Hexbyte – Tech News – Ars Technica |
Romantic scenes that never happen: your eyes meet. Your heart flutters. This person is the one—you’re sure of it, because you’re convinced they’ll live to at least 95 years old. It’s what you’ve always dreamed of.
Lifespan doesn’t usually make an appearance on people’s lists of what they’re looking for in a partner. But, according to a paper published this week in the journal Genetics, longevity correlates strongly through marriage relationships, meaning that people are pretty good at picking partners who live similar lifespans. Failing to account for that behavior has meant that estimates of the genetic contribution to longevity have been substantially overinflated.
Hexbyte – Tech News – Ars Technica | I knew the second I saw your bloodwork
Nobody is choosing partners based on how long they’ll live. As the authors of the paper sagely note, lifespan “cannot be observed until death, at which point the opportunity to mate has ended.” But as anyone who’s ever dated can tell you, people are likely to marry their match (or close to it) in characteristics like wealth and education, which play an obvious role in longevity.
J. Graham Ruby, the lead author on the paper, works for Calico Life Sciences, a research and development company funded by Alphabet. Calico’s “mission is to harness advanced technologies to increase our understanding of the biology that controls lifespan.” So Ruby used massive amounts of data from Ancestry.com to investigate the role of genes in the lifespans of more than 400,000 people born in the 1800s and early 20th century.
When it comes to complex traits like lifespan, huge numbers of genes will play a role, and so will myriad environmental factors, so the role of genes is described in terms of how much variability it can explain. Estimates of the genetic influence have ranged around 15 to 30 percent, meaning that up to 30 percent of the variation you see in human lifespan can be explained by genetic differences among people.
Estimates vary partly because of differences in data sources and calculation methods and partly because the statistic won’t be the same across different populations: countries differ in the most common causes of death, the environmental risk factors faced by people, and how much different people are exposed to the same risk factors. For example, in an impoverished country with a high risk of infectious disease and death in childbirth, the few wealthy citizens can avoid these risks through expensive healthcare. That will look very different from a wealthy, egalitarian country where cancer is one of