The idea that correlation does not imply causation is a fundamental caveat in epidemiological research. A classic example involves a hypothetical link between ice cream sales and drownings – instead of increased ice cream consumption causing more people to drown, it is plausible that a third variable, summer weather, increases ice cream consumption. appetite for ice cream and swimming, and therefore the opportunities to drown.
But what about correlations involving genes? How can researchers be sure that a particular trait or disease is truly genetically linked and not caused by something else?
We are statistical geneticists who study genetic and non-genetic factors that influence human variation. In our recently published research, we found that the genetic links between traits found in many studies might not be linked by genes at all. Instead, many are the result of how humans mate.
Genome-wide association studies attempt to link genes to traits
Since the genes you inherit from your parents remain unchanged throughout your life, with rare exceptions, it makes sense to assume that there is a causal relationship between certain traits you have and your genetics.
This logic underlies genome-wide association studies, or GWAS. These studies collect DNA from many people to identify positions in the genome that might be correlated with a trait of interest. For example, if you have certain forms of BRCA1 and BRCA2 genes, you may have an increased risk of certain types of cancer.
Likewise, there may be genetic variants that play a role in whether or not a person has schizophrenia. The hope is to learn something about the complex mechanisms that link variation at the molecular level to individual differences. With a better understanding of the genetic basis of different traits, scientists would be better able to determine risk factors for related diseases.
Researchers have run thousands of GWAS to date, identifying genetic variants associated with a myriad of diseases and disease-related traits. In many cases, researchers have identified genetic variants that affect more than one trait. This form of biological overlap, in which the same genes are thought to influence several seemingly unrelated traits, is known as pleiotropy. For example, some variants of PAHs can have several distinct effects, including changing skin pigmentation and causing seizures.
Genetic correlation analysis is one of the ways scientists assess pleiotropy. Here, geneticists study whether genes associated with a given trait are associated with other traits or diseases by statistically analyzing large samples of genetic data. Over the past decade, genetic correlation analysis has become the primary method for assessing potential pleiotropy in fields as diverse as internal medicine, social science, and psychiatry.
Scientists use the results of genetic correlation analyzes to determine potential common causes of these traits. For example, if genes associated with bipolar disorders also predict anxiety disorders, perhaps both conditions may partially involve some of the same neural circuits or respond to similar treatments.
Assorted mating and genetic correlation
However, just because a gene is correlated with two or more traits does not necessarily cause it.
Virtually all of the statistical methods that 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 is geography – people living in different parts of the world are less likely to end up together than people living in close proximity.
We wanted to know to what extent the assumption of random mating affects the accuracy of genetic correlation analyses. In particular, we focused on the potential confounding effects of assorted mating, or how people tend to mate with those who share similar characteristics with them. Matching mating is a widely documented phenomenon observed across a wide range of traits, interests, measurements, and social factors, including height, education, and psychiatric conditions.
In our study, we examined trait-matched mating, in which people with one trait (eg, being tall) tend to mate with people with a completely different trait (eg, being rich). From our database of 413,980 mate pairs in the UK and Denmark, we found evidence of cross-mating for many traits – for example, the time an individual spent in formal education was correlated not only with a mate’s level of education, but also with many other characteristics, including height, smoking habits and risk of different diseases.
We found that accounting for similarities between partners could strongly predict which traits would be considered genetically related. In other words, based solely on the number of traits shared by a pair of mates, we could identify about 75% of the putative genetic links between those traits – all without DNA sampling.
Genetic correlation does not imply causation
Assorted mating between traits shapes the genome. If people with one hereditary trait tend to mate with people with another hereditary trait, then these two distinct traits will become genetically correlated to each other in subsequent generations. This will happen regardless of whether these traits are truly genetically related to each other.
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Trait-matched mating means that the genes you inherit from one parent will correlate with those you inherit from the other. The way people mate is not random, which violates the key assumption behind genetic correlation analyses. This inflates the genetic association between traits that aren’t really linked by genes.
Recent studies support our findings. Earlier this year, researchers calculated genetic correlations using a method that examines the association between sibling traits and genes. The genetic links between traits influenced by assorted mating between cross traits have been significantly weakened.
But without accounting for mating assorted cross-traits, using genetic correlation estimates to study the biological pathways that cause disease can be misleading. Genes that affect only one trait will appear to influence several different conditions. For example, a genetic test designed to assess the risk of a disease may incorrectly detect susceptibility to a large number of unrelated conditions.
The ability to measure variation between individuals at the genetic and molecular level is truly a feat of modern science. However, genetic epidemiology remains an observational enterprise, subject to the same caveats and challenges faced by other forms of non-experimental research. While our results do not overlook all research in genetic epidemiology, understanding what genetic studies actually measure will be critical to translating research findings into new ways to treat and assess disease.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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