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Mating patterns may indeed help explain many purported biological relationships between traits, researchers say — ScienceDaily

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Many estimates of how strongly traits and diseases share genetic signals may be overstated, according to a new study led by the University of California, Los Angeles, which shows that current methods of estimating genetic relationships between traits do not account for mating patterns.

Using the powerful technology of genome sequencing, scientists have sought in recent years to understand the genetic links between traits and disease risk, hoping that discoveries in shared genetics can point to clues to fighting disease. However, UCLA researchers said their new study, published Nov. 17 in science, provides a caution against relying too heavily on genetic correlation estimates. They say such estimates are confounded by nonbiological factors more than previously thought.

Estimates of genetic correlation usually assume that mating is random. But in the real world, partners tend to bond over many common interests and social structures. As a result, some genetic correlations in previous work that have been attributed to general biology may instead represent incorrect statistical assumptions. For example, previous estimates of the genetic overlap between body mass index (BMI) and educational attainment likely reflect this type of population structure caused by “cross-trait assortative mating,” or how individuals of one trait tend to cooperate with individuals of another trait.

The study authors say genetic correlation estimates deserve more attention because these estimates have been used to predict disease risk, provide clues to potential treatments, inform diagnostic practice, and inform arguments about human behavior and societal issues. The authors said that some members of the scientific community place too much emphasis on genetic correlation estimates based on the idea that studying genes, because they are fixed, 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 are there for the same reason,” said lead author Richard Border, a postdoctoral researcher in statistical genetics at UCLA. “But the assumption was that if you could trace it back to the genes, then you’d get a causal story.”

Based on an analysis of two large databases of spousal traits, the researchers found that assortative mating across traits is closely related to genetic correlation estimates and likely accounts for a “substantial” portion of genetic correlation estimates.

“Assortative mating across traits has affected all of our genomes and caused interesting correlations between the DNA you inherit from your mother and the DNA you inherit from your father across the genome,” said study co-author Noah Zeitlen, professor in Computational Medicine and Neuroscience at UCLA Health.

The researchers also examined genetic correlation estimates for mental disorders that have sparked debate in the psychiatric community because they appear to show genetic relationships between disorders that seem to have little in common, such as attention deficit hyperactivity disorder and schizophrenia. The researchers found that genetic correlations for a number of unrelated traits could plausibly be attributed to assortative mating between traits and imperfect diagnostic practices. On the other hand, their analysis found stronger associations for some trait pairs, such as anxiety disorders and major depression, suggesting that there is indeed at least some shared biology.

“But even if there is a real signal, we still think we’re overestimating the extent of this exchange,” Border said.

Other study authors include Georgios Athanassiadis, Alphonso Buill, Andrew J. Schork, Na Kai, Alexander I. Young, Thomas Verge, Jonathan Flint, Kenneth S. Candler, Sriram Sankararaman, and Andy Dahl. The authors declared no competing interests. See the study for a full list of funders.

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