Genetic roots of psychiatric disorders clearer now thanks to improved techniques

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New technology and access to large databases are fundamentally changing how researchers investigate the genetic roots of psychiatric disorders.

“In the past, a lot of the conditions that people knew to be genetic were found to have a relatively simple genetic cause. For example, Huntington’s disease is caused by mutations in just one gene,” said Laramie Duncan, PhD, an assistant professor of psychiatry and behavioral sciences at Stanford. “But the situation is entirely different for psychiatric disorders, because there are literally thousands of genetic influences on every psychiatric disorder. That’s been one of the really exciting findings that’s come out of modern genetic studies.”

These findings are possible thanks to genome-wide association studies (GWAS), which test for millions of genetic variations across the genome to identify the genes involved in human disease.

Duncan is the lead author of a recent commentary in Neuropsychopharmacology that explains how GWAS studies have demonstrated the inadequacy of previous methods. The paper also highlights new genetics findings for mental health.

Before the newer technologies and databases were available, scientists could only analyze a handful of genetic variations. So they had to guess that a specific genetic variation (a candidate) was associated with a disorder — based on what was known about the underlying biology — and then test their hypothesis. The body of research that has emerged from GWAS studies, however, show that nearly all of these earlier “candidate study” results are incorrect for psychiatric disorders.

“There are actually so many genetic variations in the genome, it would have been almost impossible for people to guess correctly,” Duncan said. “It was a reasonable thing to do at the time. But we now have better technology that’s just as affordable as the old ways of doing things, so traditional candidate gene studies are no longer needed.”

Duncan said she began questioning the candidate gene studies as a graduate student. As she studied the scientific literature, she noticed a pattern in the data that suggested the results were wrong. “The larger studies tended to have null results and the very small studies tended to have positive results. And the only reason you’d see that pattern is if there was strong publication bias,” said Duncan. “Namely, positive results were published even if the study was small, and null results were only published when the study was very large.”

In contrast, the findings from the GWAS studies become more and more precise as the sample size increases, she explained, which demonstrates their reliability.

Using GWAS, researchers now know that thousands of variations distributed across the genome likely contribute to any given mental disorder. By using the statistical power gleaned from giant databases such as the UK Biobank or the Million Veterans Program, they have learned that most of these variations aren’t even in the regions of the gene’s DNA that code for proteins, where scientists expected them to be. For example, only 1.1 percent of schizophrenia risk variants are in these coding regions.

“What’s so interesting about the modern genetic findings is that they are revealing entirely new clues about the underlying biology of psychiatric disorders,” Duncan said. “And this opens up lots of new avenues for treatment development.”

This is a reposting of my Scope blog story, courtesy of Stanford School of Medicine.

The future of genomics: A podcast featuring Stanford geneticists

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Image by Pat Lyn

Every living organism on Earth has a genome, the complete set of DNA containing all of the information needed to develop and maintain the organism. Humans inherit three billion long strings of DNA called chromosomes from each parent, so your genome can help identify your personal ancestry. But genomes can also identify the movement of human populations based on who is similar to whom.

Carlos Bustamante, PhD, a professor of biomedical data science, of genetics and of biology at Stanford, discusses the blossoming uses of genomes on a recent episode of “The Future of Everything” radio show.

For example, Bustamante told host Russ Altman, MD, PhD, a professor of bioengineering, of genetics, of medicine and of biomedical data science, about the genomic fingerprints of the history of slavery in the United States. As part of an international collaboration, he studied the DNA of modern individuals and individuals from slave cemeteries, tracing their history to particular tribal groups in Africa.

“A lot of that history has been lost and African Americans want to reclaim parts of that history using DNA,” Bustamante said. “What’s interesting, at least in the United States, is that most of the slave ships went first to the Caribbean and Brazil. Only a couple hundred thousand people came in straight to the Port of Charleston. So the history of the slave trade is actually written in the DNA of the Caribbean, Brazilian and U.S. African descendant populations.”

But that is only one of the many genomic applications discussed on the episode. Another important use is predicting disease risks. Genetic tests are now available for many hereditary conditions, including cancer risk assessment, at Stanford.

