A few days ago, we published a paper in Nature Communications that looks at the genetics of human diseases (e.g. type 2 diabetes, rheumatoid arthritis) and complex traits (e.g. height, body mass index) across populations. In this blog post, I hope to give a simple and intuitive understanding of our work to a broad audience.

(For those that are inpatient, scroll to the bottom of this page.)

What did we study?

We studied the effects genetic variants have on diseases and traits across different populations (East Asians and Europeans, in particular). At a high level, we wanted to answer the following questions in our work.

  1. How similar/dissimilar are genetic effects across different populations, across the entire genome?

  2. For which components of the human genome are genetic effects more similar/dissimilar across populations, compared to the rest of the genome?

The metric we used to assess similarity/dissimilarity in genetic effects across populations is called cross-population genetic correlation, which, as the name suggests, measures the correlation of genetic effects in different populations. We developed a statistical tool to quantify cross-population genetic correlation for the entire genome, and for specific components of the human genome, such as gene regions, and regions that regulates expression of genes, etc.

Why do we care?

First, some background. Genetic studies have been rare for non-European populations. In fact, non-European populations only make up 20% of genetic studies, which is very small, considering about 90% of the world populations are non-Europeans. Because genetic study in non-Europeans is so limited, it makes the most practical sense to transfer what we can learn fron Europeans to non-Europeans. But the transferrability seems limited. For example, in genetic risk prediction using genetic effects learned from Europeans, the relative prediction accuracy for non-Europeans can be 50% to 80% lower compared to that for Europeans (see this paper).

This limited cross-population transferrability is often attributed to differences in frequencies and correlation patterns of genetic variants in different populations. But we are curious if the limited transferrability can be also driven by differences in the underlying genetic effects in some parts of the human genome (e.g. gene regions, regions that regulate gene expression, etc.). Investigating if this is indeed the case can provide additional understanding of the sources of limited cross-population transferrability, and may guide genetic researchers on searching for optimal strategies to perform future genetic studies.

What did we find?

We analyzed genetic studies of 31 diseases and complex traits from UK Biobank (Europeans) and Biobank Japan (East Asians). Averaging across the traits, we found that the cross-population genetic correlation across East Asians and Europeans was about 85%, although for some traits this quantity was much lower (e.g. ~30% for major depressive disorer). This results suggest that genetic effects in different populations are, overall, similar to each other. But, they are not exactly the same, suggesting that genetic effects are dissimilar, in other words, population-specific, for some parts of the genome.

We then looked for components of the human genome that have population-specific genetic effects. To our surprise, we found them in functionally important components of the genome, such as gene regions, and regions that regulate levels of expression of genes! This revealation is in direct contrast to what we expected at the beginning of this project – in the early stages of this project, we were expecting to find more shared genetic effects in functionally important regions of the genome.

Now that we knew there are more population-specific genetic effects around gene regions. We wanted to further understand what biological processes could lead to such observations. To this end, we looked at cross-population genetic correlation in different subsets of gene regions. Specifically, we looked at regions of the genes that are specifically expressed in a tissue (e.g. brain, liver, spleen, etc.). From this analysis, we realized that not all gene regions have the same level of population-specific genetic effects. For example, regions of immune genes (genes that play important roles in the immune system) have more population-specific genetic effects, whereas regions of brain genes have less population-specific genetic effects. Since immune genes are typically associated with positive selection and brain genes negative selection, we conjecture that population-specific genetic effects have something to do with gene-environment interactions at genomic regions impacted by positive selection. However, we are not 100% sure if this is indeed the right explanation, as biology is super complicated.

What are the implications?

Perhaps the most relevant implication pertains to cross-population genetic risk prediction. Most existing approaches to perform cross-population genetic risk prediction first select some candidate genetic predictors based on European studies, and then apply those predictors on non-Europeans. These predictors tend to be around gene regions, and other functionally important genomic regions. This strategy of selecting predictors is intuitive, as genetic variants important for Europeans are also likely important for non-Europeans. However, our results showing there are more population-specific genetic effects around gene regions, suggest that this strategy might not be optimal, because the effects of these predictors might be different on non-Europeans than they are on Europeans. A more accurate approach should select and prioritize genetic predictors, based not only on how important they are in the European study, but also on how likely they will have the same effect in the target non-European populations.

Since we hypothesize that gene-environment interaction is likely stronger in functionally important genomic regions, we also think it would be important for future genetic studies to collect and model environmental effects, to better reveal the true genetic effects.

We found more population-specific genetic effects in functionally important regions of the genome (e.g. gene regions, regions that regulate gene expression, etc.), compared to the rest of the genome. We found even more population-specific genetic effects around immune genes (genes that play important roles in the immune system). And we conjecture that population-specific genetic effects have something to do with gene-environment interactions at regions impacted by positive selection.