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Related Concept Videos

Polygenic Traits01:18

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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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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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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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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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Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
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Genetic risk prediction in complex disease.

Luke Jostins1, Jeffrey C Barrett

  • 1Statistical and Computational Genetics, Wellcome Trust Sanger Institute, Cambs, UK

Human Molecular Genetics
|August 30, 2011
PubMed
Summary
This summary is machine-generated.

Genetic variants from genome-wide association studies (GWAS) show varied predictive accuracy for common diseases, comparable to traditional risk factors. This research explores their clinical utility and potential in disease risk prediction.

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Area of Science:

  • Epidemiology
  • Genetics
  • Clinical Risk Prediction

Background:

  • Risk stratification is crucial in epidemiology for predicting disease onset and outcomes.
  • Classical risk factors (BMI, lipids, smoking, family history) and genetics inform disease risk prediction.
  • Genome-wide association studies (GWAS) have identified numerous common risk loci for prevalent diseases.

Purpose of the Study:

  • To evaluate the predictive accuracy of GWAS variants for 18 common diseases.
  • To compare the performance of genetic risk prediction models with non-genetic models.
  • To explore factors influencing predictive accuracy and the added value of genetic risk prediction.

Main Methods:

  • Surveying predictive accuracy of GWAS variants across 18 common diseases.
  • Measuring and comparing predictive accuracy of genetic and non-genetic risk models.
  • Reviewing uses and pitfalls of idealized risk prediction models.

Main Results:

  • Predictive accuracy of GWAS variants varies significantly across diseases.
  • The range of predictive accuracy for genetic models is comparable to non-genetic risk prediction models.
  • Factors influencing predictive accuracy and added value over classical tests were discussed.

Conclusions:

  • Genetic risk prediction shows potential clinical utility, though accuracy varies by disease.
  • Realistic expectations for future clinical implementation of genetic risk prediction are discussed.
  • GWAS variants offer a complementary approach to classical risk factors in disease prediction.