This raises a challenge, however, because our knowledge of DNA is primarily based on people of European descent. As Bustamante explained, this occurred because European countries were the first to recognize the potential impact that DNA sequencing could have on health care, once the cost of DNA sequencing technology plummeted.

“They invested quickly and by the year, say 2009, they’d done about a thousand studies and 95 percent of the participants in those studies were of European descent — be they from the countries in Europe or in Iceland.”

Since humans are 99.9 percent identical in their genetic makeup, maybe this doesn’t sound like a problem. But Bustamante said the differences may be important because they could help lead to improvements in health care. He described this lack of diversity as both a problem and an opportunity.

Take blond hair, for example. Bustamante explained that two main populations have blond hair: Europeans and Melanesians from the Solomon Islands. When the scientists started a research project, they hypothesized that a European went to Melanesia and had a lot of kids. But that isn’t what the genetics showed.

“The genetics of blond hair in Europe are different than the genetics of blond hair in Melanesia. They look the same, but it turns out that the underlying genes are different,” he said. “And why is that interesting? From the point of view of medical genetics, if this is true for blond hair — which is about as simple a trait as you can get — what about diabetes? Why would we assume the genetic basis of diabetes is the same in every population, when we know diabetes actually presents differently in different populations?”

He also argued that new drug discovery would be more successful if it was based on genetic leads. Cholesterol lowering drugs called PCSK9 inhibitors, for instance, were found by studying families with naturally high or low levels of cholesterol. Successes like these are the reason he thinks it’s important to study diverse populations.

“If we spread our bets across different human populations, we’re much more likely to find interesting biology that then benefits everybody,” he said. “Because these cholesterol lowering drugs aren’t just good for those people with high cholesterol for genetic reasons. That’s the key. You can mimic it in others and it benefits everybody.”

Of course, the potential for genomics goes beyond human applications. Altman and Bustamante also discuss plant and animal uses, including designing your dream dog.

This is a reposting of my Scope blog story, courtesy of Stanford School of Medicine.

Inherited Neanderthal genes protect us against viruses

Image by Claire Scully

When Neanderthals and modern humans interbred about 50,000 years ago, they exchanged snippets of DNA. Today, Europeans and Asians still carry 2 to 3 percent of Neanderthal DNA in their genomes.

During contact, they also exposed each other to viruses. This could have been deadly for the human species since Neanderthals encountered many novel infectious viruses while living for hundreds of thousands of years outside Africa. Luckily, the Neanderthals’ immune systems evolved genetic defenses against these viruses that were passed on to humans, according to a study reported in Cell.

“Neanderthal genes likely gave us some protection against viruses that our ancestors encountered when they left Africa,” said Dmitri Petrov, PhD, an evolutionary biologist at Stanford’s School of Humanities and Sciences, in a recent Stanford news release.

In the study, the researchers gathered a large dataset of several thousand proteins that interact with viruses in modern humans. They then identified 152 Neanderthal DNA snippets present in the genes that make these proteins. Most of the 152 genes create proteins that interact with a specific type of viruses, RNA viruses, which have RNA encased in a protein shell.

The team identified 11 RNA viruses with a high number of Neanderthal-inherited genes, including HIV, influenza A and hepatitis C. These viruses likely played a key role in shaping human genome evolution, they said.

Overall, their findings suggest that the genomes of humans and other species contain signatures of ancient epidemics.

“It’s similar to paleontology,” said David Enard, PhD, a former postdoctoral fellow in Petrov’s lab. “You can find hints of dinosaurs in different ways. Sometimes you’ll discover actual bones, but sometimes you find only footprints in fossilized mud. Our method is similarly indirect: Because we know which genes interact with which viruses, we can infer the types of viruses responsible for ancient disease outbreaks.”

This is a reposting of my Scope blog story, courtesy of Stanford School of Medicine.

Inaccurate direct-to-consumer raw genetic data can harm patients, new research suggests

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Whether or not you’ve ever had genetic testing, you probably know someone that has. Millions of people each year have their DNA analyzed by companies like 23andMe and Ancestry.com, seeking out personalized information about their heritage, health and other traits.

