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Parts of the human genome now available to study for the first time are important for understanding genetic diseases, human diversity, and evolution.
The first truly complete sequence of a human genome, covering each chromosome from end to end with no gaps and unprecedented accuracy, is now accessible through the UCSC Genome Browser and is described in six papers published today (March 31, 2022) in Science.
Since the first working draft of a human genome sequence was assembled at UC Santa Cruz in 2000, genomics research has led to enormous advances in our understanding of human biology and disease. Nevertheless, crucial regions accounting for some 8% of the human genome have remained hidden from scientists for over 20 years due to the limitations of DNA sequencing technologies.
Karen Miga, assistant professor of biomolecular engineering at UC Santa Cruz, and Adam Phillippy at the National Human Genome Research Institute (NHGRI) organized an international team of scientists—the Telomere-to-Telomere (T2T) Consortium—to fill in the missing pieces. Their efforts have now paid off.
A group of systems biologists in Berlin have developed parallel genome editing in tiny worms to produce diverse indel mutations in regulatory elements in genomic DNA and a powerful software package, crispr-DART, to analyze the indel mutations following targeted DNA sequencing. Using this new approach, they directly map gene regulatory genotypes to physical and physiological attributes in the worm. Nearly 40% of our genomes consist of regulatory elements that control when and where a gene is expressed. Although biomedical research has focused on protein-coding regions of genomes, understanding how regulatory regions control gene expression is central to understanding attributes of health and disease. While progress has been made in understanding gene regulation in cell lines and yeast, few studies have been done in live animals or in large populations. One of the technical challenges of studying regulatory regions is that they must ultimately be understood in the context of their genomic and tissue environments and developmental timing. Systems biologists at the Max Delbrück Center (MDC) for Molecular Medicine in Berlin report a new in vivo parallel genetics approach in their article “Parallel genetics of regulatory sequences using scalable genome editing in vivo” published in Cell Reports that introduce diverse mutations on a large scale in the genomes of thousands of microscopic worms called Caenorhabditis elegans and tracks their physiological effects. This offers a systematic method to link genotype to phenotype—a monumental task at the current frontiers of biology. “With cell lines, you are missing development processes, many cell-types, as well as interaction between cell types that all affect gene regulation,” says Jonathan Froehlich, a PhD student in MDC’s Systems Biology of Gene Regulatory Elements Lab in the Berlin Institute for Medical Systems Biology (BIMSB) and co-first author on the article. “We can now really test these regulatory sequences in the environment where they are important and observe the consequences on the organism.” The researchers use CRISPR-Cas9 to introduce large scale mutations in the form of genomic deletions or insertions (indels) in a parent generation of the hermaphrodite worms and studied the physiological effects in thousands of worms in subsequent generations. “One part is controlled, the part where we design the guide RNAs and tell the Cas9 nuclease where to go, but the outcome of this is semi-random,” says Froehlich. “You will have many different types of outcomes and we can see what the effect is on the animal.” The authors link several mutations in regulatory regions to specific physiological effects using the new approach. One of their expected findings was the identification of two independently functioning let-7 microRNA binding sites in the downstream regulatory region of a gene called lin-41. If at least one of the two sites was intact, the worms developed normally, else gene expression was mis-regulated and the worms developed abnormally and died. “This demonstrates nicely how this system can be used to study gene regulation during development,” says Nikolaus Rajewsky, PhD, scientific director of MDC’s BIMSB, who oversaw the project.
Via BigField GEG Tech
A new analysis traces the story of the draft genome’s impact on genomics since 2001, linking its effects on publications, drug approvals and understanding of disease. The 20th anniversary of the publication of the first draft of the human genome1,2 offers an opportunity to track how the project has empowered research into the genetic roots of human disease, changed drug discovery and helped to revise the idea of the gene itself. A recent Nature article distills these impacts and trends. The authors combined several data sets to quantify the different types of genetic element that have been discovered and that generated publications, and how the pattern of discovery and publishing has changed over the years. Their analysis linked together data including 38,546 RNA transcripts; around 1 million single nucleotide polymorphisms (SNPs); 1,660 human diseases with documented genetic roots; 7,712 approved and experimental pharmaceuticals; and 704,515 scientific publications between 1900 and 2017. The results highlight how the Human Genome Project (HGP), with its comprehensive list of protein-coding genes, spurred a new era of elucidating the function of the non-coding portion of the genome and paved the way for therapeutic developments. Crucially, the results track the emergence of a systems-level view of biology alongside the conventional single-gene perspective, as researchers mapped the interactions between cellular building blocks (see ‘No jump for big teams’). There are certain limitations to this type of analysis. For example, there is no consensus on where a gene starts and ends or, surprisingly, even what sequence exactly encodes some genes3. Multiple naming conventions are in use for some genomic elements, so sometimes our methodology did not connect them. And other links between publications and elements might not have been added to databases by the authors. Finally, the presented graphs end in 2017, because there can be a time lag between an article’s publication and entry into the databases we used. Read the full article at: www.nature.com
Via Complexity Digest
A new gene therapy appears to serve as a functional cure for the most common type of hemophilia, early clinical trial results indicate. Patients who received the one-time intravenous therapy continue to have a more than 90 percent decrease in bleeding events two to three years after their initial treatment, researchers reported Jan. 1, 2020 in the New England Journal of Medicine. The therapy fixes a broken gene in liver cells that causes production of flawed factor VIII, a protein that plays a key role in blood clotting. People with this genetic mutation have hemophilia A, the most common type of this bleeding disorder. Hemophilia A accounts for 8 out of 10 cases of hemophilia, researchers said. Hemophilia A patients must inject themselves with factor VIII every other day to prevent bleeding, said lead researcher John Pasi, a professor at Barts and The London School of Medicine and Dentistry in England. "There's been a massive reduction in bleeding in the patients, and none of them any longer need to regularly treat themselves with factor VIII to prevent bleeding," Pasi said of participants in the phase 1/phase 2 clinical trial. "That huge treatment burden of having to give yourself an intravenous injection every other day has gone away." The therapy uses a virus to carry the DNA sequence for a functional factor VIII gene into liver cells, he said. "It infects liver cells and transfers into those cells the factor VIII gene," Pasi said. "Liver cells make factor VIII and then secrete it, and it passes into the circulation." Seven initial participants in the study have a 96 percent decrease in bleeding events three years out, researchers report. Another six who joined later had a 92 percent decrease in bleeding by the end of year two. "At three years, the patients who were treated at a higher dose were expressing functional levels of factor VIII -- somewhat lower than they were at their peak, but they're still at really good levels that are hugely effective in protecting the patients against bleeding," Pasi said.
