A functional approach for studying diseases with strong genetic underpinnings
Tap into more parts of your genetic makeup than most tests
Read more chapters from your "book of life," including recipes for making essential molecules, and other segments that control genome-reading activities.
Knowing HOW to read DNA is not the same as recognizing WHAT parts are important
Automated DNA sequencing has profoundly improved our understanding of the genetic architectures that underlie many diseases. But many conventional DNA tests today still look for glitches in just a tiny part of our genome in recipes for making proteins, the essential molecules that build cells and then tell them what to do, and where in the body to do it.
Genotype Diagnostics taps into a broader part of our DNA than most tests, including regions that guide how genetic blueprints are read; rules for about customizing proteins in different cell types; and segments outside the protein-coding parts of DNA that act like "switches" to control when and how a cell uses its genetic instructions, and where the vast majority of differences among people are found.
Spot clues in DNA about the expression of harmful traits
Powerful search engines comb the chemical text of DNA for glitches connected to health problems beyond what human experts might easily discern in genetically diverse populations.
Powerful search engines look for glitches in one's genetic makeup
The enormous task of sequencing one's genome reading the order in which billions of its chemical building blocks are arranged into long DNA chains easily generates enough data from each person to fill about 400 million pages of printed text. We humans cannot quite grasp all this information on our own to meaningfully detect genetic patterns relevant to the many expressed variations in malady observed among patients diagnosed with the same health condition.
The state-of-the-art in genetic analysis draws upon robust machine-learning algorithms to reduce diagnostic uncertainty. It's the same kind of automated reasoning behind popular voice-, text- and image-recognition apps, where context is important for arriving at the right answer.
Here, powerful search engines learn to comb genetic blueprints for clinically relevant patterns using multi-layered logic that improves with experience; to spot variations that likely contribute to the onset of disease ("pathogenic risks") and, in time, to better distinguish among variants that lead to small, but harmless, differences observed in human traits ("benign mutations") from others that alter the intensity with which one's disease symptoms express ("disease modifiers").
Link genetic variations to how normal cell behaviors go awry
Model how many small glitches in a genetic blueprint may lead to disease when working in concert to disrupt the molecular circuitry inside live cells.
Functional Genomics may better account for complex biology
The promise of genetic profiles in medical diagnostics depends on a new generation of robust analytics that can profile one's disease not only by the likely sources of the problem the muddled instructions encoded in one's DNA that give rise to harmful traits but also by the ways these mutations interact to express a range of observed symptoms in progressive and spectrum disorders.
Functional frameworks offer ways to describe how the cumulative effect of many small glitches in DNA might disrupt the complex molecular circuitry inside live cells; building upon recent technical advances in molecular expression profiling with new forms of data analysis, called "deep learning," to extract features in genomes that control normal cell behaviors: that model how a live cell normally reads its genetic instructions how that information is normally turned into essential molecules.
And when directed against DNA collected from patients afflicted with a particular disease, that also predict from changes to molecular outputs how the inner workings of cells are likely disrupted and might, ultimately, express as harmful traits.
There's a bit more to it, but these are the basics.
Match the right treatment with the right patient
Target drug cocktails against the specific genetic "typos" found in each patient to improve treatment outcomes, and with fewer side effects.
The promise of "Precision Medicine"
Genetic tests are now integral parts of a new line of thinking reshaping healthcare known as "precision medicine." This is a transformative era in personalized treatment when clinicians, rather than treating a particular condition in the same way across a range of genetically diverse individuals, could better infer a patient's likely response to treatment from one's unique genetic makeup expanding the conversation about human diseases from "what's causing this?" to "who is at risk?" and "how do we treat this more effectively?"
Train computers to spot conditions with complex genetic underpinnings
Search engines that look at genomes may diagnose disorders not only by the likely sources of the problem the muddled instructions encoded in one's genome that give rise to abnormal traits but also by the ways these mutations interact to express as characteristic symptoms of disease the specific changes to molecular circuits inside living cells that increase one's risk for certain maladies.
Tailor treatment plan to each patient's individual genetic makeup
Next-generation tests could help clinicians formulate drug cocktails that better target the unique genetic makeup of each patient that infer one's likely response to a particular treatment regimen from one's molecular signatures to improve patient outcomes, and with fewer side effects.