How do scientists link genetics to health
Google researchers have developed deep learning tools that transform non-invasive biological signals, such as retinal images and blood volume pulses, into powerful predictive health biomarkers. These studies demonstrate that "aging clocks" derived from retinal scans can accurately estimate biological age and identify genetic factors linked to life span, independent of traditional blood tests. Similarly, the REGLE framework uses unsupervised learning to uncover hidden genetic information within clinical data like lung and heart waveforms, improving the discovery of disease-associated genes. Complementing these findings, a smartphone-compatible sensing technology called photoplethysmography (PPG) was shown to predict ten-year cardiovascular risk as effectively as standard office-based medical examinations. Together, these sources highlight a shift toward digital diagnostics that make complex disease screening more accessible in resource-limited settings. This body of research suggests that high-dimensional clinical data, when analyzed via artificial intelligence, can reveal systemic health insights once requiring intensive laboratory procedures.