Polygenic risk scores represent a giant leap for gene-based diagnostic tests. Here is what they mean for predictive personalized medicine: When researchers completed the first drafts of the human genome in the early 2000s, many expected that it would mark the start of a medical revolution. Geneticists started searching for the differences that might explain why one person develops diabetes or heart disease whereas another does not. The idea was simple: compare a group of people with the condition to a group without and look for differences in their DNA. The variations generally came in the form of DNA-letter swaps, known as single nucleotide polymorphisms, or SNPs. If people with a condition tended to have a T at a certain location whereas others had a C, that suggested that the SNP was associated in some way with the disease. These genome-wide association studies — or GWASs, as they came to be known — became very popular. But after years of searching, scientists could still only explain a small bit of the inherited risk for common diseases. It turned out that most of these conditions were related to many more SNPs than scientists had first expected, says Ali Torkamani, a geneticist at the Scripps Research Institute, La Jolla, California. Worse still, a majority of the variants conferred a very small risk — detectable only when surveying huge groups of people.“We didn’t have the sample size to really drive prediction as well as some people naively thought,” says Ewan Birney, director of the European Bioinformatics Institute in Hinxton, UK. By 2007, geneticists were fretting about something they called “missing heritability”. It was clear that many of these conditions had a genetic component, but GWASs clearly weren’t catching much of it. Today, things are finally changing. With access to massive data sets, as well as advances in how data are analyzed, scientists are getting better at measuring those very small risks. A prime example is the technique geneticist Kathiresan used to generate his 6.6-million SNP score, which was published in August 20181. He and his team took data from a 2015 meta-analysis that combined 48 GWASs, consisting of 61,000 people with coronary artery disease and 120,000 controls2. They then tested their polygenic predictor on 290,000 people in the UK Biobank, finding that those scoring in the highest few percentiles had on average several times higher risk of developing the disease than did the rest of the population. Of the 23,000 people who received the highest scores, for example, 7% had coronary artery disease, compared with 2.7% of the remaining population. The group conducted similar analyses for four other disorders, including inflammatory bowel disease and breast cancer, each time identifying a group who scored in the top few percentiles and were at particularly high risk. Understanding how people will react to polygenic scores is a high priority for researchers. Ripatti and his colleagues have given more than 7,000 individuals in Finland information about their likelihood of developing heart disease, based on both polygenic scores and conventional risk factors such as high blood pressure. Most of the respondents say that getting this information motivates them to make positive changes, says Ripatti. Preliminary results suggest that those with high genetic risk are the most likely to take actions such as losing weight or stopping smoking. In nearby Estonia, researchers are in the process of genotyping 100,000 individuals, adding to the 50,000 the country has already sampled. And unlike many other biobanks, participants in the Estonian project can sign up to receive feedback. Among the results being returned to them are polygenic risk scores for type 2 diabetes and cardiovascular disease, says Lili Milani, a geneticist at the Estonian Genome Center at the University of Tartu, Estonia. Similar to the Finnish work, participants are shown graphs of how lifestyle changes could reduce or increase their risk. And, says Milani, initial indications are that people are glad for the advice. For now, people are receiving their scores from genetic counsellors. But Milani is working with the Estonian government to work out how to integrate genomic data into the health-care system, so that it can be used every day by doctors. The country ultimately aims to genotype anyone who’s interested, right up to its entire population of 1.3 million, Milani says. “The goal is to build something so great that all doctors will want to recommend it and all of the population will want it.”
Will you be among the first to pick your kids’ IQ? As machine learning unlocks predictions from DNA databases, scientists say parents could have choices never before possible. Nathan Treff was diagnosed with type 1 diabetes at 24. It’s a disease that runs in families, but it has complex causes. More than one gene is involved. And the environment plays a role too. So you don’t know who will get it. Treff’s grandfather had it, and lost a leg. But Treff’s three young kids are fine, so far. He’s crossing his fingers they won’t develop it later. Now Treff, an in vitro fertilization specialist, is working on a radical way to change the odds. Using a combination of computer models and DNA tests, the startup company he’s working with, Genomic Prediction, thinks it has a way of predicting which IVF embryos in a laboratory dish would be most likely to develop type 1 diabetes or other complex diseases. Armed with such statistical scorecards, doctors and parents could huddle and choose to avoid embryos with failing grades. IVF clinics already test the DNA of embryos to spot rare diseases, like cystic fibrosis, caused by defects in a single gene. But these “preimplantation” tests are poised for a dramatic leap forward as it becomes possible to peer more deeply at an embryo’s genome and create broad statistical forecasts about the person it would become. The advance is occurring, say scientists, thanks to a growing flood of genetic data collected from large population studies. As statistical models known as predictors gobble up DNA and health information about hundreds of thousands of people, they’re getting more accurate at spotting the genetic patterns that foreshadow disease risk. But they have a controversial side, since the same techniques can be used to project the eventual height, weight, skin tone, and even intelligence of an IVF embryo. In addition to Treff, who is the company’s chief scientific officer, the founders of Genomic Prediction are Stephen Hsu, a physicist who is vice president for research at Michigan State University, and Laurent Tellier, a Danish bioinformatician who is CEO. Both Hsu and Tellier have been closely involved with a project in China that aims to sequence the genomes of mathematical geniuses, hoping to shed light on the genetic basis of IQ.
