Summary: The researchers say that estimates of how strongly diseases and traits share genetic signals might be overestimated. Instead, they suggest, mating patterns can help explain the biological relationship between traits.
Many estimates of the strength with which traits and diseases share genetic signals may be inflated, according to a new UCLA-led study that indicates that current methods of assessing genetic relationships between traits do not take into account the mating patterns.
Using powerful genome sequencing technology, scientists have in recent years sought to understand the genetic associations between traits and disease risk, hoping that findings from shared genetics could point to clues to fight back. against diseases.
However, the UCLA researchers said their new study, published November 17 in Science, cautions against placing too much faith in genetic correlation estimates. They say such estimates are skewed by non-biological factors more than previously estimated.
Genetic correlation estimates generally assume that mating is random. But in the real world, partners tend to associate due to many shared interests and social structures.
Accordingly, some genetic correlations in previous work that have been attributed to shared biology may instead represent incorrect statistical assumptions. For example, previous estimates of the genetic overlap between body mass index (BMI) and education level are likely to reflect this type of population structure, induced by “trait-matched mating”, or how individuals of one trait tend to associate with individuals of another trait.
The study authors said the genetic correlation estimates deserve closer scrutiny because these estimates have been used to predict disease risk, glean clues for potential therapies, inform diagnostic practices and shape arguments. on human behavior and societal issues.
The authors said some members of the scientific community have overemphasized genetic correlation estimates based on the idea that studying genes, because they are unalterable, can overcome confounders.
“If you just look at two traits that are high in a group of people, you can’t conclude that they’re there for the same reason,” said lead author Richard Border, postdoctoral researcher in statistical genetics at UCLA. .
“But there’s been kind of an assumption that if you can trace that back to the genes, then you’ve got the causal story.”
Based on their analysis of two large databases of marital traits, the researchers found that matched mating between traits is strongly associated with genetic correlation estimates and plausibly accounts for a “substantial” part of genetic correlation estimates. .
“Matching cross-traits has affected all of our genomes and has caused some interesting correlations between the DNA you inherit from your mother and the DNA you inherit from your father across the entire genome,” said the study co-author Noah Zaitlen, professor of computational medicine and neurology at UCLA Health.
The researchers also looked at genetic correlation estimates of psychiatric disorders, which have sparked debate in the psychiatric community because they appear to show genetic relationships between seemingly dissimilar disorders, 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 matched mating and flawed diagnostic practices. On the other hand, their analysis found stronger links for certain pairs of traits, such as anxiety disorders and major depression, suggesting that there really is at least one shared biology.
“But even when there’s a real signal out there, we’re still suggesting we’re overestimating the extent of that sharing,” Border said.
Other study authors include Georgios Athanasiadis, Alfonso Buil, Andrew J. Schork, Na Cai, Alexander I. Young, Thomas Werge, Jonathan Flint, Kenneth S. Kendler, Sriram Sankararaman, and Andy Dahl.
About this genetic research news
Author: Jason Millman
Contact: Jason Millman – UCLA
Image: Image is in public domain
Original research: Access closed.
“Mating assorted cross-traits is widespread and inflates estimates of genetic correlation” by Richard Border et al. Science
Assorted mating between traits is widespread and inflates genetic correlation estimates
The observation of genetic correlations between disparate human traits has been interpreted as evidence of widespread pleiotropy.
Here, we introduce cross-trait matched mating (xAM) as an alternative explanation.
We observe that xAM affects many phenotypes and that phenotypic cross-correlation estimates are strongly associated with genetic correlation estimates (R2 = 74%).
We demonstrate that extant xAM plausibly represents substantial fractions of genetic correlation estimates and that previously reported genetic correlation estimates between certain pairs of psychiatric disorders are congruent with xAM alone.
Finally, we provide evidence for a history of xAM at the genetic level using cross-over even/odd chromosomal polygenic score correlations.
Together, our results demonstrate that previous reports have likely overestimated the true genetic similarity between many phenotypes.
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