A new study unravels why partners often share similar traits
From education to blood pressure, partners in couples often tend to present striking trait similarities. For the first time, the reasons for this are teased apart by SIB’s Zoltán Kutalik and his team.
The team used advanced statistical methods and large public datasets to reveal that a combination of factors, from initial partner choice to convergence over time contribute to couples sharing similar traits. The intriguing results are published in the journal Nature Human Behavior and further explained in a blog post.
Investigating why couples share similar traits
In a range of traits, from physical to lifestyle, partners tend to be more similar to each other than randomly assigned pairs. According to recent research by Jennifer Sjaarda and Zoltán Kutalik at SIB’s Statistical Genetics group (University of Lausanne), this is due to several factors. The first being people selecting partners with traits similar to themselves. The second factor being partners influencing each other over time and the third being due to partners having shared influences from the environments they live in also known as confounding factors. The group were able to quantify how much each of these factors contributed to similarity in different traits. “Making interesting insights such as these come from having the unrivalled access to a large wealth of data in the UK Biobank. There is potential in future to also examine other traits such as height and longevity in this way,” Zoltán Kutalik said on this recently published study.
Making use of large public datasets
The group was able to make use of the UK Biobank, which contains data from over 500,000 adults, including more than 50,000 couples. As expected, they found striking similarities between partners. Then they sought to disentangle the contributing factors for this, to tease apart correlation and causation. To do this, they created a scenario like a randomised controlled trial through a statistical method called mendelian randomisation. This involved controlling a range of parameters to define what exactly contributes to the observed similarities between partners.
Read the blog post
par SIB Swiss Institute of Bioinformatics