If you could unravel your DNA and see if your genes indicate a high risk for certain diseases, would you do it? What if this knowledge meant you could make lifestyle changes and reduce your risk? Genomic medicine makes this possible, and it’s opening up an entire world of possibilities for our health. Computer technology and genomics have enabled today’s scientists to understand which genetic variants correlate with specific diseases and to predict a person’s likelihood of developing certain diseases.
Low-coverage and high-coverage whole-genome sequencing (lcWGS/hcWGS), gene chip testing, and polygenic scores are some of the tools and methods commonly used to understand disease risk. This article dives a little deeper into polygenic scores and whole-genome sequencing to uncover how DNA sequencing can be used to identify genetic risk for certain diseases and disorders and potentially improve outcomes for those with predispositions.
Why Is Genome Sequencing Important?
lcWGS/hcWGS and Disease Detection
Humans are 99.9% genetically identical; we all have the same six-billion-letter code of chemical building blocks (A, T, C, and G) that combine into base pairs and form our genome. What makes us different is the >1% of code that differs from person to person; these differences are known as genomic variants. Each of us is made unique by at least four million of these genomic variants (not all of which are harmful or even have any impact on health).
Low- and high-coverage whole-genome sequencing, therefore, is the comprehensive analysis of complete genomes, and it’s particularly useful in identifying those genetic variants that lead to diseases, such as the mutations that cause cancer. (While the deep sequencing of the DNA of a handful of individuals can yield a lot more information, researchers have found that low-coverage sequencing of larger samples may be more powerful and can be done at a lower cost.)
Genomic variants can be found at precise locations in our DNA, some of which are responsible for increasing or reducing the risk of disease. Where one individual may have a C in a certain location, for example, another person may have a T. While seemingly insignificant, single base-pair changes like this can make or break our health. Achondroplasia is one example — a single altered copy of the FGFR3 gene in the cells that prevents the changing of cartilage to bone.
An Example of Low- and High-Coverage Whole-Genome Sequencing at Work
Whole-genome sequencing is becoming more widely used in the identification of diseases that cannot be detected with conventional testing methods. The story of Nic Volker, a six-year-old child in Wisconsin with a rare genetic disease, is an example of the diagnostic power of WGS.
At two years of age, Nic experienced excruciating intestinal inflammation whenever he ate. It was so intense that he had to be fed intravenously, and even after four years, doctors couldn’t figure out what was wrong with him. Fortunately, genetic researchers were able to use genetic technology to analyze his genome and determine the areas of his DNA that were different from the samples of 28 other people. They discovered a mutation on the XIAP gene, which is responsible for inflammatory functions. Researchers already knew that this gene was connected to immune diseases, but Nic’s case was the first intestinal link. From there, they were able to find that a bone marrow transplant would heal the child.
How Can Genetics Help to Predict Diseases?
Disease Prediction With Low-Coverage/High-Coverage Whole-Genome Sequencing
Disease prediction is a murky subject among researchers. Some claim that most diseases are not genetic, or that they lack strong enough genetic distinctions to predict risk. Strokes and heart disease, for example, are not the result of one or multiple mutations; rather, they can be largely attributed to lifestyle and environmental factors. Bert Vogelstein, the director of the cancer genetics and therapeutics center at Johns Hopkins University wants people to understand that whole-genome sequence testing is not a crystal ball. “It may become one important determinant inpatient care, but certainly not the only one — and possibly not even a major one.” He doesn’t think gene sequencing will ever surpass the importance of preventative medicine or family history in genetic risk assessment.
Others say gene sequencing is an effective way to determine the likelihood of disease. Stanford University’s Ph.D. Michael Snyder and his colleagues have developed an algorithm that uses genetic sequences and electronic health information to estimate an individual’s likelihood of developing genetic diseases. The algorithm is called Hierarchical Estimate From Agnostic Learning (HEAL), and it uses artificial intelligence to identify molecular patterns beyond the glaring “red flag” genomic markers we’re already familiar with. Snyder claims 70% of genetic markers of disease are unknown and that there can be multiple mutations and genes involved in the actualization of a disease. He is working to use HEAL to understand the unclear genetic factors behind conditions such as autism.
Variant Detection of Monogenic Mutations
Science has grouped genetic disorders into several categories, one of which is single-gene disorders. These mutations manifest themselves as inherited disorders, such as muscular dystrophy, cystic fibrosis, sickle cell anemia, and Huntington Disease, which are all diagnosed through genetic testing. Getting diagnosed accurately is critical to a patient’s proper treatment, prevention, and genetic counseling.
Monogenic disorders or monogenic traits, as they’re also called, have very distinct inheritance patterns and can easily be identified through linkage analysis/linkage mapping, which is an analysis of a segregated genetic region with the disease phenotype in a family. Accurate diagnosis of diseases is essential for appropriate treatment of patients, genetic counseling, and prevention strategies.
Huntington Disease is a good example of the ability to identify a variant gene connected to a monogenic disorder. This disease is autosomal dominant, meaning that a mutation in one allele of the gene is required to display the disorder while recessive diseases require both alleles to be mutated to express the disease.
Polygenic Scores and Risk Assessments
A polygenic score is not a diagnostic test but rather a number summarizing a person’s genetic risk and the probability that they will develop particular diseases over time. Polygenic score analysis can be derived from hcWGS and lcWGS and/or data from gene chip arrays. Whole-genome association studies aim to identify variant loci on polygenic traits and aggregate the results to calculate a score. Genetic risk scoring is not a perfect science, but it tells enough about genetic risks to enable people to act.
The hope in providing a polygenic score is that the person will use it to make proactive changes to their lives for a brighter future. Knowing that lifestyle and environment also play a major part in the development of diseases, individuals can change their lifestyle habits and seek therapeutic intervention to slow or even prevent diseases like breast cancer, dementia, or Alzheimer’s. For example, if a woman found out she carried BRCA genes 1 and 2, which have a strong correlation with ovarian and breast cancer, she would know to get screened regularly to prevent the disease or treat it early.
The Future of Genome Sequencing and Disease Mitigation
Whole-genome sequencing (low or high coverage) and gene chip testing have become affordable, common, and highly demanded of DNA testing companies by consumers across the country. Today, more than 30 million Americans have already had their genomes sequenced for genetic diseases. While genetics cannot predict the exact future, it can provide a strong indication of what the future may hold, which is why we are likely to see these informative whole-genome sequencing tests become routine in risk prediction.