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Scientists have made groundbreaking progress in characterizing the fraction of human DNA that varies between individuals. For more than 20 years, scientists have relied on the human reference genome, a consensus genetic sequence, as a standard against which to compare other genetic data. Used in countless studies, the reference genome has made it possible to identify genes implicated in specific diseases and trace the evolution of human traits, among other things. But it has always been a flawed tool. One of its biggest problems is that about 70 percent of its data came from a single man of predominantly African-European background whose DNA was sequenced during the Human Genome Project, the first effort to capture all of a person's DNA. As a result, it can tell us little about the 0.2 to one percent of genetic sequence that makes each of the seven billion people on this planet different from each other, creating an inherent bias in biomedical data believed to be responsible for some of the health disparities affecting patients today. Many genetic variants found in non-European populations, for instance, aren't represented in the reference genome at all. For years, researchers have called for a resource more inclusive of human diversity with which to diagnose diseases and guide medical treatments. Now scientists with the Human Pangenome Reference Consortium have made groundbreaking progress in characterizing the fraction of human DNA that varies between individuals. As they recently published in Nature, they have assembled genomic sequences of 47 people from around the world into a so-called pangenome in which more than 99 percent of each sequence is rendered with high accuracy. Layered upon each other, these sequences revealed nearly 120 million DNA base pairs that were previously unseen. While it's still a work in progress, the pangenome is public and can be used by scientists around the world as a new standard human genome reference, says The Rockefeller University's Erich D. Jarvis, one of the primary investigators. "This complex genomic collection represents significantly more accurate human genetic diversity than has ever been captured before," he says. "With a greater breadth and depth of genetic data at their disposal, and greater quality of genome assemblies, researchers can refine their understanding of the link between genes and disease traits, and accelerate clinical research." Sourcing diversity Completed in 2003, the first draft of the human genome was relatively imprecise, but it became sharper over the years thanks to filled-in gaps, corrected errors, and advancing sequencing technology. Another milestone was reached last year, when the final eight percent of the genome -- mainly tightly coiled DNA that doesn't code for protein and repetitive DNA regions -- was finally sequenced. Despite this progress, the reference genome remained imperfect, especially with respect to the critical 0.2 to one percent of DNA representing diversity. The Human Pangenome Reference Consortium (HPRC), a government-funded collaboration between more than a dozen research institutions in the United States and Europe, was launched in 2019 to address this problem. At the time, Jarvis, one of the consortium's leaders, was honing advanced sequencing and computational methods through the Vertebrate Genomes Project, which aims to sequence all 70,000 vertebrate species. His and other collaborating labs decided to apply these advances for high-quality diploid genome assemblies to revealing the variation within a single vertebrate: Homo sapiens. To collect a diversity of samples, the researchers turned to the 1000 Genomes Project, a public database of sequenced human genomes that includes more than 2500 individuals representing 26 geographically and ethnically varied populations. Most of the samples come from Africa, home to the planet's largest human diversity. "In many other large human genome diversity projects, the scientists selected mostly European samples," Jarvis says. "We made a purposeful effort to do the opposite. We were trying to counteract the biases of the past." It's likely that gene variants that could inform our knowledge of both common and rare diseases can be found among these populations.
Pigs are valuable large animal models for biomedical and genetic research, but insights into the tissue- and cell-type-specific transcriptome and heterogeneity remain limited. By leveraging single-cell RNA sequencing, scientists now generated a multiple-organ single-cell transcriptomic map containing over 200,000 pig cells from 20 tissues/organs. They were able to comprehensively characterize the heterogeneity of cells in tissues and to identify 234 cell clusters, representing 58 major cell types. In-depth integrative analysis of endothelial cells reveals a high degree of heterogeneity. They also identified several functionally distinct endothelial cell phenotypes, including an endothelial to mesenchymal transition subtype in adipose tissues. Intercellular communication analysis predicts tissue- and cell type-specific crosstalk between endothelial cells and other cell types through the VEGF, PDGF, TGF-β, and BMP pathways. Regulon analysis of single-cell transcriptome of microglia in pig and 12 other species further identifies MEF2C as an evolutionally conserved regulon in the microglia. This important work describes the landscape of single-cell transcriptomes within diverse pig organs and identifies the heterogeneity of endothelial cells and evolutionally conserved regulon in microglia.
