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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.
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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.
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%...
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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

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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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Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Principles of Pharmacogenetics: Types of Genetic Variants01:27

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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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Updated: May 5, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Gene-based Genomewide Association Analysis: A Comparison Study.

Guolian Kang1, Bo Jiang, Yuehua Cui

  • 1Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105;

Current Genomics
|December 3, 2013
PubMed
Summary
This summary is machine-generated.

The entropy-based method offers a powerful approach for gene-based genetic association studies, outperforming single SNP analysis and minimum p-value methods in certain scenarios. It is also more computationally efficient.

Keywords:
EntropyGene-centricGenome-wide association studyMinimum p-value method.Monte carlo

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Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • Gene-based genetic association studies offer biological insights into complex disease etiology.
  • Existing gene-based methods, including minimum p-value and entropy-based approaches, show higher power than single nucleotide polymorphism (SNP)-based analysis.

Purpose of the Study:

  • To compare the performance of the entropy-based method against the minimum p-value and single SNP-based analyses.
  • To evaluate the strengths and weaknesses of these genetic association methods.

Main Methods:

  • Comparative analysis using simulation studies.
  • Evaluation of false-positive rates and statistical power under different genetic architectures.
  • Computational efficiency assessment.
  • Application to a real genetic dataset.

Main Results:

  • All three methods demonstrated reasonable control of false-positive rates.
  • The minimum p-value method excelled when a single disease-related SNP was present within a gene.
  • The entropy-based method showed superior performance when multiple (more than two) disease-related SNPs were present in a gene.
  • The entropy-based method exhibited greater computational efficiency compared to the minimum p-value method.
  • Application to real data identified more significant genes using the entropy-based method.

Conclusions:

  • The entropy-based method is a powerful and computationally efficient tool for gene-based association studies, particularly effective for complex genetic architectures.
  • The choice of method depends on the underlying genetic architecture of the disease under investigation.
  • The entropy-based method shows promise for identifying significant genes in complex diseases.