How is AI used in genetics?

When you think of genetics research, what comes to mind? Maybe you’re thinking of scientists staring through a microscope or mixing together bubbling reagents. But it turns out that genetics today isn’t just about what happens in the lab – it’s also about the computers analyzing the data behind the scene!

New technological advances in DNA sequencing have generated vast amounts of sequencing data – and humans simply can’t go through billions of DNA letters to spot every hidden pattern. That’s where artificial intelligence (AI) steps in. It helps researchers read, sort, and understand genetic information much faster – and often more accurately – than we could on our own.

What is AI, anyways?

Talk about AI seems to be all around us these days – but what is it really? At its core, AI means building computer systems that can recognize patterns, learn from data, and make decisions. When people talk about AI in genetics, they’re often referring to a specific branch of AI called machine learning. 

Machine learning is where computers are trained to spot patterns in large datasets and use those patterns to make predictions. Instead of being told exactly what to look for, the computer learns from examples, improving as it goes. 

There are two main types of machine learning used in genetics: 

Supervised learning is like giving the AI a cheat sheet. Scientists provide DNA data along with the correct answers – for example, which variants are linked to a disease. The AI then uses this information to learn which patterns in the DNA predict that outcome. Once trained, the AI model can examine new genomes and predict which variants are likely to be important. 

Unsupervised learning is a bit more like giving the AI a puzzle without the solution. It doesn’t know in advance which patterns matter; instead, it looks for structure in the data on its own. This can reveal hidden relationships, like discovering subtypes of a disease or ancestry groups in population genetics. 

Both approaches are incredibly useful in genetics and researchers often combine them to uncover patterns, make predictions, and generate new hypotheses about how genes affect traits. Let’s dive into some real-world examples!

Not All DNA Differences Are Created Equal

When scientists sequence a genome, they can’t just read the DNA like a book, trusting that each letter is correct. Sequencing machines break the DNA into millions of fragments, read them in pieces, and then stitch them back together. Errors can sneak in – like mistyping a letter while copying a long word. So how do researchers know which differences are real genetic variants and which are just mistakes?

It turns out that AI tools have been developed to address precisely this issue of variant identification! One such tool is DeepVariant, created by Google’s research team, which uses machine learning to distinguish real mutations from errors by analyzing patterns in how the DNA sequences line up against a reference genome.¹ Instead of relying on hand-tuned rules about sequencing errors, DeepVariant learns directly from large, labeled datasets. This approach has made variant identification much more accurate, which is essential when trying to find mutations that could cause disease. 

The Hidden Switches in Your DNA

Your DNA sequence doesn’t change from cell to cell, but your genes don’t all behave the same way everywhere in your body. A neuron and a skin cell share the same DNA, yet look and function completely differently. How is that possible? The answer lies in something called gene regulation – how genes are turned on or off in response to cues. 

Gene regulation depends on special stretches of DNA that act like switches. Some of these, called promoters, sit right next to a gene, while others, known as enhancers, can lie thousands of base pairs away but still control the gene’s activity. Enhancers work by providing binding sites for regulatory proteins called transcription factors, which help activate or silence nearby genes. Identifying which parts of the genome function as regulators is one of the major challenges in modern genetics. 

AI has become a powerful ally in this search. Deep learning models like DanQ can predict which stretches of DNA might be acting as enhancers by learning directly from sequence data.² Other models like DeepSea go a step further – they can predict how small genetic variants in these regions might change how well transcription factors can bind the DNA.³ Together, these models are helping scientists map the vast “control panel” of the genome and understand how subtle tweaks in DNA regulator sequences contribute to disease. 

Making Medicine Personal

AI isn’t just making waves in research – it’s also actively helping patients in clinical settings. In particular, AI is advancing something called precision medicine, which aims to tailor each patient’s care to their unique genome. One of the areas in precision medicine where AI is hugely useful is pharmacogenomics – figuring out how a person’s genes affect the way their body responds to medications. Two people might take the same drug but have completely different outcomes: one gets better, while the other experiences side effects. That difference often comes down to subtle variations in genes involved in drug metabolism. 

AI is helping doctors make sense of these variations. Researchers have developed machine learning models that can analyze a person’s entire genome, along with clinical data, to predict how they’ll process specific drugs.  For example, a recent GPT-4-based AI assistant uses a database of genetic guidelines, called the Clinical Pharmacogenetics Implementation Consortium (CPIC), to interpret pharmacogenetic test results and flag variants that may affect drug metabolism.⁴ This helps clinicians choose more effective medications for each patient, bringing precision medicine closer to everyday practice. 

AI in Genetics: Tread Carefully

AI is powerful, but it’s not perfect. These models learn from existing data, which means they can pick up biases. For example, many models are only trained on datasets from European populations, which can affect their ability to make accurate predictions for non-European populations. Being aware of limitations like these is critical to using AI technologies responsibly. 

Even with those limits, AI is transforming genetics research. From spotting variants to predicting drug responses, AI is helping us unlock the secrets of our DNA. Genetics might be complicated, but with AI, we’ve got a clever new partner to help us figure it out!