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Related Concept Videos

Type II Diabetes I: Introduction01:26

Type II Diabetes I: Introduction

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by insulin resistance, in which target tissues such as the liver, muscle, and adipose tissue respond poorly to insulin. It is also associated with inadequate compensatory insulin secretion, where pancreatic β-cells fail to produce sufficient insulin. Together, these abnormalities lead to persistent hyperglycemia.EtiologyT2DM develops through a complex interaction of genetic predisposition and environmental or...
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Type 2 diabetes, characterized by insulin resistance, arises when the insulin receptors on cells lose responsiveness to insulin, diminishing the cell's capacity to take up glucose, resulting in elevated blood glucose levels. To receive a diagnosis of Type 2 diabetes, a series of blood glucose tests are necessary to assess whether the blood glucose falls within normal parameters. If the result is out of the normal range, a patient may be diagnosed as prediabetic or diabetic, depending on the...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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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

Updated: Jul 19, 2026

Generation of High Quality Chromatin Immunoprecipitation DNA Template for High-throughput Sequencing (ChIP-seq)
09:52

Generation of High Quality Chromatin Immunoprecipitation DNA Template for High-throughput Sequencing (ChIP-seq)

Published on: April 19, 2013

Combining information from common type 2 diabetes risk polymorphisms improves disease prediction.

Michael N Weedon1, Mark I McCarthy, Graham Hitman

  • 1Department of Diabetes Research and Vascular Medicine, Peninsula Medical School, Exeter, United Kingdom.

Plos Medicine
|October 6, 2006
PubMed
Summary

Combining common genetic risk variants for type 2 diabetes (T2DM) identifies subgroups with significantly different disease risks. This approach may aid in future preventative strategies for polygenic diseases.

Related Experiment Videos

Last Updated: Jul 19, 2026

Generation of High Quality Chromatin Immunoprecipitation DNA Template for High-throughput Sequencing (ChIP-seq)
09:52

Generation of High Quality Chromatin Immunoprecipitation DNA Template for High-throughput Sequencing (ChIP-seq)

Published on: April 19, 2013

Area of Science:

  • Genetics
  • Epidemiology
  • Metabolic Diseases

Background:

  • Individual genetic polymorphisms offer limited clinical utility for assessing common disease risk.
  • The combined effect of multiple susceptibility alleles on disease risk is not well understood.
  • Few common risk alleles have been confirmed for many diseases.

Purpose of the Study:

  • To assess the combined effect of three common risk polymorphisms on type 2 diabetes mellitus (T2DM) risk.
  • To evaluate the clinical utility of combining genetic risk information for polygenic diseases.

Main Methods:

  • Genotyped three common variants (KCNJ11 Lys23, PPARG Pro12, TCF7L2 rs7903146) in a large case-control study (3,668 controls, 2,409 cases).
  • Analyzed individual and combined allele odds ratios (ORs) and assessed gene-gene interactions.
  • Calculated ORs for participants with varying numbers of risk alleles.

Main Results:

  • Individual allele ORs ranged from 1.14 to 1.48.
  • No evidence of gene-gene interaction was found; risks followed a multiplicative model.
  • Each additional risk allele increased T2DM odds by 1.28 times.
  • Participants with all six risk alleles had an OR of 5.71.
  • Double homozygotes for TCF7L2 and PPARG risk alleles had an OR of 3.16.

Conclusions:

  • Combining information from multiple common risk polymorphisms identifies subgroups with markedly different T2DM risks.
  • This multi-allele approach may be valuable for future preventative measures in common, polygenic diseases.