Genes linked to Crohn’s disease, an inflammatory bowel disease, might make people’s immune cells miss out on helpful messages sent by friendly gut bacteria. Good gut bacteria might not help people with Crohn’s disease. Protective microbial messages go unread in mice and in human immune cells with certain defective genes, researchers report online May 5 in Science. The findings are the first to tie together the roles of genes and beneficial microbes in the inflammatory bowel disease, says biologist Brett Finlay of the University of British Columbia in Vancouver, who was not involved in the new work. “This is a major step forward in this area,” he says. Human genes and friendly microbes work together to control inflammation, he says. “And when you muck that up, things can go awry.” In Crohn’s disease, the immune system riles up too easily, trigging chronic inflammation. Scientists don’t know why exactly people’s immune systems go haywire. But researchers have linked the disease to glitches in nearly 200 genes, including ATG16L1 and NOD2, which typically help kill bad bacteria in the gut. Researchers have also reported that people with Crohn’s have a different collection of gut microbes compared with that of healthy people, says study coauthor and Caltech microbiologist Sarkis Mazmanian.But though “there’s a huge body of literature on the genome and on the microbiome,” he says, “no one knew what the interplay was between the two.” So his team explored a potential link using a friendly gut microbe called Bacteroides fragilis. The bacteria send out calming messages that tell the immune system to tone down inflammation. Like letters inside envelopes, these messages travel in protective pouches called outer membrane vesicles, or OMVs. Feeding OMVs to mice typically protects them from developing inflamed colons, or colitis — but not mice lacking the Crohn’s-linked genes ATG16L1 and NOD2. When researchers treated those mice with a colitis-causing chemical, they succumbed to the disease, even after eating OMVs. Mice with defective versions of ATG16L1 and NOD2 “can’t reap the benefits of the beneficial microbiota,” Mazmanian says.Immune cells from human patients with the defective genes didn’t respond to OMVs either. The findings suggest that the genes that kill bad bacteria also work with good bacteria to keep people’s immune systems from going out of control, says gastroenterologist Balfour Sartor of the University of North Carolina School of Medicine in Chapel Hill. The work “opens up a new mechanism for protection,” he says.
Technology is allowing researchers to generate vast amounts of information about tumors. The next step is to use this genomic data to transform patient care.
Adrian Lee has dedicated his career to studying breast cancer, which is to say he is actually tackling many different diseases at once. “No two breast cancers are the same,” says Lee, a pharmacologist and chemical biologist at the University of Pittsburgh in Pennsylvania. “Cancer is way more complex than we know.”
Lee is using genomic technology to fully describe cancers of the breast and apply that knowledge to guide treatment decisions for individual patients. “We can now analyse multiple variables from a single specimen, such as changes in DNA, changes in RNA and changes in methylation,” he says. “Genome-wide scans allow for better systems biology and allow us to learn what's gone wrong in a particular tumor.”
Sequencing tumors is faster, cheaper and easier than ever. With many researchers collecting sequence data and uploading these to public databases such as the The Cancer Genome Atlas (TCGA), opportunities to describe the many different cancers that arise in breast tissue are upon us. “The challenge used to be generating the data,” says Nicholas Navin, a geneticist at The University of Texas MD Anderson Cancer Center in Houston. “Those issues have been resolved. Now the challenge is data processing and data analysing — interpreting the mutations and communicating those to oncologists.”
At the University of Pittsburgh, researchers are working to link the molecular signatures of people with breast cancer to a host of clinical data, including demographic information associated with risk such as age, ethnicity and body weight. They are mining electronic health records for clinical correlates, treatment interactions and outcomes. “We've got a big haystack and we're trying to find the needle,” says Lee. “But we're also trying to incriminate the needle, by linking it to lots of things.” Collecting all that data from patients' electronic records adds up, Lee says. It takes infrastructure — Pittsburgh has already accumulated 5 petabytes, or 5 million gigabytes, which is enough data to overload around 40,000 new iPhone 6 devices.
Making the connection between the reams of data coming out of sequencing laboratories and the individual women fighting breast cancer takes big-time computing power. Big data needs researchers who are comfortable with statistical noise and those who are old hands at the iterative process required to create flexible computer programs.
Big-data researchers take a large data set and look for patterns. The idea is to identify mutations that can be targeted with drug treatment. It is the essence of personalized medicine: screen a patient's tumour for a set of biomarkers to choose the best treatment to fight the cancer. Big-data researchers believe that analysing the data of the thousands of tumours that have come before will reveal patterns that can improve screening and diagnosis, and inform treatment.
Lee and his colleagues have illustrated how big-data science led to a rethink of breast cancer1. They used two public databases — TCGA and METABRIC (Molecular Taxonomy of Breast Cancer International Consortium), which contain data on the entire set of genes, RNA transcripts and proteins of thousands of breast-cancer tumours — to parse out potential differences in the molecular signatures of breast tumours in younger compared with older women. Women who are diagnosed before the age of 40 tend to have worse disease: they are more likely to have later-stage cancers, poorer prognoses and worse survival outcomes than older women.
Via Integrated DNA Technologies
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.
We are now on the verge of a health data breakthrough, in which computers will be able to do similar diagnostic tasks, by analyzing massive amounts of data, including genome sequences, risk factors, medical histories, drug interactions, and more.
Looking at this trend last year, venture capitalist Vinod Khosla made the bold claim that technology will replace 80 percent of companies eventually. The reality is probably more nuanced: Far from threatening to put doctors out of jobs, the falling prices of data analysis and genome sequencing are enabling them with tools they could only dream of even a few years ago.
At the Mount Sinai Hospital in New York, Joel Dudley, Ph.D. uses Ayasdi’s products to discover how patients with certain genes are more likely to develop some diseases (diabetes, cardiovascular conditions…) as well as how genes influence the performance of a treatment, or may reveal risks of later relapses that can be prepared for.
Already 11,000 patients at Mount Sinai have had their genome sequenced, a pool large enough for meaningful analysis, although Ayasdi tells us “those are still early days for the industry. There are no plans to act on that data directly with individual patients just yet.”
Right now the Mount Sinai community is working at organizing itself to make the useful information available to the frontline staff. And another 30,000 patients may soon sign the consent form and opt in to participate in this new way to explore which care is best for them.
The exploration of big data by the enterprise is becoming less of a competitive edge and turning into more of a must-have. Similarly, hospitals may have to adopt genetic analysis as a rule of thumb sooner rather than later. Mount Sinai is unusual today in pioneering regular genetic screenings, but it soon may become commonplace.
