Results of the UK study confirm for clinical laboratory professionals the importance of fully understanding the design and function of SNP chips they may be using in their labs
Here is another example of a long-established clinical laboratory test that—upon new evidence—turns out to be not as accurate as once thought. According to research conducted at the University of Exeter in Devon, UK, Single-nucleotide polymorphism (SNP) chips (aka, SNP microarrays)—technology commonly used in commercial genetic testing—is inadequate at detecting rare gene variants that can increase breast cancer risk.
A news release announcing the results of the large-scale study states, “A technology that is widely used by commercial genetic testing companies is ‘extremely unreliable’ in detecting very rare variants, meaning results suggesting individuals carry rare disease-causing genetic variants are usually wrong.”
Why is this a significant finding for clinical laboratories? Because medical laboratories performing genetic tests that use SNP chips should be aware that rare genetic variants—which are clinically relevant to a patient’s case—may not be detected and/or reported by the tests they are running.
UK Researchers Find ‘Shockingly High False Positives’
The conclusion reached by the Exeter researchers, the BMJ study states, is that “SNP chips are extremely unreliable for genotyping very rare pathogenic variants and should not be used to guide health decisions without validation.”
Leigh Jackson, PhD, Lecturer in Genomic Medicine at University of Exeter and co-author of the BMJ study, said in the news release, “The number of false positives on rare genetic variants produced by SNP chips was shockingly high. To be clear: a very rare, disease-causing variant detected using [an] SNP chip is more likely to be wrong than right.”
In the news release, Caroline Wright, PhD (above), Professor in Genomic Medicine at the University of Exeter Medical School and senior author of the BMJ study, said, “SNP chips are fantastic at detecting common genetic variants, yet we have to recognize that tests that perform well in one scenario are not necessarily applicable to others.” She added, “We’ve confirmed that SNP chips are extremely poor at detecting very rare disease-causing genetic variants, often giving false positive results that can have profound clinical impact. These false results had been used to schedule invasive medical procedures that were both unnecessary and unwarranted.” (Photo copyright: University of Exeter.)
Large-Scale Study Taps UK Biobank Data
The Exeter researchers were concerned about cases of unnecessary invasive medical procedures being scheduled by women after learning of rare genetic variations in BRCA1 (breast cancer type 1) and BRCA2 (breast cancer 2) tests.
“The inherent technical limitation of SNP chips for correctly detecting rare genetic variants is further exacerbated when the variants themselves are linked to very rare diseases. As with any diagnostic test, the positive predictive value for low prevalence conditions will necessarily be low in most individuals. For pathogenic BRCA variants in the UK Biobank, the SNP chips had an extremely low positive predictive value (1-17%) when compared with sequencing. Were these results to be fed back to individuals, the clinical implications would be profound. Women with a positive BRCA result face a lifetime of additional screening and potentially prophylactic surgery that is unwarranted in the case of a false positive result,” they wrote.
Using UK Biobank data from 49,908 participants (55% were female), the researchers compared next-generation sequencing (NGS) to SNP chip genotyping. They found that SNP chips—which test genetic variation at hundreds-of-thousands of specific locations across the genome—performed well when compared to NGS for common variants, such as those related to type 2 diabetes and ancestry assessment, the study noted.
“Because SNP chips are such a widely used and high-performing assay for common genetic variants, we were also surprised that the differing performance of SNP chips for detecting rare variants was not well appreciated in the wider research or medical communities. Luckily, we had recently received both SNP chip and genome-wide DNA sequencing data on 50,000 individuals through the UK Biobank—a population cohort of adult volunteers from across the UK. This large dataset allowed us to systematically investigate the performance of SNP chips across millions of genetic variants with a wide range of frequencies, down to those present in fewer than 1 in 50,000 individuals,” wrote Wright and Associate Professor of Bioinformatics and Human Genetics at Exeter, Michael Weedon, PhD, in a BMJ blog post.
The Exeter researchers also analyzed data from a small group of people in the Personal Genome Project who had both SNP genotyping and sequencing information available. They focused their analysis on rare pathogenic variants in BRCA1 and BRCA2 genes.
The researchers found:
The rarer the variant, the less reliable the test result. For example, for “very rare variants” in less than one in 100,000 people, 84% found by SNP chips were false positives.
