Posts tagged genomics
Posts tagged genomics
Scientists at The University of Manchester have used a new way of working to identify a new gene linked to neurodegenerative diseases such as Alzheimer’s. The discovery fills in another piece of the jigsaw when it comes to identifying people most at risk of developing the condition.
Researcher David Ashbrook and colleagues from the UK and USA used two of the world’s largest collections of scientific data to compare the genes in mice and humans. Using brain scans from the ENIGMA Consortium and genetic information from The Mouse Brain Library, he was able to identify a novel gene, MGST3 that regulates the size of the hippocampus in both mouse and human, which is linked to a group of neurodegenerative diseases. The study has just been published in the journal BMC Genomics.
David, who works in Dr Reinmar Hager’s lab at the Faculty of Life Sciences, says: “There is already the ‘reserve hypothesis’ that a person with a bigger hippocampus will have more of it to lose before the symptoms of Alzheimer’s are spotted. By using ENIGMA to look at hippocampus size in humans and the corresponding genes and then matching those with genes in mice from the BXD system held in the Mouse Brain Library database we could identify this specific gene that influences neurological diseases.”
He continues: “Ultimately this could provide another biomarker in the toolkit for identifying those at greatest risk of developing diseases such as Alzheimer’s.”
Dr Hager, senior author of the study, says: “What is critical about this research is that we have not only been able to identify this specific gene but also the networks it uses to influence a disease like Alzheimer’s. We believe this information will be incredibly useful for future studies looking at treatments and preventative measures.”
The ENIGMA Consortium is led by Professor Paul Thompson based at the University of California, Los Angeles, and contains brain images and gene information from nearly 25,000 subjects. The Mouse Brain Library, established by Professor Robert Williams based at the University of Tennessee Health Science Center, contains data on over 10,000 brains and numerical data from just over 20,000 mice.
David explains why combining the information held by both databases is so useful: “The key advantage of working this way is that it is much easier to identify a genetic variant in mice as they live in such controlled environments. By taking the information from mice and comparing it to human gene information we can identify the same variant much more quickly.”
And David thinks this way of working will be used more often in the future: “We are living in a big data world thanks to the likes of the Human Genome Project and post-genome technologies. A lot of that information is now widely shared so by mining what we already know we can learn so much more, advancing our knowledge of diseases and ultimately improving detection and treatment.”
New research shows that schizophrenia isn’t a single disease but a group of eight genetically distinct disorders, each with its own set of symptoms. The finding could be a first step toward improved diagnosis and treatment for the debilitating psychiatric illness.
The research at Washington University School of Medicine in St. Louis is reported online Sept. 15 in The American Journal of Psychiatry.
About 80 percent of the risk for schizophrenia is known to be inherited, but scientists have struggled to identify specific genes for the condition. Now, in a novel approach analyzing genetic influences on more than 4,000 people with schizophrenia, the research team has identified distinct gene clusters that contribute to eight different classes of schizophrenia.
“Genes don’t operate by themselves,” said C. Robert Cloninger, MD, PhD, one of the study’s senior investigators. “They function in concert much like an orchestra, and to understand how they’re working, you have to know not just who the members of the orchestra are but how they interact.”
Cloninger, the Wallace Renard Professor of Psychiatry and Genetics, and his colleagues matched precise DNA variations in people with and without schizophrenia to symptoms in individual patients. In all, the researchers analyzed nearly 700,000 sites within the genome where a single unit of DNA is changed, often referred to as a single nucleotide polymorphism (SNP). They looked at SNPs in 4,200 people with schizophrenia and 3,800 healthy controls, learning how individual genetic variations interacted with each other to produce the illness.
In some patients with hallucinations or delusions, for example, the researchers matched distinct genetic features to patients’ symptoms, demonstrating that specific genetic variations interacted to create a 95 percent certainty of schizophrenia. In another group, they found that disorganized speech and behavior were specifically associated with a set of DNA variations that carried a 100 percent risk of schizophrenia.
