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Related Concept Videos

Polygenic Traits01:18

Polygenic Traits

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When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
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A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
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Relative Risk01:12

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Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
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XPRS: a tool for interpretable and explainable polygenic risk score.

Na Yeon Kim1, Seunggeun Lee1

  • 1Graduate School of Data Science, Seoul National University, 1 Gwanak-ro, Seoul, 08826, South Korea.

Bioinformatics (Oxford, England)
|March 31, 2025
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Summary
This summary is machine-generated.

eXplainable PRS (XPRS) enhances polygenic risk score (PRS) interpretability by decomposing genetic contributions. This tool aids clinicians and patients in understanding genetic susceptibility to diseases.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Polygenic risk scores (PRS) are crucial for assessing genetic susceptibility to diseases.
  • Clinical application of PRS is hindered by a lack of interpretability tools.

Purpose of the Study:

  • Introduce eXplainable PRS (XPRS), a novel tool for PRS interpretation and visualization.
  • Enhance the clinical utility of PRS by providing insights into gene and single nucleotide polymorphism (SNP) contributions.

Main Methods:

  • XPRS utilizes Shapley additive explanations (SHAPs) to decompose PRS into gene/region and SNP contribution scores.
  • Employs multilevel visualization including Manhattan plots, LocusZoom-like plots, and tables.
  • Features a user-friendly web interface for straightforward data input and interpretation.

Main Results:

  • XPRS provides insights into specific genes and SNPs significantly contributing to an individual's PRS.
  • Multilevel visualizations highlight important genetic contributors at both population and individual levels.
  • The tool facilitates understanding of complex genetic data for actionable clinical insights.

Conclusions:

  • XPRS bridges the gap between complex genetic data and clinical actionability.
  • The software improves communication between clinicians and patients regarding genetic disease risk.
  • XPRS enhances the interpretability and clinical utility of polygenic risk scores.