Low positive predictive values of about 16% for very rare variants in the UK Biobank.
Nearly all (20 of 21) customers of commercial genetic testing had at least one false positive rare disease-causing variant incorrectly genotyped.
SNP chips detect common genetic variants “extremely well.”
Advantages and Capabilities of SNP Chips
Compared to next-gen genetic sequencing, SNP chips are less costly. The chips use “grids of hundreds of thousands of beads that react to specific gene variants by glowing in different colors,” New Scientist explained.
Common variants of BRCA1 and BRCA2 can be found using SNP chips with 99% accuracy, New Scientist reported based on study data.
However, when the task is to find thousands of rare variants in BRCA1 and BRCA2 genes, SNP chips do not fare so well.
“It is just not the right technology for the job when it comes to rare variants. They’re excellent for the common variants that are present in lots of people. But the rarer the variant is, the less likely they are to be able to correctly detect it,” Wright told CNN.
SNP chips can’t detect all variants because they struggle to cluster needed data, the Exeter researchers explained.
“SNP chips perform poorly for genotyping rare genetic variants owing to their reliance on data clustering. Clustering data from multiple individuals with similar genotypes works very well when variants are common,” the researchers wrote. “Clustering becomes more difficult as the number of people with a particular genotype decreases.”
Clinical laboratories Using SNP Chips
The researchers at Exeter unveiled important information that pathologists and medical laboratory professionals will want to understand and monitor. Cancer patients with rare genetic variants may not be diagnosed accurately because SNP chips were not designed to identify specific genetic variants. Those patients may need additional testing to validate diagnoses and prevent harm.
With improved genetic sequencing comes larger human genome databases that could lead to new diagnostic and therapeutic biomarkers for clinical laboratories
As the COVID-19 pandemic grabbed headlines, the human genome database at the US Department of Veterans Affairs Million Veterans Program (MVP) quietly grew. Now, this wealth of genomic information—as well as data from other large-scale genomic and genetic collections—is expected to produce new biomarkers for clinical laboratory diagnostics and testing.
In December, cancer genomics company Personalis, Inc. (NASDAQ:PSNL) of Menlo Park, Calif., achieved a milestone and delivered its 100,000th whole human genome sequence to the MVP, according to a news release, which also states that Personalis is the sole sequencing provider to the MVP.
The VA’s MVP program, which started in 2011, has 850,000 enrolled veterans and is expected to eventually involve two million people. The VA’s aim is to explore the role genes, lifestyle, and military experience play in health and human illness, notes the VA’s MVP website.
Health conditions affecting veterans the MVP is researching include:
The VA has contracted with Personalis through September 2021, and has invested $175 million, Clinical OMICS reported. Personalis has earned approximately $14 million from the VA. That’s about 76% of the company’s revenue, according to 2nd quarter data, Clinical OMICS noted.
“The VA MVP is the largest whole genome sequencing project in the United States, and this is a significant milestone for both the program and for Personalis,” said John West (above with wife Judy), Founder and CEO of Personalis, in the news release. “Population-scale sequencing projects of this nature represent a cornerstone in our effort to accelerate the advancement of precision medicine across a wide range of disease areas,” he added. (Photo copyright: MIT Technology Review.)
Database of Veterans’ Genomes Used in Current Research
What has the VA gained from their investment so far? An MVP fact sheet states researchers are tapping MVP data for these and other veteran health-related studies:
Differentiating between prostate cancer tumors that require treatment and others that are slow-growing and not life-threatening.
How genetics drives obesity, diabetes, and heart disease.
How data in DNA translates into actual physiological changes within the body.
Gene variations and patients’ response to Warfarin.
NIH Research Program Studies Effects of Genetics on Health
Another research program, the National Institutes of Health’s All of Us study, recently began returning results to its participants who provided blood, urine, and/or saliva samples. The NIH aims to aid research into health outcomes influenced by genetics, environment, and lifestyle, explained a news release. The program, launched in 2018, has biological samples from more than 270,000 people with a goal of one million participants.
“We’re changing the paradigm for research. Participants are our most important partners in this effort, and we know many of them are eager to get their genetic results and learn about the science they’re making possible,” said Josh Denny, MD, CEO of the NIH’s All of Us research program in the news release. Denny, a physician scientist, was Professor of Biomedical Informatics and Medicine, Director of the Center for Precision Medicine and Vice President for Personalized Medicine at Vanderbilt University Medical Center prior to joining the NIH. (Photo copyright: National Institutes of Health.)