“What we’ve done here, after a decade of frustration in the field of psychiatric genetics, is identify the way genes interact with each other, how the ‘orchestra’ is either harmonious and leads to health, or disorganized in ways that lead to distinct classes of schizophrenia,” Cloninger said.
Although individual genes have only weak and inconsistent associations with schizophrenia, groups of interacting gene clusters create an extremely high and consistent risk of illness, on the order of 70 to 100 percent. That makes it almost impossible for people with those genetic variations to avoid the condition. In all, the researchers identified 42 clusters of genetic variations that dramatically increased the risk of schizophrenia.
“In the past, scientists had been looking for associations between individual genes and schizophrenia,” explained Dragan Svrakic, PhD, MD, a co-investigator and a professor of psychiatry at Washington University. “When one study would identify an association, no one else could replicate it. What was missing was the idea that these genes don’t act independently. They work in concert to disrupt the brain’s structure and function, and that results in the illness.”
Svrakic said it was only when the research team was able to organize the genetic variations and the patients’ symptoms into groups that they could see that particular clusters of DNA variations acted together to cause specific types of symptoms.
Then they divided patients according to the type and severity of their symptoms, such as different types of hallucinations or delusions, and other symptoms, such as lack of initiative, problems organizing thoughts or a lack of connection between emotions and thoughts. The results indicated that those symptom profiles describe eight qualitatively distinct disorders based on underlying genetic conditions.
The investigators also replicated their findings in two additional DNA databases of people with schizophrenia, an indicator that identifying the gene variations that are working together is a valid avenue to explore for improving diagnosis and treatment.
By identifying groups of genetic variations and matching them to symptoms in individual patients, it soon may be possible to target treatments to specific pathways that cause problems, according to co-investigator Igor Zwir, PhD, research associate in psychiatry at Washington University and associate professor in the Department of Computer Science and Artificial Intelligence at the University of Granada, Spain.
And Cloninger added it may be possible to use the same approach to better understand how genes work together to cause other common but complex disorders.
“People have been looking at genes to get a better handle on heart disease, hypertension and diabetes, and it’s been a real disappointment,” he said. “Most of the variability in the severity of disease has not been explained, but we were able to find that different sets of genetic variations were leading to distinct clinical syndromes. So I think this really could change the way people approach understanding the causes of complex diseases.”
The largest genomic dragnet of any psychiatric disorder to date has unmasked 108 chromosomal sites harboring inherited variations in the genetic code linked to schizophrenia, 83 of which had not been previously reported. By contrast, the “skyline” of such suspect variants associated with the disorder contained only 5 significant peaks in 2011. By combining data from all available schizophrenia genetic samples, researchers supported by the National Institutes of Health powered the search for clues to the molecular basis of the disorder to a new level.
“While the suspect variation identified so far only explains only about 3.5 percent of the risk for schizophrenia, these results warrant exploring whether using such data to calculate an individual’s risk for developing the disorder might someday be useful in screening for preventive interventions,” explained Thomas R. Insel, M.D., director of the NIH’s National Institute of Mental Health, one funder of the study. “Even based on these early predictors, people who score in the top 10 percent of risk may be up to 20-fold more prone to developing schizophrenia.”
The newfound genomic signals are not simply random sites of variation, say the researchers. They converge around pathways underlying the workings of processes involved in the disorder, such as communication between brain cells, learning and memory, cellular ion channels, immune function and a key medication target.
The Schizophrenia Working Group of the Psychiatric Genomic Consortium (PGC) reports on its genome-wide analysis of nearly 37,000 cases and more than 113,000 controls in the journal Nature, July 21, 2014. The NIMH-supported PGC represents more than 500 investigators at more than 80 research institutions in 25 countries.
