The impact of many genetic variants depends on their parent of origin

In a large international study involving the Estonian Biobank and scientists from the University of Tartu, more than 30 genetic variants were identified whose impact on human health depends on whether they are inherited from the mother or the father. Using a new analytical method, researchers were able — for the first time — to conduct a large-scale association study that takes parent-specific effects into account.

Over the past couple of decades, genome-wide association studies (GWAS) have identified thousands of genetic variants that influence health indicators and disease risk. Until now, these studies have generally assumed that it does not matter which parent a particular genetic variant comes from. However, a study published this week in Nature challenges that assumption.

The research presents a new method that, based on inferred genetic relationships, allows scientists to determine whether a specific DNA segment originates from the mother or the father. The study shows that the effect of a genetic variant on human health can depend on which parent’s copy of the gene is expressed.

“We’re seeing that parent-specific genetic variants can sometimes have opposite effects: the same variant might increase a trait when inherited from the father, but decrease it when inherited from the mother,” explained the study’s lead author Robin Hofmeister, a researcher at both the University of Lausanne and the University of Tartu. This kind of bi-polar effect often goes unnoticed in traditional genetic association studies, as the opposing effects tend to cancel each other out.

Parent-specific opposing effects were found in 19 of the 30 variants. All of these are related to human growth and metabolism, affecting traits such as body mass index, blood lipid levels, and height. The study also highlights the role of parent-of-origin effect in conditions like type 2 diabetes, obesity, and cardiovascular disease.

“Our study shows that it’s not just what DNA you inherit, but who you inherit it from that can matter. We hope that our publicly available method will open the door to routinely inferring and using parent-of-origin information in future genetic studies,” said Hofmeister.

The value of the Estonian Biobank

The study was a collaboration between researchers at the University of Lausanne, the University of Bergen, and the Institute of Genomics at the University of Tartu. The new method was applied to biobank data from the UK, Norway, and Estonia — altogether involving more than 236,000 individuals.

According to Hofmeister, the Estonian Biobank played a key role in the study, as its data allowed researchers to confirm the reproducibility of parental-origin effects across different populations. “The comprehensive data makes the Estonian Biobank one of the few in the world where such findings can be reliably validated,” Hofmeister explained.

According to Professor Lili Milani, head of the Estonian Biobank and professor of pharmacogenomics at the University of Tartu, the parental origin data layer developed during the study will be added to the biobank’s database, making it available for future research. “Thanks to the inclusion of over 20% of Estonia’s population, the Estonian Biobank is highly valuable for kinship-based analyses. For comparison, while the UK Biobank includes an average of 1.6 relatives per participant, the Estonian Biobank includes around 12,” Milani noted.

The study also supports the evolutionary theory of parental conflict. “This is one of the strongest pieces of evidence yet for the long-standing evolutionary theory of genetic conflict between parental genes," said Zoltán Kutalik, professor at the University of Lausanne and head of the study. According to the conflict theory, paternally inherited genes tend to promote fetal growth, even at the cost of the mother’s health, while maternally inherited genes limit growth rate to conserve maternal resources for future offspring.

This novel analytical method allows researchers to examine parent-of-origin genetic effects for complex traits and to discover new genome regions that influence health. The method adds further value to existing datasets.