Inclusive Data Could Aid Precision Medicine
The news release notes that more than 80% of biological samples in the All of Us database come from people in communities that have been under-represented in biomedical research.
“We need programs like All of Us to build diverse datasets so that research findings ultimately benefit everyone,” said Brad Ozenberger, PhD, All of Us Genomics Program Director, in the news release.
Precision medicine designed for specific healthcare populations is a goal of the All of Us program.
“[All of Us is] beneficial to all Americans, but actually beneficial to the African American race because a lot of research and a lot of medicines that we are taking advantage of today, [African Americans] were not part of the research,” Chris Crawford, All of US Research Study Navigator, told the Birmingham Times. “As [the All of Us study] goes forward and we get a big diverse group of people, it will help as far as making medicine and treatment that will be more precise for us,” he added.
Large Databases Could Advance Care
Genome sequencing technology continues to improve. It is faster, less complicated, and cheaper to sequence a whole human genome than ever before. And the resulting sequence is more accurate.
Thus, as human genome sequencing databases grow, researchers are deriving useful scientific insights from the data. This is relevant for clinical laboratories because the new insights from studying bigger databases of genomic information will produce new diagnostic and therapeutic biomarkers that can be the basis for new clinical laboratory tests as well as useful diagnostic assays for anatomic pathologists.
Genetic data captured by this new technology could lead to a new understanding of how different types of cells exchange information and would be a boon to anatomic pathology research worldwide
What if it were possible to map the interior of cells and view their genetic sequences using chemicals instead of light? Might that spark an entirely new way of studying human physiology? That’s what researchers at the Massachusetts Institute of Technology (MIT) believe. They have developed a new approach to visualizing cells and tissues that could enable the development of entirely new anatomic pathology tests that target a broad range of cancers and diseases.
Scientists at MIT’s Broad Institute and McGovern Institute for Brain Research developed this new technique, which they call DNA Microscopy. They published their findings in Cell, titled, “DNA Microscopy: Optics-free Spatio-genetic Imaging by a Stand-Alone Chemical Reaction.”
Joshua Weinstein, PhD, a postdoctoral associate at the Broad Institute and first author of the study, said in a news release that DNA microscopy “is an entirely new way of visualizing cells that captures both spatial and genetic information simultaneously from a single specimen. It will allow us to see how genetically unique cells—those comprising the immune system, cancer, or the gut for instance—interact with one another and give rise to complex multicellular life.”
The news release goes on to state that the new technology “shows
how biomolecules such as DNA and RNA are organized in cells and tissues,
revealing spatial and molecular information that is not easily accessible
through other microscopy methods. DNA microscopy also does not require
specialized equipment, enabling large numbers of samples to be processed
simultaneously.”
The images above, taken from the MIT study, compares optical imaging of a cell population (left) with an inferred visualization of the same cell population based on the information provided by DNA microscopy (right). Scale bar = 100 μm (100 micrometers). This technology has the potential to be useful for anatomic pathologists at some future date. (Photo and caption copyrights: Joshua Weinstein, PhD, et al/Cell.)
New Way to Visualize Cells
The MIT researchers saw an opportunity for DNA microscopy to
find genomic-level cell information. They claim that DNA microscopy images
cells from the inside and enables the capture of more data than with
traditional light microscopy. Their new technique is a chemical-encoded
approach to mapping cells that derives critical genetic insights from the
organization of the DNA and RNA in cells and tissue.
And that type of genetic information could lead to new precision medicine treatments for chronic disease. New Atlas notes that “ Speeding the development of immunotherapy treatments by identifying the immune cells best suited to target a particular cancer cell is but one of the many potential application for DNA microscopy.”
In their published study, the scientists note that “Despite enormous progress in molecular profiling of cellular constituents, spatially mapping [cells] remains a disjointed and specialized machinery-intensive process, relying on either light microscopy or direct physical registration. Here, we demonstrate DNA microscopy, a distinct imaging modality for scalable, optics-free mapping of relative biomolecule positions.”