Prior to the new study, schizophrenia genome-wide studies had identified only about 30 common gene variants associated with the disorder. Sample sizes in these studies were individually too small to detect many of the subtle effects on risk exerted by such widely shared versions of genes. The PGC investigators sought to maximize statistical power by re-analyzing not just published results, but all available raw data, published and unpublished. Their findings of 108 illness-associated genomic locations were winnowed from an initial pool of about 9.5 million variants.
A comparison of the combined study data with findings in an independent sample of cases and controls suggest that considerably more such associations of this type are likely to be uncovered with larger sample sizes, say the researchers.
There was an association confirmed with variation in the gene that codes for a receptor for the brain chemical messenger dopamine, which is known to be the target for antipsychotic medications used to treat schizophrenia. Yet evidence from the study supports the view that most variants associated with schizophrenia appear to exert their effects via the turning on and off of genes rather than through coding for proteins.
The study found a notable overlap between protein-related functions of some linked common variants and rare variants associated with schizophrenia in other studies. These included genes involved in communication between neurons via the chemical messenger glutamate, learning and memory, and the machinery controlling the influx of calcium into cells.
“The overlap strongly suggests that common and rare variant studies are complementary rather than antagonistic, and that mechanistic studies driven by rare genetic variation will be informative for schizophrenia,” say the researchers.
Among the strongest associations detected, as in in previous genome-wide genetic studies, was for variation in tissues involved in immune system function. Although the significance of this connection for the illness process remains a mystery, epidemiologic evidence has long hinted at possible immune system involvement in schizophrenia.
Findings confirm that it’s possible to develop risk profile scores based on schizophrenia-associated variants that may be useful in research – but for now aren’t ready to be used clinically as a predictive test, say the researchers.
They also note that the associated variations detected in the study may not themselves be the source of risk for schizophrenia. Rather, they may be signals indicating the presence of disease-causing variation nearby in a chromosomal region.
Researchers are following up with studies designed to pinpoint the specific sequences and genes that confer risk. The PGC is also typing genes in hundreds of thousands of people worldwide to enlarge the sample size, in hopes of detecting more genetic variation associated with mental disorders. Successful integration of data from several GWAS studies suggests that this approach would likely be transferrable to similar studies of other disorders, say the researchers.
“These results underscore that genetic programming affects the brain in tiny, incremental ways that can increase the risk for developing schizophrenia,” said Thomas Lehner, Ph.D., chief of NIMH’s Genomics Research Branch. “They also validate the strategy of examining both common and rare variation to understand this complex disorder.”
Exceptional Evolutionary Divergence of Human Muscle and Brain Metabolomes Parallels Human Cognitive and Physical Uniqueness
Metabolite concentrations reflect the physiological states of tissues and cells. However, the role of metabolic changes in species evolution is currently unknown. Here, we present a study of metabolome evolution conducted in three brain regions and two non-neural tissues from humans, chimpanzees, macaque monkeys, and mice based on over 10,000 hydrophilic compounds. While chimpanzee, macaque, and mouse metabolomes diverge following the genetic distances among species, we detect remarkable acceleration of metabolome evolution in human prefrontal cortex and skeletal muscle affecting neural and energy metabolism pathways. These metabolic changes could not be attributed to environmental conditions and were confirmed against the expression of their corresponding enzymes. We further conducted muscle strength tests in humans, chimpanzees, and macaques. The results suggest that, while humans are characterized by superior cognition, their muscular performance might be markedly inferior to that of chimpanzees and macaque monkeys.
The overall number and nature of mutations—rather than the presence of any single mutation—influences an individual’s risk of developing schizophrenia, as well as its severity, according to a discovery by Columbia University Medical Center researchers published in the latest issue of Neuron. The findings could have important implications for the early detection and treatment of schizophrenia.
Maria Karayiorgou, MD, professor of psychiatry and Joseph Gogos, MD, PhD, professor of physiology and cellular biophysics and of neuroscience, and their team sequenced the “exome”—the region of the human genome that codes for proteins—of 231 schizophrenia patients and their unaffected parents. Using this data, they demonstrated that schizophrenia arises from collective damage across several genes.
