Estimates of genetic correlation often presume that mating is random. However, partners tend to pair up in the real world due to various mutual interests and social systems.

According to a recent UCLA-led study, many estimates of how strongly qualities and diseases share genetic signals may be exaggerated since existing approaches for analysing genetic links across attributes fail to account for mating patterns.

In recent years, scientists have worked to unravel the genetic links between features and disease risk using advanced genome sequencing technologies, believing that discoveries of common genetics could hint at answers for disease treatment.

However, UCLA researchers cautioned against relying too much on genetic connection estimates in their recent study published in Science. They claim that such estimates are more influenced by non-biological elements than previously thought.

Genetic correlation estimates typically assume that mating is random. But in the real world, partners tend to pair up because of many shared interests and social structures.

As a result, some genetic correlations in previous work that have been attributed to shared biology may instead represent incorrect statistical assumptions.

For example, previous estimates of genetic overlap between body mass index (BMI) and educational attainment are likely to reflect this type of population structure, induced by "cross-trait assortative mating," or how individuals of one trait tend to partner with individuals of another trait.

The study authors said genetic correlation estimates deserve more scrutiny since these estimates have been used to predict disease risk, glean clues for potential therapies, inform diagnostic practices, and shape arguments about human behaviour and societal issues.

The authors said some in the scientific community have placed too much emphasis on genetic correlation estimates based on the idea that studying genes, because they are unalterable, can overcome confounding factors.

"If you just look at two traits that are elevated in a group of people, you can't conclude that they're there for the same reason," said lead author Richard Border, a postdoctoral researcher in statistical genetics at UCLA. "But there's been a kind of assumption that if you can track this back to genes, then you would have the causal story."

Based on their analysis of two large databases of spousal traits, researchers found that cross-trait assortative mating is strongly associated with genetic correlation estimates and plausibly accounts for a "substantial" portion of genetic correlation estimates.

"Cross-trait assortative mating has affected all of our genomes and caused interesting correlations between DNA you inherit from your mother and DNA you inherit from your father across the whole genome," said study co-author Noah Zaitlen, a professor of computational medicine and neurology at UCLA Health.

The researchers also examined genetic correlation estimates of psychiatric disorders, which have sparked debate in the psychiatric community because they appear to show genetic relationships among disorders that seemingly have little similarities, such as attention-deficit hyperactivity disorder and schizophrenia.

The researchers found that genetic correlations for a number of unrelated traits could be plausibly attributed to cross-trait assortative mating and imperfect diagnostic practices.

On the other hand, their analysis found stronger links for some pairs of traits, like anxiety disorders and major depression, suggesting that there truly is at least some shared biology.

"But even when there is a real signal there, we're still suggesting that we're overestimating the extent of that sharing," Border said.