Current methods for assessing genetic relationships between traits are flawed
#Current #methods #assessing #genetic #relationships #traits #flawed Welcome to Alaska Green Light Blog, here is the new story we have for you today:
According to researchers, mating patterns can explain many of the relationships between traits previously thought to be biological.
A new study led by the University of California, Los Angeles suggests that current methods of assessing the genetic links between traits often overlook the influence of mating patterns, leading to overestimates of the strength of the genetic link between traits and diseases.
Scientists have used powerful genome sequencing technology in recent years to try to uncover the genetic links between traits and disease risk in the hope that this knowledge could lead to new treatments for diseases. However, a study conducted by UCLA and published in the journal Science warns against over-reliance on genetic correlation estimates, as these estimates can be skewed by non-biological factors that have not been fully accounted for.
Genetic correlation estimates typically assume that mating is random. But in the real world, mates tend to mate because of many common interests and social structures. As a result, some genetic correlations in previous work attributed to shared biology might 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 induced by “cross-trait assortative mating,” or how individuals with a trait tend to associate with individuals of a other property.
The study’s authors said genetic correlation estimates deserve closer scrutiny because these estimates have been used to predict disease risk, look for clues to potential therapies, inform diagnostic practices, and frame arguments about human behavior 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 because genes are immutable, studying genes can overcome confounding factors.
“If you just look at two traits that are elevated in a group of people, you cannot conclude that they are there for the same reason,” said lead author Richard Border, a postdoctoral researcher in statistical genetics at UCLA. “But there was a kind of assumption that if you could trace that back to the genes, you would have the causal story.”
Based on their analysis of two large databases of spousal traits, the 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, causing interesting correlations across the genome between the DNA you inherited from your mother and the DNA you inherited from your father,” the co-author said of the study, Noah Zaitlen, professor of computational medicine and neurology at UCLA Health.
Researchers also examined genetic correlation estimates of psychiatric disorders, which have sparked debate in the psychiatric community because they appear to reveal genetic relationships between disorders that appear to share few similarities, such as attention-deficit hyperactivity disorder and schizophrenia. Researchers found that genetic correlations for a number of unrelated traits plausibly result from cross-trait assortative mating and imperfect diagnostic practices. On the other hand, their analysis found stronger associations for some pairs of traits, such as anxiety disorders and major depression, suggesting that there really is at least some shared biology.
“But even if there is a real signal there, we still suggest that we overestimate the magnitude of this transmission,” Border said.
Reference: “Cross-trait assortative mating is widespread and inflates genetic correlation estimates” by Richard Border, Georgios Athanasiadis, Alfonso Buil, Andrew J. Schork, Na Cai, Alexander I. Young, Thomas Werge, Jonathan Flint, Kenneth S. Kendler, Sriram Sankararaman, Andy W. Dahl and Noah A. Zaitlen, November 17, 2022, Science.
The study was funded by the National Institutes of Health, the Chan Zuckerberg Initiative, the National Science Foundation, Open Philanthropy, and the Wellcome Trust.