whereas PSP frVars were enriched within neuronal enhancers and, to a lesser extent, oligodendrocytes, consistent with differential cellular impact in each disease. The majority of frVars (94%) overlapped two or more known functional annotations in human brain tissue or blood, nearly two-thirds of which were predicted to disrupt transcription factor binding, indicating their potential relevance in human disease. Forty-two high-confidence regulatory variants distributed across 15 AD loci and three PSP loci were selected for validation, using either CRISPR droplet sequencing (CROP-seq) or direct CRISPR excision in induced pluripotent stem cell–derived neurons, microglia, and astrocytes, enabling validation of 19 functional variants, implicating 20 risk genes across 11 loci. Our data provide evidence implicating C4A, PVRL2, and APOC1 as risk genes in AD and PLEKHM1 and KANSL1 as risk genes in PSP, as well as additional validation for more than a dozen other genes. MPRA-defined functional variants preferentially disrupt predicted transcription factor binding sites (TFBSs) that converge on enhancers with differential cell type–specific activity in PSP and AD, implicating a neuronal SP1-driven regulatory network in PSP pathogenesis. CONCLUSION We provide systematic characterization of common variants underlying disease risk for two distinct neurodegenerative disorders, AD and PSP. Our work illustrates the utility of integrating multiplexed reporter and CRISPR assays to efficiently characterize noncoding disease-associated variation, thereby permitting the identification of risk genes, even in complex loci with high LD. These analyses support a mechanism underlying noncoding genetic risk, whereby common genetic variants drive disease risk in aggregate through polygenic cell type–specific regulatory effects on specific gene networks. Massively parallel functional genomics identifies genes and mechanisms in dementia. We used MPRA to screen thousands of common genetic variants associated with either AD or PSP to identify 300 likely functional regulatory variants. When coupled with CRISPR editing, this approach permitted the dissection of multiple risk loci and identification of several risk genes. The ability to view the aggregate activity of genetic risk shows convergence on disease- and cell type–specific transcriptional networks. TF, transcription factor; PPI, protein–protein interaction.