How DNA Microscopy Works
The New York Times (NYT) notes that the advantage of DNA microscopy is “that it combines spatial details with scientists’ growing interest in—and ability to measure—precise genomic sequences, much as Google Street View integrates restaurant names and reviews into outlines of city blocks.”
And Singularity Hub notes that “ DNA microscopy, uses only a pipette and some liquid reagents. Rather than monitoring photons, here the team relies on ‘bar codes’ that chemically tag onto biomolecules. Like cell phone towers, the tags amplify, broadcasting their signals outward. An algorithm can then piece together the captured location data and transform those GPS-like digits into rainbow-colored photos. The results are absolutely breathtaking. Cells shine like stars in a nebula, each pseudo-colored according to their genomic profiles.”
“We’ve used DNA in a way that’s mathematically similar to photons in light microscopy,” Weinstein said in the Broad Institute news release. “This allows us to visualize biology as cells see it and not as the human eye does.”
In their study, researchers used DNA microscopy to tag RNA
molecules and map locations of individual human cancer cells. Their method is
“surprisingly simple” New Atlas reported. Here’s how it’s done,
according to the MIT news release:
Small synthetic DNA tags (dubbed “barcodes” by the MIT team) are added to biological samples;
The “tags” latch onto molecules of genetic material in the cells;
The tags are then replicated through a chemical reaction;
The tags combine and create more unique DNA labels;
The scientists use a DNA sequencer to decode and reconstruct the biomolecules;
A computer algorithm decodes the data and converts it to images displaying the biomolecules’ positions within the cells.
The visualization above was created from data gathered by DNA microscopy, which peers inside individual cells. It demonstrates how DNA microscopy enables scientists to identify different cells (colored dots) within a sample—with no prior knowledge of what the sample looks like. (Photo and caption copyright: Joshua Weinstein, PhD, et al./Cell.)
“The first time I saw a DNA microscopy image, it blew me away,” said Aviv Regev, PhD, a biologist at the Broad Institute, a Howard Hughes Medical Institute (HHMI) Investigator, and co-author of the MIT study, in an HHMI news release. “It’s an entirely new category of microscopy. It’s not just a technique; it’s a way of doing things that we haven’t ever considered doing before.”
Precision Medicine Potential
“Every cell has a unique make-up of DNA letters or genotype. By capturing information directly from the molecules being studied, DNA microscopy opens up a new way of connecting genotype to phenotype,” said Feng Zhang, PhD, MIT Neuroscience Professor,
Core Institute Member of the Broad Institute, and
Investigator at the McGovern Institute for Brain Research at MIT, in the HHMI
news release.
In other words, DNA microscopy could someday have applications in precision medicine. The MIT researchers, according to Stat, plan to expand the technology further to include immune cells that target cancer.
The Broad Institute has applied for a patent on DNA
microscopy. Clinical laboratory and anatomic pathology group leaders seeking
novel resources for diagnosis and treatment of cancer may want to follow the MIT
scientists’ progress.
Using animal blood, the researchers hope to improve the accuracy of AI driven diagnostic technology
What does a cheetah, a tortoise, and a Humboldt penguin have
in common? They are zoo animals helping scientists at Saarland University in
Saarbrücken, Germany, find biomarkers that can help computer-assisted diagnoses
of diseases in humans at early stages. And they are not the only animals
lending a paw or claw.
In their initial research, the scientists used blood samples
that had been collected during routine examinations of 21 zoo animals between
2016 and 2018, said a news
release. The team of bioinformatics
and human genetics experts
worked with German zoos Saarbrücken and Neunkircher for the study. The project
progresses, and thus far, they’ve studied the blood of 40 zoo animals, the
release states.
This research work may eventually add useful biomarkers and
assays that clinical
laboratories can use to support physicians as they diagnose patients,
select appropriate therapies, and monitor the progress of their patients. As medical
laboratory scientists know, for many decades, the animal kingdom has been
the source of useful insights and biological materials that have been
incorporated into laboratory assays.
“Measuring the molecular blood profiles of animals has never
been done before this way,” said Andreas
Keller, PhD, Saarland University Bioinformatics Professor and Chair for
Clinical Bioinformatics, in the news release. The Saarland researchers published
their findings in Nucleic Acids
Research, an Oxford
Academic journal.