Scientists have developed a powerful, inclusive new tool for genomic research that boosts efforts to develop more precise treatments for many diseases by leveraging a better representation of the genetic diversity of people around the world. The new tool will allow researchers to compare natural variations in our genes against genome sequences collected from a diverse group of people. Until now, scientists have compared these variations with a "reference genome" primarily sequenced from a few volunteers (~70% from one person) living near laboratories involved in the Human Genome Project almost 20 years ago. This represented genomes from a small number of people in a small number of countries. The new software tool, called "Giraffe," enables the use of a reference point that is far more diverse and inclusive. Instead of relying on a single reference genome, Giraffe uses a "pangenome" that incorporates information about genome sequences from people around the world. This will give scientists a much more global perspective and help them understand why diseases often strike certain groups disproportionately. "A major advantage of Giraffe is that it enables fast and sensitive comparison of short-read human genome sequences to a pangenome, which is essential for the widespread use of reference graphs that reduce bias in the human genome reference," said researcher Stephen S. Rich, PhD, of the University of Virginia School of Medicine's Center for Public Health Genomics. "Since the current effort in genomics is to move from a European-Caucasian base to a global representation, Giraffe can better define genetic variation in non-white populations and, as a result, have a major impact on precision medicine and application to understanding the genetic risk of disease."
Want to swim with the fishes? New research unravels what makes fish fins so strong yet flexible at the same time. Fish fins do not contain muscles, yet fish can change their shape with high precision and speed to produce large and complex hydrodynamic forces—a combination of high morphing efficiency and high flexural stiffness that is rare in modern morphing and robotic materials. These “flexo-morphing” capabilities are rare in modern morphing and robotic materials of human design. The thin rays that stiffen the fins and transmit actuation include mineral segments, a prominent feature whose mechanics and function are not fully understood. Now, a group of scientists and engineers use mechanical modeling and mechanical testing on 3D-printed ray models to show that the function of the segmentation is to provide combinations of high flexural stiffness and high morphing amplitude that are critical to the performance of the fins and would not be possible with rays made of a continuous material. Fish fin–inspired designs that combine very soft materials and very stiff segments can provide robotic materials with large morphing amplitudes and strong grasping forces.
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.
Just two years ago, GEDmatch was still an obscure genealogy website, known only to a million or so hobbyist DNA sleuths looking to fill in their family trees. The site was free, public, and run by two guys with a knack for writing algorithms that helped relatives find each other. All in all, it was a pretty controversy-free place. That all changed in April of 2018, when news broke that police had used GEDmatch to identify a suspect in the 40-year-old Golden State Killer case. As the site emerged as a crime-fighting tool, some users and privacy experts began to worry about how people’s genetic data might ensnare them in criminal investigations, when all they wanted was to learn about their family history. The transition has been rocky for GEDmatch. One drama after another has engulfed the website: Police searches have grown increasingly invasive; the site’s owners tried to react with changes to its terms of service that ended up backfiring; and white-hat hackers pointed out glaring security flaws. But starting Monday, that’s all someone else’s problem. Early December 2019, GEDmatch announced it was being taken over by a new owner, the forensic genomics firm Verogen. The San Diego-based company spun out of sequencing giant Illumina two years ago, specializing in next-generation DNA testing services catered to law enforcement. With the acquisition of GEDmatch, Verogen may also start offering genealogy searches like the ones that have so far identified suspects in as many as 70 cases. “Never before have we as a society had the opportunity to serve as a molecular eyewitness, enabling law enforcement to solve violent crimes efficiently and with certainty,” Verogen CEO Brett Williams said in a statement announcing the deal. The terms of the agreement were not disclosed. Reactions so far, have been mixed. “I suspect this will be the last straw for all the genealogists who don’t want to share with law enforcement,” Debbie Kennett, a genealogist and honorary research associate at University College London, told WIRED. On Monday GEDmatch updated its terms of service to reflect the new ownership, but it did not alert users via email. Kennett found out from a Facebook group discussion. When she tried to log into GEDmatch, she discovered she was locked out until she accepted the new terms. Additional options included deciding later and permanently deleting all her data from the GEDmatch servers. According to a Verogen spokesperson, whatever settings users had earlier selected for their GEDmatch profiles—opting in or out of police searches—will remain under the new terms. GEDmatch itself has not always stuck to its word on such matters. Earlier this month, the site’s users discovered their privacy settings weren’t ironclad, when reports surfaced that a Florida detective had obtained a warrant to search the site’s full database, including individuals who had opted out of cooperating with law enforcement. A few days prior, a team of genetic security researchers revealed a flaw in GEDmatch’s relative-matching algorithm that would allow a hacker to scrape more than 90 percent of users’ DNA data. Verogen’s Williams says GEDmatch has already addressed these security issues, and that his company will continue to monitor other possible vulnerabilities.