Researchers have discovered how cells repair a potentially devastating kind of DNA damage. The repair of chromosomal double strand breaks (DSBs) is crucial for the maintenance of genomic integrity. However, the repair of DSBs can also destabilize the genome by causing mutations and chromosomal rearrangements, the driving forces for carcinogenesis and hereditary diseases. Break-induced replication (BIR) is one of the DSB repair pathways that is highly prone to genetic instability. BIR proceeds by invasion of one broken end into a homologous DNA sequence followed by replication that can copy hundreds of kilobases of DNA from a donor molecule all the way through its telomere. The resulting repaired chromosome comes at a great cost to the cell, as BIR promotes mutagenesis, loss of heterozygosity, translocations, and copy number variations, all hallmarks of carcinogenesis. BIR uses most known replication proteins to copy large portions of DNA, similar to S-phase replication. It has therefore been suggested that BIR proceeds by semiconservative replication; however, the model of a bona fide, stable replication fork contradicts the known instabilities associated with BIR such as a 1,000-fold increase in mutation rate compared to normal replication. The collaborative work of graduate students working under Anna Malkova, associate professor of biology at Indiana University-Purdue University Indianapolis (IUPUI) and Kirill Lobachev, associate professor of biology at the Georgia Institute of Technology have now discovered that in budding yeast the mechanism of replication during BIR is significantly different from S-phase replication, as it proceeds via an unusual bubble-like replication fork that results in conservative inheritance of the new genetic material. They provide evidence that this atypical mode of DNA replication, dependent on Pif1 helicase, is responsible for the marked increase in BIR-associated mutations. Lobachev’s lab used cutting-edge analysis techniques and equipment available at only a handful of labs around the world. This allowed the researchers to see inside yeast cells and freeze the break-induced DNA repair process at different times. They found that this mode of DNA repair doesn’t rely on the traditional replication fork — a Y-shaped region of a replicating DNA molecule — but instead uses a bubble-like structure to synthesize long stretches of missing DNA. This bubble structure copies DNA in a manner not seen before in eukaryotic cells. Traditional DNA synthesis, performed during the S-phase of the cell cycle, is done in semi-conservative manner as shown by Matthew Meselson and Franklin Stahl in 1958 shortly after the discovery of the DNA structure. They found that two new double helices of DNA are produced from a single DNA double helix, with each new double helix containing one original strand of DNA and one new strand. “We demonstrated that break-induced replication differs from S-phase DNA replication as it is carried out by a migrating bubble instead of a normal replication fork and leads to conservative DNA synthesis promoting highly increased mutagenesis,” Malkova said. This desperation replication triggers “bursts of genetic instability” and could be a contributing factor in tumor formation. “From the point of view of the cell, the whole idea is to survive, and this is a way for them to survive a potentially lethal event, but it comes at a cost,” Lobachev said. “Potentially, it’s a textbook discovery.” During break-induced replication, one broken end of DNA is paired with an identical DNA sequence on its partner chromosome. Replication that proceeds in an unusual bubble-like mode then copies hundreds of kilobases of DNA from the donor DNA through the telomere at the ends of chromosomes. “Surprisingly, this is a way of synthesizing DNA in a very robust manner,” Saini said. “The synthesis can take place and cover the whole arm of the chromosome, so it’s not just some short patches of synthesis.” The bubble-like mode of DNA replication can operate in non-dividing cells, which is the state of most of the body’s cells, making this kind of replication a potential route for cancer formation. “Importantly, the break-induced replication bubble has a long tail of single-stranded DNA, which promotes mutations,” Ramakrishnan said. The single-stranded tail might be responsible for the high mutation-rate because it can accumulate mutations by escaping the other repair mechanisms that quickly detect and correct errors in DNA synthesis. “When it comes to cancer, other diseases and even evolution, what seems to be happening are bursts of instability, and the mechanisms promoting such bursts were unclear,” Malkova said. The molecular mechanism of break-induced replication unraveled by the new study provides one explanation for the generation of mutations. We propose that the BIR mode of synthesis presents a powerful mechanism that can initiate bursts of genetic instability in eukaryotes, including humans.
Tapping into three genome-wide association studies (GWAS), the researchers, including Peter A. Kanetsky, PhD, MPH, an associate professor in the department of Biostatistics and Epidemiology, analyzed 931 affected individuals and 1,975 controls and confirmed the results in an additional 3,211 men with cancer and 7,591 controls. The meta-analysis revealed that testicular germ cell tumor (TGCT) risk was significantly associated with markers at four loci—4q22, 7q22, 16q22.3, and 17q22, none of which have been identified in other cancers. Additionally, these loci pose a higher risk than the vast majority of other loci identified for some common cancers, such as breast and prostate. This brings the number of genomic regions associated with testicular cancer up to 17—including eight new ones reported in another study in this issue of Nature Genetics. Testicular cancer is relatively rare; however, incidence rates have doubled in the past 40 years. It is also highly heritable. If a man has a father or son with testicular cancer, he has a four-to six-fold higher risk of developing it compared to a man with no family history. That increases to an eight-to 10-fold higher risk if the man has a brother with testicular cancer. Given this, researchers continue to investigate genetic variants and their association with cancer. In 2009, Dr. Nathanson and colleagues uncovered variation around two genes—KITLG and SPRY4—found to be associated with an increased risk of testicular cancer. The two variants were the first striking genetic risk factors found for this disease at the time. Since then, several more variants have been discovered, but only through single GWAS studies. "This analysis is the first to bring several groups of data together to identify loci associated with disease," said Dr. Nathanson, "and represent the power of combining multiple GWAS to better identify genetic risk factors that failed to reach genome-wide significance in single studies."
