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The Clash of Genes and Environment in Health

Explore how genetics and environment shape our health risks.

W. James Gauderman, Yubo Fu, Bryan Queme, Eric Kawaguchi, Yinqiao Wang, John Morrison, Hermann Brenner, Andrew Chan, Stephen B. Gruber, Temitope Keku, Li Li, Victor Moreno, Andrew J Pellatt, Ulrike Peters, N. Jewel Samadder, Stephanie L. Schmit, Cornelia M. Ulrich, Caroline Um, Anna Wu, Juan Pablo Lewinger, David A. Drew, Huaiyu Mi

― 6 min read


Genes vs. Environment: Genes vs. Environment: Health Risks health outcomes. Unraveling the impact of genetics on
Table of Contents

Genes and the environment play significant roles in our health. Sometimes, our genetic makeup makes us more sensitive (or resistant) to specific environmental influences. This relationship is known as gene-environment interaction, often shortened to GxE interaction. Imagine your genes as a book and the environment as a reader. Depending on how the reader interprets the text, the meaning can change. In this case, certain exposures can either heighten or lessen the risk of developing diseases based on our genetic predispositions.

What is a Polygenic Risk Score?

To measure how genes affect our risk of health issues, scientists developed a tool called a Polygenic Risk Score (PRS). You can think of it as a “genetic report card.” This score takes several genetic variants-like little markers on a massive map of your DNA-and combines them to give an overall view of how likely you are to develop a specific trait or disease. These genetic variants are often identified from large studies that look for links between genes and health conditions.

For instance, if you were to get a health report saying, “You are at a higher risk for high blood pressure,” that’s akin to how a PRS works – it summarizes your genetic risk based on a collection of known genetic variants.

Gene-Environment Interaction Analysis

A GxE analysis focuses on identifying groups of people with certain genetic traits that might react differently to specific environmental factors. Think of it this way: two people might be exposed to the same environmental condition, say, smoking. One person, due to their genetic background, might develop respiratory issues, while the other breezes through it without a cough.

The goal of these analyses is to see how the environment interacts with particular genetic backgrounds when assessing disease risk. This can be crucial for developing effective prevention strategies.

The Role of PRS in Gene-Environment Studies

In recent years, researchers have been using PRS to delve into these GxE interactions across various health conditions such as lung cancer, diabetes, ADHD, and heart issues. By analyzing how a person's genetic risk interacts with environmental influences, researchers can better identify who might be at risk and what prevention strategies might work best for them.

Imagine having a crystal ball that tells you not just that you might get a cold this winter, but that you’d catch it easier if you hang around people who are sniffing and sneezing. That's the sort of insight scientists aim to give through these analyses.

Challenges in Detecting Gene-environment Interactions with PRS

However, using PRS to explore these interactions isn’t always straightforward. A challenge arises from the fact that standard PRS includes a wide range of genetic variants, many of which may not be directly relevant to the specific environmental factor being studied.

For example, if you think of the PRS as a pot of soup, it might contain many ingredients that don’t go together. Some ingredients could dilute the taste of the flavors you want to highlight, especially when looking for specific interactions. This can make it tougher to pinpoint which genetic factors really matter when considering environmental influences on disease risk.

Enter Pathway Polygenic Risk Scores (pPRS)

To address these challenges, researchers propose using something called Pathway Polygenic Risk Scores, or pPRS. While a PRS looks at the overall picture, a pPRS narrows the view down to specific biological pathways-think of it as zooming in on a particular section of the soup where all the good flavors live.

A pPRS only includes genetic variants that are known to interact with certain environmental factors. By focusing on these specific sets of SNPS, researchers hope to better identify interactions and boost the power to detect these associations.

Pathway-based Analysis in Action

To illustrate how this works, researchers examined Colorectal Cancer (CRC) using both PRS and pPRS. They focused on a well-known protective factor: the use of non-steroidal anti-inflammatory drugs (NSAIDs), which are commonly offered over the counter (think ibuprofen).

The researchers looked at a substantial group of people with CRC and those without it. By gathering detailed data on NSAID use and analyzing the genetic data from these individuals, they aimed to see how genes might influence the effect of NSAIDs on CRC risk.

For the analysis, they identified 204 genetic variants linked to CRC from a large genetic study. They then created an overall PRS based on these variants and a pPRS that drew upon specific genetic variants known to be associated with certain biological pathways.

The Results of the CRC Study

They found that while the overall PRS didn’t show a significant interaction with NSAID use, the pPRS did! Specifically, two pathways related to TGF-β and Gonadotropin-releasing hormone receptor showed a meaningful link to NSAID use in protecting against CRC.

This means that if you have certain genetic profiles associated with these pathways, you might get more protection from taking NSAIDs against colorectal cancer. Think of it as having a superpower against CRC if you have the right genetic makeup and use NSAIDs appropriately!

