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New Approach Identifies Candidate Causal Variants for Neuropsychiatric, Neurodevelopmental Conditions

Genomic Data

NEW YORK — Using a new approach that combines gene expression data and genome-wide associations, researchers have uncovered candidate causal variants for a number of neuropsychiatric and neurodevelopmental conditions.

GWAS have pointed to hundreds of genetic loci linked to disorders such as Alzheimer's disease, schizophrenia, or bipolar disorder, but homing in on causal variants and identifying biological mechanisms contributing to the traits has been more difficult. To get to these, researchers from the Icahn School of Medicine at Mount Sinai developed a multivariate multiple quantitative trait loci, or mmQTL, pipeline that combines multi-ancestry eQTL fine mapping with results from brain trait-related genome-wide association studies.

By applying their pipeline to a combination of various collections of brain tissue and sequencing data, the researchers identified 329 variant-trait pairs for two dozen brain-related traits, as they reported in Nature Genetics on Thursday. In particular, they identified candidate causal variants for schizophrenia, bipolar disorder, and Alzheimer's disease, some of which suggest possible biological mechanisms.

"Further integration of multiomics data with multi-ancestry fine-mapping and large-scale GWAS promises to yield further insight into the molecular mechanisms underlying disease risk," Mount Sinai's Panos Roussos and colleagues noted in their paper.

With their mmQTL approach, the researchers performed a multi-ancestry eQTL meta-analysis on RNA sequence data from various brain regions collected by the PsychENCODE, ROSMAP, and GTEx studies. This dataset encompassed more than 2,000 donors, including 474 individuals of non-European ancestry. The researchers accounted for ancestry by applying a linear mixed model to their datasets and performed statistical fine mapping of the eQTL meta-analysis together with GWAS fine mapping to uncover candidate causal variants.

This approach, which the researchers first tested using simulations, addresses drawbacks of transcript-wide association studies and fine mapping, the researchers said. The former can be hampered by the inability to tease apart correlated expression and coregulation, while fine mapping can be limited by statistical power and the influence of linkage disequilibrium, they added.

With their approach, the researchers homed in on hundreds of variant-trait pairs for 24 brain-related traits. For both schizophrenia and bipolar disorder, they identified candidate causal variants in 20 genes, including the top-ranked genes ZNF823, THOC7, and FURIN. These genes, they noted, have all previously been linked to schizophrenia or bipolar disorder, and while the candidate causal variant in FURIN has previously been experimentally validated, variants in the other genes have not.

For Alzheimer's disease, the researchers identified a candidate causal variant that hints at a possible molecular mechanism at play. The variant leads to a single amino acid change in APH1B, a subunit of the gamma-secretase complex. Though a previous GWAS also identified this variant, work to validate its functional effect was not successful. Here, though, the researchers found that the variant increases the expression of APH1B to contribute to Alzheimer's disease risk.

Meanwhile, the top hit for schizophrenia is expected to affect the expression of the zinc finger protein ZNF823 and be protective against the disorder. The researchers noted that this variant appears to disrupt a binding site for the RE1 silencing transcription factor, REST. REST is upregulated in neurogenesis and in non-neuronal cells, where it silences neuron-specific genes. Decreased binding of REST should then lead to an increased expression of ZNF823, which the researchers noted is supported by their analysis of chromatin accessibility data.

"Efforts to trace the chain of causality from variants and molecular mechanisms to pleiotropy across complex phenotypes are poised to yield insight into novel therapeutic targets," Roussos and colleagues wrote.

The researchers added that while their analysis focused on schizophrenia, bipolar disorder, and Alzheimer's disease, all results are available at their Brain eQTL meta-analysis resource page.