Nearsightedness—also known as myopia—is a major cause of blindness and visual impairment worldwide, affecting 30 percent of Western populations and up to 80 percent of Asian people. At present, there is no cure. During visual development in childhood and adolescence the eye grows in length, but in people with myopia the eye grows too long. Light entering the eye is then focused in front of the retina rather than on it, resulting in a blurred image. The refractive error can be corrected with glasses, contact lenses, or surgery. But the eye remains longer and the retina is thinner, and could lead to retinal detachment, glaucoma, or macular degeneration, especially with higher degrees of myopia. Myopia is highly heritable, although up to now, little was known about the genetic background. To find the genes responsible, researchers from Europe, Asia, Australia, and the United States analyzed genetic and refractive error data of over 45,000 people from 32 different studies, and found 24 new genes for this trait, and confirmed two previously reported genes. Interestingly, the genes did not show significant differences between the European and Asian groups, despite the higher prevalence among Asian people. The new genes include those which function in brain and eye tissue signaling, the structure of the eye, and eye development. The genes lead to a high risk of myopia and carriers of the high-risk genes had a tenfold increased risk. It was already known that environmental factors, such as reading, lack of outdoor exposure, and a higher level of education can increase the risk of myopia. The condition is more common in people living in urban areas. An unfavorable combination of genetic predisposition and environmental factors appears to be particularly risky for development of myopia. How these environmental factors affect the newly identified genes and cause myopia remains intriguing, and will be further investigated.
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UK Biobank offers easy access to genomic database for researchers around the world In the largest single release of whole genomes ever, the UK Biobank (UKBB) this week unveiled to scientists the entire genomes of 200,000 people who are part of a long-term British health study. The trove of genomes, each linked to anonymized medical information, will allow biomedical scientists to scour the full 3 billion base pairs of human DNA for insights into the interplay of genes and health that could not be gleaned from partial sequences or scans of genome markers. “It is thrilling to see the release of this long-awaited resource,” says Stephen Glatt, a psychiatric geneticist at the State University of New York Upstate Medical University. Other biobanks have also begun to compile vast numbers of whole genomes, 100,000 or more in some cases (see table, below). But UKBB stands out because it offers easy access to the genomic information, according to some of the more than 20,000 researchers in 90 countries who have signed up to use the data. “In terms of availability and data quality, [UKBB] surpasses all others,” says physician and statistician Omar Yaxmehen Bello-Chavolla of the National Institute for Geriatrics in Mexico City. Biobanks A number of efforts are releasing many thousands of whole genomes, with varying degrees of access, to accelerate biomedical research. BIOBANK Completed whole genomes Release information UK Biobank 200,000 300,000 more in early 2023 Trans-Omics for Precision Medicine 161,000 National Institutes of Health (NIH) requires project-specific consent Million Veteran Program 125,000 Non–Veterans Affairs researchers get access in 2022 Genomics England’s 100,000 Genomes 120,000 Researchers must join collaboration All of Us 90,000 NIH expects to release by early 2022 Having enrolled 500,000 middle-age and elderly participants of mostly European ancestry from 2006 to 2010, UKBB is one of the largest genetics research databases in the world. It proved its worth even before releasing whole genomes. Studies of specific DNA markers that vary among participants have revealed hundreds of new disease risk genes. Since 2019 researchers have also been probing participants’ exomes, the 2% of the whole genome sequence (WGS) that encodes proteins; the exomes from nearly all participants became available in the past 2 months. Exome studies are yielding risk genes that are very rare and can’t be found with genotyping data. But whole genomes will make it possible to explore the influence of noncoding DNA, which controls when genes are turned off or on, and of gene rearrangements, as well as missing, repeated, or extra stretches of DNA in genes. Such changes play a role in diseases such as Huntington disease.
CRISPR has sparked a renaissance in genome editing. Now, next-generation CRISPR technologies let scientists modify the genome more efficiently and precisely than before. Such tools could one day serve as therapeutics, but many challenges remain. Most drugs are small molecules that can be packaged into a pill. Genome editors are large, complicated molecules – so scientists can’t just stuff them into a pill for people to swallow, or inject them into people’s bodies. They have to find other ways to get the molecules into patients’ cells. One method relies on viruses, says Guangping Gao, a gene therapy researcher at the University of Massachusetts Medical School and president of the American Society of Gene and Cell Therapy. Scientists could potentially package genome editors into small viruses like adeno-associated viruses, for example. These viruses, which have already seen clinical use in several FDA-approved drugs, could then infect patients’ cells and dump their payloads. It could be that scientists will need to develop entirely different delivery systems. Researchers are currently experimenting with lipid nanoparticles and using electric fields to coax genome editors into cells that can then be transplanted into patients. Delivery remains a major hurdle, Gao says, but he’s still excited about genome editors’ potential. “Gene therapy is now in its golden age,” he says. And genome editors “open even more avenues for treating disease.”
COVID-19 Symptoms Swayed By Genetics More info from WIRED Magazine Some people experience Covid-19 as nothing more than a mild cold, and others exhibit no symptoms at all. Then there are the thousands who sicken and, often, die. Scientists are working hard to understand the underlying reasons for such huge discrepancies in symptoms and outcomes. No one knows the answer yet. One theory: It is locked deep in our genetic makeup. We know that age and underlying health conditions, such as hypertension, play a large role in determining how people fare once they’ve contracted Covid-19. But these alone don’t explain the wide diversity of symptoms. Studying the genetics of the virus and people who are more susceptible to SARS-CoV-2 could not only help identify and protect those more at risk but also help speed treatment and drug development. “What is it that makes some people very sick and other people hardly sick at all? There are two major possibilities,” says Kári Stefánsson, head of deCODE Genetics, an Icelandic subsidiary of Amgen Inc. that has conducted some of the most extensive studies of the virus to date. One is the genetic sequence of the virus itself: that some strains make people sicker than others, he says. The other: the unique genetics of each person who catches the disease. Some people’s genes may simply make them more vulnerable to severe illness, while others’ genetics may confer resistance. It is generally accepted that our genes do play a role in how we respond to viral infections. On the extreme end, one mutation of the gene CCR5, for example, makes those who carry it resistant to human immunodeficiency virus, or HIV. Certain genetic variants, especially in genes that influence the immune system, seem to predispose people to a host of other infectious diseases. One 2017 study looked at 23 common infections including chickenpox, shingles and cold sores and found genes that seemed to be associated with many of them. Stefánsson and other scientists suspect human genetic variations may play a similar role in people who suffer from Covid-19. There are some early indications of this with the novel coronavirus. The receptor it uses to penetrate host cells, called ACE2, can be present in varying numbers in different people based on their genetics and on environmental factors, such as what medicines they take.
