Wrist-Worn Device Helps Link Physical Activity to 14 Genetic Loci

Researchers in the U.K. have found 14 genetic regions related to activity, seven new to science. The work paves the way for better understanding of sleep, physical activity, and their health consequences, according to the scientists whose study (“GWAS identifies 14 loci for device-measured physical activity and sleep duration”) appears in Nature Communications.

The University of Oxford team showed that time sitting, sleeping, and moving is determined in part by our genes. The scientists, who also studied the activity of 91,105 U.K. Biobank participants who had previously worn an activity monitor on their wrist for a week, taught machines to automatically identify active and sedentary life from the huge amounts of activity monitor data. They then combined this data with U.K. Biobank genetic information to come up with the results highlighted in the Nature Communications paper. 

“Physical activity and sleep duration are established risk factors for many diseases, but their etiology is poorly understood, partly due to relying on self-reported evidence. Here we report a genome-wide association study (GWAS) of device-measured physical activity and sleep duration in 91,105 U.K. Biobank participants, finding 14 significant loci (7 novel). These loci account for 0.06% of activity and 0.39% of sleep duration variation. Genome-wide estimates of ~15% phenotypic variation indicate high polygenicity. Heritability is higher in women than men for overall activity (23 vs. 20%, p = 1.5 × 10−4) and sedentary behaviors (18 vs. 15%, p = 9.7 × 10−4),” wrote the investigators.

“Heritability partitioning, enrichment, and pathway analyses indicate the central nervous system plays a role in activity behaviors. Two-sample Mendelian randomization suggests that increased activity might causally lower diastolic blood pressure (beta mmHg/SD: −0.91, SE = 0.18, p = 8.2 × 10−7), and odds of hypertension (Odds ratio/SD: 0.84, SE = 0.03, p = 4.9 × 10−8). Our results advocate the value of physical activity for reducing blood pressure.”

Physical inactivity is a global public health threat and is associated with a range of common diseases including obesity, diabetes, and heart disease. Changes in sleep duration are linked to heart and metabolic diseases and psychiatric disorders. 

The genetic analysis also showed overlap with neurodegenerative diseases, mental health well-being, and brain structure, showing an important role for the central nervous system with respect to physical activity and sleep. 

“How and why we move isn’t all about genes, but understanding the role genes play will help improve our understanding of the causes and consequences of physical inactivity,” said Aiden Doherty, Ph.D., who led the work and is based at the Big Data Institute, University of Oxford. “It is only by being able to study large amounts of data, such as those provided by U.K. Biobank, that we are able to understand the complex genetic basis of even some of the most basic human functions like moving, resting, and sleeping.”

The use of machine learning in big healthcare datasets is advancing quickly, and having a profound effect on the sorts of studies that can be carried out, added Karl Smith-Byrne, DPhil, one of the lead analysts of the study. “We have carefully developed machine learning models to teach machines how to analyze complex functions like activity,” he said. “These models provide exciting new insights into human movement behaviors in large studies such as U.K. Biobank with its half a million participants.”

Researchers think such studies might also help us determine whether inactivity is a cause or a consequence of obesity.

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