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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...
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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,...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

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...
Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu01:29

Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu

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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Related Experiment Videos

Variable selection in logistic regression for detecting SNP-SNP interactions: the rheumatoid arthritis example.

Hui-Yi Lin1, Renee Desmond, S Louis Bridges

  • 1Medical Statistics Section, Department of Medicine, University of Alabama at Birmingham, 1530 3rd Avenue South, Birmingham, AL 35294, USA. hylin@uab.edu

European Journal of Human Genetics : EJHG
|January 31, 2008
PubMed
Summary

Stepwise variable selection without the hierarchical rule is a superior method for testing single nucleotide polymorphism (SNP) interactions in logistic regression. This approach demonstrates higher accuracy and fewer false positives compared to other methods, improving genetic association studies.

Related Experiment Videos

Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • Complex diseases are often linked to interactions between single nucleotide polymorphisms (SNPs).
  • Variable selection in logistic regression is a common method for analyzing SNP-SNP interactions.
  • Limited empirical data exists on the effectiveness of these selection methods for interaction analysis.

Purpose of the Study:

  • To compare the performance of nine variable selection procedures in logistic regression for detecting SNP-SNP interactions.
  • To evaluate the impact of the hierarchical rule on interaction testing.
  • To identify the most effective variable selection strategy for genetic association studies.

Main Methods:

  • Simulated genetic data for 10 SNPs across 400 and 1000 subjects, including one main effect and two 2-way interactions.
  • Compared automatic selection (stepwise, forward, backward), 2-step selection, and AIC/SC-based selection methods.
  • Assessed the effect of the hierarchical rule (including lower-order terms) versus non-hierarchical approaches.

Main Results:

  • Stepwise variable selection without the hierarchical rule showed a favorable balance of true positive and false positive rates.
  • The hierarchical rule significantly impacted interaction testing, often requiring more terms and potentially reducing the ability to analyze more SNPs.
  • Non-hierarchical procedures generally yielded higher true positive and lower false positive proportions for interaction testing.

Conclusions:

  • Stepwise variable selection, when applied without the hierarchical rule, is recommended for testing SNP-SNP interactions in logistic regression.
  • Avoiding the hierarchical rule enhances the accuracy and efficiency of detecting genetic interactions.
  • These findings were validated in a rheumatoid arthritis study, underscoring their practical relevance.