“The general public is excited about genetics because it can tell us a lot about our past ancestry and, if the right technology is used, about our future­­ ­— such as the likelihood of developing certain health problems,” said Tia Moscarello, a genetic counselor with Stanford’s Center for Inherited Cardiovascular Disease. “These tests are popular for good reason: many people want to be proactive about their health without spending a lot of money or making a trip to the doctor’s office to do it.”

Typically, these direct-to-consumer (DTC) genetic tests are less expensive than more comprehensive, clinical-grade genetic tests obtained through a health care provider.

However, the Food and Drug Administration limits what these companies can say about a consumer’s health. So many people download their raw genetic data obtained from the company, and then upload it to another company’s website for additional interpretation. But their raw data come with a disclaimer stating the information is not validated for accuracy nor intended for medical use.

“To our understanding, raw genetic data doesn’t go through quality control. We and the DTC labs know that raw data may not be accurate,” Moscarello said. For instance, a small study recently showed that 40 percent of genetic variants identified in direct-to-consumer raw data and sent for clinical confirmation were false positives — meaning that the genetic variants weren’t really present.

Moscarello has personally witnessed the impact of these false positives on patients and their families. In a recent commentary in Genetics in Medicine, she and her colleagues describe two cases of false positives seen at Stanford and two more seen at other institutions. These patients received raw data with genetic variants known to be associated with inherited heart conditions that would predispose them to sudden death, she said. Fortunately, a clinical lab determined that the results were incorrect.

Moscarello said she and her co-authors wrote the commentary to call attention to the potential harms of direct-to-consumer raw data interpretation, which extend beyond the potential for inaccurate results. She explained:

“Finding out that you or a family member are at risk for an inherited heart condition can be a very emotional, life-changing event. To go through that without an expert to talk to, or perhaps without support systems nearby, was challenging for our patients. They had to wait for an appointment with a genetic counselor who could explain the test and its limitations, and to provide support. That is usually provided prior to genetic testing, so patients can decide if they would like to proceed.”

The commentary also discussed the impact that DTC testing is having on the health care system.  For the four cases, this burden included the time and expense of four clinical-grade genetic tests, several echocardiograms and electrocardiograms for each patient, multiple visits with physician specialists, an MRI, and the implant and subsequent explant of an implantable cardioverter defibrillator, Moscarello said.

So what can be done?  The authors call for more research to determine the frequency and impact of people being affected by false positives in their raw genetic data interpretations. When a result with potential clinical significance is found, they recommend that it be sent for confirmation to a clinical-grade lab. This should occur before the consumer has to undergo costly clinical evaluations and tests, she said, concluding:

“It is clear that DTC genetic testing is here to stay, and for good reason. So it’s important to focus on maximizing the benefits of such large-scale, clinician-free testing, while minimizing the harms to consumers.

Collaboration between clinicians, consumers and the DTC genetic testing companies is a priority. I hope that DTC genetic testing companies will work with clinical genetics experts to create educational resources — so that consumers and non-specialist physicians know the data may be inaccurate, and what to do next if something is found.”

This is a reposting of my Scope blog story, courtesy of Stanford School of Medicine.

New way to understand tumor diversity combines CRISPR with genetic barcodes

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The growth of a particular tumor depends on multiple genetic factors, so it is difficult for cancer researchers to recreate and study this genetic diversity in the lab.

“Human cancers don’t have only one tumor-suppression mutation [which fuels tumor growth] — they have combinations. The question is, how do different mutated genes cooperate or not cooperate with one another?” said Monte Winslow, PhD, a Stanford assistant professor of genetics and of pathology, in a recent Stanford news release.

Now, Winslow and his colleagues have discovered a way to modify cancer-related gene and then track how these combinations of mutations impact tumor growth, as recently reported in Nature Genetics.

The researchers used a powerful gene-editing tool, called CRISPR-Cas9, to introduce multiple, genetically distinct tumors in the lungs of mice. They also attached short, unique DNA sequences to individual tumor cells — which acted as genetic barcodes and multiplied in number as the tumors grew. By counting the different barcodes, they were able to accurately and simultaneously track tumor growth.

