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Related Concept Videos

Genetic Screens02:46

Genetic Screens

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
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Human Genetics01:28

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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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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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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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Behavioral Genetics and Its Designs01:23

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Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
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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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Related Experiment Video

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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
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Methods to Assess Genetic Risk Prediction.

Christin Schulz1, Sandosh Padmanabhan2

  • 1BHF Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, 126 University Place, Glasgow, G12 8TA, UK.

Methods in Molecular Biology (Clifton, N.J.)
|January 25, 2017
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Novel genetic risk markers can improve cardiovascular disease prediction beyond traditional factors. This paper details key metrics for evaluating these new genomic risk prediction models.

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Area of Science:

  • Cardiovascular disease research
  • Genomics and personalized medicine
  • Biomarker discovery

Background:

  • Traditional cardiovascular disease risk factors have limitations in identifying all individuals at risk.
  • Novel biomarkers are needed to enhance the predictive capabilities of existing risk assessment tools.
  • Genome-wide association studies (GWAS) have identified genetic polymorphisms linked to cardiovascular traits.

Purpose of the Study:

  • To discuss the evaluation of novel genetic risk markers for cardiovascular disease.
  • To explain key metrics used for assessing the performance of risk prediction models.
  • To demonstrate the calculation and interpretation of these metrics for clinical application.

Main Methods:

  • Review of established metrics for evaluating prediction models.
  • Explanation of discrimination, calibration, and reclassification metrics.
  • Demonstration of calculating and interpreting these metrics in the context of genetic risk prediction.

Main Results:

  • Key metrics for assessing prediction models include discrimination, calibration, and reclassification.
  • Quantifying the improvement in risk prediction models incorporating genetic information is a significant challenge.
  • These metrics are essential for evaluating the clinical utility of new genetic risk markers.

Conclusions:

  • Genetic risk markers offer potential to augment traditional cardiovascular risk factors.
  • Rigorous evaluation using established metrics is crucial for integrating genetic information into clinical practice.
  • Accurate assessment of prediction models ensures reliable risk stratification and improved patient outcomes.