Collecting DNA samples for human genetic studies can be an expensive, lengthy process that has often made it difficult to include diverse populations in studies of medical and health data. University of Michigan researchers and their colleagues believe they have found a way to harness the power of social media and its ubiquitous presence to recruit a large, diverse participant pool they hope will help provide quick, reliable data for genetic studies. Their study appears in the June 13, 2019 issue of The American Journal of Human Genetics. “The ability to study very large groups of individuals is a key challenge in human genetics, which is using very rare genetic changes—each present in very few individuals—to understand human biology and health and provide leads for design of new medicines,” said senior author Gonçalo Abecasis, a professor at U-M’s School of Public Health. Katharine Brieger, a doctoral student in public health and first author of the report, said that for studies to be relevant to a broader population, they need to include samples of a wide range of racial, ethnic and socioeconomic backgrounds. “Historically, genetic studies were largely made up of people who lived near university medical centers, inadvertently excluding people who lived in more remote areas or who didn’t have the time and money to travel,” she said. “Allowing remote participation with Genes for Good allows many of these people to participate in research for the first time.” Researchers invited people to participate in the Genes for Good study through Facebook starting in January 2015. Requirements included that participants live in the United States, have a Facebook account and be at least 18. Most of the recruitment was done organically, with people finding the Genes for Good application through family and friends. As of March 2019, about 117,000 people tried the app, 80,000 people had engaged with the study, 32,000 kits had been sent and 27,000 DNA samples collected. Genotypes for the first 20,232 participants were analyzed.
A study of hundreds of years of family trees suggests a man's genes play a role in him having sons or daughters. Men inherit a tendency to have more sons or more daughters from their parents. This means that a man with many brothers is more likely to have sons, while a man with many sisters is more likely to have daughters. A Newcastle University study involving thousands of families is helping prospective parents work out whether they are likely to have sons or daughters. The work by Corry Gellatly, a research scientist at the university, has shown that men inherit a tendency to have more sons or more daughters from their parents. This means that a man with many brothers is more likely to have sons, while a man with many sisters is more likely to have daughters. The research involved a study of 927 family trees containing information on 556,387 people from North America and Europe going back to 1600. "The family tree study showed that whether you’re likely to have a boy or a girl is inherited. We now know that men are more likely to have sons if they have more brothers but are more likely to have daughters if they have more sisters. However, in women, you just can’t predict it," Mr Gellatly explains. Men determine the sex of a baby depending on whether their sperm is carrying an X or Y chromosome. An X chromosome combines with the mother’s X chromosome to make a baby girl (XX) and a Y chromosome will combine with the mother’s to make a boy (XY). The Newcastle University study suggests that an as-yet undiscovered gene controls whether a man’s sperm contains more X or more Y chromosomes, which affects the sex of his children. On a larger scale, the number of men with more X sperm compared to the number of men with more Y sperm affects the sex ratio of children born each year.
While the definitive causes remain unclear, several genetic and environmental factors increase the likelihood of autism spectrum disorder, or ASD, a group of conditions covering a “spectrum” of symptoms, skills and levels of disability. Taking advantage of advances in genetic technologies, researchers led by Alex Nord, assistant professor of neurobiology, physiology and behavior with the Center for Neuroscience at the University of California, Davis, are gaining a better understanding of the role played by a specific gene involved in autism. The collaborative work appears June 26 in the journalNature Neuroscience. “For years, the targets of drug discovery and treatment have been based on an unknown black box of what’s happening in the brain,” said Nord. “Now, using genetic approaches to study the impact of specific mutations found in cases, we’re trying to build a cohesive model that links genetic control of brain development with behavior and brain function.” The Nord laboratory studies how the genome encodes brain development and function, with a particular interest in understanding the genetic basis of neurological disorders. There is no known specific genetic cause for most cases of autism, but many different genes have been linked to the disorder. In rare, specific cases of people with ASD, one copy of a gene called CHD8 is mutated and loses function. The CHD8 gene encodes a protein responsible for packaging DNA in cells throughout the body. Packaging of DNA controls how genes are turned on and off in cells during development. Because mice and humans share on average 85 percent of similarly coded genes, mice can be used as a model to study how genetic mutations impact brain development. Changes in mouse DNA mimic changes in human DNA and vice-versa. In addition, mice exhibit behaviors that can be used as models for exploring human behavior.
Via Mariaschnee
For the data in our genome to be useful, we need to process not just the 3 billion base pairs of DNA that make up each person's genome but also the genomes of many people. The Precision Medicine Initiative is starting with the genomes of at least one million people. That's a mind-boggling amount of data already, and we'll eventually end up needing even more: "many millions" of genomes, saysDr. Eric Schadt, founding director of the Icahn Institute for Genomics and Multiscale Biology at Mount Sinai. We're still figuring out how to decode it all. At Mount Sinai, scientists are trying to "collect as much information on as many patients as we can, integrate it, build predictive models from it, and then derive from those models more refined diagnoses, risk assessments, and plans for treatment than has been possible before," Schadt told Nature Biotechnology in 2012. The team at Icahn is collecting genetic information from patients and trying to incorporate that data with everything from their clinical history to the bacteria on and around them in order to develop predictive models that will calculate how a disease will affect a person. "The technology advances [that make that possible] have been astounding," says Schadt. "The ability to generate sequencing data has moved at a super-Moore's law rate," faster than even computing technology, he says. The supercomputers we have now can process genetic information in ways that would have been "just impossible 10 years ago." Scientists all over the country are pushing for new ways to understand genomic data. Deep Genomics, a startup run by Brendan Frey, is leveraging artificial intelligence to help decode the meaning of the genome. Specifically, the company is using deep learning: the process by which a computer takes in data and then, based on its extensive knowledge drawn from analyzing other data, interprets that information.
