Scientists headed by a team at the University of North Carolina (UNC) School of Medicine have developed a new computational tool called H-MAGMA that can link noncoding genetic variants to the genes that have been associated with psychiatric and neurodegenerative disorders through genome-wide association studies (GWAS). They are making the tool publicly available so that it can be widely applied to help expand research into brain-related conditions.

The UNC School of Medicine lab of Hyejung Won, PhD, studies the genetic underpinnings of psychiatric conditions and neurodegenerative diseases. [Hyejung Won Lab, UNC School of Medicine]

Their reported studies using the new H-MAGMA tool linked disease-associated genes to specific brain cell types, and found that genes associated with psychiatric disorders are typically expressed early in life, indicating that this early period of life is critical in the development of psychiatric illnesses. In contrast, the study findings suggested that neurodegenerative disorder-associated genes are typically expressed later in life. The research, which identified new genes associated with nine brain disorders, is reported in Nature Neuroscience.

“By using H-MAGMA, we were able to link noncoding variants to their target genes, a challenge that had previously limited scientists’ ability to derive biologically meaningful hypotheses from genome-wide association studies of brain disorders,” said study senior author Hyejung Won, PhD, assistant professor of genetics at the UNC School of Medicine and member of the UNC Neuroscience Center. “Additionally, we uncovered important biology underlying the genetics of brain disorders, and we think these molecular mechanisms could serve as potential targets for treatment.”

Won and colleagues reported their findings in a paper titled, “A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating chromatin interaction profiles.”

Genome-wide association studies allow researchers to compare the genetic backgrounds of thousands of individuals who have a disease, with those of control subjects who don’t have the disease, to try and understand which genes may be involved in disease development or progression. GWAS have revolutionized our understanding of the genetic architecture related to many health conditions, including brain-related disorders. However, the authors wrote, “ … extracting biological mechanisms from GWAS data is a challenge, which is largely because the majority of common risk variants reside in noncoding regions of the genome.”

As Won noted, “To date, we know of hundreds of genomic regions associated with a person’s risk of developing a disorder … understanding how those genetic variants impact

Hyejung Won
Hyejung Won, PhD

health remained a challenge because the majority of the variants are located in regions of the genome that do not make proteins. They are called noncoding genetic variants. Thus, their specific roles have not been clearly defined.” And although the precise roles of noncoding variants may not be understood, prior research suggested that although these stretches don’t directly encode proteins, they can interact with and regulate gene expression.

Scientists had previously developed a technology known as multimarker analysis of genomic annotation (MAGMA), which can link risk variants to their cognate genes. Although the technology is now widely used, it does have some drawbacks, the investigators commented. “It aggregates single nucleotide polymorphism (SNP) associations to gene-level associations while correcting for confounding factors such as gene length, minor allele frequency, and gene density,” they wrote. “While MAGMA is a powerful tool and is broadly used, there is room for improvement.” MAGMA assigns SNPs to the nearest genes, but it is known that noncoding SNPs may actually impact on genes that are quite a long way distant from the noncoding sequence. MAGMA also doesn’t take into account tissue-specific regulatory relationships, “where disease-risk SNPs are enriched in regulatory elements of the disease-relevant tissue,” the authors noted.

The platform developed by Won’s team, called Hi-C-coupled MAGMA (H-MAGMA), assigns noncoding SNPs to their cognate genes based on long-range interactions in disease-relevant tissues. “H-MAGMA advances conventional MAGMA … by incorporating relevant functional genomic evidence and allowing developmental-stage-specific and cell-type-specific gene mapping,” the scientists explained. They used the new technology to analyze gene regulatory relationships in disease-relevant brain tissue, and identify neurobiologically relevant genes in the nine psychiatric and neurodegenerative disorders. This allowed them to link noncoding variants to interacting genes that had already been implicated in previous GWAS findings. “It also allows the comparison of different GWAS to elucidate shared biological pathways,” they wrote. “Given the importance of noncoding variants, and that they make up a large proportion of GWAS findings, we sought to link them to the genes they interact with, using a map of chromatin interaction in the human brain,” Won stated.

The nine disorders included in their analyses included the five psychiatric disorders, attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD), together with four neurodegenerative disorders, amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS), Alzheimer’s disease (AD), and Parkinson’s disease (PD).

Their results indicated differences in genetic architecture between the psychiatric, and the neurodegenerative disorders. “By comparing prenatal and postnatal expression trajectories, we found that genes associated with psychiatric disorders show remarkable developmental convergence onto mid-gestation, while genes associated with degenerative disorders were gradually increased across the life span, which reflects their increased burden with aging,” they stated.

Another key question in brain disorder research is how to determine cellular etiology—the cells involved in the root cause of disease. This is critical to our understanding of brain diseases because different cell types may act differently in response to treatment. As part of their analyses, the team looked for critical cell types for each brain disorder, and found that genes associated with psychiatric disorders are highly expressed in glutamatergic neurons, whereas genes associated with neurodegenerative disorders are highly expressed in glia. This finding further indicated divergence between the two disorder clusters, with the cellular expression profiles indicating convergence among psychiatric disorders.

Convergence among psychiatric disorders was also indicated by the cellular expression profiles, the researchers noted. “Psychiatric-disorder-associated genes were selectively expressed in excitatory neurons, while degenerative-disorder-associated genes showed more diverse cellular enrichment profiles … These results demonstrate that the shared genetic basis of psychiatric disorders translates into shared neurobiological mechanisms.” Won said, “… we classified biological processes central to the disorders. From this analysis, we found that the generation of new brain cells, transcriptional regulation, and immune response as being essential to many brain disorders.”

Won and colleagues also generated a list of shared genes across psychiatric disorders to describe common biological principles that link psychiatric disorders. “Amongst the shared genes, we once again identified the brain’s early developmental process as being critical and upper layer neurons as being the fundamental cell-types involved,” Won said. “We unveiled the molecular mechanism that underscores how one gene can affect two or more psychiatric diseases.”

As the authors stated, “Pleiotropic genes were associated with neuronal development and synaptic plasticity, which suggests that inappropriate neuronal activity and regulation may act as key components in the pathogenesis of psychiatric disorders. Pleiotropic genes also displayed mid-gestational and excitatory neuronal enrichment, which summarizes the overall pattern of psychiatric-disorder-associated genes.”

The researchers have made the H-MAGMA tool publicly available so that it can be widely applied and available to the genetics and neuroscience community to help expand research, with the ultimate goal of helping people who suffer with brain-related conditions. “Altogether, H-MAGMA can help develop neurobiologically relevant hypotheses from GWAS by incorporating higher-order chromatin interactions in a disease-relevant context.”


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