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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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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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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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Protein Networks02:26

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Genetic polymorphism in drug metabolism is crucial to the inter-individual variability observed in drug responses. Drug metabolism primarily involves the chemical modification of drugs and other xenobiotics to enhance their elimination by increasing their polarity. Two main classes of enzymes mediate this biotransformation process: Phase I enzymes, primarily cytochrome P450s, catalyze oxidation and reduction reactions, while other enzymes, such as esterases, mediate hydrolysis, and Phase II...
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Connecting common genetic polymorphisms to protein function: A modular project sequence for lecture or lab.

Christopher E Berndsen1,2, Byron H Young1, Quinlin J McCormick3

  • 1Department of Chemistry and Biochemistry, James Madison University, Virginia.

Biochemistry and Molecular Biology Education : a Bimonthly Publication of the International Union of Biochemistry and Molecular Biology
|June 10, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces modular activities connecting genetic variations like single nucleotide polymorphisms (SNPs) to observable phenotypes. Students explore DNA sequencing and protein structure simulations to understand gene function impacts.

Keywords:
genomics proteomics bioinformaticsmolecular biologyprotein structure function and foldingusing simulation and internet resources for teaching

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Area of Science:

  • Biochemistry
  • Genetics
  • Molecular Biology
  • Bioinformatics

Background:

  • Single nucleotide polymorphisms (SNPs) can alter protein structure and function, leading to observable phenotypes.
  • The biochemical basis of SNP-driven phenotypes is often unclear due to a lack of protein structural data.
  • Computational tools offer new avenues for exploring genotype-phenotype relationships.

Purpose of the Study:

  • To develop modular educational activities linking genetic variations to phenotypes.
  • To engage students in examining the molecular basis of common phenotypes.
  • To demonstrate the connection between gene structure, protein structure, and organism function.

Main Methods:

  • Phenotype testing and observation.
  • DNA sequencing.
  • Protein structure and dynamics simulation using available software.
  • Case study using the TAS2R38 bitter taste receptor gene.

Main Results:

  • A modular series of activities was created, adaptable for different student levels and resources.
  • The activities successfully link observable phenotypes to underlying genetic and protein structural changes.
  • The TAS2R38 gene and PTC tasting provide a practical example of SNP-driven phenotype determination.

Conclusions:

  • Educational activities can effectively teach students about the impact of genetic variations on protein structure and function.
  • Computational simulations are valuable tools for understanding genotype-phenotype relationships.
  • The developed modules provide a framework for exploring diverse SNPs and genomic variants.