New DNA tool predicts height, assesses risk of serious illness
A new Deoxy ribonucleic Acid (DNA)/genetic material tool created by Michigan State University (MSU), United States (U.S.), can accurately predict people’s height, and more importantly, could potentially assess their risk for serious illnesses, such as heart disease and cancer.
For the first time, the tool, or algorithm, builds predictors for human traits such as height, bone density and even the level of education a person might achieve, purely based on one’s genome. But the applications may not stop there.
“While we have validated this tool for these three outcomes, we can now apply this method to predict other complex traits related to health risks such as heart disease, diabetes and breast cancer,” said Stephen Hsu, lead investigator of the study and vice president for research and graduate studies at MSU. “This is only the beginning.”
Further applications have the potential to dramatically advance the practice of precision health, which allows physicians to intervene as early as possible in patient care and prevent or delay illness.
The research, featured in the October issue of Genetics, analyzed the complete genetic makeup of nearly 500,000 adults in the United Kingdom using machine learning, where a computer learns from data.
In validation tests, the computer accurately predicted everyone’s height within roughly an inch. While bone density and educational attainment predictors were not as precise, they were accurate enough to identify outlying individuals who were at risk of having very low bone density associated with osteoporosis or were at risk of struggling in school.
Traditional genetic testing typically looks for a specific change in a person’s genes or chromosomes that can indicate a higher risk for diseases such as breast cancer. Hsu’s model considers numerous genomic differences and builds a predictor based on the tens of thousands of variations.
Using data from the UK Biobank, an international resource for health information, Hsu and his team put the algorithm to work, evaluating each participant’s DNA and teaching the computer to pull out these distinct differences.