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.
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.”
In a recent study, published in Psychiatric Genetics, scientists found that a commonly used genetic test to predict schizophrenia risk gives scores that are 10 times higher in people with African ancestry than those with European ancestry. This is not because people with African ancestry actually have a higher risk of schizophrenia, but because the genetic markers used were derived almost entirely from studies of individuals of European ancestry. “This means that in the UK today we can test a white British subject and tell them their risk of diabetes or schizophrenia, but if they are of a different ethnicity we cannot offer them the test,” the lead author of the study said. Such tests are not yet used clinically, but in the future they could help identify those at risk of health problems in order to provide preventive treatment. Beyond clinical settings, some predict that such tests could be used to gauge the academic potential of school pupils. A recent international inventory of more than 3,000 papers describing polygenic scores found that 78% of the sequences used in research had come from individuals of European ancestry, up from 76% between 2011 and 2016. Individuals of east Asian descent accounted for 9% of samples, and non-European and non-Asian groups combined accounted for less than 4%. Ethnic minorities are also underrepresented in the UK Biobank, which is aiming to genotype 500,000 individuals. In comparison with population data from the 2011 census, black participants are underrepresented by a third (making up 1.6% versus 2.4% in the census), people of Indian and Chinese origin are underrepresented by more than a third and people of Pakistani and Bangladeshi origin by more than half. White British participants make up 94.6% of Biobank samples, compared to 91.3% of the general population.
This study was performed by members of the Social Science Genetic Association Consortium (SSGAC). The SSGAC is a multi-institutional, international research group that aims to identify statistically robust links between genetic variants and social-science-relevant traits. These include traits such as behavior, preferences, and personality that are traditionally studied by social and behavioral scientists (e.g., economists, psychologists, sociologists) but are often also of interest to health and other researchers. The SSGAC was formed in 2011 to overcome a specific set of scientific challenges. Most traits and behaviors are associated with thousands of genetic variants. Although their collective effect can be substantial (see FAQs 1.5 & 2.3), we now know that almost every one of these genetic variants has an extremely weak effect on its own. To identify specific variants with such small effects, scientists must study at least hundreds of thousands of people (to separate weak signals from noise). One promising strategy for doing this is for many investigators to pool their data into one large study. This approach has borne considerable fruit when used by medical geneticists interested in a range of diseases and conditions (Visscher et al. 2017). Most of these advances would not have been possible without large research collaborations between multiple research groups interested in similar questions. The SSGAC was formed in an attempt by social scientists to adopt this research model. Educational attainment is the amount of formal education a person completes (measured as the number of years of education completed for people in our sample, all of whom are at least age 30 or older). Although educational attainment is most strongly influenced by social and other environmental factors (see FAQ 1.7), it is also influenced by thousands of genes. People vary considerably in how much education they complete. Education is recognized throughout the social and biomedical sciences as an important “predictor” (see FAQ 1.4) of many other life outcomes, such as income, occupation, health, and longevity (Ross & Wu 1995; Cutler & Lleras-Muney 2008). Educational attainment is also among the relatively few social-scientific traits for which it is feasible to conduct a large-scale genome-wide study, because educational attainment is frequently measured by a variety of cohorts, including medical cohorts, due to its robust association with health. A large-scale study is necessary (but not sufficient) to generate scientific findings that are reproducible. The study found 1,271 genetic variants that were associated with educational attainment (using the standard statistical threshold in GWAS, which adjusts for multiple hypothesis testing). This is a substantial increase from the 74 variants identified in our last GWAS of around 300,000 individuals (Okbay, Beauchamp, et al. 2016), confirming the importance of large sample size for identifying specific genetic variants associated with behavioral traits. The current study further confirmed the finding from our earlier work that the effects of individual genetic variants on educational attainment are extremely small. The average effect size across the 1,271 genetic variants was just 1.8 weeks of schooling per allele; even the SNPs with the strongest associations only predicted around 3 weeks of additional schooling per allele. Taken together, these 1,271 SNPs accounted for just 3.9% of the variation across individuals in years of education completed.