On average, every person carries mutations that inactivate at least one copy of 200 or so genes and both copies of around 20 genes. However, knockout mutations in any particular gene are rare, so very large populations are needed to study their effects. These ‘loss of function’ mutations have long been implicated in certain debilitating diseases, such as cystic fibrosis. Most, however, seem to be harmless — and some are even beneficial to the persons carrying them. “These are people we’re not going to find in a clinic, but they’re still really informative in biology,” says MacArthur.
His group and others had been focusing on genome data, but they are now also starting to mine patient-health records to determine the — sometimes subtle — effects of the mutations. In a study of more than 36,000 Finnish people, published in July (E. T. Lim et al. PLoS Genet. 10, e1004494; 2014), MacArthur and his team discovered that people lacking a gene called LPA might be protected from heart disease, and that another knockout mutation, carried in one copy of a gene by up to 2.4% of Finns, may cause fetuses to miscarry if it is present in both copies.
Bing Yu of the University of Texas Health Science Center in Houston told the meeting how he and his collaborators had compared knockout mutations found in more than 1,300 people with measurements of around 300 molecules in their blood. The team found that mutations in one gene, called SLCO1B1, were linked to high levels of fatty acids, a known risk factor for heart failure. And a team from the Wellcome Trust Sanger Institute in Hinxton, UK, reported that 43 genes whose inactivation is lethal to mice were found to be inactivated in humans who are alive and apparently well.
The poster child for human-knockout efforts is a new class of drugs that block a gene known as PCSK9 (see Nature 496, 152–155; 2013). The gene was discovered in French families with extremely high cholesterol levels in the early 2000s. But researchers soon found that people with rare mutations that inactivate one copy of PCSK9 have low cholesterol and rarely develop heart disease. The first PCSK9-blocking drugs should hit pharmacies next year, with manufacturers jostling for a share of a market that could reach US$25 billion in five years.
“I think there are hundreds more stories like PCSK9 out there, maybe even thousands,” in which a drug can mimic an advantageous loss-of-function mutation, says Eric Topol, director of the Scripps Translational Science Institute in La Jolla, California. Mark Gerstein, a bioinformatician at Yale University in New Haven, Connecticut, predicts that human knockouts will be especially useful for identifying drugs that treat diseases of ageing. “You could imagine there’s a gene that is beneficial to you as a 25-year-old, but the thing is not doing a good job for you when you’re 75.”
HHMI researchers find that a single-letter change in the genetic code is enough to generate blond hair in humans.
Genomic surveys by other groups had revealed that the gene – Kit ligand – is indeed evolutionarily significant among humans. “The very same gene that we found controlling skin color in fish showed one of the strongest signatures of selection in different human populations around the world,” Kingsley says. His team went on to show that in humans, different versions of Kit ligand were associated with differences in skin color.
Furthermore, in both fish and humans, the genetic changes associated with pigmentation differences were distant from the DNA that encodes the Kit ligand protein, in regions of the genome where regulatory elements lie. “It looked like regulatory mutations in both fish and humans were changing pigment,” Kingsley says.
Kingsley's subsequent stickleback studies have shown that when new traits evolve in different fish populations, changes in regulatory DNA are responsible about 85 percent of the time. Genome-wide association studies have linked many human traits to changes in regulatory DNA, as well. Tracking down specific regulatory elements in the vast expanse of the genome can be challenging, however.
“We have to be kind of choosy about which regulatory elements we decide to zoom in on,” Kingsley says. “We thought human hair color was at least as interesting as stickleback skin color.” So his team focused its efforts on a human pigmentation trait that has long attracted attention in history, art, and popular culture.
Kit ligand encodes a protein that aids the development of pigment-producing cells, so it made sense that changing its activity could affect hair or skin color. But the Kit ligand protein also plays a host of other roles throughout the body, influencing the behavior of blood stem cells, sperm or egg precursors, and neurons in the intestine. Kingsley wanted to know how alterations to the DNA surrounding this essential gene could drive changes in coloration without comprising Kit ligand's other functions.
Catherine Guenther, an HHMI research specialist in Kingsley's lab, began experiments to search for regulatory switches that might specifically control hair color. She snipped out segments of human DNA from the region implicated in previous blond genetic association studies, and linked each piece to a reporter gene that produces a telltale blue color when it is switched on. When she introduced these into mice, she found that one piece of DNA switched on gene activity only in developing hair follicles.
“When we found the hair follicle switch, we could then ask what's different between blonds and brunettes in northern Europe,” Kingsley said. Examining the DNA in that regulatory segment, they found a single letter of genetic code that differed between individuals with different hair colors.
Their next step was to test each version's effect on the activity of the Kit ligand gene. Their preliminary experiments, conducted in cultured cells, indicated that placing the gene under the control of the “blond” switch reduced its activity by about 20 percent, as compared to the "brunette" version of the switch. The change seemed slight, but Kingsley and Guenther suspected they had identified the critical point in the DNA sequence.
The scientists next engineered mice with a Kit ligand gene placed under the control of the brunette or the blond hair enhancer. Using technology developed by Liqun Luo, who is also an HHMI investigator at Stanford, they were able to ensure that each gene was inserted in precisely the same way, so that a pair of mice differed only by the single letter in the hair follicle switch—one carrying the ancestral version, the other carrying the blond version.
“Sure enough, when you look at them, that one base pair is enough to lighten the hair color of the animals, even though it is only a 20 percent difference in gene expression,” Kingsley says. “This is a good example of how fine-tuned regulatory differences may be to produce different traits. The genetic mechanism that controls blond hair doesn't alter the biology of any other part of the body. It's a good example of a trait that's skin deep—and only skin deep.”
No two human beings are the same. Although we all possess the same genes, our genetic code varies in many places. And since genes provide the blueprint for all proteins, these variants usually result in numerous differences in protein function. But what impact does this diversity have? Bioinformatics researchers at Rutgers University and the Technische Universitaet Muenchen (TUM) have investigated how protein function is affected by changes at the DNA level. Their findings bring new clarity to the wide range of variants, many of which disturb protein function but have no discernible health effect, and highlight especially the role of rare variants in differentiating individuals from their neighbors.
