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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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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Genetic variations significantly influence drug response through pharmacokinetics, receptor interactions, and biologic milieu modifications. Pharmacokinetic alterations impact drug metabolism and clearance, affecting efficacy and toxicity. Variants in drug-metabolizing enzymes, such as CYP2C9 and CYP2C19, alter drug activation and elimination. For example, CYP2C9 loss-of-function variants require lower warfarin doses to prevent excessive bleeding, while CYP2C19 variants reduce clopidogrel...
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Principles of Pharmacogenetics: Types of Genetic Variants01:27

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The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
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Pharmacodynamic Models: Linear Concentration–Effect Model01:15

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The linear concentration–effect model, underpinned by the principle that pharmacological effect (E) is directly proportional to plasma drug concentration (C), emerges as a pivotal simplification of the Emax model for conditions where C is significantly less than EC50. This model portrays a linear trajectory of the concentration–effect relationship when drug levels are markedly below the EC50 threshold.Despite its inherent assumption of continuous effect augmentation with increasing...
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Enhancing polygenic risk prediction by modeling quantile-specific genetic effects.

Suin Kim1, Taewan Goo2, Taesung Park3

  • 1Department of Statistics, Korea University, Seoul, Republic of Korea.

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Summary

A new quantile regression-based polygenic risk score (QPRS) improves prediction for complex traits. QPRS captures quantile-specific genetic effects, outperforming traditional linear models for skewed phenotypes.

Keywords:
Distributional heterogeneityGenetic predictionGenome-wide association studyPolygenic risk scoreQuantile regression

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Area of Science:

  • Genetics
  • Statistical genetics
  • Biostatistics

Background:

  • Polygenic risk scores (PRSs) assess genetic susceptibility to diseases.
  • Conventional PRSs use linear models, limiting accuracy for complex phenotypes with non-linear genetic effects or skewed distributions.
  • Genetic effects can vary across the distribution of a phenotype, a factor not captured by standard PRSs.

Purpose of the Study:

  • To introduce a novel quantile regression-based polygenic risk score (QPRS) method.
  • To address the limitations of conventional PRSs in capturing complex genetic architectures.
  • To enhance the predictive performance of PRSs by modeling quantile-specific genetic influences.

Main Methods:

  • Developed a quantile regression-based PRS (QPRS) framework.
  • Utilized multiple quantile-specific genetic scores as covariates for improved prediction.
  • Evaluated QPRS performance using simulations and real-world data from the Korea Genome and Epidemiology Study.

Main Results:

  • Simulations showed QPRS significantly reduced mean squared error compared to linear PRSs, especially with variance quantitative trait loci and outliers.
  • Real-data analysis demonstrated consistent predictive performance improvements for continuous (triglycerides, glucose) and binary (diabetes) outcomes.
  • QPRS effectively models genetic influences across different phenotype quantiles, enhancing predictive power.

Conclusions:

  • QPRS offers a more robust and accurate approach to polygenic risk prediction than conventional methods.
  • The method is particularly beneficial for phenotypes with skewed distributions or complex genetic effects.
  • QPRS enhances the utility of genetic information for predicting disease risk and continuous traits.