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
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A new high-throughput genomic array offers an unprecedented look into the mouse epigenome, giving researchers an in-depth view into the basis of health and disease in a vital model organism. The array was developed by scientists at Van Andel Institute, Children's Hospital of Philadelphia and University of Pennsylvania in collaboration with Illumina and FOXO Technologies. It contains more than 296,000 probes that represent the diversity of murine DNA methylation biology. "DNA methylation is one of the pillars of epigenetics research," said VAI Associate Professor Hui Shen, Ph.D., co-corresponding author of the study that describes the array, which published today in Cell Genomics. "This array gives scientists around the world a powerful new tool to understand the role of methylation in normal processes and in diseases like cancer and many others." Since the 1980s, it has become clear that epigenetics plays a role in virtually every aspect of health and disease. This revelation has given rise to a growing field that has illuminated vital insights into myriad diseases such as cancer, which is now known to be driven by both genetic and epigenetic errors. The new array includes markers for an extensive list of biological features, including genomic imprinting, aging, cancer, variably methylated regions, germ cell development, tissue specificity, X-linked probes, and epigenetic clocks. Development of the array was led by Shen, VAI Professor Peter W. Laird, Ph.D., co-corresponding author of the study, and Children's Hospital of Philadelphia and University of Pennsylvania Assistant Professor Wanding Zhou, Ph.D., a former postdoctoral fellow in the Laird and Shen labs. "We learned many lessons from the current and previous generations of human arrays and have improved the overall quality of probe design," said Zhou, who also is a corresponding author of the study. "For example, we optimized the probe selection to cover the diverse biology of DNA methylation in mice while minimizing the chance of probe misuse and artifacts. The array also features probe sets that will enable comparative epigenome analysis between humans and mice as well as among other rodents." The array allows scientists to interrogate methylation more quickly and more deeply than previous methods. It enabled the team to develop a rich atlas of DNA methylation profiles across more than 1,200 samples, representing diverse cell types, strains and pathologies. Before now, DNA methylation data were available but not centralized. The atlas, made possible by the array, offers scientists a central repository for this critical information. Additionally, the array has been implemented in the popular bioinformatics tool SeSAMe.
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.
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.
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.
Researchers in Sweden and the U.S. have devised a new method for studying individual cells in human tissue, which could lead to even earlier detection of diseases such as cancer and ALS. The method offers a 1,400-fold increase in spatial resolution. Published in the scientific journal, Nature Methods, the study was performed by researchers from KTH Royal Institute of Technology and the Broad Institute of MIT and Harvard. The technology builds on transcriptomics, or the mapping of the cell's molecular fingerprints. RNA-sequencing provides a tool for studying different functions within tissues but the molecular profile only reveals so much. The cell's position in the tissue also has several stories to tell, including how different diseases occur. Methods for studying active genes in individual cells from tissue have evolved dramatically. At the forefront of this research are various microfluidic systems where researchers have been able to isolate individual cells and then carry out so-called massive parallel RNA-sequencing. This method has enabled the profiling of hundreds of thousands of cells from tissue, but provides no information about the cell's origin in the tissue. At the same time, other spatial and molecular methods that capture detailed information about the position of cells in frozen or solid tissue have been developed. Yet such methods still demand knowledge of which genes—or biomarkers—researchers want to investigate, which in turn requires relatively large and expensive instruments. In addition, it is usually only possible to study hundreds of genes at one time, out of thousands of active ones.
Natural hair color within European populations is a complex genetic trait. Previous work has established that MC1R variants are the principal genetic cause of red hair color, but with variable penetrance. Here, a group of geneticists have now extensively mapped the genes responsible for human hair color in the Caucasian British ancestry, participants in UK Biobank. MC1R only explains 73% of the SNP heritability for red hair in the UK Biobank, and in fact most individuals with two MC1R variants have blonde or light brown hair. The scientists identified several other genes contributing to red hair, the combined effect of which accounts for ~90% of the SNP heritability. Blonde hair is associated with over 200 genetic variants and the researchers find a continuum from black through dark and light brown to blonde and account for 73% of the SNP heritability of blonde hair. Many of the associated genes are involved in hair growth or texture, emphasizing the cellular connections between keratinocytes and melanocytes in the determination of hair color.