The slightest changes in human DNA can result in an incorrect amino acid being incorporated into a protein. In some cases, all it takes is for a single base to be substituted in a person's DNA, a variant known as a single nucleotide polymorphism (SNP). "Many of these pointmutations have no impact on human health. However, of the roughly 10,000 'missense' SNPs in the human genome – that is, SNPs affecting the protein sequence – at least a fifth can change the function of the protein," explains Prof. Yana Bromberg of the Department of Biochemistry and Microbiology at Rutgers University. "And in some cases, the affected protein is so important and the change so large that we have to wonder why the person with this mutation is still healthy."
Furthermore, two unrelated individuals have thousands of different mutations that affect proteins. Previously, scientists did not fully understand how this large number of mutations affects the coding sequences of DNA. To investigate these "silent" mutations, Bromberg joined forces with Rutgers colleague Prof. Peter Kahn and Prof. Burkhard Rost at TUM.
"We found that many of the mutations are anything but silent," declares Rost, head of the TUM Chair for Bioinformatics and a fellow of the TUM Institute for Advanced Study. The research indicates an extremely wide range of mutations. Many SNPs, for example, are neutral and do not affect protein function. Some, however, cause pathogenic disruption to protein functionality. "There is a gray area between these extremes," Rost explains. "Some proteins have a reduced biological function but are tolerated by the organism and therefore do not directly trigger any disease." The research team analyzed over one million SNPs from a number of DNA databases. They used artificial learning methods to simulate the impact of DNA mutations on the function of proteins. This approach enabled them to investigate the impact of a large number of SNPs quickly and efficiently. The study's findings suggest that, with respect to diversity in protein function, the individual differences between two people are greater than previously assumed. "It seems that humans can live with many small changes in protein function," says Rost. One conclusion the researchers draw is that the wide functional spectrum of proteins must play a key role in evolution. In addition, Bromberg says, "Protein functional diversity may also hold the key to developing personalized medicine."
http://www.pnas.org/cgi/doi/10.1073/pnas.1216613110
The goal is to put the vast collection of data on genetic variations and health into databases open to researchers and doctors all over the world. More than 70 medical, research and advocacy organizations active in 41 countries and including the National Institutes of Health announced Wednesday that they had agreed to create an organized way to share genetic and clinical information. Their aim is to put the vast and growing trove of data on genetic variations and health into databases — with the consent of the study subjects — that would be open to researchers and doctors all over the world, not just to those who created them. Millions more people are expected to get their genes decoded in coming years, and the fear is that this avalanche of genetic and clinical data about people and how they respond to treatments will be hopelessly fragmented and impede the advance of medical science. This ambitious effort hopes to standardize the data and make them widely available.
“We are strong supporters of this global alliance,” said Dr. Francis Collins, director of the National Institutes of Health. “There is lots of momentum now, and we really do want to move quickly.” In just the past few years, the price of determining the sequence of genetic letters that make up human DNA has dropped a millionfold, said Dr. David Altshuler, deputy director and chief academic officer at the Broad Institute of Harvard and M.I.T. As a result, instead of having access to just a few human genomes — the complete genetic material of a person, including genes and regions that control genes — researchers can now study tens of thousands of them, along with clinical data on peoples’ health and how they fared on various treatments. In the next few years, Dr. Altshuler said, researchers expect that millions of people will have their genomes sequenced. “The question is whether and how we make it possible to learn from these data as they grow, in a manner that respects the autonomy and privacy choices of each participant,” he said. No one wants to put DNA sequences and clinical data on the Internet without the permission of patients, he said, so it also is important to allow people to decide if they want their data — with no names or obvious identifiers attached — to be available to researchers. Medical researchers say the best way forward is to have shared databases. Do patients with a particular genetic aberration tend to do well with a particular therapy? Do patients with another mutation have greater odds of developing cancer? Dr. Collins said that cancers are so genetically complex that, most of the time, a mutation seen in a cancer patient will be uncommon. To figure out its significance, data from hundreds of thousands of patients — the world’s collected data — on that mutation are needed.
The psychiatric illnesses seem very different — schizophrenia, bipolar disorder, autism, major depression and attention deficit hyperactivity disorder. Yet they share several genetic glitches that can nudge the brain along a path to mental illness, researchers report. Which disease, if any, develops is thought to depend on other genetic or environmental factors. Their study analysed genome-wide single-nucleotide polymorphism (SNP) data for the five disorders in 33 332 cases and 27 888 controls of European ancestory. To characterise allelic effects on each disorder, they applied a multinomial logistic regression procedure with model selection to identify the best-fitting model of relations between genotype and phenotype. The research team examined cross-disorder effects of genome-wide significant loci previously identified for bipolar disorder and schizophrenia, and used polygenic risk-score analysis to examine such effects from a broader set of common variants. They undertook pathway analyses to establish the biological associations underlying genetic overlap for the five disorders and used enrichment analysis of expression quantitative trait loci (eQTL) data to assess whether SNPs with cross-disorder association were enriched for regulatory SNPs in post-mortem brain-tissue samples.Findings: SNPs at four loci surpassed the cutoff for genome-wide significance (p<5×10−8) in the primary analysis, Regions on chromosomes 3p21 and 10q24, and SNPs within two L-type voltage-gated calcium channel subunits, CACNA1C and CACNB2. Model selection analysis supported effects of these loci for several disorders. Loci previously associated with bipolar disorder or schizophrenia had variable diagnostic specificity. Polygenic risk scores showed cross-disorder associations, notably between adult-onset disorders. Pathway analysis supported a role for calcium channel signalling genes for all five disorders. Finally, SNPs with evidence of cross-disorder association were enriched for brain eQTL markers.The new study does not mean that the genetics of psychiatric disorders are simple. Researchers say there seem to be hundreds of genes involved and the gene variations discovered in the new study confer only a small risk of psychiatric disease. Steven McCarroll, director of genetics for the Stanley Center for Psychiatric Research at the Broad Institute of Harvard and M.I.T., said it was significant that the researchers had found common genetic factors that pointed to a specific signaling system. “It is very important that these were not just random hits on the dartboard of the genome,” said Dr. McCarroll, who was not involved in the new study.
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