Virtually all the statistical methods researchers commonly use to assess genetic correlations assume that mating is random. That is, they assume that potential mating partners decide who they will have children with based on a roll of the dice. In reality, many factors likely influence who mates with whom. The simplest example of this is geography – people living in different parts of the world are less likely to end up together than people living nearby.

We wanted to find out how much the assumption of random mating affects the accuracy of genetic correlation analyses. In particular, we focused on the potential confounding effects of assortative mating, or how people tend to mate with those who share similar characteristics with them. Assortative mating is a widely documented phenomenon seen across a broad array of traits, interests, measures and social factors, including height, education and psychiatric conditions.

In our study we examined cross-trait assortative mating, whereby people with one trait (for example, being tall) tend to mate with people with a completely different trait (for example, being wealthy). From our database of 413,980 mate pairs in the U.K. and Denmark, we found evidence of cross-trait assortative mating for many traits – for instance, an individual’s time spent in formal schooling was correlated not only with their mate’s educational attainment, but also with many other characteristics, including height, smoking behaviors and risk for different diseases.

We found that taking into consideration the similarities across mates could strongly predict which traits would be considered genetically linked. In other words, just based on how many characteristics a pair of mates shared, we could identify around 75% of the presumed genetic links between these traits – all without sampling any DNA.

Cross-trait assortative mating shapes the genome. If people with one heritable trait tend to mate with people with another heritable trait, then these two distinct characteristics will become genetically correlated to each other in subsequent generations. This will happen regardless of whether or not these traits are truly genetically linked to each other.

Cross-trait assortative mating means that the genes you inherit from one parent will be correlated with those you inherit from the other. How people mate is not random, violating the key assumption behind genetic correlation analyses. This inflates the genetic association between traits that aren’t truly linked together by genes.

Recent studies corroborate our findings. Earlier this year, researchers computed genetic correlations using a method that examines the association between the traits and genes of siblings. The genetic links between traits influenced by cross-trait assortative mating were substantially weakened.

But without accounting for cross-trait assortative mating, using genetic correlation estimates to study the biological pathways causing disease can be misleading. Genes that affect only one trait will appear to influence multiple different conditions. For example, a genetic test designed to assess the risk for one disease may incorrectly detect vulnerability for a broad number of unrelated conditions.

The ability to measure variation across individuals at the genetic and molecular level is truly a feat of modern science. However, genetic epidemiology is still an observational enterprise, subject to the same caveats and challenges facing other forms of nonexperimental research. Though our findings don’t discount all genetic epidemiology research, understanding what genetic studies are truly measuring will be essential to translate research findings into new ways to treat and assess disease.

This article is republished from The Conversation, an independent nonprofit news site dedicated to sharing ideas from academic experts. It was written by: Richard Border, University of California, Los Angeles and Noah Zaitlen, University of California, Los Angeles. If you found it interesting, you could subscribe to our weekly newsletter.

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Richard Border receives funding from the National Institutes of Health.

Noah Zaitlen receives funding from the NIH, NSF, DoD, and CZI.