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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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.
GWAS does not require the identification of the target gene involved in...
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Single Nucleotide Polymorphisms-SNPs01:05

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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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Comparing Copy Number Variations and SNPs02:26

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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.
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Related Experiment Video

Updated: Mar 2, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Network-based regularization for high dimensional SNP data in the case-control study of Type 2 diabetes.

Jie Ren1, Tao He2, Ye Li3

  • 1Department of Statistics, Kansas State University, 1116 Mid-Campus Drive N., 66506, Manhattan, KS, USA.

BMC Genetics
|May 18, 2017
PubMed
Summary

This study introduces a novel network-constrained method to identify key genetic markers for type 2 diabetes (T2D) by considering correlations between SNPs. This approach improves upon existing methods for analyzing high-dimensional genetic data in T2D case-control studies.

Keywords:
Case–control association studyNetwork-based regularizationRegularized logistic regressionType 2 diabetesVariable selection

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Area of Science:

  • Genetics
  • Bioinformatics
  • Epidemiology

Background:

  • Type 2 Diabetes Mellitus (T2D) prevalence is rising globally.
  • Identified genetic loci explain only a fraction of T2D heritability.
  • Existing methods for high-dimensional genetic data in T2D studies have limitations.

Purpose of the Study:

  • To develop a novel network-constrained regularization method for effective identification of important Single Nucleotide Polymorphisms (SNPs).
  • To incorporate linkage disequilibrium and interconnections among SNPs in the selection process.
  • To improve the analysis of high-dimensional genetic data in T2D case-control studies.

Main Methods:

  • A network-constrained regularization method is proposed.
  • An iteratively reweighted least squares (IRLS) algorithm within a coordinate descent framework is developed.
  • The method accounts for linkage disequilibrium among SNPs.

Main Results:

  • The novel approach effectively identifies important SNPs by incorporating their interconnections.
  • A coordinate descent-based IRLS algorithm was developed for optimization.
  • The network-based approach demonstrated advantages over competing methods in simulation and real-world data analysis.

Conclusions:

  • The proposed network-constrained method enhances the identification of significant SNPs for T2D.
  • The IRLS algorithm within coordinate descent provides an efficient optimization strategy.
  • The study validates the network-based approach using simulation and the Nurses' Health Study dataset.