“We can now generate a very large number of tumors with specific genetic signatures in the same mouse and follow their growth individually at scale and with high precision. The previous methods were both orders of magnitude slower and much less quantitative,” said Dmitri Petrov, PhD, a senior author of the study and an evolutionary biologist at Stanford, in the release.

The study showed that many tumor-suppressor genes only drive tumor growth when other specific genes are present. The researchers hope to use their new methodology to better understand why tumors with the same mutations sometimes grow to be very large in some patients and remain small in others, they said.

Their technique may also speed up cancer drug development, allowing a drug to be tested on thousands of tumor types simultaneously. Petrov explained in the release:

“We can help understand why targeted therapies and immunotherapies sometimes work amazingly well in patients and sometimes fail. We hypothesize that the genetic identify of tumors might be partially responsible, and we finally have a good way to test this.”

This is a reposting of my Scope blog story, courtesy of Stanford School of Medicine.

Big bacteria may be easier to kill, new research suggests

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The size of a cell is intrinsicThe size of a cell is intrinsically linked with its genetic makeup, growth rate and other fundamental properties. What would happen if scientists could control the size of pathogens?

That possibility isn’t completely outlandish: Stanford researchers have discovered a genetic “tuning knob” that can enlarge or shrink bacteria across a wide range — and this knob can be used to fatten up the bacteria to increase their susceptibility to certain antibiotics, as recently reported in Current Biology.

The research team is led by KC Huang, an associate professor of bioengineering and of microbiology and immunology at Stanford. Huang explained in a recent Stanford Engineering  news article:

“Most strategies to killing bacteria are linear: you find a very specific target and block it with a drug. These findings point in the direction of totally orthogonal therapies, in which you predispose cells to death by tweaking a global property like size.”

The researchers found that a single protein in E. coli, called MreB, acts as a master regulator of cell size by coordinating the construction of cell walls. So they manufactured many copies of the E. coli’s DNA, changing in each copy just one of the 347 letters in MreB’s genetic code. Using fluorescence-activated cell sorting, they then separated the individual cells with different sizes to create a library of cell-size mutants.

The team used this library to study how size impacts a cell’s physiology, including how bacterium grow and survive. For instance, they treated the various E. coli mutants with several antibiotics and found that larger E. coli were more sensitive to the drugs. A larger cell has more surface area and that increases the drug uptake, they said in the paper.

Huang said he hopes their techniques can be applied to other bacteria and used to help human health in the future. He added:

“While we don’t yet know how to twist this bacterial size dial in patients, it’s good to have such an exciting new therapeutic approach as antibiotic resistance becomes increasingly prevalent.”

This is a reposting of my Scope blog story, courtesy of Stanford School of Medicine.

 

Drug blocks Zika and other deadly viruses in cells cultures, Stanford study finds

Photo of Jan Carette by Paul Sakuma

A team of Stanford researchers is developing approaches to thwart a family of deadly viruses, called flaviviruses, by targeting the human cells that host these invading pathogens.

Flaviviruses include the dengue-fever, yellow-fever, West Nile and Zika viruses transmitted to humans by mosquitoes, as well as encephalitis transmitted by ticks. Unfortunately, approved antiviral drugs for these diseases aren’t currently available.

So, instead of the traditional approach of attacking an individual virus directly, the researchers focused on the cellular factors of their human hosts that are essential to many viral infections.

“Generally, when you develop a drug against a specific protein in dengue virus, for instance, it won’t work for yellow fever or Zika, and you have to develop new antivirals for each,” said Stanford virologist Jan Carette, PhD, in a recent Stanford news release. “Here, by targeting the host rather than a specific virus, we’ve been able to take out multiple viruses at once.”

Earlier, the team genetically profiled human cells to identity the host factors necessary for the viruses to replicate inside the cells — revealing new candidate targets for antiviral drug development. Specifically, they demonstrated the importance of the oligosaccharyltransferase (OST) complex that attaches sugar molecules to proteins. They found flaviviruses did not infect their genetically engineered cells without OST.

