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Related Concept Videos

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.
GWAS does not require the identification of the target gene involved in...
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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Incomplete Dominance

Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.
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Modern Molecular Taxonomy

Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
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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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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Mapping Nucleotide Sequences that Encode Complex Binary Disease Traits with HapMap.

Yuehua Cui1, Wenjiang Fu, Kelian Sun

  • 1Department of Statistics and Probability, Michigan State University, East Lansing, Michigan 48824.

Current Genomics
|April 23, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model to identify genetic variants linked to complex diseases. The method groups single nucleotide polymorphisms (SNPs) to improve the power of genetic association tests, aiding disease gene discovery.

Keywords:
EM algorithmNucleotide sequencecomplex diseasehaplotype.logistic regression

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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • Identifying genetic variants for complex diseases is challenging.
  • Human genome variation data, like single nucleotide polymorphisms (SNPs), are vital for disease gene discovery.
  • Existing methods may lack power when analyzing multiple SNP markers.

Purpose of the Study:

  • To develop a generalized linear model for identifying nucleotide variants associated with complex human diseases.
  • To introduce a novel haplotype grouping method to form composite diplotypes, enhancing association test power.
  • To incorporate non-genetic risk factors into the disease association model.

Main Methods:

  • A generalized linear model framework is proposed.
  • A novel approach groups haplotypes into composite diplotypes to reduce model complexity.
  • An expectation-maximization (EM) algorithm is used for efficient two-stage parameter estimation.
  • The model accommodates non-genetic risk factors.

Main Results:

  • Computer simulations demonstrate the model's reasonable power and type I error rates with adequate sample sizes.
  • Simulation results suggest balanced case-control study designs optimize estimation bias and testing power.
  • The method was successfully applied to a genetic association study of large for gestational age (LGA) neonates.

Conclusions:

  • The developed model offers a powerful tool for dissecting the genetic underpinnings of complex binary diseases.
  • The composite diplotype approach enhances the efficiency of genetic association studies involving multiple markers.
  • Balanced study designs are recommended for robust genetic association analyses.