“This study helps define a specific genetic mechanism that explains some of schizophrenia’s heritability and clinical manifestation,” said Dr. Karayiorgou, who is acting chief of the Division of Psychiatric and Medical Genetics at the New York State Psychiatric Institute. “Accumulation of damaged genes inherited from healthy parents leads to higher risk not only to develop schizophrenia but also to develop more severe forms of the disease.”
Schizophrenia is a severe psychiatric disorder in which patients experience hallucination, delusion, apathy and cognitive difficulties. The disorder is relatively common, affecting around 1 in every 100 people, and the risk of developing schizophrenia is strongly increased if a family member has the disease. Previous research has focused on the search for individual genes that might trigger schizophrenia. The availability of new high-throughput DNA sequencing technology has contributed to a more holistic approach to the disease.
The researchers compared sequencing data to look for genetic differences and identify new loss-of-function mutations—which are rarer, but have a more severe effect on ordinary gene function—in cases of schizophrenia that had not been inherited from the patients’ parents. They found an excess of such mutations in a variety of genes across different chromosomes.
Using the same sequencing data, the researchers also looked at what types of mutations are commonly passed on to schizophrenia patients from their parents. It turns out that many of these are “loss-of-function” types. These mutations were also found to occur more frequently in genes with a low tolerance for genetic variation.
“These mutations are important signposts toward identifying the genes involved in schizophrenia,” said Dr. Karayiorgou.
The researchers then looked more deeply into the sequencing data to try to determine the biological functions of the disrupted genes involved in schizophrenia. They were able to verify two key damaging mutations in a gene called SETD1A, suggesting that this gene contributes significantly to the disease.
SETD1A is involved in a process called chromatin modification. Chromatin is the molecular apparatus that packages DNA into a smaller volume so it can fit into the cell and physically regulates how genes are expressed. Chromatin modification is therefore a crucial cellular activity.
The finding fits with accumulating evidence that damage to chromatin regulatory genes is a common feature of various psychiatric and neurodevelopmental disorders. By combining the mutational data from this and related studies on schizophrenia, the authors found that “chromatin regulation” was the most common description for genes that had damaging mutations.
“A clinical implication of this finding is the possibility of using the number and severity of mutations involved in chromatin regulation as a way to identify children at risk of developing schizophrenia and other neurodevelopmental disorders,” said Dr. Gogos. “Exploring ways to reverse alterations in chromatic modification and restore gene expression may be an effective path toward treatment.”
In further sequencing studies, the researchers hope to identify and characterize more genes that might play a role in schizophrenia and to elucidate common biological functions of the genes.
Vast gene-expression map yields neurological and environmental stress insights
A consortium led by scientists from the U.S. Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) has conducted the largest survey yet of how information encoded in an animal genome is processed in different organs, stages of development, and environmental conditions. Their findings paint a new picture of how genes function in the nervous system and in response to environmental stress.
They report their research this week in the Advance Online Publication of the journal Nature.
The scientists studied the fruit fly, an important model organism in genetics research. Seventy percent of known human disease genes have closely related genes in the fly, yet the fly genome is one-thirtieth the size of ours. Previous fruit fly research has provided insights on cancer, birth defects, addictive behavior, and neurological diseases. It has also advanced our understanding of processes common to all animals such as body patterning and synaptic transmission.
In the latest scientific fruit from the fruit fly, the consortium, led by Susan Celniker of Berkeley Lab’s Life Sciences Division, generated the most comprehensive map of gene expression in any animal to date. Scientists from the University of California at Berkeley, Indiana University at Bloomington, the University of Connecticut Health Center, and several other institutions contributed to the research.
A large international consortium of researchers has produced the first comprehensive, detailed map of the way genes work across the major cells and tissues of the human body. The findings describe the complex networks that govern gene activity, and the new information could play a crucial role in identifying the genes involved with disease.