If you're of European descent, there's a good chance that you can be found. Earlier in 2018 news broke that police had devised an unexpected new method to crack cold cases. Rather than use a suspect's DNA to identify them, data from the DNA was used to search public repositories and identify an alleged killer's family members. From there, a bit of family tree building led to a limited number of suspects and the eventual identification of the person who was charged with the Golden State killings. In the months that followed, more than a dozen other cases were reported to have been solved in the same manner (Genealogy websites identify rape suspect who eluded police for 40 years). The potential for this sort of analysis had been identified by biologists as early as 2014, but they viewed it as a privacy risk—there was potential for personal information from research subjects to leak out to the public via their DNA sequences. Now, a US-Israeli team of researchers has gone through and quantified the chances of someone being identified through public genealogy data. If you live in the US and are of European descent, odds are 60 percent that you can be identified via information that your relatives have made public. Any two humans share identical versions of the vast majority of their DNA. But there are enough differences commonly scattered across the three billion or so bases of our genomes that it's now cheap and easy to determine which version of up to 700,000 differences people have. This screen forms the basis of personal DNA testing and genealogy services. These differences make it easy to identify an individual's DNA, even if they're in a large database. While any two individuals may share the same variant at one location, everyone but identical twins will have enough differences to be distinguished. And, as you might imagine, close family members share more similarities than any two random strangers. But as you move out along the branches of the family tree to more distant relatives like third cousins, the number of differences continues to grow. At this point, a different sort of analysis works better. Variations that are on the same chromosome are physically linked because they reside on the same DNA molecule, so they tend to be inherited together. Over time, exchanges between chromosomes will break up this run of linked variations, but this happens slowly. As a result, distant cousins may not have a huge number of shared variations, but the shared ones will all tend to cluster together as a run of identical variations on a small stretch of a chromosome. The size of those identical stretches will tend to go down over the generations. Many of the DNA testing and genealogy services offer this analysis as a way to find lost family members who have used the same service. But they also allow you to download the data on your variations and then upload them to independent services, which may have a larger user base, and thus a better chance of picking out family members. It was one of these services that made the match that was key to the Golden Gate Killer case. Police did enough DNA testing to have a list of the killer's variations, formatted the information appropriately, and used one of these services to identify a likely family member. From there, other genealogical information and public records could be used to build a family tree and identify likely suspects on it. Probabilities While most people would support the solving of crimes using this method, there are some basic privacy implications. Some might not be comfortable with having personal genetic information about themselves shared by their family members without permission. And, as noted above, this could be used to obtain personal health information if the participant has ever been involved in medical studies. So, the researchers behind the new study decided to quantify the risks involved. They started with a database of 1.3 million people who had been tested by a consumer genealogy company. They then chose individuals at random from this pool and searched for distant family members. They decided to go for distant family members because close family members often coordinate DNA tests and do them at the same time. This involved searching for stretches of identical variants that were long enough to indicate relation, but not as long as what you'd typically see in first cousins. In 15 percent of the cases, they were able to identify what appeared to be second cousins. Another 45 percent were third or fourth cousins for a total of a 60-percent success rate for identifying likely family members.
More than 17 years after the sequencing of the human genome (HUGO), the human proteome is still under revision. One in eight of the 22,210 coding genes listed by the Ensembl/GENCODE, RefSeq and UniProtKB reference databases are annotated differently across the three sets. Scientists have now carried out an in-depth investigation on the 2,764 genes classified as coding by one or more sets of manual curators and not coding by others. Data from large-scale genetic variation analyses suggests that most are not under protein-like purifying selection and so are unlikely to code for functional proteins. A further 1,470 genes annotated as coding in all three reference sets have characteristics that are typical of non-coding genes or pseudogenes. These potential non-coding genes also appear to be undergoing neutral evolution and have considerably less supporting transcript and protein evidence than other coding genes. The researchers believe that the three reference databases currently overestimate the number of human coding genes by at least 2000, complicating and adding noise to large-scale biomedical experiments. Determining which potential non-coding genes do not code for proteins is a difficult but vitally important task since the human reference proteome is a fundamental pillar of most basic research and supports almost all large-scale biomedical projects.
The quest to understand common diseases takes on unprecedented scope. Researchers now searched for inherited causes of insomnia in the DNA 1,310,010 people. They found 956 different genes linked to the sleep disorder, drawing closer to an explanation of what causes it and, perhaps, to new ways to treat it. The study appears to be the first gene search to involve DNA collected from more than one million people. “It’s amazingly massive,” says Stuart Ritchie, a psychologist involved with genetics research at the University of Edinburgh. On Twitter, scientists let loose with superlatives: “Holy cr*p,” “mammoth,” and “Wow!” Danielle Posthuma organized a record-breaking genetic study involving 1.3 million people to search for the causes of insomnia. The project involved crunching genetic and medical information collected from the UK Biobank and the consumer DNA testing company23andMe. It was led by Danielle Posthuma, a neuroscientist specializing in statistical genetics at Vrije University, in Amsterdam. Termed a “genome-wide association,” this type of study involves comparing the DNA of people with and without a disease. Doing so can unveil which DNA differences are responsible for 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.
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