People don’t mate randomly – but the flawed assumption that they do is an essential part of many studies linking genes to diseases and traits

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’s plausible that a third variable, summer weather, is driving up an appetite for ice cream and swimming, and hence 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 the genetic and nongenetic 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 connected by genes at all. Instead, many are a result of how humans mate.

Genome-wide association studies try to link genes to traits

Because the genes you inherit from your parents remain unchanged throughout your life, with rare exception, it makes sense to assume that there is a causal relationship between certain traits you have and your genetics.

This logic is the basis for 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 the BRCA1 and BRCA2 genes, you may have an increased risk for certain types of cancer.

Similarly, there may be gene variants that play a role in whether or not someone has schizophrenia. The hope is to learn something about the complex mechanisms that link variation at the molecular level to individual differences. With a clearer understanding of the genetic basis of different traits, scientists would be better able to determine risk factors for related diseases.

GWAS studies seek to find genetic associations between individual traits.

Researchers have run thousands of GWAS to date, identifying genetic variants associated with myriad diseases and disease-related traits. In many instances, 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 apparently unrelated traits, is known as pleiotropy. For example, certain variants of the PAH gene can have several distinct effects, including altering skin pigmentation and causing seizures.

One way scientists assess pleiotropy is through genetic correlation analysis. Here, geneticists investigate whether the 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 across fields as diverse as internal medicine, social science and

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