In the new study, recently published in Cell Reports, the Stanford researchers collaborated with scientists at Yale University to test the effectiveness of a drug called NGI-1, which inhibits the activity of the OST complex.

They showed that low concentrations of NGI-1 could be used to block the viruses from replicating without harming the host cells — successfully reducing the infection by 99 percent when treating cells immediately after they were infected by Zika or dengue virus, and by 80 percent when administered 24 hours after infection.

Their study also indicated that the viruses are unlikely to become resistant to NGI-1. “When you target a host function rather than a viral protein, it’s usually much more difficult for a virus to develop resistance,” Carette said in the release.

The researchers are now busy with follow-up studies to test NGI-1 in small animal models of dengue fever and are also developing similar drugs with improved specificity.

This is a reposing of my Scope blog story, courtesy of Stanford School of Medicine.

Researchers investigate fruit flies as a step towards understanding the human brain

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Researchers have been trying to map out the brain’s complex neural circuits to understand how diseases like Parkinson’s disrupt healthy brain communication, in hopes of designing better therapies.

That, obviously, is not easy. So a Stanford research team led by biology professor Liqun Luo, PhD, and bioengineer and physicist Stephen Quake, PhD, are instead studying fruit fly brains — yes, those pesky bugs that fly around your bananas.

Specifically, they have mapped out a blueprint for the fruit fly’s olfactory neurons — identifying how specific gene and protein activity correlates with the biological circuitry of different neuronal cell types. The researchers focused on a fruit fly’s sense of smell because the function, physiology and anatomy of its olfactory system are well known, making it a simple and ideal test bed for their research.

They measured the gene expression profiles using a single-cell sequencing technology developed for mice and human cell types, which they modified to work for the smaller and simpler fruit fly cells.

The team determined different types of neurons express genes differently during development, but gene expression between the neuron types becomes indistinguishable as the flies mature, as recently reported in Cell.

“Once the brain is wired up, the fly doesn’t need to express those genes that help them in choosing the connection partners,” said first author and Stanford biology postdoc Hongjie Li, PhD, in a recent news release. “So there is less gene expression in the adult flies.”

The researchers are a long way from using their technique to map the human brain, but they aren’t daunted by the challeng. “By further developing this approach, we hope to one day reverse-engineer and perhaps even repair defective circuitry in the human brain,” Li said in the release.

This is a reposting of my Scope blog story, courtesy of Stanford School of Medicine.

The skinny on how chickens grow feathers and, perhaps, on how humans grow hair

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How do skin cells make regularly spaced hairs in mammals and feathers in birds? Scientists had two opposing theories, but new research at the University of California, Berkeley surprisingly links them.

The first theory contends that the timing of specific gene activation dictates a cell’s destiny and predetermines tissue structure — for example, in the skin, gene activation determines whether a skin cell becomes a sweat gland cell or hair cell, or remains a skin cell. The second theory asserts that a cell’s fate is determined instead by interacting with other cells and the material that it grows on.

Now, Berkeley researchers have found that the creation of feather follicles (like hair follicles) is initiated by cells exerting mechanical tension on each other, which then triggers the necessary changes in gene expression to create the follicles. Their results were recently reported in Science.  

“The cells of the skin in the embryo are pulling on each other and eventually pull one another into little piles that each go on to become a follicle,” said first author Amy Shyer, PhD, a post-doctoral fellow in molecular and cell biology at the University of California, Berkeley, in a recent news release. “What is really key is that there isn’t a particular genetic program that sets up this pattern. All of these cells are initially the same and they have the same genetic program, but their mechanical behavior produces a difference in the piled-up cells that flips a switch, forming a pattern of follicles in the skin.”

The research team grew skin taken from week-old chicken eggs on different materials with varying stiffness. They found that the stiffness of the substrate material was critical to forming feather follicles — material that was too stiff or too soft yielded only one follicle, whereas material with intermediate stiffness resulted in an orderly array of follicles.

“The fundamental tension between cells wanting to cluster together and their boundary resisting them is what allows you to create a spaced array of patterns,” said co-author Alan Rodgues, PhD, a biology consultant and former visiting scholar at Berkeley.