“Studies on sncRNAs [small non-coding RNAs] are often largely based on homology-based information, relying on genomic sequence similarity and excluding actual expression data. To obtain information on sncRNA expression (including miRNAs, snoRNAs, YRNAs and tRNAs), we performed low-input-volume next-generation sequencing of 500 pg of RNA from 21 animals at two German zoological gardens,” the article states.
Can Animals Improve the Accuracy of AI to Detect Disease
in Humans?
However, the researchers perceived an inability for AI and machine learning to
discern real biomarker patterns from those that just seemed to fit.
“The machine learning methods recognize the typical
patterns, for example for a lung tumor or Alzheimer’s disease. However, it is
difficult for artificial intelligence to learn which biomarker patterns are
real and which only seem to fit the respective clinical picture. This is where
the blood samples of the animals come into play,” Keller states in the news
release.
“If a biomarker is evolutionarily conserved, i.e. also
occurs in other species in similar form and function, it is much more likely
that it is a resilient biomarker,” Keller explained. “The new findings are now
being incorporated into our computer models and will help us to identify the
correct biomarkers even more precisely in the future.”
Andreas Keller, PhD (left), and zoo director Richard Francke (right), hold a pair of radiated tortoises that participated in the Saarland University study. (Photo copyright: Oliver Dietze/Saarland University.)
“Because blood can be obtained in a standardized manner and
miRNA expression patterns are technically very stable, it is easy to accurately
compare expression between different animal species. In particular, dried blood
spots or microsampling devices appear to be well suited as containers for
miRNAs,” the researchers wrote in Nucleic Acids Research.
Animal species that participated in the study include:
Additionally, human volunteers contributed blood specimens
for a total of 19 species studied. The scientists reported success in capturing
data from all of the species. They are integrating the information into their
computer models and have developed a public database of their
findings for future research.
“With our study, we provide a large collection of small RNA
NGS expression data of species that have not been analyzed before in great
detail. We created a comprehensive publicly available online resource for
researchers in the field to facilitate the assessment of evolutionarily
conserved small RNA sequences,” the researchers wrote in their paper.
Clinical Laboratory Research and Zoos: A Future
Partnership?
This novel involvement of zoo animals in research aimed at improving
the ability of AI driven diagnostics to isolate and identify human disease is
notable and worth watching. It is obviously pioneering work and needs much
additional research. At the same time, these findings give evidence that there
is useful information to be extracted from a wide range of unlikely sources—in
this case, zoo animals.
Also, the use of artificial intelligence to search for
useful patterns in the data is a notable part of what these researchers
discovered. It is also notable that this research is focused on sequencing DNA
and RNA of the animals involved with the goal of identifying sequences that are
common across several species, thus demonstrating the common, important
functions they serve.
In coming years, those clinical laboratories doing genetic
testing in support of patient care may be incorporating some of this research
group’s findings into their interpretation of certain gene sequences.
The resulting genomic dataset may provide useful diagnostic insights that can be used by clinical laboratory and pathology professionals to learn how and why some people age with good health
Why do some seniors age in good health and other seniors
suffer with multiple chronic conditions? A new genetic database is using whole-genomic
sequencing (WGS) to answer that question in ways that may benefit medical
laboratories.
Because of the rapid aging of populations in the United
States and other developed nations throughout the world, there is keen interest
in how to keep seniors healthy. In fact, developing effective lab testing
services in support of improved senior health is one of the big opportunities
for both clinical laboratories and anatomic
pathology groups.
Until recent years, most clinical
pathologists dealt primarily with lab tests that used specimens such as blood
and urine. However, genetics researchers are using WGS to discover new causes
for many chronic illnesses. And the tools these researchers are developing offer
pathologists and clinical
laboratories powerful new ways to help doctors diagnose disease.
Through the use of WGS, the MGRB now features a huge
database of thousands of healthy elderly people. The data it contains may enable
pathology scientists to learn, from a genetic standpoint, why some people age
healthfully while others do not.
The researchers published their work titled, “The Medical
Genome Reference Bank: A Whole-Genome Data Resource of 4,000 Healthy Elderly
Individuals. Rationale and Cohort Design,” in the European Journal of
Human Genetics.