“Now, for the first time, we are able to pinpoint the regions of the genome that can be active in a disease and in normal activity, whether it’s in a brain cell, the skin, in blood stem cells or in hair follicles,” said Winston Hide, associate professor of bioinformatics and computational biology at Harvard School of Public Health (HSPH) and one of the core authors of the main paper in Nature. “This is a major advance that will greatly increase our ability to understand the causes of disease across the body.”
The research is outlined in a series of papers published March 27, 2014, two in the journal Nature and 16 in other scholarly journals. The work is the result of years of concerted effort among 250 experts from more than 20 countries as part of FANTOM 5 (Functional Annotation of the Mammalian Genome). The FANTOM project, led by the Japanese institution RIKEN, is aimed at building a complete library of human genes.
Researchers studied human and mouse cells using a new technology called Cap Analysis of Gene Expression (CAGE), developed at RIKEN, to discover how 95% of all human genes are switched on and off. These “switches”—called “promoters” and “enhancers”—are the regions of DNA that manage gene activity. The researchers mapped the activity of 180,000 promoters and 44,000 enhancers across a wide range of human cell types and tissues and, in most cases, found they were linked with specific cell types.
“We now have the ability to narrow down the genes involved in particular diseases based on the tissue cell or organ in which they work,” said Hide. “This new atlas points us to the exact locations to look for the key genetic variants that might map to a disease.”
Researchers may have been focusing on the wrong gene.
Scientists studying what they thought was a ‘fat gene’ seem to have been looking in the wrong place, according to research published today in Nature. It suggests instead that the real culprit is another gene that the suspected obesity gene interacts with.
In 2007, several genome studies identified mutations in a gene called FTO that were strongly associated with an increased risk of obesity and type 2 diabetes in humans. Subsequent studies in mice showed a link between the gene and body mass. So researchers, including Marcelo Nóbrega, a geneticist at the University of Chicago, thought that they had found a promising candidate for a gene that helped cause obesity.
The mutations were located in non-coding portions of FTO involved in regulating gene expression. But when Nóbrega looked closer, he found that something was amiss. These regulatory regions contained some elements that are specific for the lungs, one of the few tissues in which FTO is not expressed. “This made us pause,” he says. “Why are there regulatory elements that presumably regulate FTO in the tissue where it isn’t expressed?”
This was not the first red flag. Previous attempts to find a link between the presence of the obesity-associated mutations and the expression levels of FTO had been a “miserable failure”, he says. When Nóbrega presented his new results at meetings, he adds that many people came to him to say ‘I just knew there was something wrong here’.
So Nóbrega’s team cast the net wider, looking for genes in the broader neighbourhood of FTO whose expression matched that of the mutations, and found IRX3, a gene about half a million base pairs away. IRX3 encodes a transcription factor — a type of protein involved in regulating the expression of other genes — and is highly expressed in the brain, consistent with a role in regulating energy metabolism and eating behaviour.
When they examined the looping three-dimensional structure of the chromosome on which both genes sit in mice, zebrafish and human cells, they found that the obesity-associated regions in FTO were physically in contact with the promoter (the initial gene sequence which acts as an on/off switch) of IRX3. So the switches that turn on IRX3 are actually located far away from IRX3 itself, inside another gene. “We think of the genome as a linear thing, but it’s really a complex 3D structure that coils back onto itself,” he says.
IRX3 also appeared to be strongly linked with obesity. People with one of the obesity-associated mutations showed higher expression of IRX3, but not FTO, in brain tissue samples, the team found. Nóbrega and his colleagues also found that mice lacking the gene weighed 25–30% less than mice with a functional IRX3 gene; did not gain weight on a high-fat diet; were resistant to metabolic disorders such as diabetes and had more of the energy-burning cells known as brown fat. The same results were seen in mice in which the expression of IRX3 was blocked in the hypothalamus, a brain region known to regulate feeding behaviour and energy balance.
