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A gene association
June 2019
by Mel J. Yeates  |  Email the author
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NEW YORK—Researchers at the Icahn School of Medicine at Mount Sinai and more than 200 collaborating institutions have identified 20 new genetic associations with bipolar disorder. The study, the largest of its kind, involved more than 50,000 subjects in 14 countries. The results were published in the May 2019 issue of Nature Genetics, in a paper entitled “Genome-wide association study identifies 30 loci associated with bipolar disorder.”
 
Bipolar disorder, a neuropsychiatric condition characterized by dramatic shifts in a person’s mood, affects approximately 60 million people globally and 10 million in the United States. Unlike other illnesses, bipolar disorder has been found to equally affect men, women and people of all ethnic groups. While genetic and environmental factors have been demonstrated as playing a role in the illness, the exact cause of bipolar disorder remains unknown. The identification of genes associated with bipolar disorder may be able to help identify therapeutic targets for treatment and prevention.
 
In order to identify genes associated with the disorder, researchers conducted a genome-wide association study (GWAS). While some of the findings reinforced hypotheses regarding the neurobiology of the disease—for example, its high heritability, as previously demonstrated in twin studies —this also uncovered new biological insights. The study was initiated by the late Dr. Pamela Sklar, who was chief of the Division of Psychiatric Genomics at Mount Sinai.
 
According to the Nature Genetics article’s abstract, the study looked at “20,352 [bipolar] cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P < 1 × 10−4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (P < 5 × 10−8) in the discovery GWAS were not genome-wide significant in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity.”
 
“In the combined analysis, 30 loci were genome-wide significant, including 20 newly identified loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene sets, including regulation of insulin secretion and endocannabinoid signaling,” the abstract continues.
 
In examining the genetic relationships between bipolar disorder and other psychiatric illnesses, the researchers discovered that eight of the genes they found to be associated with bipolar disorder harbored schizophrenia associations as well. Depression, in addition to other psychiatric-relevant traits such as autism spectrum disorder and anorexia nervosa, also had genetic ties to the disorder.
 
“Bipolar I disorder is strongly genetically correlated with schizophrenia, driven by psychosis, whereas bipolar II disorder is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential biological mechanisms for bipolar disorder,” the authors note in the abstract.
 
“The crux of this international collaborative study was, in essence, to connect the dots,” said Dr. Eli Stahl, assistant professor of Genetics and Psychiatry, at the Icahn School of Medicine at Mount Sinai. “By discovering new genes associated with bipolar disorder and demonstrating their overlap with genes found in other psychiatric disorders, we bring ourselves closer to finding the true genetic underpinnings of the disease and improving patient outcomes.”
 
Mount Sinai researchers have also recently identified 413 genetic associations with schizophrenia across 13 brain regions. According to the article entitled “Gene expression imputation across multiple brain regions provides insights into schizophrenia risk,” published recently in Nature Genetics, examining gene expression at the tissue level allowed researchers to not only identify new genes associated with schizophrenia, but to also pinpoint the areas of the brain in which abnormal expression might occur.
 
While schizophrenia affects less than 2 percent of the global population, it’s one of the leading causes of disability worldwide. While it is widely believed that numerous genes contribute to increased risk of schizophrenia development, the exact genetic underpinnings are poorly understood.
 
Mount Sinai researchers used GWAS findings, coupled with transcriptomic imputation, to identify schizophrenia-associated disease with tissue-level resolution. Transcriptomic imputation is a machine learning technique that allows researchers to test associations between disease and gene expression in otherwise inaccessible tissues.
 
The researchers studied 40,299 people with schizophrenia and 65,264 matched controls, and discovered that genes associated with schizophrenia are expressed throughout development—some during specific stages of pregnancy, and others during adolescence or adulthood. Researchers also learned that different regions of the brain confer different risks for schizophrenia, with most associations coming from the dorsolateral prefrontal cortex.
 
“Our new predictor models gave us unprecedented power to study predicted gene expression in schizophrenia, and to identify new risk genes associated with the disease,” explained Dr. Laura Huckins, an assistant professor of genetics and genomic sciences and psychiatry at the Icahn School of Medicine at Mount Sinai. “In particular, it was fascinating to see schizophrenia risk genes expressed throughout development, including in early pregnancy. By laying the groundwork for combining transcriptomic imputation and genome-wide association study findings, our hope is to not only elucidate gene development as it relates to schizophrenia, but also shape the future of research methods and design.”
 
Huckins was recently awarded an NIH Research Project Grant to develop these transcriptomic imputation models further and use them to elucidate the genetic architecture of schizophrenia, bipolar disorder and anorexia nervosa.
 
Code: E061908

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