Finding New Applications for Genetic Data
According to the UNSW published study, “The MGRB is comprised
of individuals consented through the biobank programs of two contributing
studies … Each sample is from an individual who has lived to [greater than or
equal to] 70 years with no reported history or current diagnosis of
cardiovascular disease, dementia, or cancer, as confirmed by the participating
studies at recent follow-up study visits.”
The researchers noted in their paper, “Aged and healthy
populations are more likely to be selectively depleted of pathogenic alleles, and therefore
particularly suitable as a reference population for the major diseases of
clinical and public health importance.”
The MGRB plans to make its database openly accessible to the
international research community through its website once all 4,000 samples
have been sequenced. Currently, about 3,000 of the samples have been analyzed,
as noted on the Garvan website,
which is tracking the MGRB’s progress.
“The Medical Genome Reference Bank can tell us much about what it means to grow old but remain well, and is a powerful tool to help us deconstruct the genetics of common diseases,” said David Thomas, PhD (above), an NHMRC Principal Research Fellow, Director of The Kinghorn Cancer Center, and Head of the Cancer Division of the Garvan Institute in New South Wales, AU, in a statement reported by GenomeWeb. (Photo copyright: South West Sydney Research.)
Personal Genetic Data in Precision Medicine
“The integration of genomic knowledge and technologies into
healthcare is revolutionizing the way we approach clinical and public health
practice,” Caron
M. Molster, et al, noted in, “The Evolution of Public Health
Genomics: Exploring Its Past, Present, and Future,” published in Frontiers
in Public Health. Molster is Manager at the Health Department Western
Australia in Perth, and lead author of the paper.
“Public health genomics has evolved to responsibly integrate
advancements in genomics into the fields of personalized
medicine and public health,” the researchers wrote.
The 100,000
Genomes Project in the United Kingdom is sequencing the genomes of people
who have rare diseases and their families. Researchers all over the world are collecting
genomic data with plans to use it in different ways, and on various chronic
disease populations, in pursuit of precision medicine
goals.
Molster and her co-authors noted the comparable development
of genetic sequencing and precision medicine in their paper.
“Parallel to the developments in precision medicine has been
the advancement of technologies that enable the production, aggregation,
analysis, and dissemination of extremely large volumes of individual- and
population-level data on genes, environment, behavior, and other social and
economic determinants of health. These data have proven useful in finding new
correlations, patterns and trends, particularly those involving complex
interactions, in relation to diseases, pathogens, exposures, behaviors,
susceptibility (risk), and health outcomes in populations,” they wrote.
According to Paul Lacaze, PhD,
Head of the Public Health Genomics Program at Monash University, one of the
challenges in interpreting whole-genome data in order to diagnose disease is
“discriminating rare candidate disease-causing variants from the large numbers
of benign variants unique to each individual. Reference populations are
powerful filters,” he noted in the MGRB paper.
The MGRB database provides just such a powerful reference
population, giving researchers who are studying specific diseases a tool for
comparison.
Other Studies into Heathy Aging
Other initiatives to create datasets of genome information
for specific populations also are underway. The Scripps
Translational Science Institute (STSI) in La Jolla, Calif., has been
studying healthy aging since 2007. That’s when STSI launched the Wellderly Study,
according to a news
release. As of 2016, they had sequenced the genomes of 600 study
participants, as well as 511 samples for comparison from a study being conducted
separately by the Inova Translational Medicine Institute, a paper in Nature noted.
Another effort being conducted in China involves a database
called PGG.Population.
These researchers seek to “create a comprehensive depository of geographic and
ethnic variation of human genome, as well as a platform bringing influence on
future practitioners of medicine and clinical investigators,” according to
their 2018 paper published in Nucleic Acids
Research.
In this case, rather than identifying common genomic
variants among a specific population, such as the healthy elderly, the
researchers are working to understand how genetic variations are distributed
among specific populations. “The PGG.Population database documents 7,122
genomes representing 356 global populations from 107 countries and provides
essential information for researchers to understand human genomic diversity and
genetic ancestry,” wrote the researchers.
Each of these disparate datasets represents paths of
investigation that could lead to a better understanding of personal and public health.
As technologies continue to develop that enable scientists to sift through the
massive amount of WGS data being generated, a clearer picture of what healthy
aging at the genetic level looks like will likely emerge.
Precision medicine is leading to precision public health,
and clinical pathology laboratories are important parts of the public health
puzzle.