Impact of Understanding GxE Interactions on Prevention

The findings from research like this can significantly influence how we approach preventive medicine. By recognizing specific genetic factors that modify the impact of environmental exposures, healthcare providers can tailor prevention strategies better.

For instance, if a person’s genetic background suggests that they are at a higher risk for developing CRC, even with NSAID use, healthcare professionals might suggest more targeted screening strategies or additional protective measures.

Future Directions in Gene-Environment Research

As researchers continue this line of inquiry, they hope to refine their methods even further. It would be valuable to explore various genetic variants, beyond those associated with diseases, and examine how they interact with other environmental factors.

For example, consider how diet, physical activity, or even geographical location could intertwine with genetic factors when assessing disease risk. Understanding this complexity can help build a better framework for personalized medicine.

Conclusion

Gene-environment interactions present a fascinating puzzle for researchers. They reveal how our genetics and environment can work together to influence our health in profound ways. By harnessing tools like PRS and pPRS, scientists are better equipped to dissect these interactions and uncover insights that can ultimately lead to improved prevention strategies and health outcomes.

As we continue on this path, the possibilities for understanding and mitigating health risks grow, opening doors to personalized approaches that cater to individual genetic backgrounds. Who knows? Maybe one day, we’ll all have our very own health recipe for a long, healthy life, customized based on our unique genetic ingredients!

Original Source

Title: Pathway Polygenic Risk Scores (pPRS) for the Analysis of Gene-environment Interaction

Abstract: A polygenic risk score (PRS) is used to quantify the combined disease risk of many genetic variants. For complex human traits there is interest in determining whether the PRS modifies, i.e. interacts with, important environmental (E) risk factors. Detection of a PRS by environment (PRS x E) interaction may provide clues to underlying biology and can be useful in developing targeted prevention strategies for modifiable risk factors. The standard PRS may include a subset of variants that interact with E but a much larger subset of variants that affect disease without regard to E. This latter subset will water down the underlying signal in former subset, leading to reduced power to detect PRS x E interaction. We explore the use of pathway-defined PRS (pPRS) scores, using state of the art tools to annotate subsets of variants to genomic pathways. We demonstrate via simulation that testing targeted pPRS x E interaction can yield substantially greater power than testing overall PRS x E interaction. We also analyze a large study (N=78,253) of colorectal cancer (CRC) where E = non-steroidal anti-inflammatory drugs (NSAIDs), a well-established protective exposure. While no evidence of overall PRS x NSAIDs interaction (p=0.41) is observed, a significant pPRS x NSAIDs interaction (p=0.0003) is identified based on SNPs within the TGF-{beta} / gonadotropin releasing hormone receptor (GRHR) pathway. NSAIDS is protective (OR=0.84) for those at the 5th percentile of the TGF-{beta}/GRHR pPRS (low genetic risk, OR), but significantly more protective (OR=0.70) for those at the 95th percentile (high genetic risk). From a biological perspective, this suggests that NSAIDs may act to reduce CRC risk specifically through genes in these pathways. From a population health perspective, our result suggests that focusing on genes within these pathways may be effective at identifying those for whom NSAIDs-based CRC-prevention efforts may be most effective. Author SummaryThe identification of polygenic risk score (PRS) by environment (PRSxE) interactions may provide clues to underlying biology and facilitate targeted disease prevention strategies. The standard approach to computing a PRS likely includes many variants that affect disease without regard to E, reducing power to detect PRS x E interactions. We utilize gene annotation tools to develop pathway-based PRS (pPRS) scores and show by simulation studies that testing pPRS x E interaction can yield substantially greater power than testing PRS x E, while also integrating biological knowledge into the analysis. We apply our method to a large study of colorectal cancer to identify a significant pPRS x NSAIDs interaction (p=0.0003) based on SNPs within the TGF-{beta} / gonadotropin releasing hormone receptor (GRHR) pathway. Our findings suggest that focusing on genetic susceptibility within biologically informed pathways may be more sensitive for identifying exposures that can be considered as part of a precision prevention approach.

Authors: W. James Gauderman, Yubo Fu, Bryan Queme, Eric Kawaguchi, Yinqiao Wang, John Morrison, Hermann Brenner, Andrew Chan, Stephen B. Gruber, Temitope Keku, Li Li, Victor Moreno, Andrew J Pellatt, Ulrike Peters, N. Jewel Samadder, Stephanie L. Schmit, Cornelia M. Ulrich, Caroline Um, Anna Wu, Juan Pablo Lewinger, David A. Drew, Huaiyu Mi

Last Update: Dec 20, 2024

Language: English

Source URL: https://www.biorxiv.org/content/10.1101/2024.12.16.628610

Source PDF: https://www.biorxiv.org/content/10.1101/2024.12.16.628610.full.pdf

Licence: https://creativecommons.org/licenses/by/4.0/

Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.

Thank you to biorxiv for use of its open access interoperability.

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