The researchers also showed that when the cells cluster together, this activated genes in those cells to generate a follicle and eventually a feather.

Although the study used chicken skin, the researchers suggest that they have discovered a basic mechanism, which may be used in the future to help grow artificial skin grafts that look like normal human skin with hair follicles and sweat pores.

This is a reposting of my Scope blog story, courtesy of Stanford School of Medicine.

Clinical guidance on genetic testing: A Q&A

 

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Earlier this month, an FDA ruling gave 23andMe permission to market its personal genetic tests for 10 diseases, including Parkinson’s and late-onset Alzheimer’s.

But with the increase in genetic testing at home and in clinical settings comes challenges. What do physicians do with all of these data? And how do they evaluate the validity and clinical utility of genetic tests?

To tackle these questions and others, the National Academy of Medicine formed a committee to provide guidance. I recently spoke with one of the committee members, Sean David, MD, DPhil, an associate professor of medicine at Stanford, about the committee’s new recommendations and report.

What inspired you to participate in the NAM Committee on the Evidence for Genetic Testing?

“The National Academy of Medicine consensus reports have high impact on national health policy and practices, so I jumped at the chance. In our work at Stanford, we struggle with advising patients on which genetic tests to recommend, which ones to order when requested by a patient and how to interpret results from the many direct-to-consumer genetic tests. We need guidance and a framework for making these decisions. The NAM committee addressed this challenge.

Years ago, I had a patient bring in a whole stack of direct-consumer whole genome sequencing results that showed her genetic risks for different illnesses. She asked me to interpret it for her, but there was far too much for me to consume during our brief office visit. And it was unclear what criteria to use when evaluating these tests. There’s been a rapid increase in the development of genetic tests with thousands of commercially available tests, but limited evidence regarding their validity for diagnosing disease and improving patient outcomes.”

What was the committee’s mission?

“Our charge was to examine the relevant medical and scientific literature to determine the evidence base for different types of genetic tests, as well as recommend a framework for decision-making regarding the use of genetic tests in clinical care.

This is the first consensus report on this topic. Although it was designed for the Military Health System, it should still be applicable to both military and civilian populations and may set benchmarks for private insurance companies. The report also encourages different agencies to cooperate and create a clinical data repository of evidence-based genetic testing decisions, which will be available to everyone. I think someone needs to do this to set the standard. Once that’s been done, at least we’ll have something we can all use as a benchmark.”

How can this decision-making framework help guide clinical practice?

“The decision framework can be used by physicians to determine which genetic tests are really ready for prime time in the clinic. For example, we know that if people are tested based on their family history and found to be at high risk for hereditary breast or ovarian cancer, they can have interventions that will improve their survival and outcomes. By using the decision framework, a physician can come up with a quick triage decision that it’s a ‘yes’ test for someone with several family members with breast and/or ovarian cancer, and one that really all providers should know about.

Other genetic tests like tests for Alzheimer’s aren’t as clear. For instance, there could be a genetic test for a particular rare form of early onset Alzheimer’s associated with a particular mutation. If someone has that mutation, he may have a very high risk of early onset Alzheimer’s disease. Do we screen people for that? It will depend on the clinical testing scenario. If someone has family members who developed Alzheimer’s in their 40s, then it might be a good diagnostic test. Whereas, there might be another genetic test for associated risk of dementia where the causal relationship with Alzheimer’s may not be established. That’s an issue of clinical validity. So we might not offer that test routinely — to avoid giving patients information that might be misleading and might even cause some harm.

In addition, ethical, legal and social implications of genetic testing are important. For many patients — including parents of children with undiagnosed rare diseases — genetic testing may help end a diagnostic odyssey. Oftentimes geneticists will order whole genome testing without testing for something specific. There may be thousands or even millions of different genetic markers that are tested with the hope that they’ll find something that leads to a diagnosis. Evidence of clinical utility may be lacking in scenarios like these, but taking into account the value of tests to patients and their families is important — the context matters. There needs to be a certain amount of clinical judgment, and the committee isn’t saying anything against this.”

This is a reposting of my Scope blog story, courtesy of Stanford School of Medicine.