Inês Barroso, a geneticist at the Wellcome Trust Sanger Institute in Hinxton, UK, says that the work answers some of the questions around the biology of the link found in the genome-wide association studies (GWAS). “That’s always the tricky thing; a GWAS gives you an association, but it’s just a marker on the genome, it doesn’t actually say anything about which gene it’s affecting,” she says. “This strongly suggests that mediation of body mass is going to be through IRX3 rather than FTO.”
Nóbrega thinks geneticists should keep in mind this example of unexpected interactions between distant genes when dealing with genetic association studies. “There may be many other cases where people are studying the wrong gene,” he says. “We might be chasing ghosts.”
New genetic mutations shed light on schizophrenia
Researchers from the Broad Institute and several partnering institutions have taken a closer look at the human genome to learn more about the genetic underpinnings of schizophrenia. In two studies published this week in Nature (1, 2), scientists analyzed the exomes, or protein-coding regions, of people with schizophrenia and their healthy counterparts, pinpointing the sites of mutations and identifying patterns that reveal clues about the biology underlying the disorder.
When it comes to the rising prevalence of Type 2 diabetes, there are many factors to blame.
Diet and exercise sit somewhere at the top of the list. But the genes that some of us inherit from Mom and Dad also help determine whether we develop the disease, and how early it crops up.
Now an international team of scientists have identified mutations in a gene that suggests an explanation for why Latinos are almost twice as likely to develop Type 2 diabetes as Caucasians and African-Americans.
But here’s the kicker: You have to go further back on the family tree than your parents to find who’s to blame for this genetic link to diabetes. Think thousands of generations ago.
Harvard geneticist and his colleagues uncovered hints that humans picked up the diabetes mutations from Neanderthals, our ancient cousins who went extinct about 30,000 years ago.
"As far as I know, this is the first time a version of a gene from Neanderthal has been connected to a modern-day disease," Altshuler tells Shots. He and his colleagues the findings Wednesday in the journal Nature.
A few years ago, geneticists at the in Germany sent shock waves through the scientific community when they the genome of a Neanderthal from a fossil. Hidden in the genetic code were patterns that matched those in human DNA. And the data strongly suggested that humans were more than just friendly neighbors with Neanderthal.
"Now it’s well accepted that humans interbred with Neanderthals," Altshuler says. On average most of us carry about 2 percent of Neanderthal DNA in our genome. So it’s not surprising, he says, that 2 percent of our traits would be inherited from the ancient primates.
The new data don’t mean that Neanderthals had diabetes, Altshuler is quick to point out. “It just happens that this disease sequence came from them,” he says.
To identify genes that contribute to Latinos’ high rate of Type 2 diabetes, Altshuler and his team analyzed DNA from over 8,000 Mexicans and other Latinos.
The team found many genes already known to be involved with diabetes, such as one related to insulin production. But a new one also popped up in the analysis: a gene that’s likely involved in fat metabolism.
Mutations in this gene increase a person’s risk of getting Type 2 diabetes by about a 20 percent, Altshuler and the team found. If the person has two copies of the mutations, one from each parent, the risk rises by about 40 percent.
So for Mexican Americans, their for Type 2 diabetes goes from about 13 percent to 19 percent if they inherit two copies of the mutations. For other Americans, the risk gets boosted to about 11 percent from 8 percent.
"This is a genetic factor that has a modest affect on the risk of getting the disease. Not everybody that has it will have the disease," Altshuler says. "But the genes are very common in Latinos and Asians."
About half of Latinos carry the disease mutations, while 20 percent of Asians have it. On the other hand, only 2 percent of European Americans carry the mutations.
So the new genetic data help to explain a big chunk — perhaps almost a quarter — of the difference in Type 2 diabetes prevalence in Latinos versus European Americans.
"The findings are important because they give us a new biological clue about a gene involved in diabetes, which could lead to more treatments," Altshuler says. "The Neanderthal connection is interesting, but it’s not the essence of the work."