Understanding Polygenic Risk Scores with XPRS

Polygeneic Risk Scores (PRS) measure how an individual’s genes influence their risk for certain diseases. By looking at various gene versions that an individual has, researchers can estimate the likelihood of developing complex traits or conditions. This tool has become important in the healthcare field as it helps inform individuals about their risk for diseases.

#How PRS are Constructed

There are several ways to create a PRS. Different methods, such as P+T, LDPred, and MegaPRS, have been developed to calculate these scores. These methods look at the genetic markers or alleles a person has, weighing them to produce a score that reflects their genetic risk. Recently, there have been improvements in how these scores are built, especially when considering people from different ancestries.

#The Need for Explainability

When using machine learning models and tools like PRS, it is essential to make the reasons behind the scores clear. This transparency allows users to trust the results and understand what factors influence their risk scores. It also enhances communication between doctors and patients. While there are many tools for explaining machine learning results, specific tools for explaining PRS are lacking, limiting their use in medical settings. Therefore, creating user-friendly tools that clearly outline how these scores are derived is crucial.

#Introducing XPRS

XPRS stands for eXplainable PRS, a software designed to give clearer interpretations of Polygenic Risk Scores. It does this by breaking down the scores into contributions from specific genes and Genetic Variants. Although PRS calculations can be done using straightforward methods, understanding the scores can get complicated due to the large number of genetic variants involved.

XPRS helps by mapping these variants to their corresponding genes, making it easier to see which genes have the most significant impact on the overall score. It uses a method called Shapley additive explanations, or SHAPs, to assign values to each gene. This way, users can see how much each gene contributes to their risks.

#Visualization in XPRS

Visual tools are key to making the information understandable. XPRS uses various visualization techniques to present findings. At the population level, Manhattan plots and tables can show which genes are important based on how much they vary in their contributions. At the individual level, XPRS provides density plots and gene-based Visualizations to help identify the specific genes that influence an individual’s PRS.

For the software to work, users need to upload three key files: a Genotype file containing genetic data, a PRS scoring file used to calculate contributions, and a GWAS association file that includes known significant genes associated with certain diseases. The genotype file needs to be in a specific format to ensure accurate analysis.

#Key Processes in XPRS

XPRS operates in three steps.

1. Preprocessing and Variant Mapping: The first step involves aligning genetic data to prepare it for analysis. This phase ensures that the data is organized and that the right variants are included in the risk score. It includes mapping variants to their corresponding genes based on their genomic positions.

2. Calculation of Contribution Scores: After mapping, the second step calculates how much each gene and variant contributes to the overall risk score. This breakdown helps in understanding which specific factors are impacting the score.

3. Visualization: Finally, XPRS presents the results in a clear format, enabling users to explore the data visually. It depicts gene contributions at both the population level and for specific individuals, showing how various genes and variants affect risk scores.

#The Role of Input Files

To start using XPRS, users need to provide the genotype, PRS scoring, and GWAS association files. The genotype file contains the raw genetic information and must meet certain standards. The PRS scoring file is essential for calculating how each gene and variant contributes to the risk. The GWAS association file helps identify known significant genes, narrowing down the focus during the analysis.

#Customizing XPRS

XPRS allows users to adjust some key settings to improve performance. Users can modify the number of computer processing units (CPUs) used, the percentage of the most significant genetic variants they want to include, and the genomic window size for mapping. These settings enhance the software’s flexibility and allow it to run more efficiently.

#Example of Population Visualization

To illustrate how XPRS works, consider a study that analyzed a population for type 2 diabetes. The data included a large number of genetic variants, and XPRS created a Manhattan plot to highlight genes that contribute significantly to the risk of this disease.

In the plot, each point represents a gene, with its height indicating the degree of contribution. Significant genes associated with higher risks stood out, showing which ones were crucial for type 2 diabetes susceptibility.

#Individual Analysis with XPRS

For individual assessments, XPRS visualizes the contributions of specific genes and variants. For instance, by analyzing a selected individual’s genetic data, XPRS can show how their PRS compares to the overall population, pinpointing where they fall in terms of risk.

This information is vital for discussions between patients and healthcare providers, as it enables a deeper understanding of how genetic factors affect individual health.

#Limitations of XPRS

While XPRS offers valuable insights, it has some challenges. Data privacy is a concern, and users must download the software to run it securely on their servers. This requirement might make it less accessible for some users. Additionally, the effectiveness of XPRS depends on accurate mapping of variants to genes, which continues to evolve as more research is conducted.

#Closing Thoughts

Having tools like XPRS is essential for better interpreting polygenic risk scores. By revealing how specific genes and variants contribute to disease risks, it enhances the understanding of genetic predisposition. This knowledge can empower patients to make informed decisions about their health.

The software improves communication about genetic risks while ensuring that complex data remains accessible. As researchers continue to refine these tools, the potential for integrating genetic information into healthcare will only grow, leading to better-informed individuals and improved health outcomes.